The executive answer
How should pharmaceutical companies evaluate AI vendors?
Evaluate the business value, evidence, enterprise readiness, governance and risk, adoption requirements, and scalability of the proposed solution. Selecting an AI solution and successfully adopting it are two different decisions: both require a plan for responsible deployment, integration and measurable outcomes.
A RISE point of view
How should pharmaceutical companies evaluate AI vendors?
Pharmaceutical companies should assess whether an AI partner can deliver measurable business value within a regulated enterprise. Technical capability is a starting point; evidence, integration, responsible governance, adoption and the ability to scale determine whether the solution can improve real work.
Six dimensions of AI vendor evaluation
Business Value
Which consequential outcome will improve?Name the workflow, baseline, expected benefit and total cost. Tie the use case to a business priority.
Evidence
What supports the vendor’s claims?Review relevant results, methods, limitations and a validation plan using representative data and conditions.
Enterprise Readiness
Can it work in our environment?Assess data access, integration, security, support, monitoring and lifecycle ownership.
Governance & Risk
Can we use it responsibly?Define privacy, validation, traceability, human oversight and escalation in proportion to the use case.
Adoption
Will people use the new workflow?Test with users and managers; plan training, roles, change management and sustained behavior measures.
Scalability
Can value extend beyond a pilot?Examine architecture, operating capacity, vendor support, repeatability and ROI over time.
Use these dimensions to compare vendors against a defined use case, then use the seven-condition Enterprise AI Adoption Map™ below to plan the enterprise work required after selection. The evaluation should involve business, technology, data, legal, compliance, privacy and the people who own the workflow.
RISE founder Carl Wooten brings a life sciences commercial and organizational lens to this decision. RISE’s ACTO Responsible Agentic AI in Life Sciences work reflects the importance of cross-functional dialogue about responsible deployment. Explore our Enterprise AI Strategy + Adoption capability and why pilots alone do not establish adoption.
The technology may be ready before the enterprise is. Durable adoption begins when AI becomes part of how important work is designed, governed, performed and improved—not when another pilot produces an impressive demonstration.
Why promising pilots do not become enterprise performance
Many pilots are designed to answer a technical question: Can the model do this? Enterprise adoption requires a different question: Can the organization use this capability responsibly, repeatedly and at scale to improve an outcome that matters?
That transition exposes dependencies the pilot can temporarily avoid—fragmented data, unclear workflow ownership, integration demands, risk review, human accountability, capability gaps, incentives and the absence of a credible value measure.
The RISE Enterprise AI Adoption Map™
Seven conditions for durable adoption
AI value appears when these conditions reinforce one another as a system.
Strategic Value
Where can AI materially improve an enterprise priority?
Workflow Design
How must the work change for AI to matter?
Data + Technology
Can the system produce reliable, usable output at scale?
Governance + Risk
What boundaries make responsible action possible?
Human Accountability
Who remains responsible for judgment and outcomes?
Operating Capability
What must the enterprise be able to sustain?
Adoption + Value
Are people using the new workflow—and is performance improving?
What each condition must resolve
Strategic Value
Where can AI materially improve an enterprise priority?Prioritize consequential use cases against value, feasibility, risk and the organization’s ability to act—not novelty or executive enthusiasm alone.
Workflow Design
How must the work change for AI to matter?Redesign tasks, handoffs, decisions and human-machine roles around the performance moment rather than adding a tool to the existing process.
Data + Technology
Can the system produce reliable, usable output at scale?Resolve data access, quality, architecture, integration, security, monitoring and lifecycle requirements early enough to influence the design.
Governance + Risk
What boundaries make responsible action possible?Define acceptable use, validation, traceability, privacy, escalation and human review according to the consequence of the decision.
Human Accountability
Who remains responsible for judgment and outcomes?Assign clear decision ownership, review expectations and escalation paths. AI can support consequential work; it does not absorb accountability.
Operating Capability
What must the enterprise be able to sustain?Build product ownership, cross-functional delivery, vendor management, model oversight, learning and support beyond the initial pilot team.
Adoption + Value
Are people using the new workflow—and is performance improving?Measure behavior, workflow movement and business outcomes; reinforce the work through leaders, managers, incentives and continuous improvement.
The workflow—not the model—is the unit of adoption
A model can perform well while the surrounding work remains unchanged. If people still rely on the old handoffs, duplicate the output, distrust the recommendation or lack authority to act, technical performance will not become enterprise value.
Leaders should therefore govern AI at the level of the consequential workflow: what decision is being supported, what evidence is required, where human judgment remains essential and how the organization will know the new way of working is better.
How to use the map
- Start with the enterprise outcome.Define the strategic or operational result before selecting the technology or expanding the use case.
