This post is based on insights from our live webinar on June 30, 2025 featuring L&D leaders from GE Healthcare, Metrix, and Cognota | Reading time: 8 minutes.
The artificial intelligence revolution in Learning & Development isn’t coming, it’s here. But if you’re feeling overwhelmed by the endless stream of AI tools, vendor pitches, and conflicting advice, you’re not alone. The harsh reality? Most L&D teams are approaching AI adoption backwards, leading to stalled initiatives, unclear ROI, and frustrated stakeholders.
In our recent expert webinar, industry leaders from GE Healthcare, Metrix, and Cognota shared battle-tested strategies for operationalizing L&D to unlock AI’s true potential. Here’s what they learned and what you need to know.
The AI Strategy Trap: Why Technology Alone Fails
“AI on its own isn’t a strategy,” warns Ryan Austin, CEO of Cognota. “It’s a tool that amplifies whatever operational foundation you already have- for better or worse.”
The problem? Most organizations are rushing to adopt AI tools without addressing fundamental L&D operational gaps. It’s like trying to build a skyscraper on quicksand, the foundation simply can’t support the ambition.
The Reality Check: Where Most L&D Teams Are Struggling
Recent research from Cognota and Brandon Hall Group reveals sobering statistics about L&D operational maturity:
- 60% lack formal intake processes for managing learning requests
- 47% struggle with organizational silos that prevent strategy alignment
- 64% have no visibility into team workload and capacity
- 41% lack time to measure impact, while 33% don’t even know what to measure
Without these operational fundamentals, AI initiatives become expensive experiments that rarely deliver sustainable results. As Jessica Knox, CEO of Metrix, puts it: “When your workflows are siloed, your data is siloed, making it impossible for AI to work effectively.”
Industry-Specific Challenges: Life Sciences Leading the Way
Life sciences organizations face unique hurdles that make operational excellence even more critical:
- Frequent role rotations that disrupt institutional knowledge
- Mergers and reorganizations that fragment processes
- Regulatory requirements that demand robust documentation
- Rapid technological change requiring continuous upskilling
These challenges make lightweight, adaptable processes essential for AI success. Organizations that master these fundamentals create competitive advantages that extend far beyond learning technology.
Success Story: GE Healthcare’s Two-Year AI Journey
Kristy from GE Healthcare shared her team’s transformation from cost center to strategic business partner, a journey that took two years but delivered measurable results:
Phase 1: Foundation Building (Months 1-8)
- Aligned L&D goals with business outcomes (productivity, sales, retention)
- Established clear measurement frameworks
- Built stakeholder relationships across departments
Phase 2: Capability Development (Months 9-16)
- Developed internal AI literacy programs
- Created cross-functional committees for AI governance
- Implemented pilot programs with clear success metrics
Phase 3: Scale and Optimization (Months 17-24)
- Integrated AI tools into daily workflows
- Automated routine processes to focus on strategic work
- Measured and communicated ROI to executive leadership
Key insight: “Run L&D like a business,” Kristy advises. “Speak the language of your stakeholders – money, efficiency, impact, and ensure your team has the skills to deliver.”
Overcoming Common AI Adoption Roadblocks
Webinar participants identified five critical challenges and proven solutions:
- Gaining Internal Approval
Challenge: Complex procurement processes, especially in large organizations.
Solution: Start with free trials, build internal champions, demonstrate quick wins.
- Managing Resistance
Challenge: Team members worried about job security or capability gaps.
Solution: Frame AI as augmentation, not replacement; invest in upskilling.
- Identifying Relevant Use Cases
Challenge: Overwhelming options without clear business connection.
Solution: Map AI capabilities to specific business problems, not features.
- Building Organizational Capability
Challenge: Lack of AI literacy across teams.
Solution: Make AI part of everyday conversations, not separate initiatives.
- Measuring Success
Challenge: Unclear ROI metrics for AI investments.
Solution: Establish baseline measurements before implementation.
From Point Solutions to Integrated AI Ecosystems
Both GE Healthcare and Metrix described similar evolution patterns, moving from disparate tools to integrated workflows that enable:
- Faster prototyping of learning solutions.
- Personalized learning experiences at scale.
- Real-time measurement and optimization.
- Predictive analytics for learning needs.
The key insight? AI delivers exponential value when it can access and analyze integrated data across your entire L&D ecosystem.
The Future of AI in L&D: Companions, Agents, and Responsible Innovation
The panelists discussed emerging trends that will reshape L&D:
AI Learning Companions
Personalized assistants that provide just-in-time learning, coaching, and performance support tailored to individual needs and contexts.
Autonomous Learning Agents
Systems that can independently identify skill gaps, curate content, and deliver interventions without human oversight, while maintaining ethical guardrails.
Predictive Learning Analytics
Tools that anticipate learning needs based on business changes, performance data, and external trends.
Critical consideration: These innovations require careful attention to data quality, privacy, and ongoing human oversight. As Ryan Austin emphasizes, “Responsible AI adoption isn’t just about compliance, it’s about building sustainable competitive advantages.”
Your Action Plan: Getting Started Today
Based on expert insights, here’s your roadmap for operationalizing L&D for AI success:
Immediate Actions (Next 30 Days)
- Assess your operational maturity using established frameworks.
- Identify your biggest process gaps that limit effectiveness.
- Start AI literacy conversations with your team.
- Map potential AI use cases to specific business problems.
Short-term Goals (Next 90 Days)
- Implement formal intake processes for learning requests.
- Establish measurement frameworks for current initiatives.
- Build stakeholder relationships across departments.
- Launch pilot AI projects with clear success metrics.
Long-term Strategy (Next 12 Months)
- Integrate AI tools into daily workflows.
- Develop internal AI capabilities through training and hiring.
- Create governance frameworks for responsible AI use.
- Scale successful pilots across the organization.
Click here to view the full recording, and don’t forget to subscribe for additional resources, including the BHG whitepaper when it’s released.
Transform Your L&D Operations
Operationalizing L&D for AI isn’t a destination, it’s a journey of continuous improvement that starts with strong fundamentals and grows through strategic experimentation.
Ready to assess your L&D maturity? Connect with Cognota’s experts for a complimentary operational assessment or take our maturity assessment here.
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About the Contributors:
Kristy Callahan, Head of Learning and Development, GE Healthcare
