Navigating the Fog: The Product Manager's Role During R&D Feasibility

Theseus stood at the entrance to the labyrinth, holding Ariadne's thread. Without it, he knew the maze would consume him, not because the Minotaur was so fearsome, but because he'd wander forever in circles, unable to distinguish progress from illusion.

But here's what the myth doesn't tell you: Ariadne didn't just hand Theseus the thread and walk away. She stayed at the entrance, holding her end, feeling every tug and tension as he moved deeper into darkness. They navigated the labyrinth together, each playing a different role but utterly dependent on the other's judgment.

This is the essence of product management (PM) during the R&D feasibility stage. You're standing at the entrance to a labyrinth of technical unknowns, market assumptions, and resource constraints with your R&D partner, trying to figure out if there's even a monster worth fighting, and whether you can actually defeat it together.

The Partnership Paradox

Let's say your company is considering developing a new multiplex immunoassay kit for early cancer biomarker detection. The science looks promising. A key opinion leader mentioned interested at a conference. Your VP of R&D has a brilliant scientist eager to explore the chemistry. But you're still in that hazy territory where everything feels both possible and impossible at once.

Here's the uncomfortable truth about feasibility: product managers and R&D scientists approach uncertainty from fundamentally different angles, and that's exactly what makes the partnership valuable. Scientists embrace uncertainty as a puzzle to solve through experimentation. They think in hypotheses, controls, and reproducibility. Their nightmare scenario is drawing a conclusion before they have sufficient evidence.

Product managers make decisions under uncertainty and move forward despite incomplete information. We think in opportunity costs, minimum viable evidence, and strategic trade-offs. Our nightmare scenario is endless exploration that consumes resources without reaching a decision point.

Both mindsets are essential. Neither is sufficient alone. During feasibility, you're building a shared language and decision framework that honors both perspectives. This requires genuine partnership, where decision rights, communication norms, and uncertainty tolerance are explicitly negotiated, not assumed.

The Three Partnership Tensions You Must Navigate

Tension One: Problem Discovery vs. Technical Constraints

In my recent article on finding product/market fit through voice of customer, I discussed how successful products emerge from understanding workflow friction and jobs-to-be-done, not feature lists. During feasibility, that customer discovery work doesn't happen in isolation, it happens in continuous dialogue with technical exploration. The two inform each other in real time.

Your future customers will articulate solutions, not problems. They'll specify fifteen biomarkers, single-cell sensitivity, and thirty-minute protocols. As the product manager, you're the archaeologist digging beneath those specs to find the actual job to be done. When a researcher says "under thirty minutes," you need to understand why. Is it about batching samples before leaving? Sequential assays creating bottlenecks? The pain point beneath the specification changes everything.

But here's what makes feasibility different from later-stage product/market fit work: your scientist partner is simultaneously exploring what's technically possible. Maybe antibody approach A achieves target sensitivity but requires a four-hour incubation, while approach B runs faster but only works with three of your five target biomarkers.

The magic happens when you bring these discoveries together in tight loops. You learn that the customer's real pain isn't the time, it's unpredictable scheduling around shared equipment. Your scientist optimises for hands-off incubation rather than total time. You bring that back to the customer, who'd gladly accept overnight incubation for walk-away automation.

Instead, create a rhythm where customer insights and technical discoveries inform each other weekly, not monthly. Have your scientist join customer calls to hear the frustration firsthand. Bring customers into the lab to react to early prototypes with real samples. Build shared understanding, not handoffs.

Tension Two: Decision Rights and Authority

Who decides when to pivot or stop during feasibility? This question derails more partnerships than any technical challenge.

Scientists often feel that product managers don't understand the technical nuance and shouldn't have authority on scientific direction. Product managers feel that scientists are too close to the work to make objective go/no-go decisions and will optimise indefinitely if allowed. Both concerns are legitimate.

The answer isn't giving one person authority over the other, it's making decision rights explicit and contextual.

Some decisions belong primarily to the scientist: which antibody pairs to test first, whether a particular chemistry approach is worth pursuing based on preliminary data or when you've done enough experiments to draw conclusions.

Some decisions belong primarily to the product manager: whether the emerging technical constraints still align with market needs, how much additional time and budget to invest before reaching a decision point or when customer feedback changes the problem definition enough to redirect technical exploration.

Some decisions you must make together: whether to continue, pivot, or stop the overall feasibility effort. What constitutes "good enough" technical performance given market realities. How to sequence technical experiments when resources are constrained.

Name these decision types explicitly at the start of feasibility, not when you're in conflict. Create a simple decision framework: "You have authority over X. I have authority over Y. We decide Z together, and here's how we'll handle disagreement."

This sounds mechanical, but it's actually liberating. When you're clear about who owns what, you stop second-guessing each other and start focusing on the work. Trust emerges from clarity, not from hoping you'll naturally agree.

Tension Three: How You Each Handle Uncertainty

Scientists and product managers communicate uncertainty differently, and this creates invisible friction during feasibility.

When a scientist says "we need more experiments," they might mean "I've seen inconsistent results and don't understand the source of variation yet." Product manager often interpret this as hedging or perfectionism.

When a product manager says "we need to make a decision," they might mean "we have enough signal to make a directional call, knowing we'll learn more later." Scientist often interpret this as recklessness or ignoring data.

