In the rush to adopt AI, many organizations fall into the trap of chasing every new tool and trend. As Merav Yuravlivker, Chief Learning Officer of Data Society Group, notes, “Everyone is AI-ing all over the place.”
That enthusiasm is understandable, but without a thoughtful approach, companies risk wasted investments, frustrated teams, and unclear results. To get the most from AI training programs, organizations need to anchor their efforts in strategy, not hype.
Start With the Objective, Not the Tool
One of the most important shifts organizations can make is reframing how they think about AI. Instead of beginning with the technology, Merav advises starting with the problem you want to solve. “My recommendation is always start with the objective, and then determine what the parameters are and where AI can fit into that strategically.”
This perspective is especially relevant with generative AI training. While large language models can be powerful, they’re also resource-intensive and expensive to run. Merav points out that in many cases, a simpler machine learning approach, like classification or clustering, can deliver results without the complexity of generative AI.
Measure Adoption to Prove Success
Another critical step is measuring whether AI training programs are actually sticking. Merav asks a simple but powerful question: “If you’re implementing this tool across your organization, how many people are actually using it on a regular basis?”
Adoption isn’t guaranteed. Even the most advanced generative AI training won’t deliver ROI if employees don’t feel confident using the tools. Training programs should be paired with initiatives that encourage real-world use, competitions, peer showcases, or structured challenges that make the new skills part of daily work.
MUST READ: Stop “AI-ing All Over the Place”: Why Baselines, Literacy, and Community Matter
Efficiency Gains: The Before-and-After Test
The other key metric for AI training program success is efficiency. As Merav explains, “Normally with AI tools, one of the biggest benefits is an increase in efficiency because it automates a lot of processes.”
But automation only matters if it’s measurable. Companies should identify a few core processes, record how long or how many people it takes to complete them today, and then compare after the tool has been in use. “Take a couple of processes that you’re looking to automate, determine how long it takes or how many people it takes, and then do a before/after… then see what the difference is.”
These simple benchmarks can reveal whether AI is delivering true productivity gains, or if the promised benefits remain theoretical.
Don’t Overlook the Costs of Generative AI
While generative AI training has captured headlines, Merav cautions leaders to balance excitement with pragmatism. Running large models is expensive, resource-intensive, and sometimes unnecessary. And without careful validation, the “black box” nature of these tools can introduce risks.
This is why AI training programs must also teach teams how to evaluate when AI is the right fit, and when a smaller, lighter solution will do the job more effectively.
Takeaway
An effective AI training program does more than introduce new tools, it builds the foundation for lasting impact.
That means:
– Defining the problem first, before choosing a tool.
– Measuring adoption, to ensure employees are truly engaging with AI.
– Tracking efficiency, to prove business value.
– Choosing the right level of technology, balancing generative AI with simpler machine learning solutions.
As Merav Yuravlivker reminds us, excitement about AI is important, but clarity and discipline are what turn that excitement into transformation. Want to chat with Merav, book a meeting here.
FAQ: Beyond the Buzz — Making AI Training Programs Deliver Real Impact
Start with the objective, not the technology. Ask: What business challenge are we solving? Then ask: How might AI fit in to help this? Often a simpler machine-learning solution (e.g., classification or clustering) delivers value before you need full-generative-AI. Merav emphasises: begin with the objective, then determine parameters, then pick the tool.