- Map the current workflow.Identify decisions, tasks, handoffs, friction, data dependencies and the people accountable for the outcome.
- Design human and machine roles.Specify where AI generates, recommends, predicts or automates—and where human review, judgment and escalation remain required.
- Build governance into the workflow.Apply risk controls, traceability and review at the moments where consequence is created.
- Measure adoption and value together.Track whether the workflow is being used as designed and whether speed, quality, capacity or business outcomes improve.
Signals the adoption system is incomplete
- The pilot has an executive sponsor but no accountable workflow owner.
- Success is described through model performance or usage rather than a business outcome.
- Governance is treated as a late approval gate instead of a design input.
- Users are trained on the tool but the surrounding workflow and incentives remain unchanged.
- The organization cannot explain who reviews output or owns the resulting decision.
- Every use case requires a new team, architecture and governance process.
Executive tool
Use the seven-condition map.
Download the one-page Pharma + Biotech Enterprise AI Adoption Map™ for leadership discussions, strategy sessions and adoption planning.
Download the executive map One-page PDF · Complimentary RISE resourceFrequently asked questions
Enterprise AI adoption, clarified
How should pharmaceutical companies evaluate AI vendors?
Start with a specific business outcome and assess six dimensions: Business Value, Evidence, Enterprise Readiness, Governance & Risk, Adoption and Scalability. Include workflow owners and cross-functional risk and technology stakeholders before selection.
What evidence should a pharmaceutical company request from an AI vendor?
Ask for results relevant to the proposed use case, transparent evaluation methods and limitations, performance in representative conditions, references where available, and a plan to validate claims with the company’s own data and workflow.
What does enterprise readiness mean for an AI vendor?
It means the solution can meet the company’s requirements for data access, integration, security, support, monitoring, ownership and lifecycle management in the intended operating environment.
How should governance and risk affect vendor selection?
Match controls to the use case’s consequences. Assess privacy, security, validation, traceability, human review, escalation and the vendor’s ability to support the enterprise’s legal and compliance obligations.
How can pharma assess whether employees will adopt an AI solution?
Test the proposed workflow with its users and managers. Define where AI fits into tasks and decisions, what training and support are needed, who remains accountable, and what behavior will show sustained use.
How should scalability and ROI be evaluated?
Estimate total cost and expected business benefit, then agree on a baseline and measures for quality, speed, capacity or outcomes. Check whether the architecture, vendor support and operating model can extend beyond a pilot without rebuilding each use case.
What is enterprise AI adoption in pharma and biotech?
Enterprise AI adoption is the sustained use of AI within real workflows, supported by appropriate data, technology, governance, human accountability, operating capabilities and measures of business value.
Why do life sciences AI pilots fail to scale?
Pilots often prove technical possibility without redesigning the workflow, clarifying accountability, resolving data and integration constraints, preparing users or defining how value will be measured.
Should governance come before AI experimentation?
Organizations need clear risk boundaries and human-review expectations early, but governance should enable prioritized experimentation rather than become a disconnected approval exercise.
Who owns enterprise AI adoption?
Ownership is shared. Business leaders own the outcome, workflow owners shape use, technology and data teams enable delivery, risk functions establish boundaries and executives create enterprise priorities and accountability.
What are the main barriers to AI adoption in pharmaceutical companies?
The main barriers are unclear business value, insufficient evidence, fragmented data, integration complexity, governance and regulatory demands, limited organizational readiness, low user adoption, and difficulty building a repeatable operating model beyond a pilot.
Why do pharma AI pilots fail to scale?
A pilot can validate a narrow technical use case while bypassing enterprise data, integration, risk review, ownership and workflow change. Scale requires evidence in representative conditions, an accountable owner, repeatable controls, user adoption and a measurable business outcome.
What governance is needed to deploy AI across pharma?
Set risk controls in proportion to the use case, including privacy, security, validation, traceability, human oversight and escalation. Make ownership and review part of the workflow before broader deployment.
How can a pharma organization improve AI readiness and user adoption?
Identify an accountable business owner, bring data, technology and risk teams into design early, test the workflow with users and managers, train for changed decisions, and measure sustained use alongside business results.
How does pharma move AI from pilot to enterprise deployment?
Define the target outcome and baseline, validate evidence in real conditions, resolve data and integration requirements, establish governance and support, redesign the workflow with users, then expand only when value and controls remain reliable.
How should pharma leaders measure AI value?
Measure movement in the workflow and business outcome—such as speed, quality, capacity, decision confidence or customer impact—not only licenses, logins, models or pilots completed.
Move from AI activity to adoption
RISE helps leaders connect strategy, workflow, governance, human accountability and measurable value.
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