REPORT
The State of AI Readiness in Life Sciences
The Operating Model Decisions That Help Life Sciences Organizations Operationalize AI Across Content, Engagement, and Experience
Where AI Meets Scientific and Regulatory Rigor
Most life sciences organizations have moved AI past exploration, but few have built the operating model that makes deployment repeatable. This report examines what holds that progress back, and what the Leaders do differently.
Foreword
Artificial intelligence (AI) is shaping how life sciences respondents approach content, engagement, data, and operational workflows. The opportunity extends across interactions with customers and healthcare professionals and the teams that create, review, deliver, and measure those experiences. Our research asks whether ownership, governance, measurement, and ways of working are developing at the same pace as adoption.
AI initiatives often begin within individual functions, each with its own objectives, data, systems, and responsibilities. Commercial teams may focus on engagement and content, while medical, scientific, technology, and operational teams contribute expertise. Legal, privacy, regulatory, compliance, security, quality, and safety partners provide essential oversight. When these groups follow separate decision processes, initiatives become harder to evaluate, connect, and extend.
The research points to an operating model issue. Repeatable deployment requires accountable ownership, agreed measures, effective collaboration, shared learning, and practitioners who can help colleagues adopt new ways of working. These foundations must preserve scientific accuracy, privacy, security, accessibility, responsible governance, human oversight, and specialist authority.
This report examines five behaviors more common among global cross-industry Leaders. They give life sciences executives a practical framework for strengthening AI execution while maintaining the review and oversight appropriate to each workflow.
Strategy Consultant, Healthcare & Life Sciences, Adobe
About the Research
MARKETS COVERED
Central Europe, Latin America, North America, the United Kingdom and Ireland, and Western Europe.
How Leaders Were Identified and Scored
The 342 AI Leaders referenced in this report come from the full study population of 5,633 executives across 12 industries. This is a global, cross-industry cohort rather than a group specific to life sciences. It provides the benchmark used to compare life sciences findings.
How Maturity Was Measured.
Each organization was assessed across six operating model pillars, with every pillar scored on a scale of 1 to 5. The six scores were combined into an operating model maturity (OMM) composite.
Strategy
Clear AI vision with ownership and
investment commitments.
Structure
Dedicated AI ownership with budget authority.
People
Roles and culture evolving for
an AI-enabled environment.
Governance
Risk framework calibrated
for AI velocity.
Technology
Infrastructure built for scale, not just pilots.
Workflows
AI embedded into how
work is actually done.
How Leaders were identified:
To qualify as a Leader, an organization had to meet two criteria: achieve an operating model maturity composite above the top-quartile threshold of 3.27 and operate at the expanding or transforming stage of AI deployment. Applying the criteria identified 342 Leaders across 12 industries, equivalent to 6% of the 5,323 active respondents.
The designation represents advanced deployment combined with stronger operating model maturity. It is not specific to life sciences and does not establish causation. Unless otherwise stated, Leader comparisons refer to this global cross-industry cohort.
Executive Summary
Life sciences respondents are moving AI beyond early exploration, but the operating model supporting that activity has not developed evenly. Ownership remains limited, measurement is often basic, and business and information technology teams frequently work through sequential relationships. This report examines those gaps and the behaviors more common among the global cross-industry Leaders.
The Industry Picture
of the active life sciences respondent base is in the Frozen Middle, concentrated at the piloting and deploying stages. AI is active, but many respondents have not reached expanding or transforming. The finding does not show that organizations have stalled over time. It indicates where adoption is currently concentrated and where the requirements for repeatable execution become more demanding.
of life sciences respondents report that their organizations have a formal AI owner with a defined title and budget authority. Elsewhere, responsibility may be distributed across functions, committees, or individual projects. That can leave teams accountable for separate parts of an initiative with no single owner able to set priorities, allocate resources, and coordinate decisions across the work.
of life sciences respondents describe business and information technology relationships as siloed, transactional, or consultative. These models rely on separate requests, advice, and sequential handoffs rather than shared accountability. AI initiatives that depend on connected content, data, technology, measurement, and appropriate specialist review require earlier collaboration and clearer joint outcomes.
