Enterprise AI Perspective
The Hidden Workforce Behind Pharma AI—and Why It Will Shape the Future of AI
The next constraint on pharma AI may be the expert work that makes it reliable. Turning that work into reusable knowledge could shape the next generation of enterprise AI.
The short version
Pharma AI depends on experts who supply context, evaluate outputs and handle exceptions. Companies should protect that capacity, turn repeated interventions into reusable assets, and measure expert minutes per trustworthy outcome alongside quality and risk.
What MIT Sloan’s research says about hidden AI work
In a September 9, 2026 research report, MIT Sloan described a two-year field study of an academic medical center and a corporate law firm. Both offered secure AI environments and training, but their outcomes diverged.
of domain experts at the law firm disengaged from AI innovation; three organization-wide solutions remained in use.
were in use at the healthcare organization, where participation persisted and additional contributors joined.
MIT reported similar starting conditions in AI readiness and technology access. The healthcare organization sustained education, technical assistance, shared evaluation methods and collaboration, while recognizing experimentation in work and career development. The law firm’s support was more limited.
The researchers call this collective activity experimentalist work. Their comparison suggests that maintaining expert participation requires organizational support. It is evidence from two organizations, reported from a working paper—not a controlled causal experiment, a pharma benchmark, or proof that deployment count alone measures value.
The implication for pharma extends beyond burnout: these workers are helping establish what AI can and cannot reliably do inside a particular organization.
Why organizational context may become pharma’s AI advantage
Two pharmaceutical companies can use the same frontier model and still have very different AI capabilities.
One may connect claims to references, indications, audiences, approval status and expiration dates. It may document review precedents, approved content, evaluation datasets, decision rights and escalation rules. The other may hold comparable knowledge across PDFs, email, SharePoint, Veeva records, meetings and experienced employees’ memories.
The model may be identical. The usable organizational context is not. Enterprise competition may shift toward a new question:
Who has made the organization most understandable to intelligent machines?
Much of pharma’s expertise still lives inside people
An experienced MLR reviewer knows more than written regulations. They understand the product and relevant precedent, recognize when a small wording change alters a claim’s meaning, and distinguish similar situations with different risk profiles. They know which evidence is persuasive in context.
A clinical operations lead may recognize that an apparently minor protocol deviation could affect endpoint interpretation; that recurring documentation problems indicate a training gap; or that a change to an electronic data-capture form could affect downstream analysis. They understand the coordination an amendment can require across sites, vendors, data managers and investigators.
These are illustrative examples of tacit expertise, not universal clinical rules. Experienced people acquired this knowledge over time; it was rarely designed for machine use.
When experts participate in pilots, they help convert tacit knowledge into context an AI system can use. That work may become a durable asset—but only if the organization captures and maintains it.
How to turn human AI review into institutional learning
The basic human-in-the-loop pattern is familiar: AI creates an output, a person reviews it, and the person approves or rejects it. Mature enterprise systems need a second loop that improves the surrounding process.
Imagine an agent flags a claim and an experienced reviewer determines that a particular combination of evidence, audience and context requires escalation. Correcting the output resolves the immediate issue. The next question is: what should the organization learn from that intervention?
The intervention might become a new evaluation case, escalation rule, metadata requirement, claim-to-evidence relationship, permission boundary or exception class. It should not disappear when the task ends.
From expert intervention to reusable capability
- 01Human judgment
- 02Captured learning
- 03Structured context
- 04Evaluation & approval
- 05Improved AI behavior
- 06Monitoring & exceptions
New exceptions return to human judgment ↺
“Teaching AI” here does not necessarily mean retraining a model. It may mean improving retrieval, approved knowledge, instructions, workflow design or access controls. Proposed changes need evidence, ownership, review and testing before release; one person’s correction is not automatically a new organizational rule.
Every meaningful SME hour should leave behind an asset
When an expert identifies an exception, capture it. When a reviewer explains why an answer was unacceptable, create an evaluation case. When a clinical operations lead explains why a deviation matters, record the relevant conditions, evidence and decision path. When a Medical reviewer identifies a claim’s supporting evidence, structure that relationship.
Without this discipline, companies risk permanent AI babysitting: output rises, experts check more work, the same errors recur, and people repeatedly supply the missing context. Hidden verification expands alongside visible productivity.
If useful interventions become reusable tests, context, policies and knowledge, routine cases may need less expert attention. That is a hypothesis to measure, not a guaranteed saving. Human judgment remains essential for unusual, ambiguous and consequential decisions.
