At a Glance
Role: Co-Founder, Head of AI, Strategy, and Growth
Company: Inf//uent – Life Sciences Marketing
Background: Chemical Engineering
Industry Focus: Biotechnology, Cell & Gene Therapy, Vaccines, Biologics, CDMOs
Location: Denver Metropolitan Area, USA
Most Valuable AI Tool: “The honest answer is not a brand name, it is the API layer.”
AI’s Biggest Productivity Gain: “We move faster from market to insight.”
Marketing Philosophy: “AI is another channel in the mix, but it is a channel that can heavily inform all the others.”
Most Important Guardrail: “Nothing automated reaches a client or the market without a human reviewing and approving it first.”
"The productivity gain is in the analysis and the scaling, not in the judgment."
Artur Tkachev has spent his career working at the intersection of science, engineering, and marketing. From chemical engineering to commercial strategy in biotechnology, his experience spans biologics, cell and gene therapy, vaccines, and advanced life science services.
Today, as Co-Founder and Head of AI, Strategy, and Growth at Inf//uent, Artur Tkachev helps life science companies transform complex scientific expertise into clear market positioning, connected marketing systems, and measurable commercial growth.
In an industry where technical innovation alone rarely guarantees market success, he believes AI should strengthen strategic decision-making rather than replace it. In this interview, Artur shares how his team uses AI to analyze markets, uncover narrative opportunities, and build marketing systems that support long-term business growth.
Q&A
Which AI tools are most valuable to your team today?
The honest answer is not a brand name, it is the API layer. Raw access to the underlying models, ChatGPT, Claude, Gemini, is the most powerful tool we have, because it lets us work across platforms inside agentic workflows and custom applications instead of being boxed into any one vendor’s chat window.
That distinction matters more than it sounds. A consumer chat interface is built for a person typing one question at a time. The API is built for systems: running the same structured task across three different models, comparing how each one responds, chaining steps together, and wiring the output into something we have built ourselves. For the kind of work we do, that flexibility is the difference between using AI as a novelty and using it as infrastructure.
It also keeps us honest about a fast-moving market. Models change constantly, and each has its own strengths and quirks on any given task. Working at the API level means we can swap, test, and combine them as they evolve, rather than betting the workflow on a single provider and hoping it stays best in class. The tool we value most, in other words, is the one that keeps us model-agnostic.
Where has AI delivered the clearest productivity gain so far?
The clearest gain for our agency has come from GEO work, specifically, using AI to see how AI represents a market. That sounds circular, but it is the most concrete productivity unlock we have found. Manually trying to understand how ChatGPT, Claude, and Gemini describe a competitive landscape would take weeks of running queries by hand and squinting at the results. Pointing structured AI analysis at that problem compresses it into something we can actually run at scale and repeat, which is what turned GEO from a vague idea into a real diagnostic we can put in front of clients.
The reason it is such a clear gain is that the output feeds everything else. When we benchmark how AI systems represent a market, we are not just producing an AI-visibility score. We are surfacing narrative gaps, capability areas that clearly matter but that no company has claimed, questions buyers are evidently asking that the market has answered poorly. In a market as technically dense as life sciences, finding those openings by intuition alone is slow and unreliable. AI makes the gaps visible quickly, and a visible gap is the start of a content strategy.
That is the part worth being precise about, because it is easy to oversell. The productivity gain is in the analysis and the scaling, not in the judgment. AI tells us where a Contract Development and Manufacturing Organization (CDMO)’s real strengths line up with a narrative that is currently up for grabs. It does not tell us which openings are worth taking, or how to say something in a way that lands with a skeptical technical buyer. So the gain is real and immediate, it just sits in front of the human work rather than replacing it. We move faster from market to insight, and that lets us spend the saved time on the strategy and the writing, which is where the actual value still lives.
Can you share one example where AI improved a workflow, campaign, or client outcome?
