The Research Behind the Model
Updated: 5 days ago
What does research tell us about making AI actually work?
Giving people access to AI is relatively easy. Turning that access into useful, responsible, sustainable practice is much harder.
The Unified AI Adoption Model (UAAM), developed by Dr. Valeri Colón and introduced in Make AI Work, brings together two sides of that challenge:
Personal AI Adoption Framework (PAAF) focuses on how individuals build AI literacy, judgment, and effective practices.
Scalable AI Adoption Framework (SAAF) focuses on how organizations identify opportunities, support experimentation, manage risk, and scale what works.
My subsequent research, explored in Why AI Gets It Wrong: Build the Context Systems that Make AI Work, examines another part of the challenge: how the systems surrounding AI determine what information becomes available, how it is interpreted, and whether it supports reliable responses and decisions. Together, these research directions help explain why AI adoption succeeds in some settings and struggles in others.
1. AI Adoption Starts With People—Not Licenses
Decades of technology-adoption research show that access alone does not create adoption.
Davis's foundational Technology Acceptance Model demonstrated the importance of perceived usefulness and ease of use. More recent AI research expands that picture: a systematic review of ninety AI-adoption studies found that adoption is influenced by interconnected individual, social, technological, organizational, and environmental factors.
AI literacy matters too. Long and Magerko's research shows that AI literacy includes understanding capabilities and limitations, critically evaluating AI, and considering how humans, data, and ethics shape AI systems.
That research helps inform the early PAAF journey: Familiarize → Learn → Evaluate People first need to encounter AI, understand it, and determine where it actually belongs in their work.
2. AI Productivity Depends on the Task
Research shows meaningful productivity gains from generative AI—but those gains are not universal. Noy and Zhang found that professionals using ChatGPT completed writing tasks approximately forty percent faster, with quality improving by about eighteen percent. Brynjolfsson, Li, and Raymond studied 5,172 customer-support agents and found an average productivity increase of approximately fifteen percent, with larger gains among less-experienced workers.
But other studies have found smaller, mixed, or even negative effects. My systematic review of eleven empirical knowledge-worker studies from 2022–2026 found stronger gains when tasks were structured, bounded, and reviewable. Results became less consistent when work required specialized context, complex interpretation, mature codebase knowledge, or substantial coordination and review.
That is why Experiment appears in both PAAF and SAAF. The question isn't simply, "Does AI improve productivity?" It's "Does this AI improve this work, for these people, under these conditions?"
3. AI Doesn't Eliminate Cognitive Work. It Moves It.
Generative AI can dramatically reduce the effort required to draft, summarize, synthesize, and transform information. But that doesn't mean human cognitive effort disappears. My Cognitive Burden Redistribution Framework proposes that generative AI shifts some effort away from initial production while increasing the importance of problem framing, verification, contextual judgment, agency, and accountable use.
AI may write a summary in seconds. A human still needs to determine whether it is accurate, complete, appropriate, and safe to use. That is reflected in the PAAF progression: Experiment → Protect → Ingrain Effective AI use requires more than knowing how to prompt. It involves knowing what to delegate, what to verify, and when human judgment needs to take over.
4. Organizations Shape Whether Individual Adoption Succeeds
A capable employee can still struggle to adopt AI when approved tools, data, training, leadership support, or clear policies are missing. Research supports this connection. A 2025 meta-analysis covering twelve studies and 3,398 respondents found that organizational AI adoption was associated with technological, organizational, and environmental factors, including organizational readiness and top-management support.
This is why UAAM pairs individual development with organizational readiness:
PAAF: Familiarize → Learn → Evaluate → Experiment → Protect → Ingrain → Grow
SAAF: Discover → Fund → Plan → Experiment → Guardrail → Scale → Govern
The two journeys influence one another. Organizations create conditions for responsible experimentation. Employees discover where AI creates value. Organizations learn from those experiments and decide what deserves to scale. Neither journey operates independently.
5. Governance Has to Grow With Adoption
Responsible AI adoption cannot wait until AI has already spread throughout an organization. NIST's AI Risk Management Framework and Generative AI Profile emphasize ongoing governance, risk identification, measurement, and management. Recent empirical research reinforces that point. A 2026 quasi-experimental field study covering twenty teams, twenty-eight weeks, and 10,200 interactions examined AI guardrails combining policy, technical controls, workflows, monitoring, and escalation. The findings also highlighted the importance of understandable, proportionate controls: poorly designed guardrails can create friction and encourage workarounds.
