This article is the practical companion to the DRAFT framework article, which introduced a five-stage workflow for writing AI-assisted, search-optimised content without losing your voice. While the first article covered the process, this one covers the specific elements you should optimise, and explains what each one does, and why it matters for getting your content in front of the right people.

None of this requires you to be an SEO specialist. If you can structure a scientific paper, you can structure a blog article for search. The principles are closer than you think.

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Headings: your article’s architecture

Headings are not decoration. They are the structural skeleton of your article — and search engines read them that way.

Your H1 is your article title. You get one per page, and it should clearly state what the article is about. Below that, H2 headings mark your major sections, and H3 headings mark subsections within them. This nesting needs to be logical: an H3 should always sit within an H2, never float independently.

Why this matters: search engine crawlers use heading hierarchy to understand the thematic structure of your content [3]. A well-structured heading tree tells Google what your article covers, how the ideas relate to each other, and which concepts are most important. It also helps readers — particularly busy professionals scanning for the section most relevant to them.

For researchers and technical writers, think of it this way: your H1 is your paper title, your H2s are your major section headings (Introduction, Methods, Results, Discussion), and your H3s are your subsections within each. You already know how to do this. The only difference is that on the web, these headings carry SEO weight — they signal topic relevance to search algorithms, not just to human readers.

A few practical guidelines: keep headings descriptive rather than clever (save the wordplay for social media), include terms your audience would actually search for, and make sure each heading accurately reflects the content beneath it. A heading that promises something the section doesn’t deliver is worse than a bland one.

Pro tip: Read your headings in sequence, ignoring the body text. Do they tell a coherent story? Could someone scanning just the headings understand what your article covers and in what order? If not, your structure needs work — and that’s a problem for both SEO and readability.

Meta title and description: your abstract for search

When your article appears in search results (i.e. SERP = search engine result page), two things determine whether someone clicks: the meta title and the meta description. Together, they function like a miniature abstract — a compressed summary that needs to communicate value in very limited space.

Example of results displayed on the search engine result page of Google.

The meta title appears as the blue clickable link in search results. Google typically displays around 60 characters before truncating, so every word needs to earn its place. It doesn’t have to match your H1 exactly — in fact, a slightly shorter, keyword-focused version often works better for search while your H1 can be more expansive on the page itself.

The meta description is the two-line summary beneath the title. You have roughly 155 characters to convey what the article covers and why it’s worth reading. Google doesn’t always use the description you provide — it sometimes generates its own based on page content — but providing a well-crafted one increases the chances your intended message is what users see [4].

Why this matters: meta information doesn’t directly affect ranking position, but it significantly affects click-through rate — the percentage of people who see your listing and actually click on it [5]. A compelling meta description can be the difference between your article being found and being scrolled past. For expert content competing against lower-quality but more numerous AI-generated pages, a precise, authoritative meta description signals quality before the reader even arrives at your site.

For researchers accustomed to writing abstracts: the skill is the same, the constraint is tighter. An abstract might give you 300 words. A meta description gives you roughly 25. Distil the core value of your article into a single statement that answers: what is this about, and why should I read it?

Pro tip: Write three to five versions of your meta description and pick the best one. Or ask AI to generate options — this is exactly the kind of task where AI excels. Just make sure the version you choose is accurate and sounds like something you’d actually write. If it reads like a generic marketing pitch, rewrite it.

Internal linking: building your body of work

Internal links are the hyperlinks that connect one page on your site to another. When you mention a concept you’ve covered in a previous article and link to it, that’s an internal link. It seems simple, but it’s one of the most underused SEO tools — especially among experts who tend to treat each article as a standalone piece.

Why this matters: internal links serve two distinct purposes. For readers, they provide pathways to deeper or related information — the same function as cross-references in academic work. For search engines, they create a map of your content, showing how your articles relate to each other and which topics you cover in depth [6]. This is how search engines assess topical authority — the signal that you’re not just writing a single article on a subject but building a sustained, interconnected body of knowledge.

Think of it as the digital equivalent of your publication record. A single paper on a topic shows interest. A series of papers, each referencing and building on the others, demonstrates expertise. Internal linking creates that same signal for search engines.

The practical approach: every new article should link to at least two or three related pieces on your site. And when you publish something new, go back to older related articles and add a link to the new one. This bidirectional linking strengthens the connections and keeps your older content relevant.

Be intentional about anchor text — the clickable words in the link. “Click here” tells search engines nothing. “Our guide to the DRAFT framework for AI-assisted blog optimisation” tells them exactly what the linked page covers and reinforces topic relevance.

Pro tip: Keep a simple spreadsheet of your published articles, their main topics, and their URLs. When you write a new piece, scan the list for natural linking opportunities. This takes five minutes and prevents the common problem of older content becoming isolated from your growing body of work.

External links: citing your sources

External links point from your article to other websites — and they function much like citations in academic writing. When you link to an authoritative source that supports a claim, you’re doing two things: giving the reader access to the evidence, and signalling to search engines that your content is grounded in credible information.

Why this matters: Google’s systems evaluate trustworthiness partly by looking at the sources a page references [3]. Linking to primary sources — peer-reviewed research, official documentation, institutional reports — reinforces the credibility of your own content. It signals that your arguments are evidence-based, not opinion dressed as fact.

For researchers, this is second nature. You cite your sources. The web equivalent is linking to them. The key difference is that on the web, your links should point to the source itself — the journal article, the official report, the institutional page — rather than secondary summaries or aggregator sites where possible.

A few things to avoid: don’t link to low-quality or unrelated pages just to have external links. Don’t link to competitors’ content unless it genuinely adds value for your reader. And always check that your links actually work — broken links are a minor but real negative signal, and they frustrate readers.

Pro tip: When referencing a statistic or claim, link to the primary source rather than the news article that reported it. This is basic academic practice, but it’s surprisingly uncommon online — and search engines recognise the difference between content that cites originals and content that cites summaries.

Alt text on images: describing what you show

Alt text (alternative text) is a short written description attached to an image in your article’s HTML. It was originally designed for accessibility — screen readers use it to describe images to visually impaired users — and it remains one of the most fundamental requirements in the WCAG standard. Success Criterion 1.1.1 (Non-text Content) states that all non-text content must have a text alternative that serves the equivalent purpose [1]. It’s a Level A criterion — the most basic level of conformance — meaning it’s considered the bare minimum for an accessible web.

But alt text also serves an SEO function: it tells search engines what your image depicts, since crawlers can’t “see” images the way humans do.

Why this matters: if your article includes figures, diagrams, data visualisations, or photographs, alt text is how search engines index that visual content [7]. Without it, your images are invisible to search. With well-written alt text, they become an additional discovery pathway — someone searching for a specific type of diagram or chart could find your article through image search.

For researchers and technical authors, this is particularly relevant. Your articles are likely to contain figures, graphs, and process diagrams that carry significant informational value. Good alt text captures that value in words.

The approach is straightforward: describe what the image shows and why it matters in the context of the article. “Bar chart comparing publication rates across three funding models, showing a 40% increase under Model C” is useful alt text. “Chart” is not. “Image” is useless.

Keep alt text concise — one or two sentences is usually sufficient. And avoid keyword stuffing: the purpose is to describe the image accurately, not to cram in search terms. Search engines can detect the difference, and so can screen readers.

Pro tip: If you wouldn’t include the image without a caption in a journal article, don’t include it without alt text on your website. The principle is the same: every figure should be described and contextualised.

Structured data: speaking the language of search engines

Structured data — also known as schema markup — is a layer of code added to your page that explicitly tells search engines what type of content it contains. Rather than leaving Google to infer that your page is a blog article written by a specific author on a specific date, structured data states it directly in a format machines can parse.

Why this matters: structured data helps search engines categorise and display your content more effectively. For blog articles, the most relevant schema types are Article (or BlogPosting) and Person (for author information). When implemented correctly, structured data can influence how your article appears in search results — enabling richer displays that include author attribution, publication date, and article type [8].

Since Google’s March 2026 core update, structured data has taken on additional significance. Google’s AI-powered features now use schema markup as a trust and verification signal when selecting sources for AI-generated answers [9]. Accurate structured data increases the likelihood that your content will be cited in AI overviews — an increasingly important discovery channel.

For most researchers and writers, you won’t need to implement structured data yourself. Modern CMS platforms like WordPress, Squarespace, and Substack handle much of this automatically. But it’s worth understanding what it is and verifying it’s in place — particularly author information (your name, credentials, and a link to your author profile) and article metadata (publication date, date modified, article type).

If you’re using WordPress, plugins like Rank Math can manage structured data without requiring any coding. For other platforms, Google’s Rich Results Test is a free tool that lets you check what structured data a page contains and whether it’s valid [10].

Pro tip: Make sure your author schema includes a link to a dedicated author page or professional profile. Google increasingly connects content to known entities — real, identifiable people with verifiable credentials. For researchers and technical experts, this is your E-E-A-T signal: linking your structured data to a profile that shows your publications, affiliations, and expertise tells search engines exactly why you’re qualified to write on this topic [3].

URL slug: your article’s permanent address

The URL slug is the last segment of your article’s web address — the part that comes after your domain name. For this article, the slug might be /seo-for-expert-content-what-to-optimise. It seems like a small detail, but it contributes to both usability and searchability.

Why this matters: a clean, descriptive URL slug gives both readers and search engines an immediate signal about what the page contains [11]. Compare /seo-for-expert-content-what-to-optimise with /post-2026-04-draft-v3-final. The first communicates topic relevance. The second communicates nothing.

Keep slugs short, descriptive, and hyphen-separated. Include your primary keyword or topic naturally. Remove unnecessary words like “and,” “the,” or “a.” Most CMS platforms generate slugs automatically from your title, but it’s worth checking and editing them before publication — once a URL is live, changing it can create broken links and redirect issues.

Pro tip: Set your URL slug before you publish, not after. Changing a URL after publication means you need to set up a redirect from the old address to the new one — otherwise anyone who bookmarked or linked to the original URL will hit a dead end. Getting it right the first time saves you a headache later.

Putting it all together

None of these elements works in isolation. A well-structured heading hierarchy with a poor meta description still loses clicks. Excellent alt text on images doesn’t help if your headings are vague and your internal links are non-existent.

The real power of on-page SEO is cumulative: each element reinforces the others to create a page that is coherent, discoverable, and trustworthy — both to search engines and to the human beings you’re writing for.

One final element worth mentioning: readability and visual presentation. While not an SEO ranking factor in itself, your content’s visual accessibility affects how long people stay on the page — and dwell time is a signal search engines notice. WCAG 2.2 Success Criterion 1.4.3 (Contrast Minimum) requires a contrast ratio of at least 4.5:1 between text and its background [10]. Light grey text on a white background may look elegant, but it’s difficult to read for anyone with low vision — and frustrating for everyone else. Similarly, WCAG 2.2 Success Criterion 1.4.12 (Text Spacing) ensures that content remains readable when users adjust line height, letter spacing, or word spacing to suit their needs. If your CMS template is well-built, these should be handled by default — but it’s worth checking.

If you’re implementing these for the first time, start with the highest-impact items: headings, meta title and description, and internal links. These require no technical knowledge and deliver the most immediate improvement. Then layer in alt text, structured data verification, and URL slug hygiene as you build the habit.

FAQs

• Do I need to optimise every element for every article? Ideally, yes — but if you’re just starting out, focus on headings, meta title, meta description, and internal links. These four elements cover the most ground with the least effort. Add alt text and structured data verification as you become more comfortable with the process.

• Does my CMS handle any of this automatically? Most modern CMS platforms handle some elements automatically. WordPress, Squarespace, and Substack all generate basic structured data, URL slugs, and heading structures. However, automatically generated content isn’t always optimised — auto-generated meta descriptions tend to be generic, and default URL slugs can be unnecessarily long. It’s worth reviewing and refining what your CMS produces.

• How is on-page SEO different from technical SEO? On-page SEO — what this article covers — focuses on the content and structure of individual pages: headings, metadata, links, images, and text. Technical SEO deals with the infrastructure of your website: site speed, mobile responsiveness, crawlability, security certificates, and server configuration. Both matter, but on-page SEO is where content creators have the most direct control.

• Can I over-optimise my content? Yes. Keyword stuffing — forcing search terms into headings, alt text, and body copy at the expense of readability — can actually harm your ranking. Search engines are sophisticated enough to recognise when content is written for algorithms rather than people [1]. Optimise naturally: use relevant terms where they fit, and prioritise clarity over keyword density.

• Should I hire an SEO specialist? For content-level optimisation — what this article covers — you probably don’t need one. Researchers and technical writers already have the analytical skills to implement these elements effectively. Where an SEO specialist adds value is in technical SEO, competitive analysis, and long-term content strategy at scale. If you’re publishing regularly and want to build sustained visibility, a consultation can be worthwhile.

• How do I know if my optimisation is working? Google Search Console is a free tool that shows you which queries are driving traffic to your articles, your average ranking position, and your click-through rates. Set it up and check it monthly. If you see your articles appearing for relevant queries and your click-through rate is healthy, your optimisation is working. If not, revisit your meta descriptions and headings first — they’re the most common culprits.

• How much do I need to know about web accessibility? You don’t need to become a WCAG expert. But the overlap between good SEO practice and accessible design is significant enough that by following the guidance in this article — proper heading hierarchy, descriptive alt text, meaningful link text — you’re already meeting several WCAG 2.2 criteria. For researchers at public institutions, this is increasingly relevant: accessibility regulations referencing WCAG are now in force or incoming across the EU (European Accessibility Act), the US (ADA Title II), and New Zealand (Web Accessibility Standard) [11]. If your content is well-structured, clearly written, and properly labelled, you’re most of the way there.