What Leaders Do Differently
The 342 global cross-industry Leaders combine stronger operating model maturity with operation at the expanding or transforming stage. Five behaviors are more common among this cohort. These comparisons provide a benchmark for life sciences respondents but do not establish that an individual behavior caused stronger performance.
Leaders are 5.7 times more likely to have a formal AI owner with a defined title and budget authority. The role provides accountability across the program while preserving the decision rights of medical, scientific, regulatory, privacy, safety, and other specialist functions.
44% of Leaders measure return on investment comprehensively, compared with 8% of life sciences respondents. Leaders define intended outcomes, baselines, and evaluation methods before deployment, when teams can still agree on what success should mean.
81% of Leaders operate at the collaborative or integrated level, compared with 19% of life sciences respondents. Business and technology teams participate when work is defined and share accountability for delivery and the intended outcome.
34% of life sciences respondents report destructive failure patterns involving blame or repeated mistakes. Among Leaders, the figure is under 1%. Leaders are more likely to examine underperformance and apply what they learn to later work.
42% of Leaders identify finding and empowering internal champions as their most impactful culture action, ahead of executive sponsorship at 16% and formal training at 11%. These practitioners help colleagues evaluate proven approaches in a relevant working context.
PART ONE
The Industry Picture
Finding 01 — Most Life Sciences Respondents Are in the Frozen Middle
60% of the active life sciences respondent base is in the Frozen Middle, with AI underway but broader, repeatable execution still limited.
Share of life sciences respondents at each adoption stage.
Life sciences respondents are adopting AI one use case and workflow at a time. Activity may begin in content, engagement, analytics, or operations, each with its own objectives, data, systems, and review requirements. This creates progress within functions without necessarily creating a repeatable enterprise approach.
The Frozen Middle describes 60% of life sciences respondents at piloting or deploying. These stages show that AI has moved beyond exploration, but they do not indicate how long respondents have remained there. The finding identifies a concentration in deployment stages rather than a time-based stall.
Moving beyond this concentration requires teams to connect decisions that pilots can leave unresolved. Accountable ownership, data access, agreed measures, and clear decision rights help business, technology, and oversight partners evaluate which initiatives should continue, change, or expand. For life sciences respondents, progress also depends on preserving scientific accuracy, privacy, security, accessibility, human oversight, and the authority of specialist functions.
Finding 02 — Only One in Six Life Sciences Respondents Has Named a Formal AI Owner
Without a named owner, responsibility can be distributed across functions without clear authority over priorities, funding, and coordination.
Distribution of AI ownership models in life sciences.
AI initiatives in life sciences can involve commercial, medical, scientific, data, technology, operations, and oversight teams. Each may be responsible for a different decision or stage of delivery. When accountability is distributed across these groups, teams can struggle to resolve competing priorities, allocate resources, or decide which initiatives should advance.
Only 16% of life sciences respondents report that their organizations have a formal AI owner with a defined title and budget authority. Elsewhere, responsibility may be part-time, committee-led, or assigned project by project, leaving decisions dependent on agreement among teams with different mandates and priorities.
Formal ownership should establish accountability for priorities, funding, coordination, and results. It should not give one executive unilateral authority over medical, scientific, regulatory, legal, privacy, security, quality, or safety judgments. The owner must instead bring partners into decisions, clarify who has authority at each point, and keep work moving within governance appropriate to the use case.
Finding 03 — Only 11 of Every 100 Life Sciences AI Use Case Requests Reach Production
The largest reduction occurs during compliance and validation review, showing why feasibility, value, and oversight must be considered together.
Mean survivors per 100 proposed AI initiatives in life sciences, Global average: 11 per 100.
Moving an AI use case from idea to production requires decisions across business, technology, and oversight teams. The life sciences funnel follows use cases through initial evaluation, compliance and validation review, security assessment, integration, budget approval, and production launch. Each stage tests whether work is ready to continue.
