S2 Ep1: 2026 Predictions and the Janus Threshold: When Presence Beats AI

Transcript

Transcript

In this Season Two premiere, Matt Wilkinson and Jasmine Gruia-Gray explore two critical articles: Jasmine's Janus Principle on cognitive offloading in customer discovery, and Matt's six major predictions reshaping life science marketing in 2026. They debate when AI accelerates research and when human presence is the only path to conviction, pattern recognition and strategic authority.

Welcome and Introduction

Matt Wilkinson [00:00]

Welcome to a splice of Life Science marketing. Your go to show for Life Science marketing professionals in biotech medtech and diagnostics join us for sharp, strategic conversations that turn cutting edge insights into real world marketing advantage. I'm Matt Wilkinson and I'm

Jasmine Gruia-Gray [00:18]

Jasmine Gruia-Gray in each episode, we'll cut through the hype and complexity with practical plays you can use to earn trust. Stand out in crowded categories and convert attention into momentum. Hello, gentlemen, Happy New Year. Happy New Year. Matt And Happy New

Matt Wilkinson [00:36]

Year to everybody that's joining us. Welcome to Season Two of a splice of Life Science marketing lasted this long in today's episode, we're going to be talking about two articles that we've written recently. One that Jasmine's written called the Janus principle, that looks at cognitive offloading and, you know, some really interesting insights and a fantastic framework that I know that everybody should be using. And then an article that I've written that really looks into some big predictions for 2026 and certainly some ideas that marketers should be thinking about and paying attention to. So without further ado, dive in. Let's kick it off. Jasmine, in your latest article, the Janus threshold, you argue that product teams and life sciences are at a pivotal moment. We know that AI can scale voice of customer and ethnographic research fast, but delegating presence has a hidden, hidden cognitive cost. The promise is really seductive. You know, you talk about how transcripts, themes create really tidy summaries, but the risk is that AI gives you conclusions without the mental activation that makes insights stick and really helps you get under the skin of those insights. You point to evidence that people who rely on generative AI struggle to recall what they produced, and that can translate into a warning for product managers who outsource too much discovery. The punchline of your pieces isn't not to use AI, it's to use AI for volume and post experience synthesis, and to use the phase framework that you introduce to decide when you must show up yourself based on my own experience of conducting hidden needs. Research your you know your comment that we still need to go and observe workarounds or observe people in the field, and to look at those workarounds that people use potential contradictions of what they say and what they do that really resonated with me. Now, one ethnographic project I conducted while working at LGC highlighted just how important it was to visit customers, observe them in their natural habitat and look for those workarounds, pain points and contradictions. So before getting too far into this, can you just define a little bit about what you mean by the Janus threshold itself,

The Janus Threshold Explained

Jasmine Gruia-Gray [02:44]

The way I look at it, the Janus threshold is the moment you decide whether to be present in discovery or to delegate the presence to AI. So Janus is a Roman god with two faces, and this serves as a good analogy to the product manager needing to look in both directions, forward at AI's efficiency promise, and backwards at what you're trading away by not being there. So if you cross that threshold, badly meaning. You surrender thinking, you surrender processing, you surrender reflection. You become what I sometimes call the pm vending machine, someone who puts in customer requests into that vending machine and outputs features without any transformative thinking in between. And that's that's a shame you operationally lose by doing that is human agency in shaping the research itself. Yes, AI executes the plan you gave it. It can pivot mid conversation, but it cannot pivot mid conversation when a researcher mentions an unexpected work around when that core facility director actually says that she's running weekend shifts only a human asks, why? The second thing you lose is pattern recognition across contradictions, when a researcher says they trust your your kit and the sensitivity of that kit, but then runs triple replicates on every sample your brain registers the gap between the words and the behavior. AI captures what the person has said, but it misses what their actions reveal. That contradiction is where insights really live. And the third thing that you have the potential of losing is the one that keeps me up at night the most, and that's the loss of conviction when you present a strategic pivot based on AI summaries, you sound uncertain because you are uncertain. You didn't live through the discovery. You don't have that first hand memory of being in the room when that market research is being made, or when that voice of the customer is being captured, and you're just reading someone else's the AI's field notes. So when you're in a conversation with R and D or with another product manager, that uncertainty comes through, that lack of first hand experience in the VOC capture comes through.

Matt Wilkinson [05:39]

So what I'm hearing you saying is that without the deep work, we don't actually activate our, sort of our mental capacity enough to really then be able to become the expert. It's almost like we're just, we're just reading something, and we know we can read something and sort of forget it almost instantly. So you're saying that to really be able to put ourselves in the customer's shoes and to be able to embody them and be able to really represent them, we need to be doing some of that deep work ourselves.