References

[1] W3C Web Accessibility Initiative, “Web Content Accessibility Guidelines (WCAG) 2.2,” W3C Recommendation, October 2023. Available: https://www.w3.org/TR/WCAG22/

[2] W3C Web Accessibility Initiative, “WCAG 2 Overview,” W3C. Available: https://www.w3.org/WAI/standards-guidelines/wcag/

[3] Google Search Central, “Creating helpful, reliable, people-first content,” Google Developers. Available: https://developers.google.com/search/docs/fundamentals/creating-helpful-content

[4] Google Search Central, “Control your snippets in search results,” Google Developers. Available: https://developers.google.com/search/docs/appearance/snippet

[5] Google Search Central, “SEO Starter Guide,” Google Developers. Available: https://developers.google.com/search/docs/fundamentals/seo-starter-guide

[6] Google Search Central, “Links best practices for Google,” Google Developers. Available: https://developers.google.com/search/docs/crawling-indexing/links-crawlable

[7] Google Search Central, “Google Images best practices,” Google Developers. Available: https://developers.google.com/search/docs/appearance/google-images

[8] Google Search Central, “Article structured data,” Google Developers. Available: https://developers.google.com/search/docs/appearance/structured-data/article

[9] Digital Applied, “Schema Markup After March 2026: Structured Data Update,” March 2026. Available: https://www.digitalapplied.com/blog/schema-markup-after-march-2026-structured-data-strategies

[10] Google Search Central, “Rich Results Test,” Google Developers. Available: https://search.google.com/test/rich-results

[11] Google Search Central, “URL structure,” Google Developers. Available: https://developers.google.com/search/docs/crawling-indexing/url-structure

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Google has just released an official guide on optimising for generative AI features on Search, covering AI Overviews, AI Mode and the broader generative experience [1]. If you’ve been wondering whether to bin your SEO playbook and pivot to “AEO” or “GEO” hacks, this is the clarifying read you’ve been waiting for.

Here are the five takeaways I think matter most — especially if you’re a researcher, academic, founder or technical expert trying to separate signal from noise.

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#1. GEO and AEO are not separate disciplines — they are SEO

Let’s start with the headline. In Google’s own words: optimising for generative AI search is optimising for the search experience, and thus still SEO [1]. google

That single line settles a lot of debate. AEO (answer engine optimisation) and GEO (generative engine optimisation) have been marketed as new disciplines — sometimes with their own workshops, certifications and agency packages. From Google Search’s perspective, they are not.

The reason is technical, and worth understanding. Google’s generative AI features sit on top of the same ranking and quality systems that already power classic Search [1]. Two mechanisms do the heavy lifting:

• Retrieval-augmented generation (RAG), which pulls relevant pages from the Search index to ground AI responses in real, indexed content [1].

• Query fan-out, which generates a set of related sub-queries to fetch additional results for a single user question [1].

#2. Foundational SEO is still the bedrock

The guide is explicit — the SEO Starter Guide and Search Essentials continue to apply, because the generative features are rooted in the same core systems [1, 2].

In practice, that means the same fundamentals you’ve (hopefully) been investing in:

• A site that is crawlable, indexable and meets Google’s technical requirements.

• A clear technical structure with sensible HTML, no JavaScript blocking issues, and well-handled redirects.

• A good page experience — Core Web Vitals, mobile-friendliness, low latency.

• Reduced duplicate content and a clean URL structure.

There’s a helpful nuance for those of us who like clean code: Google notes that you don’t need perfect semantic HTML. The web in general isn’t valid HTML, and Google can interpret what you’ve got [1]. Use semantic HTML where you can — mostly because it helps real users, including screen readers, assistive tech and, increasingly, browser-based AI agents.

#3. Create non-commodity content that only you can produce

This is where the guide gets a little philosophical — and where I think it speaks loudest to academics, researchers and technical experts.

Google introduces a useful phrase: non-commodity content [1]. Commodity content is the kind anyone (or any LLM) could produce — a generic “7 tips for first-time homebuyers”. Non-commodity content offers a viewpoint that can’t be cheaply replicated: a first-hand account, an expert teardown, a counter-intuitive case study from your own work [1]. google

Google’s quick test is worth memorising: is this content my visitors would find satisfying? If yes, you’re on the right track [1, 3].

#4. Skip the hacks: no llms.txt, no chunking, no fake mentions

This is the section that quietly dismantles a lot of the “GEO toolkit” being sold right now.

Google explicitly lists what you do not need to do for AI search [1]:

• No llms.txt files or special AI markup. Google does not give these any special treatment for Search ranking. You can safely ignore them as a Google visibility lever.

• No “chunking” content into tiny pieces. Google’s systems already understand multiple topics on a single page. There is no ideal page length — write for your audience, not for an imagined LLM parser.

• No rewriting for AI. Google’s systems understand synonyms and meaning. You don’t need to chase every “long-tail” variation of how someone might phrase a question.

• No buying inauthentic mentions. Mentions on low-quality, low-traffic sites don’t help, and Google’s spam systems are designed to filter them out [1, 4].

• No over-engineering structured data. Structured data is still useful for rich results in classic Search, but it isn’t a requirement for generative AI features [1].

For anyone who has been pitched a “GEO audit” that mostly amounts to generating an llms.txt and seeding mentions across obscure forums — this is your permission slip to politely decline.

#5. Look ahead: agentic experiences are coming

The final takeaway is more of a heads-up than an instruction.

AI agents — autonomous systems that can book a reservation, compare specifications or pull data on a user’s behalf — are starting to interact with websites directly [1]. They may render pages, read the DOM and inspect the accessibility tree to complete tasks.

You don’t need to overhaul your site for this today. But two existing investments pay off here:

• Semantic HTML and a strong accessibility tree make your content easier for agents to parse — and easier for assistive technologies, which has always been the right call regardless.

• Clear page structures and well-labelled interactive elements help agents understand what your site actually offers.

Emerging protocols like the Universal Commerce Protocol will likely shape how Search agents interact with sites in the coming years [1]. Worth keeping on your radar, particularly if you run an ecommerce or booking-based business.

What this means in practice

If you take one thing from this guide, take this: good SEO and good content are the strategy. Not a hack. Not a separate AI playbook.

In practice:

• Keep your site technically clean and fast.

• Write from your actual experience and expertise — the kind of content that can only come from you.

• Structure your pages for humans first.

• Resist the “GEO hacks” being sold around llms.txt and content chunking.

• Stay aware of where agentic search is heading.

If your foundations are solid and you write for your target audience by adding genuine value — you’re already optimising for generative AI search. The work isn’t different. It’s the same work, done well.

Frequently asked questions

• Is SEO dead because of AI search? No. Google’s official guidance is that generative AI features in Search are built on the same ranking and quality systems as classic Search. Solid SEO foundations remain the best way to be visible in AI Overviews and AI Mode [1].

• What is the difference between SEO, AEO and GEO? From Google’s perspective, there isn’t one. AEO (answer engine optimisation) and GEO (generative engine optimisation) describe the same goal — being visible in AI-powered search experiences — but the underlying work is SEO [1].

• Do I need an llms.txt file for my website? Not for Google Search. Google does not require or give special treatment to llms.txt files for visibility in generative AI features [1]. It may still be useful for some third-party tools, but it shouldn’t be your priority.

• Should I “chunk” my content into smaller pieces for AI? No. Google’s systems understand multiple topics within a single page. There is no ideal page length — focus on writing for your readers [1].

• Does structured data still matter? Structured data isn’t required for generative AI search, but it remains useful for rich results in classic Google Search. It’s worth keeping in your SEO toolkit, just not at the expense of strong content and a clean technical foundation [1].

• What should I do first if I want to be visible in AI search? Audit the basics — indexing, page experience and content quality. Then ask the simple test Google itself suggests: would my visitors find this content satisfying? If yes, you’re on the right track [1, 3].

References

[1] Google Search Central. (2026). Optimising your website for generative AI features on Google Search. Last updated 15 May 2026. https://developers.google.com/search/docs/fundamentals/ai-optimization-guide

[2] Google Search Central. SEO Starter Guide. https://developers.google.com/search/docs/fundamentals/seo-starter-guide

[3] Google Search Central. Creating helpful, reliable, people-first content. https://developers.google.com/search/docs/fundamentals/creating-helpful-content

[4] Google Search Central. Spam policies for Google web search. https://developers.google.com/search/docs/essentials/spam-policies

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This month has been, let’s say, unexpected. I broke my hand. A post I’m genuinely proud of was a complete flop. And somewhere in the wreckage of both, I found some things worth sharing.

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Let’s get into it.

The image I wish I had for my PhD defence 18 years ago

A few weeks ago, I broke my hand. I had to wait 40 years to break my first bone ever!

I’ll spare you the elaborate story — because there is none: I missed a step. No dog pulling me (he was off lead, so that excuse was off the table anyway) and I did not slip. I just miscalculated a step, my hand took the landing, and here we are.

What surprised me wasn’t the injury. It was how much people — including myself, for a split second — wanted there to be a reason explaining why this had happened. We’re wired to reach for an explanation that softens the edges of a mistake, something that says “this happened to me” rather than “I simply got it wrong.”

But I didn’t get it wrong in some catastrophic, meaningful way. I missed a step. The consequence is real: I’m taking it easier, being patient and letting my hand heal. This resulted in writing and publishing less, and that’s ok.

On the bright side? The scientist and (former) imaging expert in me got to look at some nice images of a fracture, and I thought: “I wish I’d had that image when I defended my PhD thesis” — lol.

Why? Because I actually opened my PhD defence with the very first X-ray photograph ever taken of a human hand: on 22 December 1895, German physicist Wilhelm Röntgen asked his wife, Anna Bertha Ludwig, to hold her left hand over a photographic plate while he beamed his newly discovered X-rays at it. The resulting image — Hand mit Ringen (Hand with Rings) — shows the ghostly outline of her finger bones and wedding ring with startling clarity. Having my own hand X-ray in there would have been a great way to create “authentic content”, don’t you think?

How do you find your way back to yourself in such a noisy world?

This was, by every metric, my biggest flop.

I posted it on Easter Sunday, which, yes, wasn’t ideal timing. I reposted twice. The second time, I added a slightly more “thought leadership” angle, thinking maybe I just hadn’t framed it right. Still nothing. Fewer than 1,000 impressions across all versions combined.

I wrote that post entirely by hand. No structural help from AI — not even redundancy checks or a prompt to improve flow. Just my voice and what I was feeling at that time.

Here’s what I’ve been sitting with since: people say they want authenticity. And I think they mean it. But authenticity that doesn’t perform the right signals — the sharp hook, the bold opener, the three-part framework — seems to slip through the algorithm like water through a sieve. It doesn’t get seen, which means it doesn’t get felt.

I don’t think the answer is to stop writing that way. I think the question worth asking is: what does it mean when a genuine connection requires a performance to be found? And is there a way to be both — findable and real?

I don’t have a clean answer. But I’m thinking about it. And you know what? I truly enjoyed writing it — and I will continue to write that way from time to time. It feels really good to write for ourselves and let go.

In case you’re curious to read the original post, you can find it here.

AI is trained on average — and what to do about it

A few weeks ago I published a reflection on something that’s been quietly bothering me about AI-generated content: it’s trained on the average content available [2]. Which means, if you’re using AI without intention, you’re optimising for mediocrity.

If you missed it, you can read the full piece here.

The good news is there are concrete ways to work with AI while protecting what makes your voice yours. Which brings me to the thing I’m most excited to share this month.

The D-R-A-F-T framework

This is the practical answer to the “trained on average” problem — a workflow I’ve been developing to help you use AI while keeping your authentic voice. Not to replace you, but to augment you. Your voice, Your expertise — this is your unique selling point.

D-R-A-F-T is a sequence that keeps your own perspective at the centre of the process, rather than letting the model smooth it into something generic. I’ll walk you through each step in detail in the full article here.

What’s next

Once my hand cooperates, I’m hoping to get back to a regular publishing rhythm. Here’s what’s in the pipeline:

• More on the DRAFT framework — I want to go deeper on each step with real examples, because the concept is only useful if it’s practical

• The first article in my “beyond keywords” series — this one has been sitting in draft for longer than I’d like, but it’s almost ready

• More reflections on E-E-A-T and technical authority — specifically, how subject matter experts can build genuine credibility in a world where everyone is publishing content about everything

If any of those land for you, hit reply and let me know. I read every message, even when it takes me longer than usual to respond (broken hand, see above).

See you next month.

— Tiff.

They’re searching Google. And if your work doesn’t show up, it doesn’t exist — not for them.

A blog article is your translation layer. It’s your research rewritten for discoverability and accessibility, in terms a non-specialist can engage with. It bridges the gap between your peer-reviewed publication and the people who could fund, commercialise, or implement your findings.

However, there is a perk. AI tools promise to make this faster and easier — and they can. But for researchers and technical experts, your voice is your credibility. A blog article that reads like it was generated by ChatGPT doesn’t just sound generic — it actively undermines the authority you’ve spent years building.

The good news? You don’t have to choose between optimisation and authenticity. This article introduces the D-R-A-F-T framework — a five-stage workflow that uses AI where it’s genuinely useful, while keeping the thinking, the writing, and the final authority exactly where they belong: with you.

Why SEO matters for technical expert content

If you’ve published in a peer-reviewed journal, you’ve already proven your expertise to your discipline. But a journal article has a limited audience — specialists who know where to look and have access to the right databases. The rest of the world searches Google.

Search engine optimisation (SEO) is how you make your content discoverable to that broader audience. It’s the difference between a brilliant article that sits in obscurity and one that reaches the funders, journalists, industry partners, and policymakers who need to see it.

Here’s where researchers have a natural advantage. Google’s current ranking framework prioritises something called E-E-A-T: experience, expertise, authoritativeness, and trustworthiness [1]. This isn’t a technical trick — it’s a quality signal. Search engines are asking: does the person who wrote this actually know what they’re talking about? Have they done the work? Can they be trusted?

