67% of L&D Pros Want AI Skills They Don't Have - A Clear Opening for Education Affiliates
A new report confirms what many educators feel daily: AI is in use across L&D, but confidence and capability are lagging. That gap is where thoughtful training and clear guidance win.
Across 421 L&D professionals, 67% want AI skills and design training, 63% want workflow support, 63% need help measuring impact, 50% need integration help, and 44% want guidance on responsible AI. Meanwhile, 87% already use AI at work.
Adoption Is Up. Competence Isn't Keeping Pace.
Only 13% of teams aren't using AI now, and just 2% have no plans to adopt. Most are experimenting or actively using it day to day.
But maturity is uneven. Only 9% are scaling AI across their org, and just 6% say they're fully integrated or "AI-first." As Dr Philippa Hardman put it: "Maturity is rising-but it's far from uniform. Many teams are still early-stage; a small group is sprinting ahead."
For education affiliates and L&D leaders, that middle segment is the audience: past skepticism, hungry for competence.
What L&D Teams Actually Want to Learn
- AI skills and design training (67%): Instructional design with AI, evaluating outputs, and building learning experiences that use AI the right way.
- Workflow guidance (63%): Where AI fits in content cycles, when humans review, and what QA should look like.
- Impact measurement (63%): Proving outcomes, not just speed. As Kristen Budd noted: "L&D teams are under growing pressure to demonstrate the impact of AI."
- Integration support (50%): Connecting AI to LMS, productivity suites, and enterprise tools.
- Responsible AI guidance (44%): Practical guardrails for data, ethics, and compliance.
Translation: the market wants applied, workflow-based education-less theory, more doing.
Blockers You Can Help Remove
- Security (58%) and accuracy (52%): Teach data handling, model evaluation, and review processes that stand up to scrutiny.
- Integration challenges (46%) and lack of internal expertise (46%): Show end-to-end examples that plug into common stacks.
- Legal constraints (41%) and budget approvals (44%): Provide templates, risk checklists, and business cases leaders can sign off on.
Governance is still fuzzy. 59% avoid personal or sensitive learner data in AI, and 18% say approvals are unclear. As personalisation grows (72% expect this to be AI's biggest impact), clear policies and controls will be essential. For a helpful reference point on risk, see the NIST AI Risk Management Framework.
Where AI Work Actually Happens: Tools and Tasks
General tools: ChatGPT (74%), Microsoft Copilot (54%), Gemini (38%), NotebookLM (33%), Claude (28%), Perplexity (25%). Only 2% use no general tools.
Heavy usage sits in production: voice generation (63%), quiz and content drafting (60%), video creation (52%), and translation (38%). That's the design/develop heart of ADDIE.
- Create tool-specific courses that go beyond prompts into real workflows and QA.
- Focus content on video, voice, and translation-where the active demand is.
Want practical training in these lanes? Explore focused paths for ChatGPT, office AI tools, and AI video workflows.
Agentic AI Is the Next Move
27% are actively exploring agentic AI; 39% are interested but cautious. None said "no." That caution looks like unfamiliarity, not rejection.
- High-interest areas: AI tutors (48%), coaching/mentoring (43%), personalised guidance (43%), admin automation (38%), and course building (37%).
- Opportunity: education that explains setup, guardrails, and evaluation for these use cases.
The LMS Question Is Wide Open
Only 47% think the LMS stays central over the next three years. Opinions split on where AI will live next: inside LMS/LXPs (19%), inside productivity suites (17%), standalone AI platforms (17%), or cross-system agent layers (19%). 27% don't know yet.
That uncertainty creates space for tools-and training-that bridge systems and workflows. For source context, see Synthesia's research.
Practical Playbook for Education Affiliates and L&D Leaders
- Package by workflow, not by tool. Example tracks: AI for needs analysis, AI for storyboarding and SME collaboration, AI QA and accuracy checks, AI outcomes measurement.
- Build "deployment kits." Include policy templates, risk checklists, human-in-the-loop review steps, and sample approval forms.
- Teach impact, not speed. Add modules on baseline metrics, A/B testing, and executive-ready reporting.
- Cover integrations. Show real connections to LMS, M365/Google Workspace, and content repos.
- Introduce agentic AI safely. Start with low-risk admin automation, then move to tutors/coaches with guardrails.
Metrics That Matter (Answering the 63% Measurement Gap)
- Cycle time reduction across design/develop stages
- Cost per asset vs. quality ratings (SME review, learner feedback)
- Completion, application-on-the-job, and time-to-proficiency shifts
- Error rate/accuracy before and after human review
- Business KPIs tied to the program (sales ramp, support handle time, safety incidents)
Useful Resources to Move Fast
- Courses by job for L&D roles and education teams
- Courses by skill for AI design, workflows, and QA
- Popular certifications to build credibility with stakeholders
- Latest AI courses to stay current as tools change
The Optimism Signal (And Why It Matters)
66% believe AI will strengthen L&D's influence. 72% think the function will thrive by adapting. That mindset drives investment.
As Dr Hardman said: "Over the next 12-24 months, I expect to see a sharper divide between teams who use AI to go faster, and teams who use AI to build smarter, more personalised, more evidence-based learning ecosystems." Your courses and content can determine which side your audience lands on.
Ready to Act?
If you're building programs or recommending courses, focus on applied skills, clear workflows, real guardrails, and impact proof. That's what the data says people will buy-and what teams need to do better work.
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