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A pilot isn’t a strategy. Neither is a single tool or implementation. Start with the harder question: where will AI create ROI? It may take more time…it will also deliver more value

Since launching Kynetyk, I’ve had the chance to speak with leaders across biotech about their experience integrating AI—whether for strategy, R&D, or operational automation. While these conversations often start in different places, they frequently move in the direction of a common theme: “We’re still looking for the right way to get started.” If this sounds familiar, you’re not alone: the data tell us that most companies—even those that recognize the potential for AI integration—are in exactly the same boat.

I would argue that’s a good thing in many ways. If 2023 was the year of the chatbot, and 2024 was the year that the “Great AI Pilot” phase kicked off, the latter half of 2025 was when we learned a sobering truth: most of those early efforts never scaled to value. Taking your time up until now means that you’ve avoided much of the costly experimentation associated with brittle pilot projects, immature orchestration platforms, and rapidly scaling foundation model capabilities. It also means that your business can take a first step with the benefit of hindsight. And that’s what this article is about: how to get started with AI in a way that can actually scale to value.

Insight 1: Skip the pilot. It’s time for a plan.

If you’ve been hesitant to launch an AI pilot, you may have dodged a bullet. According to MIT’s NANDA initiative report, The GenAI Divide: State of AI in Business 2025, 95% of enterprise AI pilots fail to deliver measurable ROI. And the reasons have little to do with the technology itself. The report points to a few consistent culprits: poor integration with existing workflows, misaligned resource allocation, and a “learning gap” where generic tools can’t adapt to enterprise-specific needs. Projects stall in prototype phase. Pilots become permanent experiments that never scale. The underlying issue isn’t that companies are choosing the wrong tools. It’s that they’re starting with tools at all.

Start with goals, not tactics

A pilot is a tactic. It answers the question “can this work?” But it skips the harder, more important question: “what are we actually trying to achieve?” We believe every AI effort should begin with clearly defined ROI goals: organization-specific opportunities where AI integration can create measurable value. This isn’t a brainstorm or a wishlist. It’s a strategic exercise that requires management alignment. Leadership needs to own and articulate these goals before anything else moves forward.

Then define capabilities, not tools

Once goals are set, the next step is identifying the capabilities your organization needs to meet them. This is a step where it is easy to make the wrong move—jumping straight to evaluating vendors or spinning up pilots. But capabilities are not tools. A capability might be “the ability to synthesize internal research data to accelerate target validation.” The tool that enables it comes later.

Keeping the conversation at the capability level forces strategic thinking. It ensures you’re solving for what matters, not just what’s available.

Then—and only then—map tools, timelines, and priorities

Once you’ve defined the capabilities you need, the conversation can turn tactical. This is where you evaluate tools (whether to buy, build, or both—more on that below), sequence the work, and assign ownership.

To be clear: team input is valuable throughout this process—strategy shouldn’t be developed in a vacuum. But there’s a difference between contributing to strategy and defining it. Leadership needs to own the goals and capabilities. The final phase is where the broader team needs more agency, not just input. They’re the ones who will use these tools day-to-day, and adoption depends on their buy-in. Engaging them meaningfully at this stage—on tool selection, workflow integration, rollout sequence—smooths the path from plan to practice.

Why this structure works

This approach addresses the failure modes the MIT data exposes:

  • It considers value comprehensively. Rather than chasing a single use case, you’re mapping the full landscape of opportunity before committing resources.

  • It creates shared accountability. When management defines and owns the goals, success metrics are clear from the start. These aren’t just project KPIs—they’re organizational commitments that can be used to measure progress and align incentives across teams.

  • It engages the right people at the right level. Executives set direction. Teams shape execution. By involving broader contributors at the tactical phase—where their expertise matters most—you smooth the path to adoption without diluting strategic focus.

A pilot asks “can this tool do something useful?” A plan asks “where will AI create value, what do we need to get there, and how will we know we’ve succeeded?” One is an experiment. The other is a strategy.

Insight 2: In biotech, ROI starts with risk—not efficiency.

Most of the conversation around AI in business focuses on automation, cost reduction, and operational efficiency. And for good reason—in mature, revenue-generating companies, those gains translate directly to margin. The math is straightforward.

But your pre-revenue biotech probably isn’t that business. At least not yet.

Many biotech companies are pre-revenue. They’re scaling against time, operating lean by necessity, and pouring resources into programs whose value won’t be realized until they reach the market—if they reach the market. In this context, the primary barrier to value isn’t how efficiently the organization runs today. It’s the cumulative risk embedded in the preclinical and clinical development path. That risk is where the real ROI conversation should start.