Out of every 100 AI use case requests, life sciences respondents estimate that 11 reach production. The largest reduction occurs during compliance and validation review. The result does not make oversight an obstacle. It shows that feasibility, evidence, data, governance, risk, and approval requirements need consideration.
A stronger process brings together the people responsible for intended value, technical delivery, and specialist review together during ideation. They can identify required data, establish system ownership, define evaluation criteria, and determine which approvals apply before a proposal reaches formal review. This helps teams assess feasibility and value together without weakening scientific, medical, regulatory, privacy, security, quality, or safety requirements.
Finding 04 — Most Life Sciences Respondents Still Manage Business and IT through Handoffs
81% operate through Siloed, Transactional, or Consultative relationships rather than models built around shared accountability.
Distribution of Business-IT operating relationships in life sciences.
AI initiatives require business teams to adjust content, engagement, or operational decisions as evidence develops. Technology teams must adapt data, systems, integrations, and controls alongside them. Sequential handoffs make coordination harder.
81% of life sciences respondents describe business and information technology (IT) relationships as siloed, transactional, or consultative. In these models, business teams may submit requests and IT may deliver systems or advice without sharing accountability for outcomes. Issues can move between functions through queues, reviews, and approvals.
Collaborative and integrated relationships begin earlier. Business, technology, data, and relevant oversight partners define the use case together, agree on what success means, and identify constraints before development advances. Shared objectives and key results can maintain joint accountability through delivery and measurement. For life sciences respondents, this approach helps teams resolve data, integration, privacy, security, scientific, regulatory, and operational questions while the work is being shaped rather than after one function has handed it to another for review and final approval.
Finding 05 — Comprehensive AI Measurement Remains Rare among Life Sciences Respondents
Most life sciences respondents report basic measurement, leaving intended outcomes difficult to assess and compare.
ROI Maturity distribution in life sciences.
As AI becomes part of life sciences workflows, teams need to measure more than activity. They may track content produced, requests processed, audiences reached, or time saved. These measures can show adoption and efficiency, but they do not establish whether the initiative improved the intended business, workforce, operational, customer, healthcare professional, or patient-related outcome.
Stronger measurement begins before deployment. Teams define the intended outcome, record a baseline, and agree on how results will be evaluated. Depending on the use case, measures may address engagement, content performance, process quality, productivity, cost, or another approved outcome. Scientific, medical, regulatory, privacy, security, accessibility, and safety requirements should be incorporated at launch where relevant rather than treated as separate from evaluation.
Pre-deployment measurement protects the original comparison. Once a workflow changes behavior, timing, or output, the baseline may be difficult to reconstruct. Clear measures give executives evidence for deciding which initiatives should continue, change, or expand.
PART TWO
What Leaders Do Differently
Leader Behavior 01 — AI Advances when a Named Leader Is Accountable for It
AI work in life sciences can cross content, engagement, data, technology, operations, and specialist review. Part-time owners, committees, and ad hoc project leads may coordinate tasks without authority to resolve competing priorities or allocate resources across the program. Decisions can remain open while teams continue working within their own mandates.
Leaders are 5.7 times more likely to have formal AI ownership with a defined title and budget authority. A named owner can set priorities, align funding, establish shared measures, and coordinate decisions across participating functions. This accountability also supports operating model practices. Leaders are more likely to work through collaborative business–technology relationships and to measure return on investment comprehensively from the start of deployment.
The role should be organization-neutral and broad enough to connect business, technology, data, and governance partners. Its authority covers priorities, resources, coordination, and results. It does not replace the authority of medical, scientific, clinical, regulatory, legal, privacy, security, quality, or safety teams. Reporting lines and review processes can remain.
Leader Behavior 02 — Leaders Define Success before Deployment
44% of Leaders measure return on investment comprehensively, compared with 8% of life sciences respondents.
Share with Comprehensive or Advanced ROI framework. Leaders cohort: 342 organizations across all 12 industries.