If you’ve conducted original research, been peer-reviewed, and published in your field, you already have E-E-A-T in abundance. That’s your competitive edge — especially now, when AI-generated content is flooding the web and search engines are actively working to surface content with demonstrable human expertise [2].

The catch? Having expertise isn’t enough. It needs to be visible, structured, and accessible for search engines to recognise it and for non-specialist readers to benefit from it. That’s what optimisation does — not diluting your content, but making sure it’s structured for discovery.

So how do you optimise without losing what makes your content authoritative? With a workflow that keeps you in control.

The D-R-A-F-T framework: five stages from research to optimised article

The D-R-A-F-T framework is built on one principle: the human leads, AI supports. You bookend the process — defining the strategy at the start, fine-tuning the final output at the end — while AI handles the structural and technical layers in between.

D — Define (you)

R — Rough framework (AI scaffolds)

A — Author (you write)

F — Format and clarify (AI structures and clarifies)

T — Tune (you fine-tune)

Define (you)

You’ve already done the hard work. The research exists, it’s been peer-reviewed, it’s published. The question now is: who else needs to understand this, and what do they need to take away?

Start by defining your audience for this piece. Are you writing for potential investors? Government funding agencies? Industry partners exploring collaboration? Science journalists? The general public? Each audience changes the language, the depth, and the angle.

Then define your key argument — the “so what?” in terms your target reader cares about. This isn’t the same as your paper’s abstract. A funder wants to know potential impact. An industry partner wants to know practical applications. A policymaker wants to know relevance to current challenges.

Finally, think about intent. What is your target reader actually searching for? What question are they typing into Google that your article could answer?

This stage is entirely human. AI cannot define your strategy. It doesn’t know your field, your audience, or what makes your research significant beyond the published record. These decisions — the scope, the angle, the purpose — are yours.

Pro tip: Type your topic into Google and look at the “People also ask” box. Those questions are real queries from real users. They’ll tell you what non-specialists want to know about your subject — and they’re free keyword research.

Rough framework (AI scaffolds)

With your strategy defined, you now have something concrete to hand to AI: a target audience, a key argument, and an idea of what your reader is searching for. This is where AI becomes genuinely useful — not as an author, but as a scaffolding tool.

Feed your rough ideas to an AI tool and ask it to generate an outline. Then use it as a Socratic tutor — ask it to challenge your structure, surface assumptions you might be making, and identify what a non-expert reader would need explained. What questions would a policymaker have that you’d take for granted as a scientist? What context is missing for someone outside your discipline?

This is also the stage to ask AI to suggest related subtopics or frequently asked questions. These become the seeds for your FAQ section and help ensure you’re covering the angles your audience actually cares about.

The guardrail here is clear: you accept, reject, or reshape every suggestion. AI proposes — you decide. The scaffold supports your thinking. It doesn’t replace it.

Pro tip: Be specific in your prompts. “Write an outline for a blog about my research” gives you generic output. Try: “I’ve published a paper on [X]. I want to write a 2,000-word blog article explaining the implications for [specific audience]. Here’s my key argument: [Y]. Suggest an outline covering the main points a non-specialist would need to understand, and challenge me on any gaps.” That gives AI something to work with — and gives you something worth evaluating.

Author (you write)

Now you write. In your own voice, from your own expertise, with your own perspective.

This is the translation step — taking your peer-reviewed findings and making them accessible without dumbing them down. You’re not writing a second journal article. You’re telling the story of your research in terms your target audience can engage with.

Don’t self-censor for SEO at this stage. Write for clarity and substance first. Focus on the argument, the evidence, and the implications. Use language your defined audience will understand, but don’t strip out the nuance that makes your perspective valuable.

Most importantly, include what AI cannot produce: your personal experience in the lab or in the field, your original analysis of what the findings mean, specific examples from your work, and the honest assessment of limitations that builds trust with informed readers. This is where E-E-A-T lives — in the human-written, experience-backed content that no prompt can generate.

Pro tip: Here’s a test for every paragraph you write. Ask yourself: could someone with access to ChatGPT or Claude and my paper’s abstract have written this? If yes, it’s not adding E-E-A-T value. The moments where you draw on what you’ve personally observed, tested, failed at, or concluded — that’s what makes your content irreplaceable.

Format and clarify (AI structures and clarifies)

With your draft written, it’s time to optimise the structure and technical elements — and this is where AI saves you the most time.

Heading hierarchy. Ask AI to review your H2 and H3 structure. Are the headings logically nested? Do they reflect the content of each section? Do they include terms your audience might search for? A well-structured heading hierarchy helps both readers scanning the page and search engines understanding your content.

Metadata. Use AI to draft your meta title (the headline that appears in search results) and meta description (the short summary beneath it). These are critical — they determine whether someone clicks on your article. AI can generate options quickly, and you choose the one that’s accurate and sounds like you.

Internal and external links. Ask AI to suggest where you could link to your own related content (building topical authority) or to authoritative external sources that support your claims.

Readability. Run your draft through AI with a readability lens. Flag overly long sentences, jargon that needs defining for your target audience, and transitions that could be clearer. This isn’t about simplifying your ideas — it’s about removing unnecessary friction between your reader and your message.

Structured data. AI can suggest schema markup for your article — the behind-the-scenes code that helps search engines categorise your content by type (article), author, date published, and more. You don’t need to implement this yourself if your CMS handles it, but it’s worth understanding what it is and ensuring it’s in place.

Grammar and punctuation. Finally, use tools like Grammarly or an AI-assisted proofreading pass for mechanical polish. Spelling errors and grammatical inconsistencies undermine the professionalism of otherwise excellent content. This is the last thing AI touches before you take back control.

Pro tip: Think of your meta description as your paper’s abstract, compressed to 155 characters. It needs to convey the core value of the piece and make someone want to click. AI can generate five options in seconds — your job is to pick the one that’s both accurate and sounds like something you’d actually say.

Tune (you fine-tune)

The final pass is always human. Always.

Go through every AI-generated suggestion and verify it. Does the heading AI proposed actually reflect what the section says? Is the meta description accurate? Did the readability edit remove nuance you intended to keep?

Fact-check any claims, statistics, or references — including your own. If you’ve let AI suggest external links or supporting evidence, verify every single one. AI fabricates citations. Your audience — fellow researchers and technical experts — will notice, and your credibility is not recoverable once lost.

Add your own views where the piece needs them. If there’s a section where your professional opinion matters — where the evidence is contested, where the implications are debatable, where your experience offers a perspective the data alone can’t — this is where you write it in.

Then read it aloud. Does it still sound like you? Would a colleague recognise your voice? If something sounds off, rewrite it. AI polish should be invisible — if a reader can tell which parts were AI-assisted, the process hasn’t worked.

Your name is on this article. You are the last authority.

Where AI helps vs. where it hurts

Let’s be honest about both sides. If you’re going to use AI in your workflow, you should know exactly where it adds value and where it introduces risk.

AI is good at:

• Generating structural outlines from rough ideas

• Drafting metadata options quickly

• Identifying readability issues and suggesting clearer phrasing

• Spotting gaps in topic coverage

• Handling grammar, punctuation, and formatting

• Suggesting internal linking opportunities

AI is not good at:

• Original insight or analysis

• Lived experience and professional judgement

• Nuanced argument in contested areas

• Accurate citations — AI fabricates references confidently and frequently

• Discipline-specific accuracy

• Your voice

The risk of over-reliance is real. If your finished article could have been written by anyone with access to an AI tool and your paper’s abstract, it carries no E-E-A-T value. Search engines are getting better at detecting this — and so are readers. An article that sounds like everyone else’s AI output won’t rank, won’t build trust, and won’t serve the purpose you wrote it for.

The principle is simple: AI amplifies expertise. It doesn’t replace it. Use it for the structural and technical layers. Keep the thinking, the writing, and the final judgement yours.

FAQs

• Can I use AI to write my blog articles entirely? You can, but you shouldn’t — at least not if you want the content to rank well and build your authority. Google’s guidelines distinguish between AI-assisted content (using AI as a tool within a human-led process) and AI-generated content (where AI is the primary author). The former is fine. The latter lacks the experience, expertise, and originality that search engines — and readers — are looking for [3].

• Which AI tools are best for SEO optimisation? This article is deliberately tool-agnostic. ChatGPT, Claude, Gemini, and others can all handle the scaffolding, structuring, and proofreading tasks described in the D-R-A-F-T framework. For grammar specifically, Grammarly and similar tools work well. For keyword research, free options like Google’s “People also ask” and Google Search Console are a strong starting point. The tool matters less than the workflow.

• How long should an optimised blog article be? There’s no magic number. Length should serve depth — write enough to cover your topic thoroughly for your target audience, and no more. For a research translation piece aimed at funders or industry partners, 1,500 to 3,000 words is typical. A shallow 500-word summary won’t demonstrate your expertise, but a 5,000-word essay may lose your reader before they reach your key findings.

• Do I need to know technical SEO to follow this workflow? No. The D-R-A-F-T framework covers the most impactful optimisation elements — headings, metadata, internal linking, and structured data — without requiring technical SEO knowledge. If your website is built on a modern CMS like WordPress, most of the technical infrastructure is handled by plugins and themes. Focus on the content-level optimisation described here.

• Will search engines penalise AI-assisted content? No — Google has explicitly stated that using AI as part of the content creation process is acceptable [3]. The distinction that matters is quality and intent. Content created with AI assistance, led by human expertise, and reviewed for accuracy is treated no differently from content written entirely by hand. Content that is generated at scale with no human oversight, no original insight, and no editorial review is what risks being deprioritised.

• How often should I update published articles for SEO? Content freshness matters, particularly for topics where research is ongoing. Review your published articles every six to twelve months. Update statistics, add links to newer related work, and revise any sections where your thinking or the evidence has evolved. Search engines notice when content is maintained — and so do readers who return to a reliable source.

References

[1] Google Search Central, “Creating helpful, reliable, people-first content,” Google Developers. [Online]. Available: https://developers.google.com/search/docs/fundamentals/creating-helpful-content

[2] Google Search Central, “Google Search’s core ranking systems,” Google Developers. [Online]. Available: https://developers.google.com/search/docs/appearance/ranking-systems-guide

[3] Google Search Central, “Google Search’s guidance about AI-generated content,” Google Developers, February 2023. [Online]. Available: https://developers.google.com/search/blog/2023/02/google-search-and-ai-content

First, some definitions

As fairly you don’t spend your days reading SEO trade press, a few terms in this piece might be new, so let’s start with some definitions before we go further.

What is a Google core update? It is a broad change to how Google decides which pages to show you when you search for something. Google itself describes core updates as recalibrations designed to keep search results helpful and reliable as the web evolves [1]. The official analogy on Google’s documentation is a nice one: think of your top-20 list of favourite restaurants. Some new restaurants open. Your tastes shift. The list reorders. Restaurants that move down aren’t bad — there are just other places now better suited to the moment [1].

It’s not a punishment for breaking rules. It’s the list being redrawn.

E-E-A-T stands for Experience, Expertise, Authoritativeness, and Trustworthiness. It’s the framework Google uses to assess content quality. The simplest way to think about it: Google is trying to figure out whether the person who wrote a page actually knows what they’re talking about, and whether you can trust them. I’ve written about this before — it’s been the backbone of Google’s quality assessment since 2014, and the principles go back even further.

An aggregator is a website that collects and re-presents content that originates somewhere else. Think Glassdoor (job listings from employers), Trivago (hotel prices from hotels), Rotten Tomatoes (reviews from critics), or any “best of” listicle built entirely on other people’s products. Aggregators sit between you and the thing you actually want.

A primary source — or destination — is the opposite. It’s the original. The employer’s careers page. The hotel’s own website. The scientist’s published paper. The founder’s own product page. The thing the aggregator is pointing to.

That distinction — aggregator versus destination — is the single most important pattern in the March 2026 update.

What actually happened

Google’s first broad core update of 2026 lasted for 12 days (it began rolling out on 27 March and finished on 8 April) [2, 3]. It followed two other updates: a Discover-only update in February (the first of its kind), and a very fast spam update on 24 – 25 March that completed in under 20 hours [4, 5].

With three updates in six weeks — this could explain why your Search Console looked like a heart monitor for a while. This is what happened [4, 6]:

• Around 80% of pages that had been in the top three positions for a search query shifted positions. (For comparison, the December 2025 update moved about 67%.)

• Roughly one in four pages that had been on Google’s first page disappeared from the first hundred results entirely.

• More than half of websites being tracked saw noticeable ranking changes within the first fortnight.

Google’s official description was deliberately vague — a regular update designed to better surface relevant, satisfying content from all types of sites [3]. No companion blog post. No new guidance. The real story is in the patterns of who gained and who lost.

The three shifts that matter

Google rarely publishes a changelog. But based on visibility data and a fairly consistent trail across the SEO trade press, three shifts stand out.

1. Originality became a ranking factor

This is the biggest behavioural change, and the one I find most interesting.

Google now appears to actively reward pages that add something genuinely new to a topic — original research, first-hand experience, proprietary data, a unique perspective — over pages that simply rephrase what’s already out there [7, 8]. Long, well-structured, keyword-optimised summaries of what’s already on page one are losing ground. Pages with their own voice, their own data, their own findings are gaining.

The technical name for this is Information Gain — a concept Google has actually held a patent on for years. But March 2026 is the first update where its impact is clearly visible at scale. Early data suggests sites with proprietary research and expert commentary saw average visibility gains of around 22% [7].

If you’ve ever wondered why I keep telling scientists that their actual work — the research, the methodology, the first-hand findings — is the content asset, not the marketing collateral around it: this is why.

