The Kirkpatrick Model Is Dead. Long Live The Kirkpatrick Model. 

The Kirkpatrick Model has long been the gold standard for training evaluation. Since the 1950s, it's given L&D and contact center teams a simple way to answer a deceptively hard question: Does our training actually work?

But since then, the way we train, learn, and perform at work has fundamentally changed. 

AI agents handle customer interactions, human agents are supported by AI copilots, and learning doesn’t just happen in classrooms—it happens on the job, all the time, even mid-conversation. 

The Kirkpatrick Model was built for a completely different world. While it’s not dead, using it on its own in 2026 means ignoring the critical context of the modern contact center.

The Kirkpatrick Model gave us a clear structure for evaluating training performance:

1. Reaction
2. Learning
3. Behavior
4. Results

It laid out the importance of starting with results (level four of the Kirkpatrick Model) in order to work backwards and build out trainings from an outcome and behavior perspective. 

And with a shared language, L&D, operations, and executives teams could all align around what success looks like—an invaluable shared perspective. 

But while the core tenets of the Kirkpatrick Model still hold, there are several developments it doesn’t account for. 

Contact centers have evolved greatly in the past seventy-plus years. Here’s where the Kirkpatrick Model starts to fall short:

It Assumes Training Drives Performance

Kirkpatrick is built on the assumption that learning changes behavior, and if the right behaviors are put in place, performance will follow. 

But this chain might be too simplistic. Consider all of the other variables that impact performance:

• Systems
• Tools
• Incentives
• Reinforcement
• Culture

An agent can ace their learning and do all the right things, but still fail if:

• QA criteria is inconsistent 
• Tools are slow or fragmented
• AI tools underperform
• Managers coach to a different standard

Kirkpatrick points to training as the primary lever for performance. In reality, it's just one of many integral levers. 

It Ignores the System Around the Learner

To build off the above, the Kirkpatrick model evaluates an individual without accounting for the ecosystem around them. Consider: 

• Whether workflows support the desired behavior
• Whether managers reinforce it
• Whether incentives align with it
• Whether technology enables or blocks it

You can measure learning all day long, but if central systems are broken, performance won’t budge.

It’s Too Linear for a Nonlinear World

Kirkpatrick assumes a linear sequence of events: training -> learning -> behavior -> results.  

But that’s not how training works anymore. Instead of a linear progression, training is a continuous circle. Agents don’t learn once and then apply what they learned, they’re constantly learning new things and being coached and trained.

A static, step-by-step model struggles to capture that.

It Was Built for Humans, Not Hybrid AI-Human Teams

This is perhaps the biggest shift that the Kirkpatrick Model doesn’t account for: we’re no longer just evaluating human agent performance. 

The model doesn’t answer questions like:

• How do you evaluate an AI agent’s performance?
• How do you measure the CX associated with hybrid human-AI interactions? 
• How do you measure consistency across human and AI interactions?

The definition of “learner” has changed over time, and modern performance evaluation models need to account for that. 

There have been a few attempts to evolve frameworks beyond the Kirkpatrick Model:

• The Learning Transfer Evaluation Model (LTEM) focuses more on evidence of learning and transfer.
• The Phillips ROI Model adds a layer of financial accountability to the Kirkpatrick Model.
• The Brinkerhoff Success Case Method looks at what has actually worked in real-world scenarios.

All of these frameworks improve on parts of the problem, but none of them completely solve it. They don’t fully account for real-time environments, AI-driven workflows, or the complexity of modern hybrid customer interactions. 

No, not at all. 

While the contact center ecosystem has shifted and evolved, the foundations of the Kirkpatrick Model still hold: learning should connect to behavior, and behavior should connect to results. 

But resist using the model as a standalone source of truth, and make sure to consider the systems, technology, and context your agents are working within at each step. 

Consider the Kirkpatrick Model a way to think and a source of shared language for your team, while remaining clear-eyed about the culture, tools, workflows, incentives, and reinforcement systems it's operating within. 

And keep in mind, above all, that any training and performance evaluation model can no longer just evaluate human learners, but also needs to account for AI agents and hybrid environments. 

While the world that Kirkpatrick was built for no longer exists, the instincts underlying it are still foundational, and correct. While it may no longer be enough, it's certainly not dead.