Leaders establish measurement before deployment decisions are finalized. 44% have a comprehensive return on investment framework, compared with 8% of life sciences respondents. Measurement is established across the Leader cohort, while a share of life sciences respondents reports no measurement. This gives Leaders earlier evidence for funding, prioritization, expansion, or redesign decisions.
Before an initiative launches, teams define the intended outcome, record a baseline, and agree on how performance will be evaluated. Measures should fit the use case and may address engagement, content effectiveness, process quality, productivity, cost, workforce experience, or another result. Business, data, technology, and specialist teams should agree what evidence will support decisions to continue, change, or expand.
Timing matters because the baseline can disappear once a workflow changes. New content, engagement patterns, operational steps, or decision processes can alter the conditions teams intended to compare. Recording the starting point and evaluation method before launch helps executives interpret results responsibly without presenting activity measures as business, scientific, medical, or patient outcomes.
Leader Behavior 03 — Business and Technology Teams Share Outcomes
81% of Leaders operate at the collaborative or integrated level, compared with 19% of life sciences respondents.
Joint accountability = Collaborative + Integrated Handoff + Advisory = Siloed + Transactional + Consultative.
AI-supported work in life sciences can depend on content, data, workflow, and technology systems managed by different teams. A sequential relationship creates friction whenever requirements, evidence, access, or measurement must move between functions. Siloed and transactional models separate the work, while consultative relationships involve advice without shared accountability for the outcomes.
Among 342 global cross-industry Leaders, 81% operate at the collaborative or integrated level, compared with 19% of life sciences respondents. Leaders involve business, technology, data, and relevant partners when the initiative is defined. They develop scope together rather than transferring a completed request.
Shared accountability continues through delivery, review, and measurement. When teams carry the same objectives and key results, build decisions, governance requirements, and evaluation all move in one direction. For life sciences organizations, genuine partnership means bringing specialist functions into the work at the point their authority and expertise are needed, while keeping responsibility for intended outcomes visible across the team.
Leader Behavior 04 — Leaders Turn Underperformance into Shared Learning
34% of life sciences respondents report destructive failure patterns involving blame or repetition. Among Leaders, the figure is under 1%.
Each dot represents one of every 100 life sciences organizations. Destructive = Blame and retreat + Repetition of failures.
AI initiatives do not always perform as expected. In life sciences, teams may be working within fixed planning, review, or delivery cycles, making it tempting to move forward without examining what underperformed. When responsibility is assigned defensively or the same mistake is repeated, the organization loses evidence that could improve the next deployment or workflow decision.
34% of life sciences respondents report destructive failure patterns involving blame or repetition. Among 342 global cross-industry Leaders, the figure is under 1%. The comparison shows that Leaders are more likely to treat underperformance as information to examine, share, and apply rather than as an event to conceal or repeat in subsequent AI work.
A learning culture becomes visible in regular operating reviews. The AI owner creates time for teams to identify what happened, why it happened, and what should change. Relevant medical, scientific, regulatory, privacy, security, quality, or safety partners can help distinguish a correctable delivery issue from a concern requiring controls or escalation. Lessons can then inform later work.
Leader Behavior 05 — Leaders Give Proven Practitioners the Support to Lead by Example
42% of Leaders identify finding and empowering internal champions as their most impactful culture action.
Leaders’ top-cited culture lever. n=342 Leaders across all 12 industries.
People are more likely to adopt AI when they see a colleague apply it effectively in familiar work. In life sciences, that practitioner could work in content, engagement, analytics, operations, technology, medical, scientific, or another function. Credibility comes from showing a useful approach within a recognizable workflow and appropriate governance.
42% of Leaders identify finding and empowering internal champions as their most impactful culture action, ahead of executive sponsorship at 16% and formal training at 11%. Leaders give proven practitioners recognition, support, and permission to help an adjacent team evaluate and adapt an approach. Influence grows through demonstrated results rather than through title.