2. Authorship and credentials now carry algorithmic weight

What’s changed in March 2026 is the weight Google places on E-E-A-T signals. Two findings are worth pausing on [8, 9]:

• For sensitive topics — health, money, legal, safety (the so-called “YMYL” topics, i.e. Your Money or Your Life) — around 73% of top-ranking pages now display verifiable author credentials. Anonymous content is losing ground fast.

• Google’s systems now appear to cross-reference the named author of a page against external sources — LinkedIn profiles, speaker pages, publication history, citations. If you say you’re an expert, Google now checks.

That second point is the one that should make every domain expert sit up. The digital footprint you’ve built across LinkedIn, Substack, conferences, journal publications — Google can now use that as evidence of your expertise. It’s not just about what’s on your website anymore.

Google is also evaluating your whole site, not just individual pages. A site that goes deep on one topic outperforms a site that covers many unrelated topics [10]. A few thin pages can drag down the perceived authority of everything else you publish.

This is the part that matters most for the people I work with. If you’re a scientist or a domain expert, you already have the credentials. You already have the depth. You already have the consistency. You just need to make it visible in places Google’s systems can read.

3. AI content got filtered, not banned

Let me be clear, because this gets misreported every time: the update did not penalise AI-generated content as a category.

What it did is get much sharper at detecting content that was clearly produced at scale, with no real human review. Fluent prose that adds nothing new. Articles that read like they were written by a tireless intern who didn’t know the topic [8, 11].

The losers aren’t AI users. The losers are anyone — human or otherwise — who used a process to scale mediocre, undifferentiated content [11]. Think → AI → Think still works. AI → AI → AI doesn’t.

A useful reminder from John Mueller, Google’s Search Advocate: rewriting AI content by hand doesn’t fix it. If a site is built on low-value content, the strategy needs revising, not individual pages [12].

What content is actually getting promoted

The “three shifts” above explain the why. This section is the what.

Based on patterns observed during the rollout — particularly well-documented by SEO practitioners working closely with affected sites [12] — there’s a fairly clear list of content types and content elements that Google is now actively favouring:

• Real case studies. Not the kind with percentages pulled out of thin air. Documented examples with real outcomes — the kind a scientist or operator would recognise as evidence.

• First-hand experience. Concrete feedback from your own work. Things you’ve actually done, tested, or seen play out in practice. The “I did this and here’s what happened” structure.

• Annotated data and screenshots. Charts, dashboards, or data visualisations with arrows and callouts explaining what matters. Originals — not stock graphics.

• Expert input. Quotes or commentary from a recognised expert in your field. Or referencing your own (or others’) subject-matter posts on LinkedIn — Google appears to read those signals now.

• Genuinely deep, well-researched writing. Not AI-drafted prose nobody re-read. Content that engaged seriously with the question and didn’t just take the first answer ChatGPT or Claude offered.

• Topical authority on a specific niche. Supported by good backlinks — the kind earned through real credibility, not bought in bulk.

• Visible E-E-A-T signals. Author bios with credentials. Verifiable sources. Transparent attribution.

• Content written for readers, not for the algorithm.

And on the other side — content types that took a hit:

• Listicles. “Top 10...” style articles seem to have been deprioritised in many niches, possibly as part of a broader cleanup of overly promotional content [12].

• Mass-produced AI content. As discussed above — sites that scaled hundreds of generic AI articles are among the worst affected [6, 12].

• Anything obviously written for the algorithm rather than the reader.

If you’re translating this into your own publishing strategy: the format that wins right now is one expert, one specific question, one piece of original evidence, told properly.

Winners and losers

The visibility patterns map onto a single distinction, and it’s the one we set up at the start: destination versus aggregator.

Sites that gained ground

• Government and institutional sources (think Census.gov, BLS.gov) on fact-driven queries [4, 13]

• Specialist sites that go deep on one subject area [10, 13]

• Established brands with a strong direct presence in their space [13]

• Primary destinations — the employer’s careers page, the actual research publication, the original publisher [13]

Sites that lost ground

• Job aggregators like ZipRecruiter and Glassdoor — losing ground to the employers themselves [4, 13]

• Broad consumer health portals — losing ground to clinical sources and original research [13]

• Comparison sites and “best of” lists built on other people’s data [13, 14]

• Affiliate-heavy sites built around pushing you to buy something — among the hardest-hit overall [6]

• Mass-produced AI content with no editorial backbone [6, 11]

In one sentence: if your site sits between a user and the thing they actually want, this update was unkind. If your site is the thing they want, it probably worked in your favour [14].

What this means for founders

If you’re building a company, the message from March 2026 is simple: owned value compounds.

• Be a primary source, not a layer. Aggregators are losing ground to the destinations they used to send traffic to. If you’re a SaaS founder, that means investing in original product data, customer outcomes, proprietary research. Content nobody else can produce.

• Make your team’s expertise visible. Your engineers, scientists, operators are a competitive asset. Named author bios, LinkedIn-linked credentials, topic-consistent publishing — these now feed directly into how Google evaluates your domain.

• Treat brand as infrastructure. Strong brands won this update. People searching for your name directly, a real About page, verifiable contact info, consistent business listings — all of this feeds the trust component of E-E-A-T.

• Don’t outsource your moat. If your content strategy is “spin up an AI workflow and publish 200 articles a month,” March 2026 just turned that strategy from a shortcut into a liability.

The framing for any founder thinking about content investment in 2026: publish less, with deeper authorship. One genuinely original case study, methodology piece, or first-party benchmark will do more for your visibility than fifty thin posts.

What this means for tech experts

For technical professionals — engineers, researchers, security specialists, AI practitioners — March 2026 reinforces something I’ve been saying for a while: technical credibility is a content moat.

• First-hand experience markers matter. Phrases like “in my audits of…”, “when I tested…”, “across the deployments I’ve reviewed…” are now visible signals to Google’s quality systems [8]. Generic technical content without these markers is increasingly indistinguishable from scaled AI output.

• AI workflows need a human spine. Most of us are using AI to draft technical content — fine. But the content needs genuine expert review, original benchmarks, working code samples that someone actually ran. Without those, fluent prose is a liability, not an asset [11].

• Page experience is a baseline, not a differentiator. If you read SEO trade press, you’ll see references to Core Web Vitals — Google’s measurements of how quickly your page loads (LCP under 2.5 seconds), how responsive it feels when users click (INP under 200ms), and how stable the layout is as it loads (CLS under 0.1) [12]. These are now table stakes. Failing them is a ranking liability. Passing them is the floor, not the ceiling [8, 13].

• Structure for AI citation, not just ranking. AI Overviews, ChatGPT, Perplexity, Gemini — they cite the same kinds of content this update rewards: original, well-structured, expert-authored, clearly attributable. Research suggests roughly 44% of citations in AI tools come from the first 30% of a page’s text [9] — meaning your introduction and early sections carry disproportionate weight.

The consolation, if you’re a tech expert who’s been resisting the content-mill model: the algorithm has just made depth profitable again.

A practical playbook

If your traffic moved between late March and mid-April — or if you simply want to be in good shape for the next core update (likely June or July 2026 [11]) — here’s the disciplined response. This aligns closely with Google’s own published guidance on responding to core updates [1].

1. Wait at least a full week after the rollout completed before drawing conclusions. Google explicitly recommends this. The rollout finished on 8 April, so meaningful analysis starts from around 15 April onwards [1, 3].

2. Compare the right dates. In Google Search Console, compare a week of post-update data (mid-April onwards) against the week immediately before 27 March [1]. That gives you the cleanest signal of what changed.

3. Distinguish small drops from large drops. Google’s own guidance: a small drop (e.g. position 2 to 4) doesn’t need drastic action. A large drop (e.g. position 4 to 29) warrants a deeper assessment [1].

4. Analyse different search types separately. Did the drop come from regular Google search, Google Images, Video, or the News tab? They behave differently and may need different responses [1].

5. Confirm whether you’re looking at the core update or the spam update. A drop on or after 27 March points to the core update. A drop on 24 – 25 March points to the spam update. Some sites were affected by both [15].

6. Look at who gained in your niche, and what they’re doing differently. For your priority search terms, what do the new winners have? More first-hand experience? Original data? Named author with credentials? Deeper coverage of the topic?

7. Strengthen authorship. Add named author bios with verifiable credentials. Link them to LinkedIn and primary sources. Publish under named bylines, consistently [9].

8. Improve the content that needs improving — don’t panic-delete. Google’s official guidance is that deleting content is a last resort. Try improving or restructuring first. Only remove content if it’s genuinely unsalvageable [1, 10].

9. Invest in proprietary value. First-party data, case studies, original methodologies, in-the-wild experiments — these are what feeds the originality signal [7].

10. Get the technical floor right. If you have a developer, ask them to clear any “Poor” or “Needs Improvement” warnings in Google Search Console’s Core Web Vitals report. If you don’t, this is a reasonable thing to outsource [8].

The recovery window is real but not immediate. Google has stated that improvements can take effect within days, but often take several months — sometimes recovery comes only with the next core update [1, 11]. The work you do now compounds toward that next cycle.

The bigger picture

Each core update since late 2024 has tightened the bar. March 2026 tightened it further. The era of ranking on aggregated, repackaged, or thinly-edited content is ending — not suddenly, but steadily and consistently [14].

If you take one thing from this digest, take this: the algorithm hasn’t asked you to do anything new. It has simply made the thing scientists and technical experts have always done — work hard on something specific, tell the truth about it, put your name on it — into the dominant ranking signal.

That’s not bad news. That’s the playing field finally levelling out.

FAQs

• When did the March 2026 core update roll out? It began on 27 March 2026 and completed on 8 April 2026 — 12 days from start to finish [2, 3].

• Was it a penalty? No. Google’s own guidance is explicit: core updates are recalibrations of how the algorithm assesses content, not punishments for specific violations. A drop means other pages are now considered more useful for the affected queries [1].

• What is an aggregator, and why does it matter here? An aggregator is a website that collects and re-presents content from elsewhere — job sites that pull listings from employers, comparison sites that pull prices from retailers, “best of” listicles built on other people’s products. The March 2026 update consistently moved visibility away from aggregators and toward the original sources they aggregate [4, 14].

• What types of content did Google promote in this update? Real case studies, first-hand personal experience, annotated data and screenshots, expert input and quotes, genuinely deep research, topical authority on specific niches, and visible E-E-A-T signals (author bios, verifiable sources). Listicles and mass-produced AI content lost ground [12].

• Did it penalise AI-generated content? No, not as a category. It targeted content produced at scale without meaningful human review, regardless of who or what wrote the underlying draft [8, 11]. Expert-edited, AI-assisted content is performing well. Sites publishing AI content without human supervision are reporting visibility losses of 35 – 60% in early French analyses, with dedicated AI content farms hit harder still [6, 12].

• What is Information Gain? A Google concept (and patent) for measuring how much genuinely new information a page contributes relative to what already ranks. March 2026 is the first update where its impact has been clearly visible at scale [7].

• How is this different from the February 2026 update? The February 2026 update only affected Google Discover (the personalised content feed on Android phones and the Google app). March 2026 is a broad update affecting general search rankings globally [5].

• What should I do if my site dropped? Wait at least a full week after rollout completion (so from around 15 April onwards) before analysing. Compare the right dates in Google Search Console. Distinguish small from large drops. Look at what your competitors who gained are doing differently, strengthen E-E-A-T signals, and improve content where it lacks original value. Avoid wholesale rewrites or panic deletions — Google’s own guidance is that deletion should be a last resort [1].

• When is the next core update expected? Based on Google’s recent cadence, the next broad core update is expected around June or July 2026 [11]. Google also runs smaller, unannounced updates between major ones, so improvements can be recognised at any point [1].

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References

[1] Google Search Central. (2025). Google Search’s core updates and your website. https://developers.google.com/search/docs/appearance/core-updates

[2] Search Engine Land. (2026, April 8). Google March 2026 core update rollout is now complete. https://searchengineland.com/google-march-2026-core-update-rollout-is-now-complete-473883

[3] Search Engine Journal. (2026, April 8). Google confirms March 2026 core update is complete. https://www.searchenginejournal.com/google-confirms-march-2026-core-update-is-complete/571459/

[4] Search Engine Land. (2026, April). March 2026 Google core update more volatile than December — here’s what changed. https://searchengineland.com/march-2026-google-core-update-what-changed-474397

[5] Search Engine Roundtable. (2026, April 8). Google’s March 2026 broad core update has completed rolling out. https://www.seroundtable.com/google-march-2026-core-update-complete-41145.html

[6] Quasa.io. (2026). Google March 2026 core update: Massive volatility, traffic winners and losers explained. https://quasa.io/media/google-march-2026-core-update-massive-volatility-traffic-winners-losers-explained

[7] Shortlist.io. (2026). Google’s March 2026 core update: What went down and how to adapt your SEO game. https://shortlist.io/blog/google-march-2026-core-update/

[8] ClickRank. (2026). Google March 2026 core update: What changed and what to do. https://www.clickrank.ai/google-march-2026-core-update/

[9] Launchcodex. (2026). Google March 2026 core update: What you need to know and how to adapt. https://launchcodex.com/blog/seo-geo-ai/google-march-2026-core-update/

[10] OrangeMonke. (2026). Google March 2026 core update: What you need to know. https://orangemonke.com/blogs/google-march-2026-core-update/

[11] Lifted Websites. (2026). March 2026 Google core update: What actually changed. https://liftedwebsites.com/march-2026-google-core-update/

[12] Bart, A. (2026, March 21). Mise à jour Google Mars 2026 — On décrypte + guide de survie. Apprendre le SEO sans migraine.

