Podcast Summary – AI Transformation in Learning: How AI Agents and Digital Coworkers Redefine Work with David Chestnut of Accenture
A different kind of L&D conversation
Before he was Accenture’s Principal Director for Talent Strategy and Agent Reinvention, David Chestnut was a technologist who loved solving human puzzles. He understood systems, but he also “understood how humans feel and how they learn and how they behave,” which made consulting a natural fit.
His career in learning and development wasn’t planned. It was, as he puts it, “a happy accident.” After years as a Salesforce Solution Architect, he was invited to take a few months off consulting to teach. Seven years later, that “temporary assignment” had evolved into leading Accenture’s global learning strategy for technology, reaching more than 800,000 employees.
Today, Chestnut sits at the intersection of learning and artificial intelligence, helping organizations reimagine what it means to work alongside digital coworkers. “We’ve got these amazing new coworkers called agents,” he says. “And our job now is to learn how to lead them.”
From knowledge to Behavior
One of Chestnut’s most provocative statements lands like a wake-up call for every L&D professional:
Knowledge, content at rest, is dead. Knowledge is no longer the domain of human beings.
He explains that the traditional learning funnel (from awareness to knowledge to skills) has shifted dramatically. With AI able to retrieve and apply knowledge instantly, the human advantage now lies in behavioral execution and contextual judgment.
That means designing learning ecosystems that allow people to experiment safely, make mistakes, and iterate in real time.
He uses a delightfully practical metaphor to make it stick: “You’ve got to plan for spontaneity.” In his personal life, that means keeping a go-bag packed so a weekend trip can actually happen. In learning, it means equipping employees with tools, safety, spaces to experiment, and, most importantly, time, so “behaviors can emerge.” That’s the new center of gravity for L&D.
Creating a Future State Description
Stop “teaching tools.” Start “onboarding coworkers.”
As Chestnut sees it, AI isn’t a tool. It’s a teammate.
We’ve done ourselves a disservice by teaching people they’ve got to get techie to live in this world. We’re not building calculators; we’re building coworkers.
He draws a clear line between the old mindset, “learning software,” and the new one: managing digital teammates. Just as leaders onboard a new hire, they must now onboard and coach their intelligent systems.
With bounded LLMs, retrieval, and guardrails, the work isn’t to memorize commands; it’s the onboarding and managing digital teammate. Like any new hire, you don’t blindly trust them on day one. You inspect, coach, and calibrate trust over time.
He cautions against a common enterprise anti-pattern: rolling out a tool and demanding “be 40% more efficient” without teaching what it’s for, where it fails, and how to supervise it. Trust, he reminds us, “is gained in drips and lost in buckets.” L&D must teach leaders and teams how to earn those drips.
That’s where change management consulting becomes essential; helping leaders understand how to build, maintain, and repair trust with both people and technology.
Knowledge is the domain of agents and bounded GPTs. We [L&D] are firmly in the skills-to-behaviors realm.
5 Ways Change Management Consultants Drive Effective New Initiatives
The Three I’s: a practical framework for agent design
To lead effectively in a world of digital coworkers, Chestnut offers a framework that resonates deeply with instructional designers and learning strategists—the Three I’s:
- Intelligence: the model’s reasoning capability (e.g., GPT-4 vs. GPT-5),
- Information: the authoritative sources it must reference as “truth”, and
- Instructions: the role definition, structure, and process that govern its behavior.
It’s a lesson every instructional designer can appreciate; clear guidance and consistent structure lead to quality outcomes.
If a model is less intelligent, you compensate with richer data and more precise instructions. If it’s highly advanced, you can allow flexibility and creativity. “It’s the same logic we use in instructional design,” he says.
The Four A’s: Rethinking Work Through an L&D Lens
As a manager of human-plus-digital teams, Chestnut looks at his own workflows through the Four A’s:
- Abolish what no one uses;
- Automate end-to-end where inputs/outputs are predictable;
- Augment high-value moments (e.g., intake conversations) while auto-generating the artifacts (e.g., the brief);
- Acknowledge activities AI shouldn’t touch.
