AI ROI Is Like Training for a Marathon, Not Buying Running Shoes 

Enterprise leaders have largely moved past the “Should we use AI?” phase. The real conversation happening in boardrooms and IT leadership meetings in 2026 is far more practical—and more difficult: 

Are we actually getting value from AI, and can we prove it? 

Despite soaring investment in AI, many organizations struggle to demonstrate sustained return on investment. Analysts consistently report a growing gap between early AI pilots and measurable, enterprise-scale impact—often because the economics of AI don’t behave like traditional IT programs. [forbes.com] 

A useful way to think about this challenge is not through an IT lens at all, but through something far more familiar: training for a marathon. 

Buying Running Shoes vs. Training to Finish the Race 

Buying great running shoes is easy. You can spend a lot of money very quickly and feel confident you’ve “invested” in running. That’s where many AI programs stall. 

AI platforms, copilots, and large language models are the shoes. They’re important—but owning them doesn’t mean you’ve become a runner. 

Finishing the marathon requires a training plan, consistent habits, nutrition, progress tracking, and recovery. In enterprise AI terms, that means use-case focus, governance, workflow integration, adoption, and—most critically—measurement. 

This distinction explains why AI excitement remains high, while ROI often feels elusive. Enterprise AI budgets have surged, yet boards and CFOs are increasingly asking for concrete proof of value in financial terms.  

Why AI ROI Feels Harder Than Traditional IT ROI 

Traditional IT investments typically behave in linear ways: buy software, deploy it, reduce costs or increase output. AI value compounds differently. 

Research shows that AI value tends to emerge over time, as usage evolves from individuals to teams to enterprise-wide workflows, rather than appearing immediately at golive.  

Continuing the marathon analogy: 

• Early training feels like effort with little visible payoff. 

• Mid-cycle gains come as endurance improves. 

• Breakthrough results appear only when training becomes systemic and repeatable. 

Similarly, early AI pilots often demonstrate flashes of productivity—faster drafts, quicker answers—but struggle to translate those gains into P&L impact without structural changes. 

The ROI Trap: Measuring “Miles Jogged” Instead of “Races Finished” 

One of the most common enterprise mistakes in AI measurement is tracking gross activity rather than net outcomes. 

Organizations often highlight: 

• Hours “saved” by AI tools 

• Number of AI users 

• Volume of AI-generated output 

But recent research shows that a significant portion of time saved through AI is often offset by validation, correction, and rework—especially in high-risk domains. [forbes.com] 

That’s like tracking miles jogged per week, while ignoring whether the runner is actually getting faster—or injured. 

Effective AI ROI measurement focuses on net operational impact, not raw usage. That includes: 

• What work was avoided entirely? 

• What cycle time actually disappeared? 

• What risks were reduced or downstream costs prevented? 

From Pilots to Muscle Memory 

Many enterprises now acknowledge the “pilot purgatory” problem—AI projects that work in demos but stall in production. Analysts report that a significant percentage of AI initiatives never deliver measurable ROI because they’re applied alongside existing workflows rather than embedded into them.  

Back to the marathon: Training only on weekends doesn’t build endurance. It has to be part of daily life. 

High-performing AI programs share a similar pattern: 

• AI is embedded directly into the tools employees already use 

• Outputs appear in the flow of work, not in separate systems 

• Adoption becomes organic, not forced 

When AI becomes muscle memory instead of a novelty, ROI becomes easier to defend. 

Governance Is the Training Plan, Not the Handcuffs 

Another misconception is that governance slows AI value. In reality, mature governance correlates strongly with measurable impact, because it creates consistency, trust, and repeatability.  

A marathon plan doesn’t limit a runner—it protects them from burnout and injury. 

Similarly, governance: 

• Defines which use cases matter 

• Clarifies ownership and decision rights 

• Sets guardrails for risk, compliance, and cost 

• Enables leaders to scale what works with confidence 

Without it, AI investments may produce flashy results but lack staying power. 

What Winning AI ROI Looks Like 

Organizations that successfully realize AI ROI behave differently. They don’t ask, “Where can we use AI?” They ask, “Which outcomes matter most, and how will AI change the economics of achieving them?” 

Leading enterprises: 

• Define success metrics before implementation  

• Tie AI initiatives to operational KPIs, not tool adoption 

• Expect some pilots to fail—and plan for it 

• Invest in workforce fluency to compound returns over time [forbes.com] 

In marathon terms, they’re not chasing gadgets—they’re training for race day. 

The Bottom Line 

AI ROI isn’t about proving that AI works. That question is already settled. 

The real challenge is proving that AI changes outcomes in ways that matter—to customers, to employees, and to the financial health of the business. 

If your AI program looks like a closet full of expensive running shoes, it may be time to rethink the training plan. Because in enterprise AI, as in endurance sports, the reward goes not to the best-equipped, but to the best-prepared.