What is the SME burden curve—and how should pharma measure it?
Adoption, prompts, users, agents deployed and estimated time saved do not establish that a workflow can scale. An agent could double content production while doubling reviewers’ validation time. The dashboard reports productivity; the reviewers experience more work.
Proposed management metric
SME minutes per trustworthy completed outcome
Total expert minutes spent supporting the workflow during a defined period ÷ completed outcomes that meet its predefined quality, safety and acceptance criteria.
Include time spent on unsuccessful attempts, rework and escalation in the numerator so failures cannot disappear from the measure. Report one-time setup and asset-building time separately from recurring review time. If no outcome meets the acceptance criteria, report no trustworthy completions rather than a zero burden.
Agree what “trustworthy” means before counting outcomes, using an appropriate review or audit method. Segment routine, complex and high-risk cases; keep comparable volumes and case mix when comparing periods. Pair the metric with error severity, rework and escalation performance so a lower number cannot conceal weaker oversight.
The burden may rise initially while experts find failure modes, explain exceptions and build evaluations. Over time, recurring burden should fall for comparable routine cases if the system is improving.
If it does not, investigate whether:
- The model is not reliable enough or critical context is missing.
- Systems are poorly integrated or the workflow is badly designed.
- The use case genuinely requires expert judgment and should not be automated.
- Visible work has been automated while verification shifted to less-visible experts.
An AI workflow has not scaled if higher output depends on scarce experts performing proportionally more hidden validation.
Why this changes enterprise AI architecture and governance
Future enterprise platforms need a way to decide whether a correction should become institutional knowledge. That can require expanding evaluation libraries, metadata, taxonomies, knowledge graphs, approved examples, exception registries, decision rules and escalation policies.
Models may change as providers and capabilities evolve. The surrounding knowledge can compound, although model changes still require reassessment: a new model will not necessarily use existing context in the same way.
McKinsey’s September 2026 operating-model research associates advanced AI adoption with redesigning roles, workflows and governance. It also cites earlier survey findings that organizations classified as “reinventors” more often reported meaningful enterprise value than those focused on basic enablement. These are reported associations, not proof that a particular operating model causes value.
The World Economic Forum’s Future of Jobs Report 2025 places growing AI-related skills alongside analytical thinking, resilience and leadership. Its employer expectations reinforce the importance of human capabilities; they do not validate this article’s specific implementation targets.
The regulatory scope must stay clear
The FDA–EMA Good AI Practice principles emphasize human-centric design, defined context of use, multidisciplinary expertise, data governance, risk-based assessment and lifecycle management in drug development. They are not promotional MLR or commercial-content requirements.
ICH E6(R3), Principles and Annex 1, emphasizes quality by design, proportionate approaches, reliable trial data and clear responsibilities. It is not an AI-specific guideline. Applying these principles to AI-supported clinical research requires evaluation against the actual trial, intended use and applicable jurisdiction.
A model cannot simply be called “good.” It must be fit for a defined purpose, using specified information, under particular conditions and appropriate oversight. That judgment needs maintenance as models, sources and workflows change.
The hidden workforce can build a reusable layer of knowledge
In a future pharma content ecosystem, a claim could carry relationships to its evidence, indication, permitted audience, approval market, dependent assets and channels, expiry date, relevant labeling changes and review requirements.
Apply the same discipline to clinical endpoints, protocol requirements, data-quality signals, safety events, medical inquiries and workflow exceptions. The system becomes more capable because the organization becomes easier to understand.
This also supports the shift toward AI-mediated pharma engagement: reliable external answers depend on governed internal context.
Seven priorities for a pharma AI operating model
Organizations should treat expert AI teaching as a strategic capability and execute it through a staged plan. The following priorities retain accountability within existing business, quality and governance functions.
Priority 1 · Within 90 days
Establish executive accountability and protect SME capacity
Accountable owner: Chief Digital and Data Officer, Chief Information Officer or equivalent AI executive, with Medical, Regulatory, Clinical Operations and Commercial sponsorship.
Near-term steps
- Name an executive sponsor for the AI learning system and a single accountable program leader.
- Select two or three high-value, bounded workflows—for example, MLR review support, clinical-trial deviation triage or medical-information response drafting. Keep consequential decisions with authorized people.
- Allocate explicit SME capacity for experimentation, evaluation and exception capture. Treat it as planned work, not volunteer effort.
- Add AI teaching responsibilities to pilot charters, role expectations and performance objectives where appropriate.
- Hold a monthly steering review covering value, risk, SME burden and reusable knowledge created.
