AI, analytics, and performance thinking: Reimagining learning measurement
Traditional training measurement often feels like a chore: gathering level 1 feedback, reporting completion rates, and expecting downstream results. But in 2025, with the rise of AI and performance analytics, this reactive model will no longer be sufficient. Today’s organizations want real-time visibility into performance, not just learning. And that means rethinking how we define, capture, and leverage learning outcomes. Kirkpatrick meets Six Boxes® Performance Thinking, ushering in a new era of measurement where evaluation becomes a continuous performance feedback loop rather than a postmortem.
Why learning effectiveness still matters and why it needs to evolve
Executives don’t invest in training just because it’s good. They invest because they expect results such as:
Increase CSAT Faster onboarding Reduce errors and escalations Increase retention and sales conversion
Traditional models (Kirkpatrick, Phillips, Brinkerhoff) helped connect training to value. But they are not built to:
AI-driven content Simulation-based practice Micro-coaching at scale Real-time behavioral tracking
This is where performance thinking and AI come together.
A modern take on a classic model that is still relevant today.
Kirkpatrick’s 4th level (upgrade)
Level 1 Level 2 Level 3 Level 4 Reactions Learning behavior results NLP sentiment from open text feedback Adaptability ratings, confidence scores, simulation performance Behavioral signals from systems (CRM, call logs, workflow tools) Dynamic dashboards that map learning to business KPIs
The AI makes each level continuous rather than temporary.
Philips ROI model
Phillips adds Level 5 (Return on Investment). Historically, measurement has been complex, but AI has made it possible to:
Automatic mapping of training to KPI movements (reducing escalations, improving first call resolution, etc.). Predictive ROI modeling using pre- and post-cohort data. Visualize training value by region, audience, and content type.
ROI becomes more than just an annual review, it becomes a real-time metric.
Brinkerhoff Success Method (SCM)
AI can:
Transcribe and analyze interviews with small and medium-sized businesses. Automatically generate summaries of success stories. Cluster high and low performers for design feedback.
SCM will no longer be anecdotal, it will be scalable.
The Six Boxes® Performance Thinking Model: From Learning to Performance Ecosystem
While the above model evaluates training outcomes, Karl Binder’s Six Boxes® Performance Thinking narrows the focus and asks, “What actually drives job performance?” The six boxes are:
Expectations and Feedback Tools and Resources Skills and Knowledge Motivation Competencies Results and Incentives
Training is in box 3, but results depend on all 6. AI can help diagnose and optimize all six of the following:
expectations
Detect coordination gaps through surveys and conversation analysis. Tools and resources
Track post-training tool usage (e.g. clickstream data). skills and knowledge
Micro-assessment using simulation scoring and AI. motive
Analyze engagement patterns, attrition signals. capacity
Identify cognitive overload and resource bottlenecks. result
Monitor reward and recognition loops via HRIS.
With AI and Six Boxes®, you can move beyond the question, “Did they learn?” “Can they play?”
AI turns scorecards into signals
Here’s how L&D is using AI to advance impact measurement.
1. Microfeedback loop
Chatbots embedded in modules capture learner responses “in the moment”. Confidence meter before and after practice Nudges based on friction points (e.g. “Would you like to look at this module again?”)
2. Simulation and LLM scoring
Agents interact with customers generated by AI. AI scores tone, empathy, compliance, and accuracy. The progress dashboard shows your skill development curve.
3. Behavioral signals from research
CRM Log → Percentage of agents applying dispute handling techniques. Escalation data → Post-training trends by team. Search the knowledge base → Topics that learners struggle with the most.
4. Performance Dashboard
Bring your LMS, QA, HRIS, and CRM together in one view. Track time to competency, 90-day retention, tool adoption, and CSAT delta. Drill down by content, cohort, or country.
L&D use case: Bringing it all together
Let’s say you’ve launched a sales enablement program globally. Using modern tools and models, you can:
Track reliability and simulation pass rates (Kirkpatrick 2). Analyze conversion rate improvement (Kirkpatrick 4 + Phillips ROI). Interview top closers and use AI to summarize what went well (Brinkerhoff SCM). Map barriers across Six Boxes® (e.g. unclear expectations, wrong sales templates). Use AI to create real-time enablement nudges via chatbots.
This is not a consequential measurement. It is learning design as a feedback system.
From content creation to performance ecosystem
AI moves L&D from content producer to performance enabler.
It will tell you what is working now, not months from now. This maps learning to outcomes across roles, regions, and personas. This gives you the tools to diagnose, adapt, and iterate faster.
And when combined with performance thinking, it aligns learning to work, not just knowledge.
Guardrails to maintain learning integrity
AI needs boundaries. L&D must:
Validate AI scoring using human SMEs. Check for bias in predicting outcomes. Protect your privacy in data analysis. Tailor prompts and feedback to DEI, accessibility, and business context.
Final thoughts: The future is signals, not surveys
Kirkpatrick tells us what happened. Philips shows its value. Brinkerhoff shows what worked best. Six Boxes will tell you what to fix next. AI shows everything in real time.
This is how L&D gets a seat at the table. Not by measuring learning, but by enabling performance.