Live AI: Joe Leslie

Live AI A Framework for Building Organizational AI Fluency

AI fluency isn't taught.

It's built.

Joe Leslie, Global AI Learning & Development Lead, Golin Ketchum · May 2026

Global spending on workforce training stands at approximately $400 billion a year.1 Yet McKinsey’s 2025 State of AI survey, covering nearly 2,000 companies across 105 countries, found that while 88% are now using AI in at least one business function, only 39% can point to any measurable financial return from it.2 The investment is real and growing, yet the results are proving elusive.

The pattern is consistent across industries and geographies. Businesses have moved quickly to train their people on AI tools, often with sincere commitment and real budget.

But the training model most have defaulted to, explain the tool, demonstrate it, send people back to their desks, was not designed to produce the kind of fluency that AI demands. Fluency with AI is a practice skill, and most training infrastructure was built around knowledge transfer, not practice.

Live AI is the principle that AI capability develops through use, not study. Fluency comes from repeated practice on real work in real conditions, in your role, not from instruction about what AI can do followed by a hope that behavior changes. The environment where capability is built and the environment where it is applied are the same. That is what makes it live.

There is a second reason Live AI matters. Practice leaves a trace behind, session after session, a record of how people are really working with AI under real conditions. Passive training never produces anything like it. And that record is what makes increasingly sophisticated coaching possible as time goes on.

1. The Problem With How Organizations Train AI The dominant format for corporate AI training is passive: information gets explained, demonstrated, and made available, and application is left to follow on its own. Most learning and development functions were built around exactly that, delivering content, not building capability, which is a large part of why passive formats and AI fluency do not mix well. Research on skill transfer consistently shows that training investment rarely produces the behavioral change it intends, with completion rates and capability development routinely diverging. For AI training specifically, the gap between investment and impact is just as pronounced.

Through conference sessions, industry conversations, and close to a decade working at the sharp end of AI learning strategy, I have seen this pattern surface consistently across organizations of every size and type. It is not unique to any one company or sector. The pattern has been formally documented in organizational transformation research.

Endeavor Intelligence’s Two-Wave Transformation Blueprint identifies what it calls the ‘Training Day’ fallacy: the assumption that “a training event will change behavior in an unchanged environment,” with success measured by workshop completion rather than

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any change in how work is done.3 Teams complete AI training with real engagement and yet return to their work largely unchanged. The tool was explained. The skill was not developed.

The reason is structural. Passive training creates a gap between where learning happens and where performance is required. That gap is not bridged by motivation or by the quality of the content. It is bridged by practice, context, and repetition under real conditions. Learning transfer, the degree to which skills developed in training carry over into work performance, is one of the most studied and most persistently disappointing phenomena in learning science. The table below shows where Live AI differs.

Where it happens

Passive AI Training

Live AI

Separate learning

Inside the work

environment What it develops

Knowledge about AI

Fluency with AI

How capability forms

Instruction, then application

Through use, continuously

What gets measured

Completion and recall

Behavior and output quality

Learning transfer to the job?

Required, and research

No transfer gap by design

shows it rarely happens at scale

2. The Intellectual Foundation Live AI draws on principles that learning science has supported for more than a century.

The pattern holds across all of it: people build capability by engaging with real conditions, and whenever training gets separated from where the work happens, results suffer.

Dewey argued that true understanding is constructed through engagement with real problems, not transferred through instruction, writing in Experience and Education (1938) that “every experience enacted and undergone modifies the one who acts and undergoes,” shaping the quality of the experiences that follow.4 Piaget and Vygotsky formalized this further: learners build understanding through direct interaction with their environment, not by receiving it, and the most effective growth happens just beyond current capability with appropriate challenge and support.5 6 Lave and Wenger’s situated learning theory showed that knowledge cannot be separated from the context in which it is practiced. That is part of why AI training initiatives can produce adoption numbers that look strong on a dashboard, numbers that often have little relationship to how work gets done.7

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Bernhardt and Rose’s 2023 survey of AI’s role in L&D, published by the Learning Guild, traces this practitioner lineage from Gery through Cross, documenting the accumulated case that performance-context and learning-context must converge for capability development to hold.8 By 2025, Rose and Bernhardt were framing the destination in terms that directly anticipate the Live AI principle: “the disappearance of the line between being trained and doing the work,” where learning becomes part of performance itself rather than a separate activity that precedes it.9 Bloom’s 2 Sigma Bloom’s 2 Sigma research showed that students receiving individual tutoring performed roughly two standard deviations above their conventionally-taught peers. The driving factor was feedback specificity: responses calibrated to the individual learner rather than averaged across a group.10 AI is the first technology with the true potential to deliver that kind of personalized, responsive feedback at organizational scale.11 Live AI is the framework for using that potential deliberately.