Apprendre le SEO sans migraine 💊

🤖 Mise à jour Google Mars 2026

Comme une habitude qu’on n’aime pas trop. Toutes les années en Mars c’est reparti pour un tour, la grosse mise à jour de Google qui porte le nom de “March Core Update…

Read more

4 months ago · 47 likes · 6 comments · Amandine 🦦

[13] YellowJack Media. (2026). Google March 2026 core update: What changed. https://www.yellowjackmedia.com/google-march-2026-core-update-what-changed/

[14] Opositive. (2026). Google March 2026 core update: Volatility, winners and losers. https://news.opositive.io/google-algorithm-update/google-march-2026-core-update-volatility-winners-losers/

[15] ALM Corp. (2026). Google March 2026 core update: Confirmed timeline, SEO impact, and what site owners should do next. https://almcorp.com/blog/google-march-2026-core-update/

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Now, to the real stuff. March was very personal to me. More personal than I expected. I turned 40, published two articles on Substack, posted more on LinkedIn than I have in months, and — a year later — finally found the words to talk about Moritz.

Here’s how March went.

To turning 40!

I debated whether to write publicly about turning 40. Birthdays on LinkedIn can turn quickly into performative wisdom or forced gratitude. But I kept coming back to a format I’d seen from Samuele Lisi — a year-by-year timeline of key learnings. Simple, honest, no fluff.

So here’s my version. Not 40 things — just the ones that shaped the turns.

• 2008. I started my PhD at 22 — somehow, I was younger than some of the students I was supervising during lab work! (One of the assistants even mistook me for a student on my first day!) I had to learn fast that credibility is not about age. It’s about showing up prepared.

• 2013. August is the month where I both completed my PhD and met the person who would become my husband.

• 2014. I moved to the Netherlands for a postdoc. I did not know yet that this would be the first stone of my academic career. I always thought I would only ‘stay one more year’ – probably a lame excuse for not learning Dutch as I never thought I would stay longer than a year. That lasted almost 8 years...

• 2016. The year I turned 30... became engaged... and landed a tenured position at M4i. The thing you’re supposed to want. And realising, slowly and sometimes painfully, that “supposed to” is a dangerous mindset, and that things ‘you’re supposed to have’ might not be the ones that make you happy.

• 2021. I decided to end my academic career for Boehringer Ingelheim. Terrifying. The skills academia didn’t see as valuable — translating complexity, thinking strategically, connecting people to ideas — turned out to be exactly what the outside world needed.

• 2021 (again). Adopting Moritz. A retired lab beagle with eight years of pharmaceutical testing behind him and an unparalleled nose for treats. He deserved the world, and we tried to give it to him.

• 2023. The move of a lifetime to go on the other side of the world. Here we are, New Zealand. Starting TS sci.com. Learning that building a consultancy is less about expertise and more about trust. And that trust takes time to build.

• 2025. Saying goodbye to Moritz. Welcoming Jaxx. Understanding that new beginnings don’t erase what came before — they carry it forward. And not feel guilty about it. Also landed a new job at the University of Auckland – starting a new career.

• 2026. I turned 40 but that you already knew. And I finally started I’ve wanted for a long time but was too afraid to do: I launched my newsletter Strategy360. Finally not apologising for doing things my way.

If I have to keep one learning, it's this: stay true to yourself. If you feel you have to change who you are to fit in, you're either not surrounded by the right people or simply not in the right place. And that's ok — nothing is set in stone.

“What if I choose the wrong career path?”

This question hit differently in March.

I published Academic career or your own company — you can’t do both on Strategy360, a piece that started with something I was told during a yearly evaluation as a tenured assistant professor: “You’ll have to choose. It’s either an academic career or having a company.” The article unpacks that moment — and the data behind the gender gap in academic entrepreneurship that makes comments like these not just personal, but systemic.

Around the same time, Isabelle Kholer from NextMinds posted something on LinkedIn that stopped me mid-scroll. “What if I choose the wrong career path?” — a question she hears constantly from master students, PhD students, and postdocs trying to figure out their next move.

Her answer? There is no wrong career path. And the metaphor she uses — a scaffolding instead of a ladder — is one I wish I’d heard fifteen years ago.

A ladder only goes up. A scaffolding goes sideways, backwards, in directions you didn’t plan. You build it as you go.

This makes me think of grapevines. A grapevine doesn’t grow straight up — it spreads laterally, reaches for whatever structure is nearby, wraps itself around things you didn’t plant for it. And somehow, it produces something remarkable. The growth that matters most doesn’t always look like climbing.

That’s what my career has been. Pharmaceutical sciences to academia to industry to strategic communication. Not a ladder. A scaffolding — with a few unexpected vines.

→ Read the full article: Academic career or your own company — you can’t do both

The anatomy of a post that worked before the age of AI

I asked Claude to analyse a LinkedIn post I wrote in 2021.

It was a farewell post — the end of seven years in the Netherlands. No AI to help with writing. 250 likes. 56 comments.

The analysis was sharp, honest, and a little humbling. It told me what worked, what I missed, and ended with this: “You don’t write like a LinkedIn caricature. That’s your biggest asset.”

I sat with that for a while. Because what made that post work — the Gouverneur, Ron by name, the iKnife that I never bothered to explain — was exactly the kind of detail that no tool can generate for you. It came from being there. From actually feeling something.

Authentic voice was always rare. AI didn’t invent that problem. It just made the noise so much louder that the real thing stands out even more.

I wrote about all of it — the original post, the full Claude exchange, and why I think this matters especially if you’re a scientist or technical professional trying to build a presence that actually sounds like you.

→ Read the full article: The anatomy of a post that worked before the age of AI

On the freelance front

Réseau Biotechno has officially launched their new website — a project I worked on from design and UX/UI audit through to information architecture, and SEO. Seeing the team announce it publicly and reading the feedback was one of the highlights of March. “Un projet dont je suis fière.” (“A project I am proud of” )

I also signed two new projects in March that I’m excited about — one creative, the other in collaboration with a talented freelancer and marketing consultant. I can’t say much yet, but I’m hoping to share more in the next issue or two. More in due time.

In loving memory of Moritz

A year ago, we had to say goodbye to our little buddy, and I couldn’t write today without acknowledging it.

Moritz was a cool dude — “smart and elegant,” as my husband used to say. He was a pharma hero who spent the first eight years of his life working as a laboratory dog for drug testing. We adopted him when he retired and gave him a life full of cuddles, love, and pig ears — the life he deserved.

Born in Germany, he travelled to France, Switzerland, and Austria, and as far as New Zealand, where he spent the last two and a half years of his life. Moritz loved people and knew exactly where to find the good treats — his sense of smell was unparalleled.

Saying goodbye was devastating. But I can today, finally, look back and say that we gave him a great life. We have great memories to go back to, and he will always be in our hearts. He was never far from me when I was walking along the beach on the weekend.

Much love to you, my sweet Moritz — my very Good Boy. xx

One of our happiest memories. Photo of happy Moritz playing in the sand, Papamoa beach. December 2024.

What’s coming next

I’m working on a full rewrite of my website — new services structure, new copy, new messaging. The bones are there, and I’m hoping to have it live in the coming weeks.

On Strategy360, drafting the “SEO beyond keywords” series continues. And I have a few ideas I’ve been sitting on — including a deeper piece on how I actually use AI in my content workflow, and what I mean when I say I use it to think with me, not to write for me.

As always — if something in this issue resonated, hit reply. I read every message.

— Tiff.

I was born and raised in the mountains — in the French Alps, to be exact.

When I was a kid, it was a family tradition to go hiking on the weekend, always ready with a picnic (with French baguettes, of course, not to sound cliché). Sometimes, it would be a barbecue by the river. Playing in the cold water. Though I sometimes complained as a kid about the long hikes, as a grown-up I’ve learned to embrace them — and draw strength from them. One place in particular is very close to my heart, and although I literally travelled around the world, this remains my comfort place: la Sambuy. I learned to ski at very small ski resort when I was not yet three years old!

📷 La Sambuy, French Alps — the mountain that raised me. I was not even three years old when I first stood on those slopes.

I have always loved to go back to the mountains – forests are my go-to place to decompress, let go of constraints, and recharge my batteries. The silence, the stillness of the trees, the gentle whispering of the leaves in the wind, are what make me feel restored after a good long hike.

📷 One of the best views on Earth to me. Looking down onto Lac d'Annecy from La Sambuy — "entre lac et montagnes," exactly as the opening of my article goes.

Paradoxically, while the forest’s calm gives me strength, coastal walks bring me focus and help me reconnect with myself. Since I fell in love with New Zealand in 2018 — my husband and I spent our honeymoon here, two weeks that barely scratched the surface of these islands, and yet we knew — I’ve learned to love just walking along the beach — it’s easy to find yourself alone there. The only effort is going forward in the sand. Listening to the whooshing of the wind – sometimes howling – this is what keeps the daily noise away. Observing the movement of the clouds and the crashing of the waves on the shore, no two photos are alike.

This unpredictable nature is truly inspiring. There is no brief to follow here, no protocol, no expected outcome. Nothing is perfect – but nature tries again and again, accepting that perfection does not exist and that no replicate would be like another. In science, not being able to reproduce a situation feels counterintuitive — even like a failure. But standing here, watching the waves, I’ve come to understand it differently. Not everything has to go as planned. Some things are worth releasing. Some battles are not worth fighting. What matters is staying open — to the unexpected, to a different perspective, to the possibility that what didn’t go as planned might still lead somewhere worth going. The result is still stunning.

📷 Papamoa Beach, Bay of Plenty, New Zealand. Easter 2025 — our place, our memories.

Navigating between seas and mountains is what keeps me inspired – each offering something unique and profound in its own way. I have learned to receive their gifts – and be at peace with whatever comes next; this is what makes me move forward.

And you? How do you find a way back to yourself in this noisy life?

Strategy360 isn’t going anywhere. I’m writing this with a warm tea near the fireplace, and a new issue nearly ready to ship.

Happy April Fool’s Day.

But here's the thing — you clicked! Instead of scrolling past as you do for most likely 98% of the LinkedIn content you see passing by, you actually clicked. That means something. Maybe some part of you was curious enough, or maybe you are just nosy enough to want to see what this was all about before it "disappeared" — and why I failed while I was just starting this newsletter.

But I know my network, and if you’re here, I am choosing to believe that it’s because you have excellent taste!

The next issue drops in the coming days.

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If you’re not subscribed yet, do it now. And here are three reasons to convince you why you should stick around a little longer:

1. You won’t find this content elsewhere. Strategy360 bridges the gap between (scientific) credibility and digital strategy — a space almost nobody is writing about with real depth. If you work in research, biotech, academia, or science communication, this is built for you.

2. It’s built on experience, not trends. I do not recycle AI trends. You won’t find 47-step frameworks. If any, they will be actionable. Just two decades of academic research and a career pivot into digital strategy, distilled into something actually useful.

3. It's written by someone who means it. No corporate polish, no hidden agenda. Just honest thinking from someone navigating the same messy intersection of science, strategy, and figuring it out in public. What you see is what you get.

Thanks for being here — whether you came out of curiosity, concern, or just mild FOMO. I’ll take it.

Now hit subscribe before I actually change my mind. 😉

— Tiff

I write about communication, brand, and strategy for people whose expertise runs deep but whose visibility does not yet reflect it. I also share the story of a non-linear career — from academia to biotech to consulting — in the hope it might help someone find their own way.

Strategy360 is also a space to think about the bigger picture: the gender gap in diverse fields, the AI shift, and what both mean for the way we communicate and build.

If any of that resonates, the subscribe button is right below.

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A few weeks ago, a connection of mine sent me a message after reading one of my LinkedIn posts. She wrote: “The post kind of stood out to me between the LinkedIn noise since it felt authentic and relatable to you and your moves, and not like ‘oooh be grateful for my carefully phrased wisdom’.”

I loved that message. Not out of vanity but because it pointed at something I had been struggling to articulate myself: that the thing people respond to on social media has very little to do with polish, and almost everything to do with specificity. With the feeling that a real person wrote it.

This article is about a post I wrote in early 2021, before AI writing tools became part of most people’s workflows. It generated 250 likes and 56 comments, and I have been thinking about why. Recently, I asked Claude to analyse it. The results were illuminating and so on point, and I am sharing the full exchange here in full transparently, because I think the process itself is part of the story.

But before I start, let me be clear about the argument I want to make. This is not a nostalgic piece. I am not about to tell you that writing was better before AI, or that you should feel guilty for using these tools. The truth is more interesting: authentic voice was always rare — and so it remains in the age of AI. Most people were already writing like LinkedIn caricatures long before ChatGPT or Claude existed. What has changed is the volume of polished, empty content — which means that if you have something real to say, and you know how to say it, the opportunity to stand out has never been greater.

Setting the scene: the post and the moment behind it

In March 2021, I left the Netherlands after more than seven years in academia. I was ending my academic career at the University of Maastricht and M4i (the Maastricht MultiModal Molecular Imaging Institute), and moving to Germany to join Boehringer Ingelheim. It was a significant transition — the kind of moment that calls for a farewell, for acknowledgement, for most important to me, for saying thank you to people who had mattered.

But sadly, this event also happened in the middle of a pandemic. There was no party. No drinks at the Gouverneur, no last dinner at Toscanella Apuana. Just a quick goodbye at the office to those who were present, and the need to mark the end of something important in a way that felt real.

Here is the post as it appeared — you can read the original and the full comment thread on LinkedIn.

I really enjoyed working hand in hand with clinicians, our brainstorming sessions, and learned a lot from the challenges that lie with bringing new technologies to the clinical settings. I am proud we have made the first steps towards implementation of the iKnife to the operating room at the MUMC+. Being at the forefront of building the new biomolecular imaging courses and being able to integrate and teach my own research to the next generation of young scientists was a pure luxury. I truly enjoyed every second of teaching and my interactions with very enthusiastic students, to whom I hope I could pass on some of my enthusiasm and passion for our research and technologies! I feel lucky I had the chance to meet and accompany talented PhD candidates along their PhD journey. They taught me a lot, and I wish them all a brilliant career and a lot of happiness for their future.

