The People Strategy For AI Adoption Framework

The people strategy for AI adoption framework is a model for understanding where your organization stands on its AI journey and what to focus on next. It is grounded in published maturity research and has been refined in the HRSF AI Collaboratory through co-creation with CHROs, academics, and AI practitioners.

People strategy for AI adoption: latest version (October 2026)

October 2026 · Application-ready, research-grounded

The People Strategy for AI Adoption

A practitioner-built maturity guide for leaders redesigning work, decisions and culture

AI adoption maturity is the demonstrated ability to redesign work, decide, develop people, and govern AI for sustainable value.

Start hereStrategic Intent

Optimizer

Same output, lower cost. A margin play. Expect higher employee anxiety and plan the conversation for it.

AND/OR

Expander

Same people, new capacity for work previously out of reach. A growth play. Shapes every metric and leadership message downstream.

Maturity ProgressionFrom experimentation to systemic intelligence
Stage 1Experimenting
Stage 2Operationalizing
Stage 3Integrating
Stage 4Reinventing

A cycle, not a line: Progress is uneven and iterative. Functions, workflows, and human systems can develop at different rates.

Definitions
Teams innovate, test bounded uses, learn where AI adds value, and set basic safeguards.
Teams use AI reliably in defined processes, with trained users, accountable owners, and measured results.
Organizations redesign workflows, roles, and decision rights for measurable outcomes across functions.
Organizations continuously learn and use AI to enable new offerings and ways of operating.
Core Question
What can AI do?
How do we use AI reliably?
How does AI change how we run the business?
How does AI redefine our business model?
Value Realization
Isolated productivity
Functional efficiency
Cross-enterprise impact
New value creation
What to Measure
Active use, learning, and early value
Task time, quality and rework
Workflow outcomes and cost-per-outcome
New capacity, revenue and business value
Context ModifiersFactors that determine the pace of adoption
  • Regulatory intensity (e.g., Health, insurance, legal, pharma)
  • Organization scale (e.g., Small firms lack large data sets)
  • Data sensitivity (e.g., PII, HIPAA, IP exposure)
  • External contracts (e.g., Customer and vendor data terms)

The 7 Human Systems (what evolves at each maturity level)

1
Work DesignHow do we redesign work so people’s roles, skills, and workload improve alongside AI?

Roles unchanged; skills self-taught; workload steady

AI as tool

Roles add AI use cases; prompting and review skills; routine tasks offloaded

AI as assistant

Roles redesigned around workflows; orchestration and judgment skills; workload rebalanced

AI as contributor

Fluid, team-based roles; continuous reskilling; workload shifts to higher-value work

AI as catalyst
2
Workforce CapabilityDo people have the skills and judgment to use AI well and to question its output?

AI literacy and prompting; self-found use cases; safe space to play, fail and share.

Curious, self-taught tool users

Role-based hands-on upskilling; prompting, review, and evaluation skills; adapting to tool changes.

Role-specific fluency with trained users

Validating AI output with domain expertise and systems thinking; reskilling at scale before automation.

Judgment as the core skill

Accountable oversight of AI agents; cross-functional collaboration skills valued; new learning paths for early-career staff.

Humans lead oversight and innovation
3
Decision & GovernanceWho decides and who is accountable, with people able to intervene and recover?

Baseline guardrails: data use, vendor terms, human-in-the-loop; approved tools, basic rules, acceptable risk levels.

Safe to try

Formal policies, incl. customer and vendor data agreements; an accountable owner per use case; principles-based policy.

Clear rules, clear owners

Workflow owners are accountable; guardrails built into tools; decision-impact review and bias testing for people decisions; continuous monitoring for drift.

Governance in the flow of work

Risk-based human oversight; kill switches and rollback tested; every human and agent decision has an owner; employees can challenge AI.

Oversight matched to risk
4
Leadership & CultureHow do leaders build trust, invite challenge, and own workforce effects?

Leaders demonstrate AI use and its limits, invite bounded experiments, and share learning without stigma.

Leaders go first

Leaders role model AI use, set the roadmap, and explain what is changing and uncertain.

Leaders clarify direction, capitalize on trust and reward initiative

Leaders align functions on shared outcomes, reinforce employee voice, and hold teams accountable.

Leaders set shared outcomes and accountability

AI-forward mindset: leaders continuously redesign work, support challenge and learning, and own outcomes.

Leaders redesign and own outcomes
5
Motivational DriversWhat makes people want to use AI, and how are they rewarded for using it well?

Curiosity, play, and psychological safety; legacy reviews and pay unchanged.