Champions do not replace ownership, training, or specialist review. They connect those structures to work by documenting what worked, explaining where it may apply, and identifying where conditions differ. Leaders can support them with time, a budget, access to expertise, and clear boundaries. For life sciences respondents, this helps adoption spread through evidence while maintaining the oversight each use case requires.
5 Moves that Build the Operating Model
These actions translate the Leader behaviors into practical steps for life sciences executives. They build on existing business, medical, scientific, data, technology, and governance capabilities rather than requiring a wholesale organizational redesign.
- Name one AI owner with authority across the program.
Appoint a named executive with a clear mandate and budget authority for AI priorities, coordination, and results. The role may sit in any function positioned to work across the organization. Existing reporting lines and specialist decision rights should remain intact. The owner should convene the business, technology, data, and oversight partners required for each initiative, resolve resource conflicts, and ensure that scientific, medical, regulatory, privacy, security, quality, and safety judgments remain with the appropriately qualified teams. - Define the intended outcome before the next deployment.
For every AI initiative, define the intended business, workforce, operational, customer, healthcare professional, or patient-related outcome before work advances. Set a baseline against the current process and agree how results will be evaluated. The participating functional and specialist partners should distinguish activity measures from outcome measures and incorporate relevant accuracy, privacy, security, accessibility, quality, safety, and human-oversight requirements into the evaluation plan. - Make business and technology jointly accountable from the brief.
Bring business, data, technology, and relevant governance teams together when the use case is defined. Establish shared objectives and key results for delivery and the intended outcome rather than passing a completed request from one function to another. Clarify data access, system ownership, integration requirements, review responsibilities, and decision authority before development advances. Joint accountability helps teams resolve issues while the work is being shaped without weakening necessary specialist review or approval. - Build structured learning into operating reviews.
Create a regular review in which the AI owner and participating teams examine what performed as expected, what did not, and what should change. Separate correctable delivery issues from scientific, medical, regulatory, privacy, security, quality, or safety concerns that require escalation or different controls. Record decisions and make relevant lessons available to other teams. Leaders should reward accurate reporting and thoughtful correction rather than encourage teams to conceal underperformance or continue an approach simply to preserve the original schedule. - Give proven practitioners the support to extend their work.
Identify practitioners who have applied AI effectively within a specific, appropriately governed workflow. Give them time, recognition, a modest budget, and access to the expertise needed to document their approach and help one adjacent team evaluate it. Champions should explain the conditions in which the practice worked, the evidence used to assess it, and the boundaries that must remain. Their role is to support adoption through credible examples, not to replace formal ownership, training, governance, or specialist authority.
Readiness Depends on the Operating Model.
Life sciences respondents have moved AI beyond early exploration, but adoption alone does not create repeatable execution. The research points to a more practical requirement — an operating model that connects ownership, measurement, collaboration, learning, and adoption across the teams responsible for value, delivery, and oversight.
The global cross-industry Leaders offer a useful benchmark. They are more likely to appoint a formal owner, define success before deployment, share accountability across business and technology, learn from underperformance, and empower proven practitioners. For life sciences executives, these behaviors matter when they are applied with scientific accuracy, privacy, security, accessibility, equity, responsible governance, safety, and human oversight appropriate to each use case.
The next step is to choose one priority workflow and make its operating decisions explicit before further investment: name the accountable owner, identify the teams with specialist authority, establish the baseline and intended outcome, and agree how delivery, review, and learning will work. That creates a disciplined starting point for deciding whether the initiative should continue, change, or expand.
About Adobe
Adobe empowers organizations to create impactful digital experiences. Our portfolio of customer-experience products and services helps businesses put every interaction in context, understand what each customer needs right now, and design and deliver digital experiences that build loyalty and drive growth. Visit adobe.com to know more.
About Incisiv
Incisiv is an industry insights and strategy firm combining close to a decade of industry trend data with primary research, messaging architecture, and content development to help enterprises navigate transformation, evaluate solutions, and unlock growth. Visit incisiv.com to know more.
Recommended for you
https://main--da-bacom--adobecom.aem.page/fragments/resources/cards/thank-you-collections/healthcare