Maastricht was also a lot of fun outside the labs of UNS50, and I will never forget the crazy parties, beers at the Gouverneur, cocktails at Beez, (very) special dinners at Toscanella Apuana, and also our many drinks and cheering up events with the great “young PIs” group!

247 likes. 56 comments. No paid promotion. No strategic content planning. Just a person, a moment, and a post written from the gut. No AI at that time.

I asked Claude.

The autopsy: what an AI noticed about a human post

Four years later, I wanted to understand why it worked — and what it could have done better. So I shared the post with Claude and asked for an honest analysis: what worked, what didn't, and what the writing revealed about voice.

Here is the full exchange, unedited.

What works really well

"The post is warm, specific, and deeply human. You name people, places, and moments (the Gouverneur, Toscanella Apuana, Ron by name) rather than staying vague, which is what drove engagement. That specificity is rare on LinkedIn and it makes readers feel like they're witnessing something real, not reading a press release. The emotional tone is consistent — grateful, proud, forward-looking — without tipping into sentimentality. Your voice comes through clearly: thoughtful, genuine, with a lightness that balances the gravitas of the moment. The French-inflected sentence rhythm is also subtly present ("I am forever grateful... which made me grow and which I take along with me") and it gives the prose a certain elegance."

What could have been improved at the time

“The main structural issue is that the post is essentially one long block of text, which would have reduced readability on mobile (even in 2020, the majority of LinkedIn traffic was mobile). Breaking it into shorter paragraphs — one per theme: the work, the teaching, the PhD students, the social life, the gratitude to Ron — would have made it much easier to skim and would have encouraged people to read to the end rather than stopping halfway.

The opening is strong but slightly buried. ‘Today marks officially the end of a wonderful journey’ is good, but ‘officially’ is a minor hedge that slightly softens the impact. Something like ‘Today marks the end of a wonderful chapter’ would have been cleaner.

The transition to the next step (Boehringer Ingelheim in Biberach) comes very late and reads almost like an afterthought after the ellipsis. For career-related reach — new connections, opportunities — leading with a clearer ‘where I’m going’ signal earlier, or at least making that closing line more intentional, would have helped. ‘To be continued…’ is charming but slightly anticlimactic for anyone who wanted to know what was next.

There’s also a missed hook for engagement. With a post this personal and this well-received, a direct question at the end — something like ‘Has anyone else made a leap this significant? I’d love to hear your story’ — could have pushed the comment count even higher. The 250 likes suggest enormous goodwill from the network; comments were likely organic, but a nudge would have deepened the conversation further.

Finally, the iKnife mention is fascinating and completely unexplained. For anyone outside the direct field, it reads as a tantalising detail with no context. A single parenthetical explaining what it is (’a real-time tissue analysis tool for cancer surgery’) would have dramatically widened the appeal and possibly drawn in people beyond the immediate circle.”

On voice and style

"What stands out across the writing is a strong instinct for concrete detail, genuine warmth without performative humility, and a tendency to move from the personal to the professional and back again fluidly. You don't write like a LinkedIn caricature. That's the biggest asset and it's consistent across everything you write."

I want to sit with that last line for a moment: you don't write like a LinkedIn caricature. Because I think it gets to the heart of what actually drives engagement — not just on LinkedIn, but anywhere people are trying to build a presence and be heard.

The argument: why authentic voice matters more now, not less

Here is what I notice when I scroll through LinkedIn today. The posts are often well-structured. They have hooks. They have white space. They have calls to action. They follow the frameworks. And they are, many of them, completely indistinguishable from one another.

This is not entirely AI’s fault. The caricature existed before the tools arrived — the inspirational pivot story, the grateful humble-brag, the “I used to think X until I realised Y” formula. But AI has dramatically increased the supply of this kind of content. And when supply floods the market, scarcity increases in value.

What is scarce right now is specificity. The iKnife. The Gouverneur. Ron, by name. The detail that could only come from a person who was actually there, who actually felt something, who is writing from lived experience rather than from a content framework.

This is not an argument against using AI in your writing process. I used Claude to analyse this post, and the analysis is sharper and more structured than anything I could have produced in a single sitting. AI is extraordinarily useful for structure, for gap analysis, for pressure-testing an argument. What it cannot do is manufacture the raw material — the specific memory, the genuine feeling, the voice that has been shaped by a particular life.

That is what you bring. And in a feed full of polished, hollow content, it is more valuable than it has ever been.

The scientists and technical professionals I work with often tell me they struggle to write about themselves because they don’t think their work “translates” for a general audience, or because they feel exposed putting personal experience alongside professional content. But what this post demonstrates — and what my connection’s message confirmed — is that the translation problem is almost never the real barrier. The real barrier is the belief that professional credibility requires a certain kind of distance, a certain kind of polish, a certain kind of voice that sounds nothing like you.

It doesn’t. In fact, that distance is exactly what gets you lost in the noise.

The most powerful thing you can do, right now, is write like yourself. Name the places. Name the people. Say what you actually felt. Let the “iKnife” — your tool — be unexplained for a second because the reader’s curiosity about it is itself a form of engagement.

Your voice, specific, unique, and unmistakably yours, is the one thing that cannot be generated.

Frequently asked questions

• Does this mean I shouldn’t use AI to help me write? Not at all. The most effective approach is to use AI for the things it does well — structure, editing, gap analysis, SEO optimisation — while ensuring the raw material comes from you. Think of it as a thinking partner, not a ghostwriter. The post analysed in this article was written entirely by hand; the analysis was done with AI. Both have their place.

• How do I find my authentic voice if I’ve been writing “professionally” for years?

  Go back and read something you wrote before you were thinking about strategy or audience. A message to a colleague, a note to a student, an email you dashed off without overthinking. Notice what’s different. That difference is often where your voice lives.

• Isn’t specificity a risk — what if people don’t know the references? The iKnife example in this post proves the opposite. Unfamiliar references create curiosity, not confusion, as long as the emotional context is clear. You don’t need to explain everything. You need to make people feel that something real is happening.

• What makes a post perform well on LinkedIn in 2026? The fundamentals haven’t changed as much as people think. Content that feels personal, specific, and genuinely useful still outperforms content that is optimised for algorithms but hollow at the centre. The difference is that the bar for “polished” has been lowered by AI tools, which means the bar for “real” has been raised in perceived value.

• Is this approach relevant for scientists and technical professionals specifically? Especially for scientists and technical professionals. Audiences in these fields are often highly sceptical of marketing language and performative content. Specificity and intellectual honesty are not just stylistic preferences — they are trust signals. Writing that sounds like you thought it through, because you actually did, carries enormous weight in scientific communities.

Go further

1/ Explore how I help scientists build strategic websites → Visit tiffanysiegel-sci.com
2/ Follow along for weekly strategic insights → Connect on LinkedIn

I wasn’t handing in my resignation. I wasn’t pitching a startup. I was simply expressing an ambition. And I was shut down on the spot.

At the time, I wasn’t mature enough to understand how wrong that response was — on so many levels.

The ambition that wasn’t welcome

Let me revisit the scene. I was an early-career scientist with real enthusiasm for my research. But I also had this feeling that there was more I could contribute beyond the lab and the classroom. The idea of building something — creating value, solving a problem, bringing something to society — was really challenging and motivating to me.

I didn’t have a concrete business idea at that point, and I was aware of it. I wasn’t conducting research I had personally funded. The technology I worked with wasn’t something I had invented. The patents belonged to collaborators. And so, in my mind, I didn’t feel I had “earned” the right to think about entrepreneurship.

That’s imposter syndrome talking, of course. Years ago, I truly believed you had to invent something from scratch — a novel technology, a groundbreaking discovery — to justify building a company around it.

It took me years to realise that value doesn’t only come from invention. It comes from perspective, from translation, from bridging gaps that others don’t see. We also live in a world where truly novel inventions are harder to come by than they were twenty years ago — but the need for people who can connect, communicate, and strategise around existing innovation has never been greater.

That’s exactly where I found my value when I started my freelance activity. But that realisation came much later.

What bothered me then — and infuriates me now

Back then during my evaluation, I did not push back nor react. I accepted the comment. Moved on. But looking back, two things bother me deeply.

Dismissing enthusiasm instead of nurturing it

The first is that my evaluator’s response completely disregarded the enthusiasm and motivation of an early-stage researcher whose only ambition was to create something meaningful. I wasn’t trying to piggyback on someone else’s work. I was dreaming out loud — in a place I trusted was safe — about contributing to society in a different way. And I was told, basically, that I could not do it and to stay in my lane.

Universities talk endlessly about innovation, entrepreneurship, and knowledge transfer. Yet when a young researcher expresses entrepreneurial curiosity, the response is “pick one”? That’s not mentorship. That’s gatekeeping.

Interestingly, research confirms this matters. A study published in PLOS ONE found that the academic environment has a fundamental impact on students’ decisions to start new ventures — and that women are especially sensitive to whether that environment is supportive or discouraging [1]. When a supervisor shuts down entrepreneurial ambition, the effects are not neutral. For women in particular, that discouragement sticks.

What makes this worse is that the damage often starts long before the evaluation room. Research on the intergenerational roots of the entrepreneurial gender gap has found that the gap doesn’t only emerge at the execution stage — when women try to actually start a business — but is already present at the level of entrepreneurial tendency [2]. By the time women reach the point of launching a venture, many have already internalised a self-perception that they’re not cut out for entrepreneurship. That internalisation is driven in part by parental and environmental role modelling [2] — and, I would argue, by comments like the one I received.

The gender dimension I couldn’t ignore

Here’s the second thing that bothers me — and this is the one I find harder to talk about, because it requires me to name something I observed throughout my entire academic career — although I unconsciously made “an effort” to not see it.

The comment came from a male professor.

And throughout my career, I watched male colleagues build startups, take on consulting contracts, sit on advisory boards, and commercialise their research — all while maintaining their academic positions. Nobody told them to choose.

I wish this were just my personal experience. But it isn’t.

What the data actually shows

The gender gap in academic entrepreneurship is not anecdotal. It is well-documented, persistent, and systemic — and it plays out everywhere, including right here in Aotearoa New Zealand.

The pipeline leaks early

A 2023 study published in PNAS examined the role of faculty advisors in training PhD students as new inventors at leading US research universities. The findings were striking: female PhD students were less likely to be matched with top inventor advisors, and both male and female advisors transformed their female students into new inventors at a 4 to 8 percentage point lower rate than their male students [3]. The pipeline for academic entrepreneurship is leaky from the very start — and the leak disproportionately affects women.

Research from 2012 on gender differences in scientists’ participation in corporate scientific advisory boards found that, holding constant professional achievement, network ties, and research focus, male scientists were almost twice as likely as female scientists to serve on these boards [4]. The researchers found no evidence that women were choosing not to participate — the gap was driven by demand-side factors, meaning women simply weren’t being invited. While this study is now over a decade old, there is little evidence to suggest the landscape has shifted significantly since.

The numbers closer to home

This isn’t just a US or European problem. The University of Auckland’s Gender Investment Gap research — led by Jenny Rudd and Dame Theresa Gattung — found that in 2024, female-only startups in Aotearoa received just $4.59 million from a total investment pool of $159.9 million. That’s 2.9% of every dollar invested [5]. By project count, only 10% of funded startups were female-led, compared to 68% male-led [5].

Globally, only 15% of tech startup founders are women. New Zealand actually ranks comparatively well, with 45% of startups having at least one female founder — but this masks the stark funding disparity [6]. And while Aotearoa has the world’s highest percentage of women graduates who go on to found startups (13.4%, with nearly half from the University of Auckland), the gap between intention and investment remains wide [7].

At the same university, a 2021 survey found that 28% of male undergraduates aspired to run their own business within five years, compared to just 18% of women — and both figures lag behind the global averages of 37% and 30% respectively [8]. Women also reported lower confidence in their ability to start a business, despite no evidence of lower capability [8].

In Australia, the picture is similar: only 37% of university STEM enrolments are from women, and women represent just 15% of all people working in STEM jobs [9]. In 2023, just 18% of total Australian startup capital went to ventures with at least one woman founder [10].

The funding gap is global

Zooming out, of the $289 billion invested globally in venture capital in 2024, just 2.3% went to female-only founding teams — approximately $6.7 billion — while 83.6% went to all-male teams [11]. A global meta-analysis published in the Journal of Business Ethics in 2024, spanning three decades of data and over a million data points, confirmed a prolonged and worldwide bias against women in entrepreneurial bank finance, with women’s loan applications rejected more often and at higher cost [12].

In the US, for example, only 11% of university startups have women founders [13], and in the country’s premier federal programme for academic entrepreneurship — the NSF I-Corps — only 20% of participants since its inception have been women [14].

These are not edge cases. This is the system.

The “double masculinity” problem

Researchers have described what happens at the intersection of STEM and entrepreneurship as “double masculinity” [15]. STEM fields are already male-dominated — and entrepreneurship carries its own set of masculine-coded expectations around risk-taking, self-promotion, and competitive ambition. Women operating at this intersection face compounded bias.

A systematic review of the gender gap in higher STEM studies identified gender stereotypes, the “leaky pipeline,” and stereotype threat as the strongest drivers of women’s underrepresentation — with influences from family, educational environment, and peer groups all playing reinforcing roles [16]. The review found that self-concept, self-efficacy, and self-perception need to be actively fostered in order for women to choose their paths based on goals rather than gendered expectations.

And this isn’t just about stereotypes in the abstract. A longitudinal study tracking over 1,300 young people into adulthood found that women actually demonstrate greater skill variety than men during adolescence — but this advantage reverses as men accumulate broader educational and professional experiences [17]. The entrepreneurial gap isn’t innate. It’s manufactured by the environments we move through.

