Most organizations start their AI strategy using generative AI as a tool, then try to find a problem to solve with it. In a recent webinar, From AI Vision to Real Results: A Leader’s Playbook for Getting Generative AI Right, Gary Lamach, Marko Harvot, and I discussed the broad nature of AI, spanning analytics, machine learning, automation, and more. And generative AI is only one part of that space. A robust AI strategy will evaluate opportunities across the entire landscape.
So, where do organizations go wrong? Many organizations feel pressure to “do something” with AI, especially as competitors claim to be seeing results. That pressure often leads to the wrong starting point: “How can we quickly start using these AI tools?” instead of “What problem are we trying to solve, and is AI the right solution?” Understanding what AI can do is an important first step, but it doesn’t create value on its own. Without prioritizing the most important and clearly defined problems, teams end up generating activity rather than impact. Rather than focusing on the tool, we should look to the organization’s people and processes first. These can be the areas that are the biggest challenges to AI adoption and value creation.
A people problem.
A survey conducted during the webinar reflected this disconnect: 52.5% of respondents identified people as the primary obstacle to effective AI use, 37.5% pointed to process, and only 10% to technology. In other words, the biggest problems can’t be solved by simply using the tools. In the webinar, ELB Learning discussed a pain-first framework approach to address this, starting with business goals, identifying pain points where work breaks down, quantifying the cost, and only then assessing whether AI is a good fit.
We also discussed the gap between individual productivity gains and real organizational improvement. Generative AI can speed up tasks, but faster task completion does not automatically translate into better outcomes or a more effective organization. In practice, time savings are often absorbed into more work rather than yielding better results. Productivity has benefits for an organization, but doesn’t create strategic assets.
Busy isn’t better.
This is where organizations can get misled. A growing list of AI projects can create the appearance of progress, even when only a few initiatives actually move the needle. This aligns with research findings that the vast majority of AI initiatives fail to deliver meaningful returns, despite high levels of activity and investment. What we’ve observed, both in organizations and in the classroom, is that success depends less on the specific tool and more on how well people, process, and technology are aligned. That includes building capability and trust, adjusting workflows to reflect how work actually gets done, and integrating tools to support measurable outcomes such as cost reduction, revenue growth, and customer impact. That shift, from experimentation to operational use, is where AI starts to matter and where strategy plays a critical role.
Watch the full webinar session below. And click here to learn more about ELB Learning’s AI transformation services.
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Matthew Pecsok is an experienced educator and industry professional with over 25 years of experience in information systems and analytics. He is a full-time faculty member at the University of Utah, where he teaches courses in data mining, statistics, and data engineering.