Neither interpretation is correct. You're simply using different uncertainty thresholds appropriate to your roles.

The fix isn't to adopt each other's threshold, it's making your uncertainty explicit and discussing what additional information would meaningfully change the decision.

Structure feasibility reviews around:

• What did we think was true last week?
• What did we learn this week that changed our thinking?
• What are we still uncertain about?
• What would we need to see to increase our confidence from 40% to 70%?
• Is that level of confidence sufficient to make a decision, or do we need to de-risk further?

This framework acknowledges that you'll make many feasibility decisions without perfect information, while respecting that some uncertainties are worth resolving before proceeding. You're building shared calibration of what different confidence levels mean and when they're sufficient.

Practical Moves That Build Partnership

Create a living feasibility canvas. Not a monthly PowerPoint deck, but a one-page visual showing what you know, what you don't know, what you're testing next, and what would cause you to pivot or stop. Share it weekly. Tools like Miro, FigJam, or Mural work well for this, pick whichever one your team will actually update consistently.

Run hypothesis-driven sprints together. Every two to four weeks, write down the specific hypothesis you're testing together, "We believe we can achieve 10 pg/mL sensitivity with our current antibody pairs while maintaining a two-hour protocol." The scientist designs the experiments. You line up customer feedback on whether that specification solves the real problem. Review the results together with a friendly customer or clinical advisor. Decide together what you learned and what comes next.

Establish your "good enough" thresholds early, together. Before you start heavy experimentation, align on what "feasible" actually means. Not perfect, not optimal, feasible. For your assay, maybe it's "detection of at least three biomarkers, sensitivity within one order of magnitude of the gold standard, and total protocol time under four hours." Define this together, the scientist brings technical realism, you bring market context.

Create explicit communication rituals. When you learn something significant from a customer conversation, text your scientist partner immediately. When they see unexpected results that might change the approach, they should do the same. Brief, frequent updates create shared context and prevent whiplash from surprise announcements at formal reviews.

Protect the partnership through conflict. You will disagree during feasibility. Maybe your scientist wants two more weeks to optimise sensitivity when you believe the current performance is good enough. Maybe you want to pivot based on customer feedback when your scientist believes they're close to a breakthrough. Assume good intent. Get curious about the other person's reasoning before advocating for your position. Make the disagreement explicit and discuss what information would resolve it.

Finding Your Way Out Together

Theseus emerged from the labyrinth because he maintained his connection to Ariadne while navigating the maze. The thread didn't make the journey easier; it made the journey possible by keeping them in communication despite the distance and darkness.

Your job during R&D feasibility isn't to be the hero who solves every problem. It's to be the partner who maintains connection with your R&D counterpart while you both navigate uncertainty. You bring different perspectives, different strengths, different ways of handling the unknown. The partnership works because of those differences, not despite them.

Your assay kit may or may not prove feasible. But if you've built a strong partnership during exploration with clear decision rights, honest communication about uncertainty, and mutual respect for each other's expertise you'll emerge with something more valuable than any single product. You'll have a working relationship capable of navigating the next labyrinth, and the one after that.

The best product managers don't just manage products. They build partnerships that make impossible-seeming products possible.

Ready to strengthen your product-science partnership? Download our free Feasibility Decision Framework and start having clearer conversations about uncertainty, decision rights, and when to pivot or proceed. Or better yet, share this article with your R&D partner and discuss which partnership tensions feel most relevant to your current project. The strongest products emerge from the strongest partnerships.

Frequently Asked Questions

How long should the R&D feasibility stage last for a typical assay kit product?

There's no one-size-fits-all timeline, but most life science tools companies spend 8-16 weeks in focused feasibility exploration for assay kits. The key is time-boxing your investigation around specific hypotheses rather than letting exploration continue indefinitely. If you're beyond four months without clear go/no-go criteria being met, you likely need to revisit your decision framework or acknowledge you've moved from feasibility into early development. The goal isn't to perfect the product - it's to establish whether the core technical approach is viable and whether the market opportunity justifies continued investment.

What's the difference between R&D feasibility and proof of concept?

Proof of concept typically demonstrates that something is scientifically possible in controlled conditions ("Can we detect this biomarker using this chemistry?"). R&D feasibility goes further by asking whether that possibility can become a commercially viable product ("Can we detect this biomarker reliably enough, cost-effectively enough, and in a workflow that customers will actually adopt?"). During feasibility, you're testing not just technical viability but also manufacturing scalability, regulatory pathway clarity, competitive differentiation, and market timing. Think of proof of concept as answering "Can we?" and feasibility as answering "Should we?"

How can product managers and R&D scientists improve their partnership during feasibility?

Start by making decision rights explicit - discuss who has authority over what types of decisions and which decisions you must make together. Create communication rituals that keep you connected beyond formal meetings. Practice making your uncertainty explicit rather than hiding it - say "I'm 60% confident this will work because X, but uncertain about Y" instead of hedging. Most importantly, invest time early in understanding each other's constraints and motivations. The PM should understand enough about the technical approach to spot implications for customers. The scientist should understand enough about market dynamics to recognise when technical optimisation is solving the wrong problem. Partnership isn't about thinking alike - it's about thinking together.

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