This resonates deeply with my own experience. I didn’t have female entrepreneurial role models in my immediate academic circle. The people I saw building companies, consulting on the side, and commercialising research were overwhelmingly men. And when I expressed even a quiet interest in doing the same, I was told it wasn’t compatible with my academic career.

The study on PhD entrepreneurship I mentioned earlier confirmed this pattern: what actually made the biggest difference for women wasn’t university programmes or incubators — it was the opportunity to work in a gender-balanced environment [1]. The presence of female role models and peers mattered more than any institutional initiative.

But here’s what’s changing

There is a silver lining, and it’s coming from an unexpected place.

Recent data from the Female Innovation Index shows that women who founded deep tech startups in Europe are actually raising more than men in that sector [18]. About 33% of all venture capital raised by female entrepreneurs in Europe is now going into deep tech — a sector closely linked to academia, where women tend to be more equally represented in the research pipeline.

More than 80% of the 50 largest funding rounds raised by female-founded startups in 2024 went to those with scientific backgrounds — in areas like synthetic biology, generative AI, and drug development [18]. The academic pathway, it turns out, is becoming a strength rather than a limitation for women entrepreneurs.

And here in Aotearoa, there are signs of momentum too. The Aotearoa Centre for Enterprising Women at the University of Auckland is working to build better data, stronger networks, and more visible role models for women entrepreneurs [7]. The Gender Investment Gap research itself is a form of change — because as Jenny Rudd put it, “you can’t fix what you can’t measure” [5].

This is encouraging. But it also highlights how much was lost in the years when women like me were told to stay in their lane.

What I know now

I didn’t build a company during my academic career. Not because I couldn’t — but because I was told I shouldn’t, and I believed it.

When I eventually left academia and started my independent activity, I discovered that my value wasn’t in inventing a new technology. It was in everything I had built along the way: the ability to translate complex ideas, to think strategically, to connect the dots between scientific expertise and digital visibility. The very skills that academia didn’t recognise as “entrepreneurial” turned out to be exactly what scientists and technical professionals needed.

If you’re a researcher reading this and you’ve been told — explicitly or implicitly — that entrepreneurship isn’t for you, I want you to question that. Look around. Look at who is building companies alongside their academic careers, and who is being told they can’t.

And if you’re in a position of influence — a supervisor, a department head, a mentor — think carefully about how you respond when a young researcher expresses an entrepreneurial dream.

Your words carry more weight than you think.

FAQs

• Can you really have an academic career and run a company at the same time? Yes — and many academics do. University policies vary, but most institutions allow (and increasingly encourage) academics to engage in consulting, spin-off creation, and industry collaboration. The key is transparency and alignment with your institution’s intellectual property and conflict-of-interest policies.

• Is the gender gap in academic entrepreneurship improving? Slowly. While the deep tech sector in Europe shows promising signs — with women founders raising proportionally more than men [18] — significant disparities persist in startup creation, venture funding, and advisory board representation. At the current rate of improvement, it would take until approximately 2065 to reach gender parity in venture capital allocation [11].

• Why do women participate less in academic entrepreneurship? Research points to structural rather than merit-based explanations. Women have smaller professional networks connecting academia to industry, receive fewer invitations to advisory boards, and often face environments that discourage commercial pursuits [1, 4]. Crucially, the gap isn’t just about barriers at the execution stage — entrepreneurial self-perception is shaped much earlier, through parental role modelling and environmental signals [2].

• What does the picture look like in New Zealand? Aotearoa has some bright spots — including the world’s highest rate of women graduates who go on to found startups [7]. But the funding gap is stark: female-only startups received just 2.9% of venture capital investment in 2024 [5]. The University of Auckland’s research suggests that closing this gap requires action from investors, not just founders.

• What can universities do to support women in academic entrepreneurship? Evidence suggests that gender-balanced environments make the biggest difference [1]. Universities can also provide targeted mentoring, ensure women are included in industry networking events, actively nominate women for advisory board positions, and foster self-efficacy and entrepreneurial confidence from the PhD stage [3, 16].

• Do you need to invent something new to start a company as an academic? Not at all. Many successful companies are built on translating, applying, or combining existing knowledge in new ways. Strategic communication, consulting, and service-based businesses are equally valid paths — and often better suited to the transferable skills academics develop.

References

[1] Muscio, A. & Vallanti, G. (2022). The gender gap in Ph.D. entrepreneurship: How do students perceive the academic environment? PLOS ONE, 17(4), e0261495. https://doi.org/10.1371/journal.pone.0261495

[2] Lecker, J. (2021). What stands behind the gender gap in entrepreneurship? Untangling the intergenerational parental role. PLOS ONE, 16(12), e0260793. https://doi.org/10.1371/journal.pone.0260793

[3] Ganguli, I., Gaulé, P. & Guinan, E.C. (2023). Faculty as catalysts for training new inventors: Differential outcomes for male and female PhD students. Proceedings of the National Academy of Sciences, 120(35), e2200684120. https://doi.org/10.1073/pnas.2200684120

[4] Ding, W.W., Murray, F. & Stuart, T.E. (2012). From bench to board: Gender differences in university scientists’ participation in corporate scientific advisory boards. Academy of Management Journal, 55(3), 698–721. https://doi.org/10.5465/amj.2011.0020

[5] University of Auckland Gender Investment Gap (GIG) Research (2024). Rudd, J. & Gattung, T. https://www.thegenderinvestmentgap.co.nz/

[6] Startup Genome (2022). Only 15% of Tech Startup Founders Are Female. https://startupgenome.com/insights/only-15-of-tech-startup-founders-are-female

[7] Aotearoa Centre for Enterprising Women, University of Auckland Business School. https://www.enterprisingwomen.ac.nz/research/

[8] University of Auckland (2025). Why aren’t more women launching start-ups? Based on GUESSS 2021 data. https://www.auckland.ac.nz/en/news/2025/02/20/why-aren-t-more-women-launching-startups--.html

[9] Australian Government Department of Industry, Science and Resources (2024). The state of STEM gender equity in 2024. STEM Equity Monitor. https://www.industry.gov.au/news/state-stem-gender-equity-2024

[10] Australian Government (2024). Status of Women Report Card 2024. https://genderequality.gov.au/status-women-report-cards/2024-report-card

[11] Founders Forum Group (2025). Women in VC & Startup Funding: Statistics & Trends — 2025 Report. https://ff.co/women-funding-statistics-2025/

[12] Malmström, M., Burkhard, B., Sirén, C., Shepherd, D. & Wincent, J. (2024). A meta-analysis of the impact of entrepreneurs’ gender on their access to bank finance. Journal of Business Ethics. https://mendoza.nd.edu/news/entrepreneur-gender-gaps-greater-in-societies/

[13] Northwestern University InQbation Lab, FoundHer programme. https://www.invo.northwestern.edu/innovation-commercialization/about/news/foundher-takes-on-the-gender-gap-in-entrepreneurship.html

[14] Epstein, D. et al. (2025). Gender diversity in academic entrepreneurship: Social impact motives and the NSF I-Corps program. Research Policy, 54(2). https://doi.org/10.1016/j.respol.2024.105108

[15] Ahl, H. & Marlow, S. (2020). Stemming the gender gap in STEM entrepreneurship. International Entrepreneurship and Management Journal, 17, 7–25. https://doi.org/10.1007/s11365-020-00642-5

[16] Verdugo-Castro, S., García-Holgado, A. & Sánchez-Gómez, M.C. (2022). The gender gap in higher STEM studies: A systematic literature review. Heliyon, 8(8), e10300. https://doi.org/10.1016/j.heliyon.2022.e10300

[17] Krieger, A., Block, J., Stuetzer, M., Obschonka, M. & Salmela-Aro, K. (2022). Closing the gender gap in entrepreneurship: The importance of skill variety. PLOS ONE, 17(7), e0270976. https://doi.org/10.1371/journal.pone.0270976

[18] TechCrunch (2025). Female Innovation Index 2025: The decline in funding has hit male and female-led startups equally, but women out-raise men in DeepTech. https://techcrunch.com/2025/03/04/funding-to-women-founded-startups-declined-12-in-2024-but-that-wasnt-far-behind-the-broader-market/

This newsletter has been in the back of my mind for months (dare I say years…). Not as a vague “I should probably start a newsletter” thought — more like an obsession that wouldn’t go away. The kind that follows you through your morning coffee, pops up during meetings, and keeps you scribbling notes at odd hours.

So why did it take so long?

Because I practise what I preach. And what I preach is: strategy before execution. Always.

I could have launched months ago with a handful of articles and a “let’s see what sticks” approach. But that felt wrong — especially given that this publication is about strategic communication. If I was going to write about doing things intentionally, I needed to start that way myself.

So I spent the last few months getting clear on what I wanted Strategy360 to be. Who it’s for. What it isn’t. How the pieces fit together. I mapped out content pillars (which I have tried to avoid for months), tested ideas on LinkedIn, studied what resonated and what fell flat, and — honestly — gave myself permission to take the time I needed.

The result is what you’re reading now. Not perfect — there will be changes and improvements along the way — but intentional. And I’d rather launch with clarity than rush with confusion.

What Strategy360 is about

If you’re a scientist, academic researcher, or technical professional who’s ever felt like the digital world speaks a language you weren’t taught — this is your space.

Strategy360 sits at the intersection of science and strategic communication. I write about SEO, branding, and digital visibility — but always through the lens of someone who’s spent nearly 20 years in research, biotech, and academia before making the leap into digital strategy.

The articles live here on Substack, organised across three themes: SEO for technical experts, Building your brand, and Career transitions. You can read them whenever you like — they’re not going anywhere.

This monthly newsletter is different. It’s the behind-the-scenes layer. Personal updates, reflections on what I’m learning, a curated round-up of what I’ve published, and — because I genuinely want to hear from you — a question at the end.

What I’ve been up to (January–February 2026)

These past two months have been a lot. Let me catch you up.

Three months into my new role. I joined the University of Auckland as a Digital Content Developer in January, working with the TeachWell platform to create educational resources for teaching staff. It’s a role that combines my love of learning design with my background in science communication — and after years of navigating very different environments, it feels like the pieces are finally fitting together. I’m learning a lot, and I’ll share more about this experience in future posts, but before all, I am most grateful to my colleagues (if they read me, they’ll know) who have welcomed me and made me feel like I’ve landed where I was meant to.

“Quel gâchis!” If you follow me on LinkedIn, you might have seen this one. Over the Christmas break in France, a family member responded to my latest career move with: “What a waste!” To them, I was throwing away years of education and scientific experience. It stung — even though I know better. I wrote about it because I think many of you have heard similar comments, and I wanted to say what I believe to be true: a diploma doesn’t define us, and a non-linear career isn’t a failed one. It’s a strategic one. That post resonated more than I expected, and it confirmed something important — the career transition conversation matters to this community.

Back to mentoring with FEMS. In February, I restarted my involvement with FeMS+ (Females in Mass Spectrometry) after a one-year break. Mentoring women in STEM through career pivots is one of the most rewarding things I do, and I’m excited to be back. If mentoring is something you’re curious about — whether giving or receiving — I’d love to hear about your experience.

Réseau Biotechno website rebrand. I completed a full website rebrand for Réseau Biotechno, a French biotechnology career network that connects young life scientists with industry professionals. This was a project that combined everything I do — SEO strategy, visual identity, content restructuring, and metadata optimisation. I’ll be writing a proper case study about this soon, because it’s a perfect example of what happens when strategy leads the design process. But here’s the news for you, in avant-première!

Discover the website en avant-première

On Substack right now

I’ve published two articles so far, and they’re both about something I feel strongly about: why you need strategy before you need a website.

Why your website needs strategy before design. This one started as a LinkedIn reflection on a pattern I kept seeing early in my freelance work — beautiful websites that did absolutely nothing for the people who owned them. The article expands on that story with a practical framework and the hard truth about what happens when you skip the strategic foundation. → Read: Why your website needs strategy before design

Are you ready for a website? 8 questions to find out. The companion piece. A diagnostic tool disguised as an article — eight questions to ask yourself before you invest in a website, each with what a strong answer looks like and the red flags to watch for. I also created a downloadable checklist you can use to work through the assessment at your own pace. (special shout out to Samuele Lisi for encouraging me adding infographics to my content, that’s been a blast to create and I hope you’ll appreciate!) → Read: 8 questions to ask yourself before building your website to know if you’re ready

What’s coming next

I’m building something I’m genuinely excited about — a series called SEO beyond keywords.

Here’s the premise: most scientists and technical professionals have been told that SEO is about keywords, meta tags, and gaming algorithms. But SEO has evolved dramatically over the past three decades, and what search engines actually reward now — experience, expertise, authoritativeness, trustworthiness — are qualities that researchers already possess through their careers. You just haven’t been taught to make them digitally visible.

The series will trace SEO’s evolution, challenge some popular misconceptions, explore how large language models are changing the game, and connect it all back to a 360° strategic approach. It’s the backbone of what Strategy360 is about, and I can’t wait to share it with you.

First article coming soon. Stay tuned.

A question for you

I want to hear from you — genuinely.

What’s the one thing about digital visibility, SEO, or online presence that feels most confusing or overwhelming to you as a scientist or technical professional?

Hit reply and tell me. I read every single response, and your answers will directly shape what I write about next.

Thank you for being here for this very first issue. It means more than you know.

See you next month,

Tiff.

Make sure you do not miss the next issue:

PS2: If you know a fellow scientist, researcher, or technical founder who might benefit from Strategy360, I’d be grateful if you forwarded this to them. Growing this community one thoughtful reader at a time is exactly how I want to do it.

