The ROI Trap

Are we measuring the wrong things?

"What's the ROI on electricity?" Factory owners asked in 1910s America. As historian Paul David noted in his seminal work on technology adoption, it took decades for American factories to realize electricity's true value—not because the technology wasn't transformative, but because they were measuring the wrong things.

Samuel Insull, Thomas Edison's former secretary who built Chicago's electrical system, recalled endless meetings with factory owners fixated on comparing the direct cost per horsepower of electric motors versus steam engines. "They couldn't see that the real advantage had nothing to do with the cost of power," he wrote. "It was about restructuring the entire way we thought about manufacturing."

This question, absurd as it sounds today, was commonly asked by factory owners in the early 1900s. Many initially saw electricity as merely a replacement for steam power, calculating its value through the narrow lens of energy costs. They missed the revolutionary impact it would have on manufacturing, the workplace, and productivity.

Today, we may be making a similar mistake with AI.

Historical Parallels

Consider how we think about internet connectivity in modern organizations. When was the last time you saw a CFO ask for an ROI calculation for providing high-speed internet to employees? We understand intuitively that network connectivity is a fundamental enabler of productivity, innovation, and employee satisfaction. Today, if the internet isn't available, millions of people literally can't get their jobs done. This isn't just a utility—it's a necessity. Companies that tried to economize on internet connectivity in the early 2000s quickly found themselves at a disadvantage. Slow internet became more than an inconvenience; it became a reason why talented employees left for competitors. The same pattern is likely to emerge with AI.

And again with the Cloud...

The evolution of cloud computing offers another instructive parallel. In the early 2010s, many organizations approached cloud adoption with the same ROI-focused mindset we see with AI today. They created detailed cost comparisons between on-premises servers and cloud installations, before concluding that cloud was "like renting" and "too expensive."

What these calculations missed was transformational impact. The real value wasn't in cost savings—it was in the ability to experiment rapidly, scale instantly, and innovate continuously. Organizations that invested in the cloud and made the capabilities ubiquitous to its staff found velocity grow and invention flourish.

Valid Cost Concerns

While this post argues against over-focusing on ROI, the financial reality of AI adoption—especially for smaller organizations—cannot be ignored. Small and medium-sized businesses face legitimate constraints, like annual AI licensing costs, training and integration require significant upfront investment, and ecurity and compliance measures, which all add additional overhead.

However, organizations can address these challenges through thoughtful adoption strategies. Consider starting with specific high-impact departments or use cases, then expanding based on demonstrated value. Cloud-based AI services with consumption-based pricing offer more flexible entry points than traditional enterprise software. The key is viewing AI investment not as an all-or-nothing proposition, but as a scalable journey that can begin modestly and grow with your organization's needs and capabilities.

Beyond Traditional Metrics

Traditional ROI calculations usually fail to capture the transformative nature of technologies which are different at a foundational level. They focus on more easily measurable direct impacts while missing the broader organizational effects (you may know better ones):

Productivity Amplification: Just as high-speed internet enables workflows that weren't possible with dial-up, AI amplifies cognitive tasks in ways that transform how work gets done. The value isn't just in time saved—it's in the new possibilities opened up.

Cultural Impact: Organizations that provide broad access to powerful AI tools send a clear message about valuing their employees' time and capabilities. Those that don't risk being seen as technologically regressive, much like companies that still restrict internet access.

Innovation Enablement: When AI capabilities are universally available, employees find novel applications that weren't part of the original business case. This organic innovation is hard to capture in traditional ROI calculations.

Not everything that counts can be counted, and not everything that can be counted counts

Creating artificial scarcity around AI access—through restrictive licensing or tiered access models—introduces hidden costs that can outweigh any apparent savings:

Shadow AI: Employees denied access to corporate AI tools will find alternatives, creating security risks and fragmented workflows.

Productivity Drag: When workers toggle between AI-enabled and AI-restricted tasks, they experience the same cognitive friction as switching between high-speed and dial-up internet.

Innovation Barriers: Limited AI access creates two classes of workers: those who can leverage AI for innovation and those who cannot.

This isn't an argument against measurement—quite the opposite. As management guru Peter Drucker famously noted, "What gets measured, gets managed." The challenge lies not in whether to measure, but in what and how we measure. Traditional ROI calculations excel at capturing incremental improvements in existing processes. We can and should measure direct cost savings, time saved on existing tasks, reduction in errors, and so on. Transformational metrics are harder to measure (but critical to understand), include things like new capabilities enabled, innovation velocity, employee satisfaction and retention, organizational adaptability, etc.

The key is maintaining a balanced perspective. While we should absolutely measure AI's impact, we must avoid the trap of optimizing solely for what's easily measurable.

The Path Forward

The companies that will thrive in the AI era won't be those that found the perfect ROI calculation. They'll be the ones that recognized AI as a fundamental business utility—like electricity, internet connectivity, or mobile devices. They'll make it universally available, focusing not on controlling access but on enabling responsible use. This doesn't mean abandoning financial prudence. But it does mean shifting from a scarcity mindset to an enablement mindset. The question isn't whether to provide AI access, but how to do it effectively and responsibly. The real risk isn't overspending on AI—it's underinvesting and falling behind. Just as no modern company tries to compete with restricted internet access, future organizations won't be able to compete with restricted AI capabilities. The time to build this foundation isn't when you're already behind—it's now.