Jasmine Gruia-Gray [06:07]

Yeah, there's, there's so many neurological studies that show there's a lot of thinking, there's a lot of brain activation that's going on when you're in the room, when you with another person or with a group of people there, there's a lot of neural synchrony that goes on in that room that activates thinking, processing and reflection, and giving that up to an AI summary is actually a disservice to the job that you're meant to be doing.

The PHASE Framework

Matt Wilkinson [06:48]

So I know that you propose a phase framework as part of the article. Are you able to share what that looks like?

Jasmine Gruia-Gray [06:56]

Yeah, so phase stands for purpose. Hypothesis, action, solutions and endorsement. It's it's a quick acronym to help you ask yourself some questions to be able to decide whether it makes sense to have AI be present, or whether you should be present. So purpose. P for purpose. Am I clear on what this research is designed to answer if you're still figuring out whether some scenario is a real opportunity or just an edge case, delegating that to AI, and for AI to be present in voice of the customer is a mistake. H for hypotheses? What assumptions am I carrying into this research? If my hypothesis is that customers are using some kind of kit for a specific type of work in an underserved segment, and I'm wondering whether they pay a premium price for that solution. That's a hypothesis that I need to be present for to test in real time, and not abdicate that to AI, A is for action. Is this evidence for a decision already made, or evidence that could change the decision? S is for solutions? Could this research surface alternative solutions? I haven't considered there is really vital cognitive thinking in being in the room to be able to answer that question that, again, you don't want to abdicate to AI an E for endorsement. Will I need to defend these findings in a very deep, detailed sort of way? When I go back to R and D and say, we're not building some sort of instrument or kit or software solution, we're going to build an integrated platform instead, because I heard ABC during voice of the customer from from four or five core facility managers, that kind of deep connection of the dots and the ability to defend my position really requires you to be in the room for that.

Matt Wilkinson [09:32]

So I absolutely love that framework. And actually it brings me to a point where some work I conducted with University of Leicester last year now where we'd gone out, and we interviewed a number of different organizations. I was in the room or on the calls for all of those conversations, so I was part of the learning process, so I'd already been in the room, but then to go through and actually to conduct the thematic analysis, I was able to use reasoning models to do that markup, to markup transcripts, and to be able to kind of create those summaries, but because I'd already been there, I was able to check that it was a true representation of what had happened, rather than me sort of approaching it for the first time. So I think it's that. Can we use it to do some of the manual, tedious work, but can we make sure that we're doing the really important things? And of course, one of the things about VOC that's so important is actually being in the room anyway, and showing the customers that you value them with that, with your time. And so I think that there's the two parts there. So it is to making sure that you're part of that. And when I've been part of those, going in and talking to customers in the past and visiting facilities. You know, I would have loved to have had some of these AI tools that would allow me to generate transcripts on the fly so that I didn't miss any of those quotes. Often, ethnographic research is best conducted on video, and you know, when you're capturing everything on film, but so many people feel uncomfortable with that. But if we can make sure that we're using these tools to capture more information, then sort of do the downstream analysis. You know, as long as we're present, I get the sense that we're in a really strong place, right?

Being Present to Build Relationships and Check AI

Jasmine Gruia-Gray [11:07]

So I think you bring up a really important point about being present, to continue to build relationships with with the people that are providing the VOC, as well as having a firsthand understanding of the discussion from the VOC to be able to check what the AI summaries are giving you back. Because, as we all know, there are points of hallucination. There are analyzes that it falters on, and if you're not present to have that first hand experience, you wouldn't be able to check that effectively, absolutely.

Matt Wilkinson [11:47]

So if we only remembered, you know, one thing from from this article, what should it be?

Jasmine Gruia-Gray [11:52]

AI can tell you what a customer said, right? It's, it's capturing those notes, effectively, only you as the product manager, as the marketing manager, can understand what it meant in that context. The difference between a strategic product manager and one with better tools is whether you were in the room when the Insight was built.

Matt Wilkinson [12:17]

I love that. And of course, in any role, with whether we use AI or not, we're still responsible for the work that we deliver, and so we really should make sure that we're we're there, we're present for capturing that information and making sure that we really do understand why we're proposing things, rather than just outsourcing everything to the AI.

