AI in Adult Learning: From Courses to Capabilities in the Flow of Work

How to use AI to deepen learning (not dumb it down), reinforce coaching, and decide what to buy vs build—grounded in research and industry evidence.

The big shift isn’t just more content; it’s learning in the flow of work—support that shows up at the moment of need, inside the tools people already use. Josh Bersin popularized this shift years ago, and it has only accelerated with AI copilots embedded across enterprise platforms.

At the same time, C-suites are pressing for value from AI, while analysts warn HR to evolve operating models and governance in parallel. In other words: yes to AI—but with purpose, safeguards, and a clear portfolio strategy.

The science: what actually improves adult learning

Adults learn best when they have agency, relevance, and opportunities for reflection, followed by practice over time—the core pillars of andragogy. These principles recognise that adults bring prior experience, intrinsic motivation, and a desire to solve real-world problems, not just absorb information. In career coaching and executive coaching, this translates into helping individuals connect new insights to lived experience, take ownership of their growth, and apply learning in the moments that matter. 

What’s changing now is that AI enables these timeless learning dynamics to happen continuously and at scale. Intelligent systems can personalise content based on individual goals, surface reflection prompts at critical work junctures, and automate spaced practice without losing the human nuance that drives transformation. Rather than reinventing how adults learn, AI gives organisations the tools to operationalise what we already know works—self-directed, relevant, and reflective learning that compounds through real-world application.

• Andragogy (Knowles): Adults are self-directed, bring prior experience, and want problem-centered, immediately relevant learning. Build for autonomy and application. eLearning Industry+1
• Transformative learning (Mezirow): Lasting change requires critical reflection on assumptions—not just information exposure. Valamis
• Self-Determination Theory (Deci & Ryan): Motivation—and thus persistence—rises when autonomy, competence, and relatedness are supported. Self Determination Theory+1
• Spaced repetition & retrieval practice: Durable memory comes from spacing and effortful recall, not cramming or re-reading. AI can schedule this automatically. PMC+4PubMed+4AERO+4
• Coaching works: Meta-analyses show workplace coaching improves outcomes; AI should amplify, not replace, human coaches.PMC

What AI looks like in the flow of work

Think micro-interventions that respect attention and trigger reflection. Here’s what they might look like for your employee users in their day to day interactions:

1. Retrieval nudges in productivity tools
   Short prompts (“Before your 1:1, list two behaviors you observed since last week’s feedback module”) that cue recall and application. ScienceDirect
2. Contextual performance support
   Inline tips, checklists, or short clips surfaced in CRM/ERP/HRIS when a learner hits a relevant task—Bersin’s core “flow” idea. JOSH BERSIN
3. AI-assisted reflection
   Guided journaling questions aligned to program goals (“What assumption did you challenge in this negotiation?”) grounded in Mezirow’s reflective practice. Valamis
4. Spaced boosters
   Auto-scheduled, 90-second boosters that revisit key ideas over weeks, tuned by spacing science. PubMed
5. Coach + AI copilot
   Human coaches set goals; the AI helps track habits, drafts practice plans, and summarizes patterns between sessions—keeping autonomy and human judgment in the loop. PMC
6. Team rituals
   HBR highlights embedding learning into routines (retros, stand-ups). AI can generate quick reflection prompts tied to current sprint/OKRs. Harvard Business Review
   

Something to keep in mind: Human–AI combos aren’t always superior; they excel when AI augments structured tasks and humans own meaning-making and creativity. Design accordingly. PubMed+1

Design principles: make people think more, not less

The real opportunity with AI in adult learning, career coaching, and executive coaching isn’t automation — it’s augmentation. In a world where cognitive shortcuts are everywhere, AI should not replace the deep reflection, self-direction, and sense-making that define adult learning. Instead, it should serve as a catalyst for inquiry — prompting individuals to pause, notice patterns, and challenge assumptions in the flow of work. In coaching contexts, this means using AI to reinforce—not flatten—the developmental process: surfacing powerful questions between sessions, nudging reflection after key leadership moments, and making tacit learning visible. When designed well, AI becomes less about delivering quick answers and more about building metacognition and agency — helping people think more critically about how they work, lead, and grow.

