To Make Enterprise AI Pay Off, Start With The Outcome Not The Technology

Enterprise AI projects are failing to deliver value at an astonishing rate. But the root cause is often strategic, not technical. According to Boston Consulting Group, 74% of companies fail to capture meaningful value from their AI investments. The reason is simple: they start with the technology instead of the outcome. The organizations that succeed begin by defining the business result they want to achieve, then design the process and technology to make it happen.

As the Director of Product Management for AI/ML at industrial supplies and equipment provider Grainger, Hardi Gokani's work centers on a single question: what job are we trying to get done? Before her time at Grainger, Gokani led a cloud transformation at CVS Health that saved the company over $75M. Today, Gokani is a leading voice on the gap between AI experimentation and enterprise ROI, including a guest appearance on "The CAIO Connect" podcast.

"The mistake many companies make is starting with the technology. They jump into AI without clearly defining the outcome they’re trying to achieve," Gokani said. But her approach flips that sequence. "First, identify the outcome you want to enable, the specific 'job to be done' for your user. Next, design the ideal process that would deliver that outcome. Only after those steps should you choose and embed the right technology. If you start with the tech, you end up optimizing for the tech. If you start with the outcome, you optimize for the business."

According to Gokani, the disconnect between technical execution and business value is a key contributor to the "pilot graveyard." The problem is rarely the algorithm, she noted. In fact, 90% of AI failures stem from challenges with people and processes. To solve for this, she proposed a framework to connect technical metrics to C-suite priorities.

• From model to money: "Many AI projects fail because they cannot clearly connect a technical pilot to a top-line business goal. A 'metrics laddering' framework bridges that gap. The first rung is the AI Metric, which measures the model’s direct output, such as prediction accuracy. The next is the Process Metric, which tracks operational improvements like faster cycle times or higher efficiency. At the top is the Business Metric that matters most to leadership, whether that is revenue growth, cost savings, or customer satisfaction," Gokani explained.

• Right metric, right goal: Next, she illustrated how different objectives require different optimization strategies. "If the goal is to improve customer satisfaction, the recommendation model should prioritize accuracy to ensure each suggestion truly fits the user’s needs. But if the objective is to expand market share, volume becomes more important, and the focus shifts to the number of recommendations displayed."