“What’s the ROI on this training program?”

If you’re in L&D, you’ve heard this question. And if you’re proposing AI training, you’ll definitely hear it. Leadership wants to know that their investment will pay off before they commit resources.

The challenge is that training ROI is genuinely difficult to measure. Not because the value isn’t there, but because isolating the impact of training from other variables is methodologically complex.

That said, difficult isn’t impossible. Here’s how to build a measurement approach that satisfies legitimate business questions about AI training value.

Why Traditional Training Metrics Fall Short

Most training evaluation stops at what I call “happy sheets”—post-training surveys that ask if participants enjoyed the experience and found it valuable.

These metrics are nearly useless for ROI purposes because:

• Satisfaction doesn’t correlate strongly with learning
• Self-reported value isn’t actual demonstrated value
• They measure immediate reactions, not lasting impact
• They don’t connect to business outcomes at all

If you’re only measuring Level 1 in Kirkpatrick’s model (reaction), you can’t demonstrate ROI. Research from the Association for Talent Development confirms that organisations using multi-level evaluation see significantly better training outcomes. You can only demonstrate that people didn’t hate the training.

Building a Measurement Framework

A proper ROI measurement approach starts before the training begins and continues for months afterward. Here’s the framework I use.

Step 1: Define the Business Problem

What specific business outcomes do you expect AI training to improve? Get concrete:

• Customer response times
• Document processing speed
• Report creation efficiency
• Content production volume
• Error rates in specific processes
• Employee time spent on specific tasks

These become your target metrics. If you can’t name specific outcomes the training should improve, you’re not ready to train.

Step 2: Establish Baseline Measurements

Before training, measure current performance on your target metrics. This gives you a comparison point.

Methods include:

• Time studies of task completion
• Quality audits of current outputs
• Surveys of current capability and confidence
• Analysis of existing performance data

Document these carefully. You’ll need them later to show change.

Step 3: Track Learning Outcomes

During and immediately after training, measure whether people actually learned what you taught. Options include:

• Pre and post knowledge assessments
• Skill demonstrations or practical tests
• Supervisor observations of capability
• Self-efficacy measurements

This is Kirkpatrick Level 2. It doesn’t prove ROI, but it’s a necessary step. If people didn’t learn anything, there’s no path to business impact.

Step 4: Measure Behaviour Change

This is where most measurement efforts fail. Two to three months after training, assess whether people are actually using what they learned.

Methods include:

• Follow-up surveys asking about tool usage
• Manager observations of work practices
• Analysis of system usage data where available
• Review of work outputs for evidence of new methods

If behaviour hasn’t changed, learning didn’t transfer. This is common—research suggests most training doesn’t result in lasting behaviour change. If you find this, focus on improving transfer before measuring results.

Step 5: Measure Business Results

Finally, measure the business outcomes you identified in Step 1. Compare post-training performance to your baseline.

This is the crux of ROI, but it requires:

• Enough time for changes to manifest (usually 3-6 months)
• Controls for other variables that might explain changes
• Honest assessment of what can be attributed to training

Step 6: Calculate Financial Impact

Translate business results into financial terms:

• If customer response time improved by 20 minutes per case, and you handle 1,000 cases monthly, that’s 333 hours saved monthly
• At $50/hour fully loaded cost, that’s $16,650 monthly savings
• Annual impact: approximately $200,000

Compare this to training costs:

• Development or licensing: $X
• Delivery time and facilitation: $Y
• Participant time away from work: $Z
• Total investment: $X + $Y + $Z

ROI = (Annual Impact - Total Investment) / Total Investment × 100

The Attribution Problem

Here’s the honest challenge: even with this framework, proving that training caused the improvement is difficult. Other factors might explain the results:

• New tools were implemented alongside training
• Processes were redesigned
• High performers who would have improved anyway
• Seasonal variations in workload
• Other organisational changes

You have a few options for addressing this:

Control groups. Train some people and not others, then compare. This is the gold standard but often impractical.

Time series analysis. Look at trends before training. If performance was flat for twelve months and improved immediately after training, attribution is more credible.

Convergent evidence. If multiple metrics all improve, if participants report using new skills, if managers observe behaviour change, the case strengthens even without perfect causal proof.

Acknowledge limitations. Be honest about what you can and can’t prove. “We can’t definitively attribute all improvement to training, but the evidence suggests training contributed significantly” is more credible than overclaiming.

Building the Business Case

When presenting ROI to leadership, structure your case clearly.

Lead with the Business Problem

“Our analysis team spends 40% of their time on routine data compilation that AI tools could accelerate. At current headcount and salaries, that’s $1.2 million in annual labour cost.”

Present the Proposed Solution

“A comprehensive AI training program would cost approximately $150,000 including development, delivery, and participant time.”

Show Expected Returns

“Based on pilot results and benchmarks from similar implementations, we project a 50% reduction in routine compilation time, representing $600,000 annual value.”

Provide Measurement Plan

“We’ll measure baseline compilation times before training, assess capability immediately after, track tool usage at 60 days, and measure compilation times again at 90 days.”

Acknowledge Uncertainty

“These projections assume successful adoption. We’ve built change management support into the program to maximise that probability, and we’ll adjust based on early results.”

Quick Wins vs. Long-Term Value

Some AI training ROI is visible quickly. If training helps someone complete a four-hour task in one hour, the savings are immediate and obvious.

Other value takes longer to materialise. If training improves strategic thinking quality, the impact might not show up for months or years, and it will be harder to measure.

When building your measurement approach, include both:

Quick win metrics that demonstrate value fast and build momentum Longer-term metrics that capture deeper impact over time

Don’t sacrifice long-term value for easy measurement. Some of the most important outcomes are the hardest to quantify.

When ROI Isn’t the Right Question

Sometimes focusing on ROI misses the point.

If everyone in your industry is building AI fluency and you’re not, the question isn’t “what’s the ROI of training?” It’s “what’s the cost of falling behind?”

If your employees are anxious about AI and disengaged as a result, training that addresses that anxiety has value beyond productivity metrics.

If AI adoption is strategically important but the specific returns are uncertain, some investment may be warranted even without clear ROI projections.

ROI is one input to decision-making, not the only input.

Making Measurement Sustainable

Whatever measurement approach you adopt, make sure you can sustain it.

Elaborate measurement systems that require significant ongoing effort tend to fade away. Build measurement into existing processes where possible:

• Use data you already collect
• Add a few questions to existing surveys
• Incorporate observation into regular check-ins
• Automate data collection where feasible

The goal is consistent, sustainable measurement that provides ongoing intelligence, not a one-time evaluation project that gets forgotten.

The Honest Truth About Training ROI

Most organisations don’t measure training ROI rigorously because it’s genuinely hard. The methods I’ve described require effort, time, and acceptance of uncertainty.

But even imperfect measurement is better than none. Having a defensible estimate of impact—with honest acknowledgment of limitations—puts you ahead of programs that can only point to satisfaction surveys.

And the discipline of defining expected outcomes upfront often improves program design. When you know what you’re trying to achieve, you’re more likely to achieve it.

Leadership deserves to understand whether their investments are paying off. Even if perfect measurement isn’t possible, good-enough measurement is worth the effort.