Curiosity first, pay unchanged

Job-specific clarity and protected time; rewards tied to effective AI use and critical validation.

Reward effective use

Goals, recognition, and employee voice; multi-stakeholder review of AI-driven impact; pay anchored to skill agility.

Recognize cross-enterprise impact

Purpose, mastery, and collective learning; fluid roles with pay anchored to value creation and agility.

Pay follows value and agility
6
Operating ModelHow do we organize so ownership is clear, teams coordinate, and people keep a voice?

Siloed experimentation; each function’s risk posture sets its pace; no shared ownership of AI.

Local and uneven

Hybrid (central + function-led); tools rationalized; cross-functional AI teams (IT, legal, HR) form; workflows still follow the org chart.

Central standards, local ownership

Platform + product model; cross-functional pods; clear ownership and coordination across boundaries.

Redesign how work flows across functions

Fluid pods from a clean sheet; hybrid roles; agents in the org chart.

Build the organization around humans + AI
7
Economics & ValueWhat value does AI create, and how do we count workforce impact as part of it?Co-owned by HR + Finance

AI costs hide inside IT budgets; people may hide time saved; experimenting is unbudgeted.

Cost is hidden

Token and usage costs tracked per team; capacity gains logged but not redeployed; savings tested.

Spend visible, savings tested

Economics shifts to total cost of work: the best human–AI mix per workflow, at real price.

Total cost of work

AI reshapes pricing, go-to-market and the human–AI labor mix while protecting talent capital.

Price value, not effort

Developed through the 2026 HRSF AI Collaboratory, drawing on the lived experience of HR, talent, and business practitioners navigating AI adoption, and grounded in research.

Scan to read the full paper and framework.For the full model, refer to: Sarkar-Barney, S., Arjannikova, S., Grantham, C., Rogowski, R., Pui, S. Y., & Baill, B. (2026, October). The people strategy for AI adoption: An explainer and action guide to the seven human systems [Working paper]. HR Strategy Forum, AI Collaboratory.

White Paper
Detailed Framework (Web)
Framework (Print)

Original people strategy framework for CHROs (May 2026)

AI Adoption Maturity

A People Strategy Framework for CHROs

AI maturity is not about technology sophistication. It is the extent to which the organization has redesigned work, decision-making, and leadership to create value with AI.

Maturity progression: from experimentation → systemic intelligence
Stage 1Experimenting
Stage 2Operationalizing
Stage 3Integrating
Stage 4Reinventing
Core Question
What can AI do?
How do we use AI reliably?
How does AI change how we run the business?
How does AI redefine our business model?
Value Realization
Isolated productivity
Functional efficiency
Cross-enterprise impact
New value creation
The 5 Human Systems (what actually evolves)
1. Work Design
Individual tool usage
Defined use cases
AI embedded in workflows
Human–AI collaboration as default
2. Workforce Capability
Self-directed experimentation
Targeted upskilling
Role redesign & reskilling at scale
Skills shift to judgment, oversight, innovation
3. Decision & Governance
Ad hoc / reactive
Emerging policies
Embedded governance in workflows
Adaptive, AI-augmented governance
4. Leadership & Culture
Curiosity, fragmented ownership
Functional alignment begins
Enterprise alignment & shared accountability
AI-native leadership mindset
5. Operating Model
Siloed experimentation
Hybrid (central + function-led)
Platform + product model
AI-native, continuously learning org
Download the Original people strategy framework (PDF)

References

Pui, S. Y. (2026). AI maturity framework [Unpublished model]. Synthesized from Salem & Chandra (2025); MIT CISR (2024); Workday (n.d.); and MITRE (n.d.). 

Drawn from: Salem, S., & Chandra, R. (2025). Enterprise awareness for adopting an AI maturity model: A research study. 

Weill, P., Woerner, S., & Sebastian, I. (2024). Enterprise AI maturity model. MIT Center for Information Systems Research. https://cisr.mit.edu/publication/2024_1201_EnterpriseAIMaturityModel_WeillWoernerSebastian

Workday. (n.d.). AI adoption maturity models. https://www.workday.com/en-us/topics/ai/adoption-maturity-models.html

MITRE. (n.d.). MITRE AI maturity model and organizational assessment tool guide. https://www.mitre.org/news-insights/publication/mitre-ai-maturity-model-and-organizational-assessment-tool-guide

Sarkar-Barney, S., Arjannikova, S., Grantham, C., Rogowski, R., Pui, S. Y., & Baill, B. (2026, October). The people strategy for AI adoption: An explainer and action guide to the seven human systems [Working paper]. HR Strategy Forum, AI Collaboratory.