• Default to questions over answers. Use the AI to ask good questions, not just give tips—this drives retrieval and reflection. ScienceDirect
• Preserve autonomy. Offer choices (timing, modality, difficulty). Motivation hinges on perceived control. Self Determination Theory
• Make it specific and timely. Tie prompts to real work moments (“before the customer call”)—that’s the “flow” advantage. JOSH BERSIN
• Space it. Schedule short follow-ups at expanding intervals; let users opt into more/less challenge. PubMed
• Social without surveillance. Create safe, coach-like spaces (peer circles, prompts for recognition) while avoiding creepiness in monitoring. (See governance below.) NIST Publications
  
  

Measurement: show learning and performance lift

When organisations invest in AI-enabled adult learning, career coaching, or executive coaching, the real measure of success isn’t course completions—it’s capability transfer and behavioural change that drive performance. Traditional metrics like participation rates or satisfaction surveys tell us little about whether leaders are thinking differently, applying new skills, or growing in role. AI gives us the opportunity to move beyond these vanity metrics by capturing richer, longitudinal data—how often people retrieve concepts, reflect on their experiences, or translate learning into decisions at work. The goal is to demonstrate that learning, coaching, and technology together create measurable improvement in both individual growth and organisational outcomes: a visible “learning and performance lift.”

• Learning leading indicators: retrieval attempts completed, reflection prompts answered (quality/length), spaced-booster adherence.
• Behavioral application: frequency of targeted behaviors in retros/OKRs, peer feedback trends (coach-verified).
• Business outcomes: cycle time to proficiency, manager quality scores, sales/CS metrics where skills apply. (Analysts emphasize tying AI to value creation, not novelty.)

So with these impact measures in mind, can you achieve learning through AI through your own internal technology, or is it better to buy in tools and resources from experts external to the organisation?

Buy vs Build (and the emerging Blend)

When to “Buy” (platforms/copilots/plugins)

You need speed-to-value, solid vendor governance, and integrations into systems of work (e.g., Office, Salesforce, HRIS). Mature vendors increasingly support in-the-flow nudges, analytics, and governance out of the box; Gartner recommends structured build-vs-buy questions and a portfolio view. Gartner+1

When to “Build” (targeted custom)

You have unique workflows, sensitive data, proprietary taxonomies, or domain-specific coaching models that off-the-shelf tools can’t express. You can resource ongoing model ops, prompt/guardrail tuning, data pipelines, and evaluation.

The pragmatic middle: “Buy–Build–Blend”

Use commercial platforms for orchestration and compliance; extend with lightweight custom microservices (e.g., retrieval-nudge service hitting your knowledge base) where differentiation matters. Industry discussion increasingly frames this blended approach as the new norm. agilepoint.com

A quick decision checklist (evidence-informed)

1. Use case clarity: Retrieval, reflection, simulation, performance support? Prioritize those with proven learning science (spacing/retrieval) and measurable business links. PubMed+1
2. Data readiness: Do you have clean content, skills taxonomies, and feedback loops? HBR notes AI ROI tracks data quality investments. Harvard Business Review
3. Governance fit: Can the option align with NIST/ISO controls, privacy, and bias management? NIST Publications+1
4. Integration surface: Will it live inside core tools (email, chat, CRM, project boards) to truly hit the flow moment? JOSH BERSIN
5. Total cost of ownership: Beyond licenses—consider content curation, prompt engineering, evaluation, and change enablement. Analysts advise an executable AI portfolio with clear ownership. Gartner
6. Human coaching model: Preserve space and budget for humans; aim AI at reinforcement, tracking, and synthesis between sessions. PMC

The Bottom line

AI won’t replace adult learning fundamentals—it can finally operationalize them at scale. Anchor your approach in autonomy, reflection, spacing, and coaching; deliver it in the flow of work; and govern it like any other enterprise capability. Do that, and you’ll see real behavior change—not just another content library.