The clearest example is a tool we built ourselves. As AEO and GEO started to matter, we went looking for a way to measure how AI systems actually represent a company, and we found that the available tools did not give the full picture. Most of them run a handful of brand-name queries across a couple of AI providers and call it analysis. In a technical market like life sciences, that is nowhere near enough to understand what is really being surfaced about a company, or why.
So we built our own. Rather than a few prompts, our GEO Brand Standing Benchmark runs a structured battery of queries, on the order of a hundred per study, across ChatGPT, Claude, and Gemini, with the specific model versions documented so the work is repeatable. We do not just count whether a brand gets mentioned. We calculate Share of Voice by normalizing each company’s mentions against the whole competitive set, then build composite measures on top of that: narrative strength, capability ownership, recommendation dominance, narrative fragility, source influence, sentiment, geographic visibility, and the knowledge gaps where AI clearly wants an answer the market has not supplied.
A real finding makes it concrete. When we benchmarked the ADC CDMO market, the data showed one provider clearly leading AI visibility, with a strong secondary tier behind it, but most of the high-value technical capabilities, complex molecule handling, high-containment facilities, regulatory differentiation, were actively contested, with no single brand owning them in the AI narrative. That is not a vanity ranking. That is a map. It tells a CDMO with genuine strength in one of those areas exactly where a focused content push could let it take ownership of a narrative that is currently up for grabs.
Two things we are deliberate about. We report findings as directional, not definitive, because citation rates in these markets are low and AI answers shift by model and by day, and we say so plainly in the work. And the tool is a diagnostic, not the deliverable. Its value is that it points to the few themes worth going deep on. The strategy and the content are still where the actual outcome comes from.
How has AI changed the way your team approaches creative or strategic decision-making?
It has given us a new input, not a new boss. A lot of marketers right now are reorganizing everything around AI search visibility, as if it were the whole game. We think about it differently. AI is another channel in the mix, but it is a channel that can heavily inform all the others, and that second part is where it has actually changed how we work.
Concretely, it has become a way to find narrative gaps. When we look at how AI systems describe a market, we can see where the story is thin, where a capability is clearly important but no company has claimed it, where buyers are evidently asking a question the market has not answered well. Those gaps are strategic gold, because they are precisely the places where a client’s real strengths can line up with an opening nobody else has taken.
So the decision-making loop now looks like this. Use AI to surface where the gaps and contested narratives are in a target market. Find the spots where a client has genuine, defensible strength that maps onto one of those gaps. Then build a content strategy that does two things at once: it highlights a real capability, and it takes ownership of a narrative the AI is currently telling us is unclaimed. That is a sharper way to choose where to compete than instinct alone, and it keeps the creative work pointed at openings that actually exist rather than ones we assume are there.
What has not changed is the judgment call at the end. AI tells us where the gaps are. It does not tell us which ones are worth taking, or how to say something in a way that lands with a skeptical technical buyer. That is still ours.
How are clients responding to AI being used in your agency’s workflows and delivery process?
They’re responding well, because we are not shipping them AI slop, and they can tell the difference. That is the whole answer, but it is worth unpacking, because the fear behind this question is real and reasonable. Clients have all seen the flood of generic, machine-extruded content, and nobody wants to pay an agency to hand them more of it.
Our rule is simple. AI is used to enable strategic content marketing, never to replace the expertise that makes it worth reading. Every output gets paired with two things a machine cannot supply: genuine market knowledge and a human dissecting the result, editing it, questioning all of the claims, and injecting the specific point of view that makes content credible to a technical audience. Nothing leaves the building as raw model output.
When that is the standard, AI stops being a thing clients worry about and becomes a net gain they feel. They get more, faster, without the quality drop they were braced for. The conversation shifts from “is this just AI-written” to “this is clearly built by people who know our market, and there is more of it than we expected.” That is the response we want, and it only happens because the human expertise is non-negotiable, not decorative.
What has been the biggest challenge when integrating AI into your agency’s work?
Keeping up. The space moves faster than almost anything we have worked in. Models change constantly, capabilities shift, and the ground rules for something as central to our work as AI search visibility get rewritten on a regular basis. Staying genuinely current is not a one-time setup cost, it is an ongoing discipline, and it is the hardest part of the job.