“I can turn this project over and trust I’m going to get the result I’m used to,” she explains, summing up why experience matters. Lori believes those with a “helper” mindset (people drawn to serve and support others) naturally thrive in L&D.
He gives a simple but powerful example. Instead of typing notes during an intake meeting, record the conversation in Copilot, and let an agent draft the learning brief automatically. “That’s augmentation,” he explains. “You’re still present, but you’ve automated the artifact creation.”
The result? More focus on strategy, empathy, and innovation; the human elements machines can’t replace.
We were the people who understood people. But really, we are the experts in how work gets done. If as L&D leaders or HR leaders you’re not elbowing into that C-suite conversation and staking your claim as the voice of work and workers in this new era of digital and agents, you’re missing the boat.
That shift reframes HR’s value proposition. Instead of focusing solely on hiring, developing, or retaining talent, HR and L&D become co-designers of the operating model itself; defining how outputs are created, how automation integrates with human work, and how behaviors scale across teams.
This mindset turns HR from a support function into a strategic enabler of transformation. It also opens new lanes for collaboration across functions such as operations, IT, and change management.
In this redefined model, learning leaders don’t just deliver training; they engineer the conditions for performance. They help the business interpret what the “future of work” means for every role, technology, and process. As Chestnut notes, that requires fluency in both human systems and digital ones; a capability L&D is uniquely positioned to lead.
Calibrating trust: how to lead digital coworkers
If you’ve ever handed a vague “PLS FIX” deck to a junior analyst at 2 a.m., you already know the outcome: unclear instructions yield unpredictable results.
The same is true with agents. Inspect what you expect.
When outputs disappoint, ask the model to self-critique and show where you under-specified intelligence, information, or instructions. Being “a better leader” of your digital team produces better outputs. No prompt-engineering mystique required.
This mindset to lead digital coworkers like humans represents the next wave of AI in Learning and Development.
Inspect what you expect. Be a better leader, get better outputs.
That nuanced balance (trusting enough to collaborate, but not enough to lose perspective) is at the heart of responsible AI adoption. It’s the same principle L&D practitioners have long taught in leadership and emotional-intelligence programs: awareness, empathy, and boundaries.
The Digital Parado Effect also underscores why psychological safety matters in digital transformation. Employees who feel comfortable experimenting with AI are more likely to learn from errors, refine their prompts, and develop confidence working alongside intelligent systems.
“It’s okay to be fond of a well-tuned agent,” David reflects. “We work better with the people (and the systems) we’re fond of.”
In today’s human-plus-machine workplace, that fondness can be a feature, not a flaw. What matters is that leaders recognize the relationship and design learning experiences that keep empathy, ethics, and human judgment at the center.
Creating the Future of Leadership Development
Getting started tomorrow: a simple path for every leader
- Pick one sanctioned personal tool (Copilot, enterprise GPT, Claude, etc.).
- Treat it like a new assistant: give it your writing samples, your frameworks, and your priorities; then give it work; with checks.
- Every time you’d normally Google, start with your “co-intelligence.”
- Over time, calibrate trust the way you did with Excel. You didn’t check every formula forever; you learned when to trust it.
What to learn next (according to David)
Chestnut recommends following Ethan Mollick for practical, leader-friendly updates and stresses seeking sources that separate signal from marketing noise; universities, balanced analysts, and curated feeds. He even “uses an agent to scrub the web” for relevance, then reviews outputs with a critical eye.
The Creative Horizon: What Words Can Now Build
As the conversation closes, Chestnut reflects on what AI means for creativity itself. He reflects on how, for the first time, we’re seeing the things that we thought were the last bastion of only humans (words, art, music) suddenly be within reach of technology.
He doesn’t see that as a loss of human uniqueness, but as an invitation to evolve. “Developing rigor for these new types of relationships is part of what it means to lead in this era,” he says. “We’re dealing with a form of intelligence that simulates humanity. Why not treat it like humanity and get more out of it?”
It’s a fitting close to a conversation that bridges technology and humanity, systems and emotion, data and trust.
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