Beyond ‘Implement AI’: A Technical Leader’s Framework for Meaningful Execution

Every week, another executive announces their company is “implementing AI.” The hype around artificial intelligence (AI) seems to be everywhere. For example, PWC reported that 49% of tech leaders claimed in 2024 to have AI “fully integrated” into their core business strategy, and they have seen 20-30% gains in productivity as a result. Given the buzz, it can be easy for business leaders to feel like they’re missing out—or at least curious about AI.

But here’s the uncomfortable truth: most of these AI initiatives are destined to fail, not because the technology isn’t ready, but because leadership treats AI like a checkbox activity rather than a strategic transformation. As AI popularity continues to grow, technical leaders are drowning in pressure to “do something with AI” without a clear framework for execution.

The result? Millions of dollars in wasted spend, demoralized teams and technology deployments that solve yesterday’s problems with tomorrow’s tools.

How to Drive AI Success

The answer to making AI a success at your organization is building a practical framework that moves beyond the simple “implement AI” mandate to address the real questions of what technical leaders must achieve with this innovation. This approach demands asking essential questions like: Why do we want to use AI? What challenges are we hoping AI will solve? How will we measure the success of our AI implementation?

Write down the answers to all these questions and share the documentation across departments and stakeholders to align on success metrics before implementing AI. This will ensure everyone is on the same page, taking a strategic approach for true digital transformation, rather than a “just do it because everyone else is” approach.

Indeed, AI might not be a good fit for everyone at this moment. A report from McKinsey, for example, found that organizations with more than $500 million in annual revenue are transforming with AI much faster than smaller organizations. This is likely not because they are nimbler, but because they have the large budget to throw at “figuring things out”—something smaller organizations with tight budgets don’t have the luxury to do.

To realize ROI in AI, leaders must examine the foundation of their business to identify gaps where AI can act as an automation driver or a tool for productivity. By addressing the existing pain points, you will ready your business for external uses of AI. Better to fail internally among staff than to fail with customers, right? When brand reputation is on the line, it’s essential that customers don’t receive ill-equipped AI solutions that influence their experience.

For this reason, selecting the right AI solution for specific use cases is a good starting point to reap early ROI with the technology, allowing you to expand the business use cases as advocacy for it grows internally.

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The Importance of Data in Reaping ROI

Data is the foundation for so many important business decisions. So, making sure your chosen AI solution is using clean datasets—not dirty, invalid data—is crucial. The output from the AI engine will be mismatched to your business needs if it’s not using clean data. Worse, you could even be making competitive decisions based on stale information. If your AI only has dirty data running through it, it means that you’ll never be able to reap the full ROI potential from AI.

For this reason, data cleansing might be the first order of business before any AI solution is implemented. This takes dialogue with your departments to check which datasets need cleanup and which ones are accurate. Once you begin using AI, it’s key to have a human feedback loop as well, to ensure its data analysis is churning out good recommendations. AI is never a “set it and forget it” technology, as much as the hype might otherwise lead you to believe.

Best Practices for Evaluating AI Solutions

When AI adoption is a top-down directive, that’s great. But that alone doesn’t make its implementation a success. Instead, you must build broad buy-in for AI among your internal staff first. A lot of what employees likely have seen in the news involves AI replacing jobs, so expect some heavy skepticism across all departments. Don’t be pushy. You need to establish clear, small wins to build confidence among your workforce that you aren’t looking to replace them en masse.

When it comes to selecting the right AI solution for your business, sometimes it’s a good idea to bring in a third-party expert, one who is already heads-deep in the technology landscape. This allows your staff to focus on core business priorities, rather than searching the marketplace for the right fit for each need. Some solutions might not be needed with your business maturity, and the AI’s maturity—so a service provider would know the full scope of what’s out there, and they can connect the benefits of each AI solution to your unique challenges.

In the end, the primary focus should remain on your business strategy for the long term, empowering a culture of adaptability and nimbleness, rather than getting distracted by shiny objects. AI is just a tool in a toolbox of a lot of other technological innovations—there are surely going to be more on the horizon. What can your business do to create a foundation for continuous acceleration? The answer to this question will determine where and when you should implement AI, and how to measure its success.

About The Author Of This Article – Mike Simms, Vice President, Data and AI at Columbus

Mike Simms is Vice President – Data & AI at Columbus with significant experience in SQL Server, Azure Data Tools and AI, Power Platform, as well as Microsoft Dynamics AX & F&SC.

 

 

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