Risk‑tiered validation. Ship weekly with audit‑proof control.

The AI Pilots That Never Ship

I've been talking with a lot of you about AI lately, and the same pattern keeps coming up: you're running 5-10 promising pilots, but six months later they're still pilots instead of production systems.

It's not a science problem. It's a transition problem—from experiment to infrastructure.

What I'm Seeing

The teams stuck in "pilot mode" usually have:

• 5+ AI tools that "work" in testing but aren't integrated into actual workflows

• Different data formats across discovery, clinical, and manufacturing teams

• No clear definition of what "validated" means for each use case

• No single person accountable for keeping any given model working

• Finance asking hard questions about mounting vendor costs

Sound familiar?

The quick test: Ask your team which AI pilots are actually on the critical path for a decision you need to make in the next 6 months. If the answer is "none" or "we're still evaluating," you're in pilot paralysis.

What's Working for the Teams That Broke Through

Three companies I'm working with recently made the jump from pilots to production. They didn't solve it by building better models—they solved it by changing how they think about AI.

The shift: Stop treating it like R&D. Start treating it like your LIMS—operational infrastructure with owners, SLAs, and integration into existing workflows.

What that actually meant:

1. They picked two workflows and went deep

Not ten pilots across everything—two specific use cases where:

• The data was already clean enough

• The regulatory risk was understood

• There was a clear decision the model would inform

Usually that meant starting with low-risk internal tools (target screening, site selection) before touching anything patient-facing.

2. They named System Owners

Not project managers—people accountable for ongoing model performance. Someone who makes the call to take a model offline if it's not working, and who interfaces with the team actually using it.

This is often where it breaks. If you don't have someone with both technical depth and workflow understanding, you're not ready to deploy.

3. They tiered validation based on actual risk

The breakthrough was realizing you don't validate a discovery triage tool (low-risk, internal use) the same way you validate a manufacturing QC model (high-risk, touches regulatory filings).

Low-risk (internal tools):

• Discovery target screening

• Literature monitoring

• Hypothesis generation

→ Validation: Basic performance benchmarks, version control, internal review

Medium-risk (decision support):

• Trial site selection

• Patient cohort identification

• Supply chain forecasting

→ Validation: External validation set, documented decision logic, drift monitoring, rollback plan

High-risk (patient/regulatory):

• Patient selection for dosing decisions

• Manufacturing QC release criteria

• Safety signal detection

→ Validation: Full qualification, SOPs, independent validation, human-in-the-loop, audit trails

Why this matters:

If you treat target screening tools (low-risk) the same as manufacturing QC (high-risk), your low-risk tools will never get deployed because the validation burden is disproportionate.

This sounds obvious, but most teams I talk to are applying the same validation burden to everything, which kills momentum.

The Pattern That Actually Delivers ROI

The companies seeing durable returns aren't getting one big win—they're getting repeated small gains that compound because the infrastructure is reusable.

Where I've seen the clearest returns:

• Discovery: 2-3x faster screening cycles (if your validation process can keep up—that often becomes the new bottleneck)

• Clinical ops: 10-20% fewer screen failures, faster site activation (but only if ops actually changes their workflow to use it, not run it in parallel)

• Manufacturing: 10-15% fewer deviations, faster review cycles (but only if QA trusts it enough to change their review cadence)

The key: The gain only shows up if you change the workflow to incorporate the tool. Running AI "alongside" existing processes just adds work, and teams abandon it within a few months.

One Example That Might Resonate

I was working with a Phase 2 oncology company that had been running pilots for 18 months. Target screening, patient stratification, site selection, deviation prediction—all promising, none in production.

The breakthrough: They picked two workflows (target screening and site selection), named owners, defined what success actually looked like (not "accuracy" but "does this change how we make decisions?"), and documented the decision logic from day one.

Within three months, both were in production. More importantly, when they started their third workflow, they reused 60% of the data contracts and governance framework. The third tool took six weeks instead of six months.

The feedback from their Head of Data Science: "We stopped debating whether AI works and started focusing on making it work reliably."

The Question I Keep Getting

"How do we know if we're ready for this?"

Honest answer: Most pre-IND companies shouldn't build AI infrastructure yet. You don't have enough programs or data to justify reusable systems.

You're probably ready if:

• You're IND-stage or later (multiple programs to reuse infrastructure)

• Your data is already structured (or you have resources to structure it)

• You have someone internal who can own model performance technically

You're probably not ready if:

• You're pre-IND with a single asset

• Your data is mostly PDFs and inconsistent spreadsheets

• You don't have anyone who understands both the science and the models

• Your core R&D workflows are still in flux (don't automate unstable processes)

The hard truth: Infrastructure makes sense when you have enough repetition that reusable systems create leverage. Before that, just use vendor tools and run pilots.

How to Start

Step 1: Pick two workflows (not ten)

Choose based on:

• Clean data (already structured and accessible)

• Clear use case (specific decision this model will inform)

• Understood risk (know the regulatory implications)

Good first candidates:

• Target screening/triage (if discovery data is structured)

• Literature monitoring (almost always low-risk)

• Trial site selection (if you have historical enrollment data)

Bad first candidates:

• Patient dosing decisions (high-risk, requires full validation)

• Anything where data is mostly unstructured

• Workflows where the team doesn't trust AI yet

Step 2: Name System Owners

Not project managers. People who:

• Own model performance (accountable if accuracy degrades)

• Can take a model offline if it's not working

• Interface with the team using the tool

Usually a senior scientist or ops lead who understands both the science and the workflow.

If you don't have someone with this mix: You're not ready. Hire for it, develop someone internally, or bring in interim support.

Step 3: Match validation to risk

Don't apply the same rigor to everything:

• Low-risk tools: Get them deployed fast with basic checks

• High-risk tools: Take the time to validate thoroughly

The mistake is treating everything as high-risk, which means nothing moves.

Step 4: Integrate into workflow (not alongside)

The test: Can the person using this tool describe how their job changed?

If they say "it's helpful" but their workflow hasn't changed, you haven't integrated—you've added a nice-to-have they'll stop using in three months.

What Good Looks Like

You'll know this is working when:

• Your team stops saying "we're testing AI" and starts saying "this is how we do X now"

• Finance can trace AI spend to specific workflow improvements

• Your third deployment takes a fraction of the time your first one did

• Regulators ask about your models and you can answer in minutes, not scramble for weeks

Most reliable early signal: The team using the tool changes their SOP to incorporate it, rather than running it as a parallel check.

When AI Infrastructure Is Premature

This approach works best for:

• IND-stage or later (enough programs to reuse infrastructure)

• Organizations with structured data

• Teams with at least one person who can own model performance technically

It's probably too early if:

• You're pre-IND with a single asset

• Your data is mostly unstructured

• You don't have anyone internal who understands both the science and the models

• You're still figuring out core R&D workflows

The honest answer: Most early-stage biotechs should run pilots and use vendor tools without building infrastructure. Infrastructure makes sense when you have multiple programs and enough repetition that reusable systems create leverage.

Worth Talking About?

I'm curious if you're seeing the same pattern—pilots that show promise but never quite ship. And if you are, whether the "tier your validation" framing resonates or if I'm missing something about your specific constraints.

Let me know what you're thinking.

—Roop