We’re living through the biggest technology shift since the internet, and everyone knows it. Every conference, every board meeting, every casual conversation seems to circle back to AI. As a technology leader, here’s what I’m seeing: while everyone’s talking about AI, far fewer are actually doing anything meaningful with it.

The disconnect is real, and it’s costing organizations opportunities every day.

The Gap Between Promise and Practice

Last month, I participated in three different executive meetings where AI came up. In each one, the conversation followed the same pattern: excitement about possibilities, followed by vague commitments to “explore AI initiatives,” ending with no action plan or even clear next steps. Sound familiar?

This isn’t unique to any specific industry or company size. I’ve read in the news and connected with colleagues where businesses large and small spin their wheels on AI strategy while their competitors quietly implement practical solutions. I’ve seen small businesses paralyzed by the perceived complexity while missing obvious automation opportunities. I’ve witnessed large businesses caught in endless loops of analysis, trying to build perfect governance frameworks before they start any AI work. Don’t get me wrong—governance is critical. But you need to develop it alongside AI implementation, not as a prerequisite that blocks all progress.

The problem isn’t that AI is overhyped. It’s that we’re approaching it backwards.

Start with Problems, Not Possibilities

Most AI discussions I encounter begin with the technology: “What can ChatGPT do for us?” or “Should we implement machine learning?” This is like asking what you should build with a hammer before you know what you need to build or what needs fixing.

The organizations getting real value from AI started somewhere else entirely. They began with business problems that were costing them time, money, or competitive advantage. Then they asked whether AI could help solve those specific challenges. Then they focus on an implementation of AI to target and deliver a specific business outcome.

Here’s a simple framework I have started talking about with my teams:

Identify the Pain: What manual processes consume disproportionate resources? Where do errors occur most frequently? What decisions require extensive research but follow predictable patterns? What tasks or workflows are repetitive and could be automated?

Quantify the Impact: How much time does this problem cost? What’s the financial impact of delays or errors? How does it affect customer experience or employee satisfaction? What could our employees focus on or do differently if they didn’t need to do this and what would be the impact to innovation or the bottom line?

Match the Tool: Only after understanding the problem clearly do we evaluate whether AI is the right solution. AI is a very loose term as well. Since there are different types of AI available and some are better than others with specific use cases our analysis will identify the best solution to drive the desired business outcome most effectively.

Practical AI Implementation: Three Real Examples

Let me share three examples from organizations I’ve worked in, to demonstrate how this approach plays out in practice.

Example 1: Customer Service Triage The problem wasn’t that customer service was bad. It was that experienced agents spent 40% of their time on routine questions while complex issues waited in queue. We implemented two AI solutions: a chatbot for self-service support and an AI-powered ticket classification with auto-responses for common issues. Result: 30% increase in issue resolution and 60% reduction in response time for complex problems, allowing agents to focus on work that actually required human expertise.

Example 2: Workforce Scheduling Optimization Our workforce management system was generating schedules, but store managers spent hours each week making manual adjustments for coverage gaps, availability changes, and seasonal demand fluctuations. We implemented AI-powered scheduling optimization that learns from historical patterns, local events, and individual employee preferences to generate more accurate initial schedules. Store managers now spend 70% less time on schedule adjustments, and employee satisfaction with schedules improved significantly due to better consideration of their availability and preferences.

Example 3: Learning Path Personalization With 250,000+ employees across different roles, experience levels, and learning styles, our one-size-fits-all training approach wasn’t maximizing effectiveness. We deployed AI to analyze learning completion patterns, assessment results, and role performance data to create personalized learning paths. The system now recommends specific modules, adjusts pacing based on comprehension, and identifies knowledge gaps before they impact performance. Training completion rates increased 40% and time-to-competency for new employees decreased by three weeks.

Notice what these examples have in common: they augment human capabilities rather than replace them. They solve specific, measurable problems. And they integrate with existing workflows instead of requiring complete process redesigns and overhauls.

The Implementation Reality

Here’s what nobody talks about in the AI success stories: implementation is messy, iterative, and requires organizational change management just like any other major technology initiative.

Data Quality Matters More Than Algorithm Choice: Your AI is only as good as your data. If your systems contain duplicate records, inconsistent formats, or missing information, you should fix that first. With the large scale data warehouses and the philosophy many business have of lets just collect all the data and we’ll figure out how to use it later, data becomes the problem. I’ve seen several AI projects fail due to poor data quality vs. any other factor.

Start Small and Scale: Resist the temptation to transform everything at once. Pick one process, prove the value, learn from the experience, then expand. The organizations achieving sustainable AI success treat it like any other technology rollout: careful planning, pilot programs, measured expansion. Going use case by use case may seem more challenging and costly, but when you consider the pitfalls of expanded data sets, high volume responses and the impacts of hallucinations on user perception it is better to start small and expand.

Culture Change Is the Hard Part: The technology is often the easy part. The hard part is getting people comfortable with new workflows, helping them understand how their roles evolve, and maintaining change momentum over months or years. I like to ask, “in your role, what can you do, that you always wanted to, but were too busy before?”

What IT Leaders Need to Know

If you’re leading technology strategy in your organization, here are the some key considerations I recommend for AI implementation:

Budget for the Full Stack: AI isn’t just software licensing. Factor in data infrastructure, integration work, training, and ongoing model management. The total cost of ownership often runs 3-4x the initial technology investment.

Security and Compliance Can’t Be Afterthoughts: AI systems process and generate data in new ways. Your existing security policies may not cover AI-specific risks like model poisoning, data leakage through prompts, or algorithmic bias. Update your governance frameworks before you deploy to production and ensure they are informed by the work leading to implementation.

Vendor Management Gets Complicated: AI vendors offer everything from hosted APIs to on-premise solutions to custom development. You will also see some AI networks coming together to satisfy the requirements of your implementation, such as Copilot working with ChatGPT. Each approach has different implications for data control, cost predictability, and integration complexity. Understand these trade-offs before committing to any particular approach.

The Bottom Line

AI represents a genuine opportunity to solve real business problems and create competitive advantages. But success requires the same disciplined approach as any technology initiative: clear problem definition, realistic planning, careful execution, and ongoing measurement.

The organizations winning with AI aren’t the ones with the most sophisticated algorithms or the biggest AI budgets. They’re the ones applying technology strategically to solve specific problems while managing change effectively.

The good news? You probably already have the skills and frameworks to lead successful AI initiatives. The challenge is cutting through the hype to focus on what actually matters: solving real world problems and creating value.

What problems is AI uniquely positioned to solve in your organization? Start there, and the technology decisions become much clearer.

What’s your experience with AI implementation? I’d love to hear about the problems you’re tackling and the approaches that are working. Connect with me on one of my socials below to continue the conversation.