Share Strategy360

I’ve seen it happen enough times to recognise the pattern: someone invests in a beautiful website, launches it, and then… nothing. No traffic, no engagement, no leads. Not because the design was wrong, but because there was no strategic foundation underneath it.

That’s why I now work through these eight questions with every client before a single wireframe gets drawn. They’re designed to surface whether you have the clarity to build something that actually performs — or whether there’s foundational work to do first.

If you want to understand why strategy matters before design, I’ve written a companion piece that covers the full argument: Why your website needs strategy before design →

This piece is about the how. Let’s find out where you stand.

How to use this assessment: Work through each question honestly. Pay attention to whether your answers sound more like the “strong answer” or the “red flag.” You don’t need perfect answers to all eight — but you do need to know where your gaps are before you invest.

1. Who is your primary audience?

Why it matters: Your website isn’t for everyone — and if you try to speak to everyone, you’ll connect with no one. Scientists and technical professionals often struggle here because their work spans multiple audiences: funders, collaborators, students, the public, potential clients.

Pro tip: It is ok to stagger your message and communication strategy into different types of content and channels. Not all messages have to be heard by everyone all the time.

A strong answer sounds like: “Early-career researchers in biotech who are transitioning into industry consulting and need to establish credibility outside academia.”

A red flag sounds like: “Anyone interested in what I do.” If you can’t name a specific group and describe what they need from you, the website will lack focus — and so will everything on it.

2. What problem do you solve for them?

Why it matters: People don’t visit websites out of curiosity. They’re looking for something — a solution, an answer, a person who understands their challenge. Your site needs to make that connection immediately.

A strong answer uses their language, not yours: “I help founders translate complex science into investor-ready narratives.”

A red flag is describing your process instead of their problem: “I offer strategic communication consulting services.” That tells someone what you do, not why they should care.

3. What action do you want visitors to take?

Why it matters: Every page on your site should guide visitors toward something. Without a clear call to action, you’re leaving the next step entirely up to them — and most people won’t take it.

A strong answer: “Book a 30-minute discovery call” or “Download my free readiness checklist.”

A red flag: “I just want them to know about me.” Awareness is not a conversion strategy. What happens after they know about you?

4. What makes you different from alternatives?

Why it matters: Your audience has options. Other consultants, other researchers, other service providers. Your website needs to communicate — quickly — why you are the right choice for them.

A strong answer is specific and grounded in experience: “I combine 15 years of pharmaceutical research with hands-on digital strategy — I speak both languages.”

A red flag is generic claims: “I’m passionate and dedicated.” Everyone says that. What can you offer that others genuinely can’t?

5. Do you have content ready to populate the site?

Why it matters: This is the question that catches most people off guard. A website needs words, images, case studies, and proof of expertise. If you’re planning to “figure that out later,” you’re not ready to build yet.

A strong answer: “I have a working bio, three case studies, published articles I can reference, and a clear idea of my core service pages.”

A red flag: “I’ll write the content once the site is designed.” This is backwards and I can tell you: it does not work! Content shapes structure and the design — not the other way around. Designing a site without content is like designing an experiment without a hypothesis — you’ll generate data, but you won’t know what it means. Or even like ordering reagents before you’ve designed the experiment — it is expensive, and half of it won’t be what you actually need.

6. How will people find your website?

Why it matters: A website without a traffic strategy is invisible. This is the question that connects your site to the real world — and it’s where search engine optimisation (SEO), social media, and your broader communication ecosystem come in.

A strong answer: “I’ll drive traffic through LinkedIn content, conference appearances, and organic search for key terms my audience is searching for.”

A red flag: “I’ll just share the link when people ask.” If discovery depends entirely on you manually sending URLs, your website isn’t doing its job.

7. How does this website support your broader business goals?

Why it matters: Your website doesn’t exist in isolation. It should be part of a bigger picture — supporting your consulting pipeline, your thought leadership, your funding applications, or your career transition.

A strong answer: “The website is my credibility anchor. It supports my LinkedIn content, gives me a place to host long-form insights, and funnels potential clients into my discovery process.”

A red flag: “Everyone has a website, so I should too.” That’s not a goal — that’s peer pressure. And it usually leads to a site that ticks a box without delivering value.

8. What does success look like 6 months after launch?

Why it matters: If you can’t define what success looks like, you won’t know whether your investment was worth it. This question forces you to think in measurable outcomes, not vague aspirations.

A strong answer: “Three qualified leads per month from organic search, a 40% increase in LinkedIn profile views from website cross-traffic, and at least two speaking invitations that reference my published content.”

A red flag: “I just want it to look good.” Looking good is a baseline. What do you want the site to do?

What to do if you’re not ready (and that’s okay)

If working through those questions made you realise you don’t have solid answers yet — that’s actually a good sign. It means you’re thinking critically about this instead of rushing into a build.

Not being ready for a website doesn’t mean you’re behind. It means you’re being honest about where you are — and that’s where the real work begins.

Building your strategic foundation might look like defining your audience and testing your messaging on LinkedIn before committing it to a website. It could mean developing a content plan so you know what your site will actually say. Or it might mean getting clear on your service offering and positioning first — because a website built on a shaky foundation will need to be rebuilt the moment your business evolves.

I work with clients at this stage too. Sometimes the most valuable thing I can do is help someone build the strategy that comes before the website — so that when they’re ready, we’re building on clarity, not guesswork.

I've written about why that strategic foundation matters so much → if you want the full picture.

So here is what I would encourage you to do: don’t treat “not ready” as a failure or an obstacle. Treat it as a phase and an opportunity to know better your business, and/or yourself – what you want to achieve. The readiness assessment isn’t a pass/fail test — it’s a diagnostic tool. It tells you where to focus your energy next.

And very importantly: be realistic about timelines. Strategic foundation work isn’t a weekend project. Allow yourself to spend a few weeks — or even a couple of months — getting clear before you invest in the build. Your future website (and your budget) will thank you.

Final thoughts

I’ve tried to skip these questions myself — thinking my intuition was enough — but I was so wrong! Not only do I have clarity now, but documenting the process and thinking has been a considerable gain of time. I don’t need to restart from scratch every time I have a new idea for content or a concept to work on, because I’ve built the foundations.

These eight questions aren’t just for my clients. They’re for anyone who wants their website to be more than digital decoration.

Strategy first. Always.

Frequently asked questions

• What if I can only answer some of the questions confidently? That's completely normal — and it's useful information. The questions where you struggle are showing you exactly where to focus before investing in a build. Even answering five or six well gives you a strong starting point; just be honest about the gaps.

• Can I use this assessment for an existing website, or is it only for new builds?It works for both. If you already have a website, running through these questions is an excellent way to diagnose why it might not be performing. The answers will tell you whether you need a redesign, a content overhaul, or a strategic rethink.

• What if my business is still evolving — should I wait? Not necessarily. If your audience and core offering are clear enough to answer most of the eight questions, you're ready. Build for where you are now with flexibility for growth. A strategic site can evolve with you; a directionless one will need replacing.

• How is this different from a creative brief? A creative brief typically focuses on visual preferences — colours, fonts, references. This assessment sits upstream of that. It's about the strategic decisions that should inform the creative brief. Get these right, and the creative brief practically writes itself.

• Do I need to work with a strategist, or can I do this on my own? You can absolutely work through these questions independently — that's why I've shared them. But if you find yourself going in circles or struggling to be objective about your own positioning, a strategist can help you move faster and see blind spots. That's part of what I do in my discovery process.

Go further

1/ Understand the full case for strategy-first web design → Read Why your website needs strategy before design
2/ Explore how I help scientists build strategic websites → Visit tiffanysiegel-sci.com
3/ Follow along for weekly strategic insights → Connect on LinkedIn

A researcher needed a personal site to showcase their work. A startup founder wanted a redesign that “matched better with what they were doing” — but without changing structure or copy.

I would design something clean and professional. We’d launch it. And then… nothing.

The client wouldn’t maintain the site, wouldn’t update or enrich the content. No promotion on social channels, no integration into their communication. No one knew the site existed because there was no plan to drive people there.

If this sounds familiar, you’re not alone. It took me several of these experiences to realise the problem wasn’t the design — it was that we’d skipped the most important step entirely.

Here’s what I’ve learned about getting it right.

The real cost of building without strategy

When I say “a website without strategy is just expensive digital real estate,” I’m not being dramatic. The cost goes far beyond the invoice.

There’s the opportunity cost — the months (sometimes years) your site sits there while your competitors are actively using theirs to generate leads, build their authority and credibility, and grow their audience. Every day a website exists without purpose is a day it could have been working for you.

There’s the credibility risk. For scientists, researchers, and technical professionals, your online presence often forms a first impression with funders, collaborators, media, and/or potential clients. An outdated or unfocused website doesn’t just fail to help — it can actively undermine the expertise you’ve spent years building. A site that says nothing strategic about you still says something about you.

And then there’s decision fatigue. Without a strategy to guide you, every choice becomes harder. What goes on the homepage? How should the navigation be structured? What content do you need? You end up making dozens of design decisions based on gut feeling instead of data and goals — and the result usually reflects that.

The difference between a website and a strategic digital asset is this: one exists, the other performs. “Just getting something online” might feel productive, but for technical professionals who need to communicate complex values to non-expert audiences, a generic site can do more harm than having no site at all.

What strategy actually means (and why it’s not just a buzzword)

I know “strategy” can sound like a vague, overused term — the kind of word that gets thrown around in meetings without anyone agreeing on what it means. So let me try to explain what I mean, and be specific.

When I talk about website strategy, I mean clarity about four things:

• who you’re trying to reach;

• what you want them to do;

• why they should care about you specifically;

• how this website fits into everything else you’re doing to build your visibility.

This is fundamentally different from “I need a website that looks professional because I have to be online.” Professionalism is a minimum standard, not a strategy. A beautiful website with no clear audience, no defined goals, and no connection to your broader communication — that’s decoration, not a tool.

And don’t get me wrong – I did the same mistake with the first iteration of my own website, and I ended up with something generic, imprecise, that did not connect with my audience.

Here’s what lacking a strategy looks like in practice. Imagine two biotech consultants, both launching personal websites. One says, “I need a clean site with my CV, services, and a contact form.” The other says, “My primary audience is early-stage founders looking for regulatory guidance. I want them to book a discovery call. My differentiator is that I’ve been on both the research and industry side. My website needs to connect with my LinkedIn content and support the talks I give at conferences.”

Same starting point. Completely different outcomes. The first gets a digital brochure. The second gets a strategic asset that actively supports their business goals.

The strategy conversation isn’t about making things complicated — it’s about making sure that every design decision has a reason behind it.

How strategy transforms the design process

When you start with clarity, everything changes. The design process becomes faster, more focused, and significantly less stressful — for you and for your designer. You might even enjoy contributing to the process of designing your website and seeing it coming to life!

Instead of debating homepage layouts in the abstract, we know exactly who’s landing there and what they need to see first. Instead of guessing at navigation structure, we build it around user journeys that support your goals. Instead of writing placeholder copy and hoping to “fix it later,” the content is already aligned with your messaging and SEO strategy.

This is why I now combine SEO strategy into every project from day one. When strategy leads, SEO isn’t an afterthought bolted on at the end — it’s woven into the site architecture, the content hierarchy, and the way every page is structured. Your site doesn’t just look right; it’s built to be found.

And here’s something that often surprises people: this approach actually saves time and money. Fewer revisions, fewer “actually, can we change the whole direction?” moments, fewer post-launch regrets. When the strategic thinking is done upfront, the design process flows — because every decision has a reason behind it.

The websites I build now? They work. Not because I became a better designer, but because we stopped skipping the step that makes design meaningful.

Are you ready to build? Find out first

If you’re reading this and thinking, “Okay, but how do I know if I actually have enough strategic clarity to move forward?” — I’ve created a companion piece that walks you through exactly that.

It’s an 8-question readiness assessment designed to surface whether you have the strategic foundation to build something that works — or whether there’s groundwork to do first. Each question includes what a strong answer looks like, what red flags to watch for, and practical guidance to help you get unstuck.

👉 Read next: 8 questions to ask before building your website — a readiness assessment

Final thoughts

Strategic thinking isn’t reserved for big agencies or corporate marketing teams. It’s a skill — and if you’ve written a research proposal, structured a scientific argument, or built a case for funding, you already have excellent foundations – maybe even without knowing it.

The take-home message is simple: before you invest in a website, invest in the thinking behind it. Ask yourself the eight questions above. Be honest about the answers. And if you need help working through them, that’s exactly what a discovery process is for. And where I come in to help!

Strategy first. Always.

Frequently asked questions

• Do I really need a strategy for a simple personal website? Yes — even a simple site benefits from knowing who it’s for and what it should achieve. “Simple” should mean focused, not directionless. A one-page site with clear strategy will outperform a five-page site without it.

• How long does the strategy phase typically take? For most of my clients, the strategic foundation work takes two to four weeks. This includes the readiness assessment, audience definition, messaging development, and content planning. It’s a relatively small time investment that shapes everything that follows.

• Can’t I just build the site and add strategy later? You can — but I would not recommend it as it is significantly more expensive and time-consuming. Retrofitting strategy onto an existing site usually means restructuring content, rewriting copy, and sometimes redesigning key pages. It’s far more efficient to get it right from the start.

• I’m an academic researcher, not a business. Does this apply to me? Absolutely. Whether you’re building a personal academic site, a lab group page, or a portfolio for industry transition, the same principles apply. You have an audience (funders, collaborators, potential employers), you need them to take action (read your work, reach out, cite you), and you need a plan to get them there.

Go further

1/ Not sure if you're ready to build? → Take the 8-question readiness assessment
2/ Explore how I help scientists build strategic websites → Visit tiffanysiegel-sci.com
3/ Follow along for weekly strategic insights → Connect on LinkedIn