L&D Must Evolve Into a Human-Machine Performance Function

If L&D continues to act like a content factory, it will fall behind. The teams that succeed will treat AI as a core tool and focus on performance as the real product.

Consider the following:

• Thanks to advances in AI, creating, updating, and curating content now happens almost instantly. Personalization and automation are now standard in L&D. Teams are moving from building individual assets to managing systems that can produce, adapt, and measure learning at scale.

• Today, business agility relies on being ready with the right skills, not just doing yearly assessments. Real-time diagnostics help teams quickly spot gaps, try targeted solutions, and see results fast.

• Having lots of data only helps if people know how to use it. When AI-savvy team members connect learning data to business results using integrated tools, organizations gain a real advantage. This means shifting toward analyzing and measuring performance with both people and machines.

• Learners now expect experiences that adapt to their role, behavior, and performance—much like Netflix recommends shows. Traditional, unchanging courses just don’t measure up anymore.

• External realities are raising the stakes. Employers expect large skill shifts within five years, and a growing share of work activities is now technically automatable with generative AI. Many leaders also doubt their organizations can keep pace without upskilling.

Enter the Human‑Machine Performance Function

The Human‑Machine Performance Function (HMPF) is an operating system for L&D that blends human expertise with AI, data, and workflow design to deliver measurable business outcomes. It shifts attention from events to effects: from programs delivered to performance moved.

In my new ATD Press book release, Applying AI in Learning and Development: From Platforms to Performance, I tie this shift to a simple idea: clean, connected data plus human judgment turns AI into a performance engine rather than a novelty.

But what does that look like in action?

• People and roles. A Human‑Machine Performance Analyst sits at the center. Responsibilities include data analysis, AI system management, strategic alignment, ROI storytelling, and continuous improvement.

• Data and integration. Connect learning, performance, and workflow systems. Treat “clean data” as the source of truth so AI insights map to real outcomes.

• AI platform and workflows. Use AI where it compounds human effort: creation, curation, generation, and automation, with humans preserving creativity and relevance.

• Governance and risk. Define rules for privacy, bias, and escalation so humans can override or audit algorithmic decisions. Pair innovation with controls.

• Measurement. Link learning work to business KPIs and build a cadence of reporting that leaders trust.

Table: Traditional L&D vs. HMPF

Generative AI shifts the frontier of knowledge work and can unlock large productivity gains if organizations pair it with process redesign and worker support. Skills‑based operating models strengthen this link by organizing around capabilities, not just jobs. Data governance and quality practices are preconditions for any AI‑heavy system to perform.

This isn’t just a theory. It’s a practical shift that turns AI from a content tool into a system for improving performance, with people still responsible for meaning, ethics, and value.

In my next post, we will take a closer look at the role of the Human-Machine Performance Analyst.