A good example: in 2026, Google published guidance that openly told site owners to skip a number of the popular GEO tactics that an entire cottage industry had been selling, things like special AI-only files and content chunking, because their AI features run on the same core search systems as everything else. Around the same time, independent analysis found that one of those much-hyped files produced no measurable lift at all. If you had built your whole approach around last year’s conventional wisdom, you were suddenly out of date. We read the primary documentation from the model providers directly, and we still have to revisit our assumptions constantly.
The practical consequence is that we treat our own methods as living things. The benchmark methodology, the channel recommendations, the technical guidance we give clients, all of it gets reviewed against what the platforms are actually saying and doing now, not what was true six months ago. It is more work. It is also the only responsible way to operate in a space this young, and frankly it is part of what clients are paying us for: to have already done the keeping-up so they do not have to.
What guardrails or best practices are most important for responsible AI use in your team?
One rule sits above all the others: nothing automated reaches a client or the market without a human reviewing and approving it first. No exceptions. AI can draft, scale, and accelerate as much as it wants inside our process, but the gate at the end of that process is always a person who knows the work and signs off on it.
That single guardrail does a lot of quiet work. It catches the confident-sounding errors that AI is prone to, the subtly wrong technical claim, the statistic that does not hold up, the phrasing that misreads how a regulated, risk-sensitive audience will hear it. In life sciences especially, a plausible-but-wrong sentence is not a small problem, so a human check on factual accuracy and claim fidelity is not optional.
Underneath it sits a related principle we apply to our own analysis: do not overclaim what the tools can actually support. When we report on AI visibility, we are explicit that the findings are directional, that citation rates are low, that results vary by model and over time. Responsible AI use is partly about what you let it produce, and partly about being honest regarding the limits of what it tells you. Both come back to the same instinct, which is to keep human judgment firmly in the loop rather than ceding it to a system that does not actually understand the stakes.
Where do you see the biggest opportunity for AI in marketing agencies over the next 12 months?
AEO and GEO, without much hesitation. The way buyers form their first impression of a company is shifting, and in a complex space like life sciences, AI systems are increasingly the first-pass research layer for sponsors, investors, and commercial teams trying to figure out who is credible before any human conversation happens. Helping clients understand and shape how they show up in that layer is the clearest opportunity in front of agencies right now.
The reason it is an opportunity and not just a trend is that almost nobody has done it well yet, and the work compounds. AI citation rewards depth over breadth: the companies that build genuine authority on a specific set of topics tend to hold that position as models keep citing them, which means early, disciplined movers build an advantage that gets harder to displace over time. For a focused agency, that is a rare combination, a real client need, a narrow window, and a payoff that accrues to whoever starts first and does it seriously.
We would add one note of discipline, because it is central to how we think. The opportunity is real, but it is a top-of-funnel opportunity. AI visibility shapes who makes the shortlist, not who wins a complex eight-figure CDMO contract, and any agency promising otherwise is overselling it. The biggest opportunity is not to chase AI visibility as a transformation. It is to integrate it sensibly into a real content strategy, sized correctly against every other channel, so it genuinely informs the rest of the mix instead of hijacking it. Done that way, GEO is not a fad. It is just good marketing, pointed at a new surface.
Final Thoughts
Artur Tkachev believes that effective marketing is built on structure, not just activity. Throughout the interview, one idea appears again and again: AI should strengthen strategic thinking, not replace it.
Whether he’s benchmarking AI visibility, identifying narrative gaps, or helping life science companies build stronger commercial systems, Artur’s approach remains rooted in human expertise. AI accelerates research, scales analysis, and uncovers opportunities, but meaningful growth still depends on experience, judgment, and a clear understanding of the market.
As AI continues reshaping the marketing landscape, Artur Tkachev’s perspective is a reminder that lasting competitive advantage doesn’t come from adopting the latest tools – it comes from using them to build better systems, communicate scientific value more effectively, and create measurable commercial growth.
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