Suggested success criteria
- Every pilot has a business owner, technical owner, quality or compliance owner and SME lead.
- Consider formally allocating at least 10% of participating SME capacity to evaluation and workflow redesign; validate feasibility with functional leaders.
- Track pilot participation and completion rates by function.
- Do not broaden deployment without documented context of use, decision rights and escalation paths.
- Distinguish deliberate early investment from rising SME burden caused by workflow failure.
Priority 2 · Within 120 days
Build cross-functional AI teaching pods
Accountable owner: Business-unit AI product leaders, supported by Enterprise Architecture, Quality, Compliance and Learning and Development.
Near-term steps
- Combine domain experts, process owners, AI engineers, data stewards and quality or compliance representatives in small teams.
- Give each team one workflow and one measurable outcome rather than a broad mandate to explore AI.
- Define the context of use, acceptable outputs, prohibited actions, human decision points and escalation conditions.
- Review outputs weekly, classify failures and decide which interventions should become reusable assets.
- Share templates for decisions, exceptions, evidence sources and unresolved questions.
Suggested success criteria
- Each priority workflow has a functioning team with documented roles and decision rights.
- Aim to classify at least 90% of pilot issues: missing context, model error, workflow design, data quality, integration or policy ambiguity.
- Every high-severity failure has an owner, corrective action and retest date.
- SME participation remains stable or increases through the pilot.
- Aim to address at least 80% of recurring issues through documented changes to context, workflow, evaluation, permissions or escalation logic.
Priority 3 · Within 90 days of launch
Create an enterprise exception registry
Accountable owner: Quality or AI Governance, with operational ownership in each workflow product team.
Near-term steps
- Define a common failure taxonomy covering severity, process, root cause, regulatory relevance, required human action and recurrence.
- Capture exceptions during review rather than relying on retrospective interviews.
- Record the original output, expert intervention, supporting evidence, final decision and recommended system change. Apply access controls, data minimization and retention rules to sensitive records.
- Decide whether an exception should become a policy, evaluation case, metadata requirement, knowledge relationship, permission change or approved training example.
- Review repeated failures monthly and challenge the underlying process where needed.
Suggested success criteria
- Aim to log at least 95% of material AI interventions in priority workflows.
- Set five business days as an illustrative maximum for governance review of high-severity exceptions; immediate containment and applicable safety reporting must not wait for that review.
- Repeated exceptions decline month over month.
- Aim for at least 70% of logged exceptions to result in a documented action, with a rationale for those requiring no change.
- Trace material decisions from AI output through human intervention, evidence and corrective action.
Priority 4 · Within 180 days
Convert expert judgment into evaluation assets
Accountable owner: AI product owners and model risk or validation leaders, with SMEs accountable for domain validity.
Near-term steps
- Build representative evaluation sets from routine cases, edge cases, historical failures and deliberately ambiguous scenarios.
- Separate cases by risk tier and define minimum acceptable performance for each tier.
- Evaluate citations, escalation, completeness, traceability, permission compliance and appropriate refusal as well as output quality.
- Run evaluations before deployment, after material changes and on a scheduled basis.
- Have SMEs approve the interpretation of results and identify where aggregate scores conceal meaningful risks.
Suggested success criteria
- Every priority workflow has a versioned evaluation set with provenance and risk classification.
- Test all material production changes before release according to applicable change control; a percentage target must never excuse skipping required validation.
- Cover every known high-severity exception type.
- Improve routine-case accuracy and appropriate automation without degrading high-risk escalation performance.
- Consider a target of at least 25% fewer repeated production failures against the first-month baseline, adjusted for volume and case mix.
Priority 5 · Within 180 days
Structure the knowledge AI will eventually need
Accountable owner: Chief Data Officer or Enterprise Information Architect, with business data owners and platform teams.
Near-term steps
- Identify critical entities and relationships: claims, references, indications, audiences, assets, approvals, endpoints, protocol requirements, safety events and review decisions.
- Document authoritative sources, ownership, update frequency, retention requirements and access restrictions.
- Prioritize metadata and relationships that would eliminate the most repeated SME interventions.
- Connect knowledge to existing systems wherever possible rather than creating an unmanaged parallel repository.
- Use change control so labeling, protocols, policies or source-content updates trigger review of dependent AI behavior.
Suggested success criteria
- Every priority workflow has a knowledge map and authoritative source inventory.
- Use 80% source traceability as an illustrative interim discovery target; production inputs must satisfy the workflow’s actual provenance and approval requirements.