Six Major Shifts Hitting Life Science Marketers in 2026

Jasmine Gruia-Gray [12:35]

Matt life science, marketers face six major shifts in 2026 buyers now trust peers over vendor claims. They expect websites that answer their questions without friction and use AI tools to find solutions before ever contacting sales. Success now depends on account level metrics rather than lead volume strategic investment in events with measurable ROI and measurement systems that track actual revenue influence the gap between knowing these trends and building the capability to respond will determine who wins this year. What inspired this article. I'm so curious to hear your thoughts.

Matt Wilkinson [13:24]

Well, I love to keep up to date with and what everybody's predicting for the coming year. What what really inspired this was that it's very unusual that everybody's predicting things that sort of correlate so so succinctly. So you know, Forrester, McKinsey, Gartner and IDC all converged on pretty similar themes, all independently and all within weeks of each other. Maybe that's a sign of groupthink, I don't know, but those six themes really hit home, and so I figured it was worth sharing. So trust has become our only defensible moment in the sciences. We know that peer review is our most important form of kind of social proof, followed by key opinion leader recommendations and your and also your own. You know lab mates and the people that you work with recommending things. We know that poor buyer enablement really does cost deals. So people love to self serve. They love to be in a scientist in particular, trained to do research in the library, now online, but trying to sort of find out answers themselves. So we really need to enable buyers to come to a sales conversation with a hypothesis, and if we don't give them those tools, you know we're failing them right from the start. But AI is changing the discovery process. It's not just about content creation, but most people are starting to use AI in actually looking for answers. And while that may not be everywhere across the sciences, it's definitely a trend that's coming in with these shifts how people are using AI. It's also changing the way that people are engaging in our websites. We know that we're getting more no click searches, so people not visiting websites because they've already got the answers. But it also means that we're sort of changing the way that what's known as a marketing qualified lead has become more of a vanity metric, and we really need to be looking at sort of account level engagement, because it might be the one person from a company's gone in. But actually, recent research has shown that the buying group, certainly for bigger purchases, are getting bigger and bigger, and that adds complexity. And so we really need to be making sure that we're getting a view across who's engaging across an account, rather than just at an individual level. We know that human connection is really, really important, and so we know that that investing in connecting with individuals in target accounts, it really does prove ROI and it steals as well, if you're not doing that effectively, and that, and all of those things mean that our measurement frameworks need to be rebuilt and looked at again. I mean things like cookie deprecation and other changes around privacy, as well as sort of AI kind of creating this sort of opaque wall about who's engaging with things really means that we really need to shift what we're measuring and how.

From Golden Age to Platinum Age

Jasmine Gruia-Gray [15:58]

Yeah, all fabulous points for me, what resonated the most was the human connection in combination with trust. I'm sure that lots of folks are like you and I, where it's one thing to use AI to do the homework, the background checking, it's another thing to look at websites and read the materials and the data, but it's something completely different to be in front of a salesperson, a field application specialist, and really have the story behind the data and that human connection of that field application specialist using the technology that, to me, is is irreplaceable. So I'm curious what resonates the most for you, and could you sort of elaborate on that a bit more?

Matt Wilkinson [16:53]

I think it's actually the fact that so many of the things that we held true in sort of the what we might want to call the golden age of digital marketing are kind of coming to an end. 15 years ago, we were sort of hailing the idea that we can email lots of people, that we can get social media to engage with lots of people. All of a sudden, we could reach people in different ways. We could track we could get ROI off marketing far better than we ever could before. That's kind of disappearing, and I think we're moving into a more complex world, you know, that's, that's, you know, there's more channels, there's more opaqueness again, but I think it's also means that there's a simpler element to it again, which is now that customers can come to a conversation far more prepared than ever before, because of all these tools. We really have to be the sense makers, and it's something that I talked about at SAMPS in Boston, but we really have to point bring a point of view. We really have to make sense of what our offer is in the context of our customer. So that means that we have to do our due diligence, we have to do our research onto the customer. We have to get to know their problems, and we have to spend time really understanding what it is that's going to make them successful, so that we can be building the trust as the trusted partner, not just for providing the right solution, but for providing the right platform for them to be successful. And I think that's the really important thing. They want somebody that's going to not just be here's, here's a kit that works, or here's, here's something that's going to answer this part of the solution. They want somebody that's now going to be able to walk side by side with them and take responsibility for making sure stuff works.

Jasmine Gruia-Gray [18:27]

What I'm interpreting from what you're saying is we as marketers and product marketing teams need to go back to the basics, the basics of understanding your persona, who your target audience is, understanding their pain points, the gains that you're providing and telling a coherent story. Is that right? Absolutely.