Transfer-appropriate processing, established by Morris, Bransford and Franks, reinforces the same point: retention and application are strongest when the conditions of learning closely match the conditions of performance.12 Live AI is the direct application of that principle to AI capability building.

3. What Live AI Looks Like in Practice The Live AI principle shows up differently depending on how far an organization has taken it. Early on, that might mean nothing more than designing AI learning experiences around real work problems instead of invented or vague ones. Further along, it can mean building systems where the AI tool being learned doubles as the instrument of diagnosis, coaching, and development, all at once.

What I have come to refer to in my talks and applied work as Adaptive AI Coaching is the system that becomes possible when Live AI is running at scale: behavioral data from real AI use, analyzed continuously, generating specific coaching at the individual, team, and organizational level. To my knowledge, the most complete current expression of this in a real organizational setting is the AI Coaching Companion, developed at Golin Ketchum in early 2026.13 While the learning theory and L&D strategy behind the system were developed as an extension of ideas and structures enacted by the author of this paper, the technical architecture and true brilliance were engineered by David Lance, Golin Ketchum’s AI Tech Lead, whose work made the behavioral data infrastructure possible.

Furthermore, the conditions for its development were created by Jeff Beringer, Golin Ketchum’s Chief AI Officer, whose approach to responsible AI innovation established the environment in which an experiment of this kind could be built and tested, within the organization’s responsible AI governance framework.

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The Analytics Gap That Made It Necessary Most businesses deploying AI at scale encounter the same gap once the initial rollout is complete. The available analytics tell them how many people are using the tool, how often, and in broad terms which features are being accessed. They cannot answer the question that drives behavior change at the individual, team, and department level: how well is this person, or this account team, or this practice area, using AI, and where is the biggest opportunity to improve?

This diagnostic gap has been captured precisely in the practitioner literature. Bernhardt and Yates, writing in the Endeavor Report series (2025), describe what they call Metric Myopia: attention narrowed to a single figure, often a vanity metric like completion, hiding the second-order effects that decide whether progress sustains.14 Closing that gap requires behavioral evidence from real working conditions, with enough resolution to coach a single person, a level of detail no group average could ever offer.

Case Study: The AI Coaching Companion Golin Ketchum, together with its group of agencies, DeVries Global, The Brooklyn Brothers, and Virgo Health, operates as a global communications network of approximately 1,700 employees across 50-plus offices worldwide. Named PRWeek’s Global Agency of the Year 2025 and PRovoke Media’s Global SABRE Agency of the Year in 2026, the group set out to become the first fully AI-integrated global PR firm. A guiding principle shaped the approach from the outset: technology accounts for roughly 10% of what makes AI adoption work; the remaining 90% is people, culture, and how the work is structured. In early 2024, the agency began a systematic program to build AI fluency across its workforce. By early 2026, adoption among client-facing staff had grown from 15% to over 93%, with 97% participation in AI training, supported by GOLearn, the internal learning platform, CoLearn, a dedicated peer-to-peer AI mentorship program, and The Prompt, its monthly AI newsletter, and more than 1,200 AI-assisted workflows created across the business.15 The more significant development, though, was what the agency built from the behavioral data that adoption generated.

Before turning to the Coaching Companion specifically, it is worth noting two other Live AI expressions within the agency. The AI Champions network, around 90 experienced AI practitioners who model and share capability across offices and practice areas, and CoLearn, the dedicated peer-to-peer AI mentoring program, are both Live AI programs in their own right. In both cases, fluency develops through peer practice on real work: through mentoring relationships, lateral knowledge sharing, and the kind of contextual learning that no structured course can replicate. Together they represent the social layer of the Live AI approach.

