AI has moved from a sensitive topic to a standard expectation in pharmaceutical organizations. Leadership teams are asking for it. Departments are experimenting with it. Pilot programs are underway across the enterprise.

Yet despite this momentum, most AI initiatives stall before they deliver meaningful impact. The reason is simple: proving that AI works is one challenge; scaling it across complex pharma workflows is another entirely.

Based on our recent webinar, “Integrating AI to Scale & Accelerate Workflows and Improve Pharma Product Launches,” leaders across life sciences commercial, AI, and tech implementation teams share a practical look at how these organizations can enter the next phase of AI, going beyond the demo and into real, everyday workflows that accelerate success.

Start with Trust, Not Technology

There’s an uncomfortable reality right now: trust in AI is slipping. Not because the tools don’t work, but because the hype cycle has triggered fear, particularly around jobs and resourcing. The fastest way to reestablish this trust is honing in on low-risk, employee-facing use cases. Some examples of these include:

• Internal knowledge retrieval
• Drafting skeletons of routine documents
• Supporting development or testing teams
• Simple no-code agent building

The reason these use cases work? They hit the three sweet spots for AI to make a measurable impact: low compliance risk, quick productivity gains, and a safe space for teams to build comfort and AI literacy.

Pick Problems, Not Buzzwords

Too often, organizations start with the tool. The classic, “We bought an AI platform! Now what?” scenario takes precedence over defining the actual problem. While these flashy, innovation-forward initiatives may look impressive on the surface, they frequently lead to orphaned pilots and low adoption across the business. The result is predictable: little to no meaningful impact.

The antidote is simpler than it sounds. Start with a clearly defined business problem. Then determine the right solution—whether that involves GenAI or not.

AI isn’t a single solution. It’s a toolbox. And the right answer isn’t always a shiny large language model. It could be straightforward machine learning or robotic processing automation. It could be natural language processing or even a smaller, specialized model. Choosing the right tool starts with the right question. Instead of asking, “Where can we use AI?”, organizations should ask, “What problem are we trying to solve?”

Adopt a Crawl-Walk-Run Method

Scaling AI doesn’t happen overnight. It’s a sequence, not a single launch—one that requires organizations to crawl before they walk, and walk before they run.

Crawl:

Start with simple, low-risk internal use cases such as productivity tools, basic agents, controlled sandboxes, and workflow experiments that let people see AI is working without feeling the stakes are so high.

Goal: Familiarity over flashiness, getting internal teams to create a shared understanding of what AI can realistically do.

Walk:

Once teams understand the value of AI and appropriate guardrails are in place, the next step is to move it into operational workflows. That means embedding AI into the systems people already use—such as Veeva, CMS, or DAM platforms—rather than introducing yet another portal with another login.

Goal: Reduce friction and duplication while proving AI can reliably accelerate existing processes without sacrificing compliance.

Run:

This is the stage everyone wants to jump to, where AI becomes a true part of the launch engine. Connected, cross-functional workflows allow medical, legal, and commercial teams to operate from the same AI foundation. With that alignment in place, teams can respond in near real time to market dynamics and country-specific requirements.

Goal: Intelligence at scale, supported by clean data, strong governance, and clear ownership.

Most AI failures come from teams trying to run before the data, systems, and people are ready. The teams that scale AI treat it as a product: always evolving, always iterating. 

Why Pilots Stall and How to Design Ones That Don’t

If you’ve ever launched a pilot that fizzled out, you’re in good company. This is true for most organizations, often for the same set of reasons:

Failure Pattern #1: No clear problem or ROI

Issue: Teams chase hype instead of real business needs.

Fix: Set explicit success metrics upfront (cycle time, cost, throughput, adoption).

Failure Pattern #2: Pilots built on desktops

Issue: Great demos or one-off projects, but terrible for scaling across the business.

Fix: Use production-grade data, infrastructure, and full-stack teams early.

Failure Pattern #3: Dirty, fragmented data

Issue: The model is not the bottleneck—the data is.

Fix: Invest in data readiness, including governance, cleanliness, and accessibility.

Failure Pattern #4: No owner after go-live

Issue: No plan for who monitors, retrains, or intervenes when AI drifts.

Fix: Assign ownership before you start the pilot, not after.

Failure Pattern #5: Innovation happens in a silo

Issue: Legal, regulatory, and procurement come in at the end and hit the brakes.

Fix: Involve them from the start and define guardrails together.

Governance Without Killing Innovation

Every sponsor asks the same thing: “How do we move fast without increasing risk?”

The answer isn’t to loosen oversight, but to strengthen the right kind of oversight.

Effective governance models look like:

• Human-in-the-loop by design, rather than an afterthought
• Guardrails for hallucinations (retrieval-augmented grounding, constraints)
• Reproducibility over cleverness, with the same input producing the same output
• Cost monitoring to keep storage, tokens, and workflow complexity from getting out of hand
• Clear escalation paths when AI hits uncertainty

Good governance makes AI safer and faster. Review teams spend time on exceptions instead of redoing the entire workflow by hand.

Treat AI as a Capability, Not a Campaign

The next wave of AI transformation won’t be defined by who has the best model, but by who can scale effectively, connect teams across functions, measure real impact, and iterate continuously. The true differentiator lies in execution beyond the pilot phase. And it starts with small, compounding wins: literacy, trust, data readiness, workflow alignment, and realistic ROI.

If you’re exploring where AI can make a meaningful impact on your product launches or help reduce review friction in your MLR/PCR process, our team can guide you through a workflow assessment and heat-mapping exercise to identify what “crawl,” “walk,” and “run” looks like for your organization. Schedule a call with one of our experts to get started.