- Missing-context exceptions decline across evaluation cycles.
- Critical relationships, such as claim-to-evidence or deviation-to-endpoint impact, are machine-readable for selected workflows.
- Every production knowledge source has a named owner and documented update responsibilities.
Priority 6 · Within 180 days
Manage the SME burden curve as an executive metric
Accountable owner: Chief Operating Officer or transformation office, with AI product owners and functional leaders reporting.
Near-term steps
- Baseline SME minutes per trustworthy completed outcome before or at pilot launch.
- Track verification time, escalation, rework, repeated errors, exception volume and cases completed without expert intervention.
- Segment routine, complex and high-risk work so reduced involvement is not mistaken for reduced oversight.
- Determine whether each new capability removes work, shifts work or creates work.
- Set explicit stop, redesign and scale criteria for every pilot.
Suggested success criteria
- Consider a target of at least 20% fewer SME minutes per trustworthy routine outcome, unless a documented risk decision supports a different target.
- Rework and repeated-error rates fall while quality and compliance remain stable or improve.
- Consider a target of at least 30% of routine cases completed with no more than one expert intervention, only where permitted by the approved workflow.
- High-risk cases continue to meet required human-review and escalation controls.
- Every pilot receives a documented scale, redesign or stop decision based on burden, value, risk and learning velocity.
Priority 7 · Within 12 months
Make institutional learning part of the AI operating model
Accountable owner: Executive AI steering committee, with Human Resources, Finance, Quality, Compliance and functional leadership.
Near-term steps
- Recognize AI teaching, evaluation and exception analysis in capacity planning and performance management.
- Include reusable knowledge created, repeated failures eliminated and SME burden reduced in scorecards.
- Create an internal community for sharing evaluation cases, taxonomies, patterns and lessons.
- Require new initiatives to explain what organizational knowledge they will create and how it will be maintained.
- Review reuse across products, functions, geographies and workflows without weakening local accountability.
Suggested success criteria
- Annual operating plans include experimentation and evaluation capacity for participating functions.
- Share reusable evaluation cases, exception patterns and knowledge assets across at least two business units where appropriate.
- New initiatives have a documented learning plan and accountable knowledge owner.
- Demonstrate year-over-year growth in useful reusable assets and fewer repeated SME explanations.
- Employee surveys show experts understand how their contributions improve workflows and organizational capability.
This plan is not about documenting every human thought or encoding outdated bureaucracy. Some rules are essential; others are historical accidents. Expert review should challenge processes as well as explain them.
Is the organization converting expert intervention into reusable capability faster than it is creating new verification work?
The Pharma AI Futurist takeaway
Even an extraordinarily capable model may not know why one regulatory exception matters more than another, why a source is trusted, which precedent applies, or when a technically correct answer is unacceptable. These may be limits of context rather than intelligence.
Enterprise AI is moving from giving employees tools, to building workflow agents, to capturing the knowledge those agents need. The next stage could be an organization that continuously improves how human expertise, AI execution and institutional learning work together.
Every new generation of models could become more useful when the company already has maintained context, evaluations, policies and institutional memory. The competitive advantage may lie in understanding how the business works and making that knowledge usable.
The opportunity is to reduce repetitive hidden verification—not to make experts disposable. Domain experts correcting AI today should help create assets tomorrow’s systems can reuse, while remaining accountable for the judgments that require their expertise.
Alongside “How many employees use AI?” ask “What does the organization learn every time an expert works with AI?” Alongside time saved, ask how much expert knowledge becomes a capability others can use.
The future of enterprise AI may depend less on teaching people to use AI—and more on teaching AI how the organization actually works.
For a practical starting point, use the pharma AI readiness assessment to identify operating gaps, or explore how to design a governed 90-day pilot.
Sources & research notes
Research and official guidance checked September 16, 2026. The SME burden metric, learning loop and operating plan are the author’s proposed framework. Clinical examples illustrate context-dependent judgment; this article is not medical or regulatory advice.
- MIT Sloan: Why some organizations turn AI experiments into business value while others quietly failSeptember 9, 2026 · Report on a working paper
- McKinsey & Company: The key to AI value is hiding in plain sight: Your operating modelSeptember 9, 2026
- World Economic Forum: The Future of Jobs Report 2025: Skills outlookJanuary 2025
- FDA and EMA: Guiding Principles of Good AI Practice in Drug DevelopmentJanuary 2026
- ICH: E6(R3) Good Clinical Practice: Principles and Annex 1January 6, 2025 · Step 4 guideline (PDF)