Efficiency gains have a ceiling—risk reduction doesn’t

When you’re already running lean, there’s only so much cost to cut. A 15% efficiency gain on a small operations team is real, but it’s not transformational. Meanwhile, a single better-informed decision at a critical development inflection point (e.g., target selection, trial design, patient stratification) can materially change the probability of program success. In biotech, that’s where the value lives.

This doesn’t mean AI-driven efficiency is irrelevant. It means it shouldn’t be the only or first priority.

Where AI can reduce program risk

Risk reduction through AI isn’t abstract. It shows up in concrete ways:

  • Better decisions. AI can synthesize internal and external data to surface insights that inform critical go/no-go moments—helping teams see patterns, validate hypotheses, or challenge assumptions before committing resources.

  • Augmenting high-risk workflows. Some activities carry disproportionate executional risk: complex data interpretation, regulatory submissions, cross-functional handoffs. AI can act as a check, a second set of eyes, or an accelerant in these moments—reducing the chance that execution errors derail an otherwise sound program.

  • Compressing timelines without compressing rigor. Speed matters in biotech, but not at the expense of quality. AI can help teams move faster on low-risk tasks, freeing capacity and attention for the decisions that require more deliberation.

Reframe the question

When evaluating where AI can create value, the instinct is often to ask: “Where can we save time or money?” In biotech, the better first question is: “Where can we reduce the risk that our program doesn’t reach its potential?”

Efficiency will matter more as the organization matures. But for companies still navigating the path from pipeline to market, risk reduction isn’t just a valid lens for AI investment—it’s the right starting point.

Insight 3: Buy or build? The answer is probably both.

The “buy vs. build” question gets treated as a binary choice. It isn’t.

The reality for most small biotech companies is more nuanced. You likely don’t have the engineering capacity to build and maintain production-grade software that will be used extensively across teams and workflows. That’s not a criticism—it’s just not where your resources should go. At the same time, you probably don’t need an enterprise license for a polished platform just to stand up a data pipeline or consolidate internal knowledge.

The right answer depends on what you’re trying to solve.

When to buy

Consider buying when a tool meets three criteria:

  • It addresses a well-defined capability. There’s a clear, recurring need—something your team will use repeatedly, not a one-off experiment.

  • It impacts multiple opportunities for value. The capability connects to several of the ROI goals you’ve already defined. It’s load-bearing, not peripheral.

  • It requires production-level stability. If the workflow drives GxP activities, extensively integrates external partners, or will be used by many users much of the time, you need reliability, version control, and support. That’s hard to build and maintain in-house without dedicated engineering resources.

Vertical AI tools built for biotech workflows—protein design, clinical trial management, regulatory writing—often fit this profile. When a vendor has already solved the hard problems and validated the tool across similar organizations, buying saves time, reduces risk, and gets you to value faster.

When to build

Building makes sense in different circumstances:

  • Edge cases and team-specific needs. Not every workflow justifies a procurement cycle. Sometimes a team needs a lightweight automation tailored to how they actually work—something off-the-shelf tools don’t quite fit.

  • Internal orchestration and data infrastructure. Connecting systems, routing information, consolidating knowledge—these can often be handled with tools you already have access to. Platforms like Copilot Studio, Claude with Skills, or simple scripting can get you surprisingly far without a major investment.

  • Experimentation. Letting your team tinker with AI—building small automations, testing ideas—builds fluency and surfaces unexpected opportunities. Not every build needs to scale. Some just need to teach you something.

The principle

Buy where the capability is broadly applicable and needs to be rock solid. Build where the need is specific, the stakes are lower, or the goal is learning. Most organizations will end up doing both—and that’s the right outcome.

The mistake is treating this as an either/or decision and over-investing in one direction. Over-buying leads to shelfware and bloated costs. Over-building leads to fragile systems and distracted teams. A clear-eyed view of what each approach is good for will serve you better than a rigid philosophy.

If you’re still figuring out where to start, that’s not a problem—it’s the right place to be. The companies that get the most from AI won’t necessarily be the ones that moved fastest. They’ll be the ones that started with a plan, focused on the right kind of value, and made smart choices about what to build and what to buy.

That’s exactly what we help biotech companies do at Kynetyk. If any of this resonated, we’d welcome the conversation.

Dr. Joshua Ziel is a former biotech CEO, board member, and co-founder of Kynetyk—an AI Agentics company helping biotech and life sciences organizations design and implement AI strategies that actually scale. Reach out at josh@kynetyk.ai.