Matt Wilkinson [18:52]

I think there's then the additive level of this, where, with AI, we can now start using those tools to not just tell one story or a few stories based on segmentation, but actually looking down at getting far more targeted with that context, and being able to really understand what is going to matter to individual companies and targets as well as the individuals in them. And I think that that then gives us a huge opportunity to use AI, if we do it right, to connect far better with people and make sure that our offers are far more personalized and far more tailored towards the customer itself. So hopefully if we do this right, we'll be able to use AI to actually do a better job of connecting with other human beings. And I think that's where I would hope that commercial teams in 2026 end up.

One Opportunity to Focus on in 2026

Jasmine Gruia-Gray [19:43]

Fabulous. What is the one opportunity you suggest someone in marketing or product marketing dig into further?

Matt Wilkinson [19:53]

We know that marketing is a broad church, so I would look at the thing that is going to make the most difference to the customer. So look at, how do you interact most with your customer, whether that's in person, at events, whether that's online, and look at those key touch points with the customer. And really focus on, how can I make sure that with all of this change, we're trying to make sure that we're doing the best possible job for our customer.

Jasmine Gruia-Gray [20:17]

So what I've learned in this discussion is we as marketing and sales organisations need to pivot from sort of what was called the golden age of digital marketing to maybe what we can call the platinum age. The Platinum age being hyper personalization, hyper connection and staying human, even with the perhaps foundation of AI behind it all. So that was a fantastic discussion. Matt, thanks so much. We talked about two areas. One is the Janus threshold and cognitive offloading. And as part of the blog post that I wrote, I created this framework called PHASE that helps product managers and product marketers think about how and when to use AI versus how and when to be present in the room and prevent cognitive offloading. And then you gave us five or six areas where we should be thinking what 2026 is going to look like, and maybe pivot from the golden age of digital marketing into what we might want to call the platinum age of personalisation and staying human. Thanks again for a fantastic discussion.

Matt Wilkinson [21:43]

Thank you. Thank you for listening to a splice of Life Science marketing. We hope you enjoyed the episode.

Speaker 1 [21:49]

If this conversation helped you, the single biggest way you can support the show is to subscribe and leave a review on YouTube, Spotify or Apple podcasts. We'd really appreciate it, and it makes a huge difference.

Matt Wilkinson [22:04]

You can find out more about us and the topics we discuss at strivenn.com or on LinkedIn. Thank you so much for listening. We hope to see you next time.

Q&A

How do I apply the PHASE framework to my next customer interview without overthinking it?

Print the five questions on a card and ask them 48 hours before your interview. Purpose: what am I trying to learn? Hypothesis: what assumptions am I testing? Action: could this change a decision? Solutions: might alternatives surface? Endorsement: will I defend this to R and D? If three or more answers are yes, block your calendar and show up. If fewer than three, delegate to AI and review the transcript yourself within 24 hours while memory is fresh.

What's one practical way to shift from lead volume to account-level engagement this quarter?

Pick your top 20 target accounts and create a simple dashboard tracking engagement breadth, not depth. Count how many people from each account touch your content, attend events or engage with sales in a 30 day window. Set a threshold of three people minimum before labelling an account as actively engaged. This shifts your focus from individual MQLs to buying group activation and reveals which accounts have momentum versus isolated interest.

How can I use AI for VOC analysis without losing the insight that builds conviction?

Attend the first three customer conversations yourself, even if AI captures transcripts. Use AI to generate themes across all ten interviews, but personally review every transcript where the AI flagged a contradiction between what customers said and what they described doing. Those gaps are where product pivots hide. Schedule a 90 minute session to map contradictions on a whiteboard before presenting findings. This hybrid approach gives you volume with conviction.

What's the fastest way to test if my website enables buyers properly before they contact sales?

Run a 15 minute test with someone outside your team. Give them a realistic purchase scenario and ask them to research your solution without contacting anyone. Watch where they get stuck, what questions go unanswered and how long it takes to find technical specs, pricing signals or proof points. Record the session. The friction points they hit are exactly what your real buyers experience. Fix the top three blockers before investing in new content.

How do I make a case for event investment when budgets are tight and ROI is questioned?

Reframe events as account acceleration, not lead generation. Before your next conference, identify 15 target accounts you know will attend. Pre-schedule meetings with at least two people per account. Track post-event pipeline velocity for those accounts versus accounts that didn't attend. Compare deal cycle length and win rates over 90 days. Present results as cost per accelerated deal, not cost per lead. This shifts the conversation from expense to strategic investment in shortening sales cycles.