How It Works

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The AI Coaching Companion connects to the organization’s enterprise AI environment via a compliance-grade API, ingesting interaction data across more than 1,000 licenses into two databases: one for quantitative analysis and one enabling semantic search across conversation content and project instructions. An AI agent can answer open-ended analytical questions across the full data set. The data never leaves the organization’s environment, non-work activity is filtered out before any analysis runs, and generating a full coaching report costs a matter of cents. The cumulative data set covers more than three million usage data points across every office, brand, practice area, and seniority level.

Three Levels of Coaching The system operates at three levels, each designed to support a different kind of intervention.

At the individual level, the system produces personalized coaching reports built from each person’s usage history. He had 598 recorded conversations and 13 active AI projects behind him. The good work was happening inside those projects. Day to day, though, most of his sessions were just basic chat with no context loaded. The recommendation was to migrate his recurring tasks into that same project structure, so every session would begin already loaded with full context, producing responses calibrated to his clients, briefs, and ways of working. A finance leader with 50 conversations and 51 file uploads received a different report built around a clear strength: sophisticated documentgrounded contract analysis. The coaching delivered three custom project recommendations with ready-to-use instructions, designed to bring that same documentgrounded depth to a wider range of her daily tasks.

At the account team level, the system maps adoption and behavioral quality across a full client team. A team of 68 people, with 91% adoption and an elevenfold rise in conversation volume over the prior year, received coaching built around a central insight: the team’s next phase of growth was about standardizing what already worked. Fifty team members were writing influencer bios in fifty different ways, and another twenty-two were interpreting contract language on their own, with no shared framework between them.

The coaching report identified five custom AI projects derived from what the team’s own best performers had already built, complete with named co-development partners and deployment instructions.

At the talent community level, the system produces specialist coaching reports comparing usage and behavioral quality across people in the same role or practice area. A creative community of 103 people across four agency brands and three regions showed 88% adoption, with 14 members at expert-level prompting sophistication. The primary quality gap was bare openers: short prompts with no context that produce weak first drafts. Eight prompt templates were built from the community’s top performers’ methods, seven

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shared AI projects were proposed to eliminate context-rebuilding, and eight champions were identified across all brands and equipped with prompt templates to model best practice for their teams.

A different kind of outcome emerged from an audit of six junior healthcare communicators. Analysis of more than 1,500 combined conversations and over 90 pages of research identified a consistent pattern: every session began with the same context reconstruction, client emails, transcript processing, pharmaceutical editing conventions, media flagging, congress communications, all rebuilt from scratch every time. The response took the form of a purpose-built AI project pre-loaded with format templates, style guides, and role-specific context derived from the team’s own usage patterns. No traditional training course was required. The friction was removed at the source.

Why This Is Live AI The AI Coaching Companion demonstrates the Live AI principle at its most complete for three reasons. First, it works alongside existing L&D infrastructure rather than replacing it, behavioral evidence from real work adds a layer of specificity that surveys and assessments alone cannot provide. Second, the coaching draws from the team’s own best work: the strongest teaching examples come from the team’s own top performers, emerging from real client work and made available as models. Third, the approach is instrument-agnostic. The specific platform used is Claude, but what makes it work is the feedback loop: behavioral data from real work, analyzed to identify patterns and gaps, returned to individuals and teams as specific coaching. Any company that builds that loop is applying the Live AI principle.

Live AI does not just train people to use a tool. It builds an organizational learning system that sharpens with every session.

4. Where Live AI Leads: Adaptive AI Coaching Live AI creates something most organizations have never had before: a continuous, highresolution record of how their people are working with AI in practice. What you build from it determines whether Live AI produces a one-time capability lift or something that compounds over time.

The natural evolution of Live AI is Adaptive AI Coaching: the continuous, data-driven process of identifying the gap between current AI behavior and optimal AI behavior, and responding, either by coaching people toward better practice, or by building tools that remove the need for the behavior entirely. Where Live AI describes how fluency is built, Adaptive AI Coaching describes what you build on top of that fluency once you can see it clearly.

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What does a mature Adaptive AI Coaching system watch for? A lot, in practice.

Engagement by team, department, seniority, and geography. How deep people go with the tool, not just whether they log in. Which features and platforms they gravitate toward. It also draws on semantic analysis of real conversations to surface training gaps, and maps capability patterns by role. On top of that, it can identify who the informal champions are and see how performance compares from region to region. One further signal matters just as much: how much of people’s AI use happens inside persistent, context-rich workspaces built up over time, the kind that spare someone from starting cold every session. Each of those signals feeds into a training planner, turning behavioral evidence into real, trackable interventions, complete with the practical details a facilitator would need to run one.

