Chief Digital and Information Officer, FUJIFILM Biotechnologies
Biopharma companies are adopting artificial intelligence (AI) at a faster pace than ever. Generative AI tools are compressing tasks that once consumed full workweeks into mere hours. Advanced analytics applied across global manufacturing networks are surfacing efficiency gaps that traditional reporting often misses. In research and development (R&D), AI-assisted molecule screening is accelerating the front end of drug development in ways that would have seemed implausible a decade ago.
While these use cases hold promise, many biopharma manufacturers have yet to advance them beyond proof of concept. The friction is predictable and it surfaces in the same places consistently: incomplete data foundations, change management that starts too late, and technology tacked onto processes that were never redesigned to support it.
What happens frequently with AI adoption is magical thinking: the assumption that deploying a capable model on top of existing infrastructure will produce reliable outcomes. Without a clear framework for what comes next, biopharma manufacturers risk stalling at the pilot stage indefinitely.
This four-stage approach is designed to help biopharma manufacturers understand where they fall on the AI maturity curve and how to overcome the barriers preventing them from advancing their AI initiatives.
Stage 1—Building the digital backbone
The first step to AI maturity is all about understanding the core fundamentals of your data. Can you acquire it? Can you govern it? Do you have clear ownership of it?
Without the right data, you cannot gather meaningful or actionable insights. Without governance, the insights that you do generate may be inaccurate, incomplete, or biased. And a lack of clear ownership means no one is accountable for whatever outputs do surface. When all three are absent together, the conditions are prime for batch failures, non-compliance, and loss of trust with partners. For biopharma manufacturing, the stakes are uniquely high, as every process in the chain ultimately connects to a patient outcome and a compromised data foundation puts that chain at risk.
To effectively move through this stage, leaders must establish a strong and complete data foundation, repair data silos, and standardize systems across sites. They should also assign clear ownership and data governance guidelines to ensure responsible AI use. Getting the foundation right is imperative for unlocking every stage that follows.
Stage 2—From pilots to performance
While Stage 2 is where organizations are ready to introduce pilots, it is also where most AI implementation efforts stall. Many organizations test a proof-of-concept in a controlled environment and see early metrics look encouraging only to then realize that the initiative is unable to lift off or move forward.
Process is the first variable that determines whether a pilot survives contact with the real operation. Bolting new AI tools on existing processes produces incremental results at best, because the underlying process was never built to take advantage of the full capabilities of AI. Instead, leaders must treat deployment as a reason to rethink their processes entirely. Change management is critical to this effort, as leaders need team buy-in to successfully make a process redesign.
AI needs to be built with people, and the framing that an organization uses from the earliest conversations about its deployment shapes everything that follows. Leaders must help their people see AI as something that enhances their performance and augments what they already do well. Framing AI as a force multiplier changes how teams engage with it. That cultural mindset shift, like any cultural initiative, requires ongoing reinforcement and a willingness to revisit the framing as AI evolves and new use cases emerge.
To test the effectiveness of their processes, leaders should establish KPIs for their organization’s target versus current state. For example, a manufacturing operation with a goal of 85% equipment utilization running at 30% has a clear process problem, and applying AI to it will not help. The process must be examined, redesigned, and stabilized with AI in mind. A process that has been redesigned and stabilized will show up in the KPI before it shows up anywhere else, and that improvement is the clearest signal that the organization is ready to scale.
Stage 3—Scaling systems
Stage 3 is the inflection point. It’s the shift from integrating AI into individual processes to connecting it across functions, systems, and decisions across the organization.
Most organizations underestimate how different this challenge is from what came before. The work of Stage 2 is largely about redesigning a specific workflow, bringing the team along, and proving that the pilot can perform under real conditions. Stage 3 requires AI to function as connective tissue across the organization rather than as a capability that lives in one department or use case.
To scale AI to this level and drive measurable outcomes, organizations need to weave AI into the fabric of their operations. For biopharma manufacturing, one example of this in practice is the integration of AI across the full manufacturing network. An organization at Stage 2 of their AI maturity journey likely uses analytics and AI tools within functional boundaries—one system informing upstream bioprocessing, another supporting quality review, another tracking equipment performance across the floor. Each may be performing well in its own context. What Stage 3 introduces is the ability for data and intelligence to flow across those functions continuously.
When that cross-functional integration is in place, the cumulative effect is significant. Supply chain planning draws on real-time production data rather than scheduled reporting cycles. Quality functions shift from reactive review to proactive monitoring. Manufacturing leadership makes resource allocation decisions based on network performance in real time.
This level of integration also introduces a governance challenge that Stage 2 did not fully require. When AI influences decisions across multiple functions simultaneously, accountability structures need to match that scope. Data standards, access protocols, and decision rights must be defined at the organizational level, not just within individual teams. The Stage 1 foundation work on data ownership and governance becomes load-bearing again at Stage 3, because cross-functional AI is only as coherent as the data architecture underneath it.
Stage 4—Being a differentiator
Stage 4 is where AI becomes a true source of competitive advantage, and the organizations that arrive here have earned it through the work of the previous three stages.
Efficiency gains, while meaningful, are a floor rather than a ceiling. An operation that uses AI solely to reduce cost is leaving the more consequential value on the table, because the combination of use cases is what produces results that actually change the competitive position of the organization.
In biopharma manufacturing, the strongest expression of that combination is the ability to deliver life-changing therapies to patients faster and at a higher standard of quality, without compromising safety or compliance in the pursuit of either. That requires AI working across the full value chain simultaneously: compressing development timelines in early-stage work, improving process consistency and yield in manufacturing, and accelerating the documentation and review cycles that precede release. While each of those use cases contributes incrementally in isolation, together, they change the speed and reliability of what an organization can deliver to its partners and, ultimately, to patients.
To strategically differentiate with AI, biopharma manufacturing leaders should map AI use cases against where they can create more value for partners. Organizations that bring AI-enabled transparency into the partnership—sharing real-time manufacturing data, flagging risks earlier, and compressing communication cycles—become structurally harder to replace.
This is especially true with the application of AI to technology transfer, the process of moving a therapy from development into commercial manufacturing. Traditionally one of the most time-consuming and risk-intensive transitions in the contract development and manufacturing organization (CDMO) model, AI-enabled technology transfer can match historical process data, expedite the identification of comparability risks, and sequence validation work required before scale-up. Shortening that timeline without increasing risk not only improves partnership value, but also the top line and patient outcomes.
For biopharma manufacturers, reaching Stage 4 ultimately means AI has become inseparable from the organization’s ability to fulfill its core purpose: getting safe, effective therapies to the patients who need them.
A framework for what’s next
Advancing through these four stages is a deliberate progression, and each layer of the framework remains active long after you have moved beyond it. For biopharma manufacturers, the promise of AI is real, but it is only accessible to organizations that move through adoption and implementation with intention, building each capability on a foundation that can support it. How much of the next wave of tools, models, and use cases any organization can absorb will come down to how seriously it took the foundational work first.
Mike Tomasco is chief digital and information officer at FUJIFILM Biotechnologies.