SAAF reflects this progression: Experiment → Guardrail → Scale → Govern Experimentation generates evidence. Guardrails establish boundaries. Scaling expands proven uses. Governance sustains responsible adoption. The goal is to make responsible AI use part of how work happens, rather than relying entirely on policies people must remember to consult.
6. AI Performance Depends on the Context Systems Around It
My more recent research explores a problem that traditional AI adoption measures do not fully explain:
Why can a capable AI model produce an incorrect answer even when the organization has the correct information?
In Why AI Gets It Wrong, I introduce AI Context Systems as the social, technical, informational, and operational systems that produce the temporary working picture from which AI responds or acts. I use the mnemonic OTIS to describe these interconnected dimensions:
Operational: Workflows, roles, approvals, timing, and current conditions.
Technical: Models, connectors, permissions, APIs, memory, and system configurations.
Informational: Documents, records, metadata, policies, and data sources.
Social: Human knowledge, decisions, expectations, assumptions, and working practices.
AI does not operate directly on organizational reality. It operates on a temporary context state assembled from the information available to it. That distinction matters. An AI assistant may retrieve an outdated policy even though the current policy exists. It may confuse an active user account with authorized access. It may interpret a cleared project blocker as evidence that an entire project is on track. In each case, the issue may involve the model, but it can also originate in the context system surrounding it.
My exploratory context-systems research examines how AI responds when information is missing, stale, conflicting, overloaded, or changing. It also introduces seven dimensions of context fitness: Relevance, Sufficiency, Validity, Coherence, Usability, Adaptability, and Efficiency. These dimensions provide a way to examine whether the context supporting an AI response is appropriate for the task.
This extends the practical application of UAAM. Individuals need skills to recognize questionable outputs and missing context. Organizations need processes that make information available, current, authorized, and usable. Better prompting matters. Better context systems matter too.
From Research to Real-World AI Adoption
My two books approach the challenge from complementary directions. Make AI Work translates research on technology adoption, learning, productivity, and governance into practical frameworks, activities, and workplace practices. Why AI Gets It Wrong examines what happens after AI enters real workflows, where documents conflict, permissions restrict information, decisions change, and human knowledge isn't always captured.
Together, they encourage organizations to ask:
Where can AI genuinely help?
How will we know whether it is helping?
What context does AI need to perform the task?
What should humans continue to own and evaluate?
What organizational conditions support reliable AI use?
What should be scaled, improved, or stopped?
UAAM remains a "toolbox, not a to-do list" and a "framework, not a formula." The Context Systems research reinforces that philosophy: effective AI adoption depends on the relationship between people, tasks, technology, organizational processes, and the context available at the moment of use.
Research-Informed—and Still Being Researched
Research-informed does not mean empirically validated. UAAM synthesizes established and emerging research into a practical adoption framework. Existing studies support many of its underlying principles, including AI literacy, experimentation, task fit, human oversight, organizational readiness, and governance.
My Context Systems research provides an additional conceptual and exploratory foundation for understanding AI reliability in organizational settings. Its initial findings do not establish universal causal relationships or independently validate UAAM. Both areas invite continued empirical testing. The approach follows the same principle it recommends for AI adoption: Experiment. Measure. Learn. Refine.
Try It Now
Choose one AI-supported task your team uses regularly. Identify what information the AI needs, where that information comes from, what may be missing or outdated, and how someone verifies the result before using it. Use this Prompt:
Act as an AI adoption and context-systems researcher. Review this AI-supported workflow: [describe the task, users, AI tools, information sources, permissions, workflow steps, and expected outcomes]. Evaluate the workflow using the Personal AI Adoption Framework (PAAF), Scalable AI Adoption Framework (SAAF), and the four OTIS context-system dimensions: Operational, Technical, Informational, and Social. Identify adoption barriers, context gaps, risks, and improvement opportunities. Return a table with: issue, affected framework or context dimension, supporting evidence, recommended action, responsible role, and how to measure improvement.
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