What sets Adaptive AI Coaching apart is a single decision: whether to coach the behavior or build around it. Coaching means surfacing what someone is doing, showing them what better looks like, and supporting them in closing that gap through practice. Building means recognizing that a gap exists because someone is stuck doing something repetitive and context-independent, and constructing a tool that removes the need to do it by hand at all.

That decision only becomes possible once behavioral data is detailed enough to make it a reasoned choice rather than a guess. The healthcare communications case from Section 3 is a clear example: six practitioners rebuilding identical context every session, a repetitive task with no real skill value attached to it. The right response there was a purpose-built tool that removed the need for that work altogether, a build response, and one that only becomes visible once behavioral data shows the pattern underneath it.

Live AI is the entry point. It creates the conditions for real AI fluency to develop and generates the behavioral signal that makes Adaptive AI Coaching possible. Together, the two frameworks describe one continuous arc. One creates the conditions for AI capability.

The other reads that capability continuously and keeps raising its ceiling. This paper addresses the first half of that arc. The second half is the subject of a companion paper,

Beyond Training to Performance: The Observation Shift: Seeing How Well People Work With AI, published in July 2026.

5. The Live AI Principle in One Paragraph AI capability develops through use: through repetition on real problems, through the accumulated judgment that comes from hundreds of sessions on work that matters. The companies building durable AI fluency share a different profile, one where training hours and course libraries barely register. What matters instead is whether people are using AI deeply and consistently on real work, inside an environment built to surface what is working and close the gaps where it is not. That is what Live AI means in practice. It is grounded in over a century of learning science, demonstrated in organizational practice,

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and available to any business willing to design for performance rather than completion.

For any employer, there is a simpler, more useful question to ask: are your people live with AI?

We create change that matters.

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Notes Prepared by Joe Leslie, Global AI Learning & Development Lead, Golin Ketchum | May 2026 For external publication.

1. Global corporate training market. Multiple industry sources including SkyQuest and Research and Markets place the 2025 market at approximately $400 billion. Figure used as an order-of-magnitude reference. ↩︎ 2. McKinsey Global Survey, “The State of AI in 2025: Agents, Innovation, and Transformation.” Survey conducted June 25 to July 29, 2025, 1,993 respondents across 105 nations. Published November 2025. ↩︎ 3. Endeavor Intelligence, Two-Wave Transformation Blueprint, October 2025. The anti-patterns list includes: training events without interface changes; adoption measured as logins; no owner for people change. ↩︎ 4. Dewey, J. (1938). Experience and Education . New York: Macmillan. p. 35. ↩︎ 5. Piaget, J. (1952). The Origins of Intelligence in Children . New York: International Universities Press. ↩︎ 6. Vygotsky, L.S. (1978). Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA: Harvard University Press. ↩︎ 7. Lave, J. & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation . Cambridge: Cambridge University Press. ↩︎ 8. Bernhardt, M. & Rose, T. (2023). AI’s Increasingly Important Role in L&D. The Learning Guild eBook, pp. 17-22. ↩︎ 9. Rose, T. & Bernhardt, M. (2025). From Workflow Learning to Boundaryless Performance. TrainingZone, March 2025. ↩︎ 10. Bloom, B.S. (1984). The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher, 13(6), 4-16. ↩︎ 11. Bernhardt, M. & Rose, T. (2023). AI Aids in Connecting Learning and Performance Ecosystems. The Learning Guild, March 2023. ↩︎ 12. Morris, C.D., Bransford, J.D. & Franks, J.J. (1977). Levels of processing versus transfer appropriate processing. Journal of Verbal Learning and Verbal Behavior, 16(5), 519-533. ↩︎ 13. This paper originally referred to Golin prior to its June 2026 merger with Ketchum. References to the organization have been updated to Golin Ketchum for consistency with current naming. Figures, dates, and organizational details describe the organization as it existed at the time, prior to the merger. ↩︎ 14. Bernhardt, M. & Yates, K. (2025). Performance Ecosystem. Endeavor Report 2.0, November 2025. ↩︎ 15. Jeff Beringer, AI25: Class of 2026, PRWeek, February 2026. ↩︎

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