If you're interested in learning more about how BOLDLY can help your organisation, we invite you to explore our website or contact us here.

Frequently Asked Questions: AI and Adult Learning in Organisations

1. Why should HR or L&D leaders care about AI in learning now?

AI is reshaping how knowledge is accessed and applied — moving from formal courses to continuous capability building in the flow of work. For HR, this means learning can finally become performance-adjacent, measurable, and embedded in the systems people already use daily (Slack, Teams, CRM, etc.). The opportunity is not just efficiency — it’s effectiveness: helping people think better, reflect more deeply, and apply concepts when they matter most.

2. Doesn’t AI risk making learning too automated or impersonal?

That’s a legitimate concern — and why design intent matters. AI should augment, not replace, human learning experiences. The goal is to trigger reflection, prompt recall, and nudge application, not to spoon-feed answers. When used well, AI strengthens human agency: it helps employees think more critically, not less.

3. What kinds of learning use cases are most AI-ready?

The strongest ROI comes from use cases that already have a feedback or application loop — for example:

• Manager capability programs (AI prompts between coaching sessions)
• Sales or customer service training (spaced micro-practice scenarios)
• Leadership development (guided reflection and journaling tied to real work)
  These align with adult learning principles like retrieval, reflection, and reinforcement.
  
  

4. How can we ensure AI-enabled learning still supports reflection and agency?

Design for questioning over telling. AI should ask reflective, context-specific questions (“What did you notice in today’s meeting?”) rather than prescribing behaviour. Provide options, timing flexibility, and transparent data use — supporting autonomy and psychological safety.

5. How do we measure success beyond completion rates?

Shift from “inputs” (hours, completions) to “signals of transfer”:

• Reflection engagement (quality and frequency)
• Behavioural change indicators (self/peer check-ins)
• Business outcomes linked to program goals (e.g., time-to-proficiency, team engagement scores)
  AI can automate data capture here, offering new visibility into learning impact.
  
  

6. What are the key governance issues with AI in learning?

Trust is non-negotiable. CPOs should align with recognised frameworks such as:

• NIST AI Risk Management Framework (AI RMF 1.0) – ensures reliability, fairness, and transparency.
• ISO/IEC 42001 – sets out an auditable standard for AI governance.
• Ethical design principles – clarify consent, explainability, and bias management.
  AI ethics in learning isn’t optional — it’s a precondition for adoption.
  
  

7. Should we buy a commercial AI learning platform or build our own?

• Buy when you need speed, integration, and compliance. Mature vendors now include reflection, micro-learning, and analytics features natively.
• Build when your intellectual property (e.g. proprietary leadership models, competency data) or security posture is unique.
• Blend when you can combine vendor infrastructure with your custom microservices or coaching tools.

The “buy vs build” decision should weigh total cost of ownership, data readiness, and governance maturity — not just upfront cost.

8. What’s the role of human coaches in an AI-augmented ecosystem?

Coaches remain central. AI can handle the tracking, summarising, and prompting between sessions — freeing coaches to focus on sense-making, empathy, and transformation. Think of AI as a continuity layer that reinforces human coaching, not a substitute for it.

9. How do we start small and de-risk adoption?

Start with a single program that already has strong leadership sponsorship. Pilot:

• One retrieval-nudge use case (reinforcing key program messages)
• One reflection prompt series (linked to real work events)
  Set up minimal governance and measurement, learn quickly, and scale from there.
  
  

10. What’s the biggest mistake organisations make when adopting AI in learning?

Treating it as a technology purchase instead of a capability shift. AI in learning isn’t about chatbots or new content libraries — it’s about operationalising learning science at scale. Success depends on design intent, governance, and how well you preserve human reflection inside the workflow.