Readiness Is Built, Not Generated: Why AI Access Isn’t Making Your Workforce Ready

A reap of NovoEd’s session at the Brandon Hall Group AI Summit, October 2026

Speaker: Farnaz Ronaghi, Co-founder and Chief Product and Technology Officer, NovoEd

The Short Version

Farnaz opened her session at the Brandon Hall Group AI Summit with a simple observation: everyone in the room, and nearly everyone they work with, now has an AI tool that can pull up a policy, a framework, or the steps of a process in seconds. Finding answers is no longer the hard part. The hard part, she argued, is what comes next. Knowing something and being ready to act on it in a real job moment are two very different things. Over the course of the talk, she made the case that AI transformation is stalling on people rather than technology, and that L&D can change that by connecting learning, practice, and coaching into one readiness cycle instead of running them as separate islands.

A Few Terms From the Session:

Farnaz introduced a handful of ideas early on that framed the rest of the talk.

• Performance readiness: The judgment, confidence, and context to act well in the moments that matter. This was the outcome Farnaz centered the whole session on, and she was clear that it gets built over time.
• The broken shortcut: The assumption that access to answers equals readiness to act. Her point was that AI didn’t create this shortcut. It just made it impossible to ignore.
• Tactic knowledge: The things experts know but can’t easily put into words, like how work really gets done in an organization or what good looks like on a specific team. It lives in managers and experts, and AI can’t generate it.
• The Readiness Cycle: The model Farnaz walked through in the second half of the talk, connecting structured learning, immersive practice, and 1:1 coaching around an organization’s own context.

What Farnaz Shared in the Session

She started by separating knowing from doing. “Finding answers is no longer a challenge,” she told the audience. “But knowing something doesn’t mean our people are ready to put it to work in a real job moment.” That distinction became the thread for everything that followed.
She grounded the problem in research. Drawing on Brandon Hall Group’s work on AI transformation, she pointed out that the bottleneck is human capability, not technology. In that research, 39% of organizations lack the foundational infrastructure to turn AI access into skills, 44% have the structure to launch AI initiatives but tend to mistake deployment for adoption, and only 17% have reached the stage where AI adoption is widespread across L&D.
She named what’s slowing L&D teams down. Most teams are stitching together separate tools to build content, launch it, chase engagement, and measure results. Farnaz was careful to frame this as a structural issue rather than a people issue. “That’s not a failure of effort,” she said. “It’s the paradigm.” The outcome is predictable: knowledge reaches a fraction of the workforce, and behavior change rarely follows.
She made the case that leaders matter more, not less. Leading through AI, she explained, takes two things: understanding the technology well enough to make decisions with it, and relating to how people feel as they adopt it. “The first rolls out tools. The second brings people along.”
She showed the audience what AI-powered practice actually looks like. Rather than describe it, Farnaz dropped the room into the middle of a live AI Roleplay. A salesperson was practicing a conversation with their manager about how they’d been using AI on sales call transcripts. At the end, the learner received immediate feedback scored against the methodology in the organization’s own content, along with feedback on their delivery. She then showed how L&D built that roleplay from just a few written sentences.
She closed the loop with the Readiness Cycle. Structured learning kicks off a new mindset. Immersive practice builds confidence through repetition, without anyone needing to schedule it. And 1:1 coaching is where judgment accelerates. What ties it together, she explained, is the insight layer underneath: because practice happens in the same system as learning, managers can see where people are developing and where they’re stuck, and start their coaching conversations from there.

How We See Things at NovoEd

Most “AI-ready” workforces aren’t ready yet. Rolling out a copilot to every employee is a real milestone, but it’s a deployment milestone. Treating it as proof of readiness is one of the most costly assumptions in L&D today.
Generic practice can build confidence in the wrong behaviors. A roleplay that doesn’t reflect your methodology, your customers, or your standards prepares people for a job that isn’t quite theirs.
Your best experts are probably spending time in the wrong place. When senior people run baseline rehearsals, they have less time for the judgment-building work only they can do.
Leadership development belongs at the center of AI strategy. If employees don’t trust their leaders to guide them through change, adoption won’t scale no matter which tools are in place.
L&D deserves to be measured on readiness. Completions and hours tell you what happened. Readiness tells you whether it worked.

Why NovoEd Approaches This Differently

• Everything runs in one connected system: Learning, AI practice, and 1:1 coaching live in the same platform, so what shows up in practice informs what happens in coaching.
• Practice is built from your own content: Scenarios are generated from materials organizations already have, like e-learning files, PDFs, internal sites, call transcripts, and event recordings, and feedback is scored against their methodology.
• Practice goes beyond roleplay: As Farnaz noted in the session, learners can also respond to scenarios, record their screen to show they’re using job tools correctly, or submit case studies and emails, each with immediate feedback.
• Managers get visibility without extra work: A weekly email shows each manager where their team is progressing and where a conversation would help most.
• AI supports every stage of a program: Courses can be built in minutes, enrollment and communications run automatically, practice and coaching scale across the workforce, and confidence is measured before and after.

Farnaz’s Three Takeaways

• Take the inefficiency out of L&D’s day, so your team’s time goes to strategy instead of working the gaps between tools.
• Let AI do the baseline coaching, and use what it shows you to see where your people are actually developing.
• Bring your experts in on top of that, to build the kind of judgment that goes beyond scenario confidence.

“That’s how L&D becomes the readiness engine for the business.”

Questions We Heard Around This Topic

What is performance readiness?
It’s the judgment, confidence, and context to act well in real job moments. In her session, Farnaz positioned it as the outcome L&D should own, rather than course completion or knowledge transfer.

Why is AI transformation stalling in so many organizations?
According to the Brandon Hall Group research Farnaz cited, the main barrier is human capability, not technology. Many organizations either lack the infrastructure to turn AI access into skills or treat deployment as if it were adoption.

Can AI replace human coaches?
Not when it comes to judgment. AI can provide realistic, repeatable practice and immediate feedback at scale. But tacit knowledge lives in managers and experts, and that’s where human coaching still matters most.

What is the Readiness Cycle?
It’s the model Farnaz presented at the summit: structured learning, immersive practice, and 1:1 coaching, grounded in an organization’s own context and connected by insights from every practice attempt.

What is performance readiness?
It’s the judgment, confidence, and context to act well in real job moments. In her session, Farnaz positioned it as the outcome L&D should own, rather than course completion or knowledge transfer.

Why is AI transformation stalling in so many organizations?
According to the Brandon Hall Group research Farnaz cited, the main barrier is human capability, not technology. Many organizations either lack the infrastructure to turn AI access into skills or treat deployment as if it were adoption.

Can AI replace human coaches?
Not when it comes to judgment. AI can provide realistic, repeatable practice and immediate feedback at scale. But tacit knowledge lives in managers and experts, and that’s where human coaching still matters most.

What is the Readiness Cycle?
It’s the model Farnaz presented at the summit: structured learning, immersive practice, and 1:1 coaching, grounded in an organization’s own context and connected by insights from every practice attempt.