FEATURE: ARTIFICIAL INTELLIGENCE
Jason Rivera, CTO, Verra Mobility, says companies rushing to adopt AI without identifying clear business problems risk creating an expensive innovation gap. He outlines a structured framework for evaluating AI applications to ensure real-world impact. my previous experience at companies like 3M, I’ ve observed that successful technology implementations begin with understanding customer problems – not with the technology itself.
The problem with current AI approaches
Reversing the innovation process
In boardrooms around the world, executives are demanding to know not only what their company’ s AI strategy is but also how AI will actually make money. Despite significant global investment in AI technologies, McKinsey’ s 2025 State of AI report reveals that“ more than 80 % of respondents say their organizations aren’ t seeing a tangible impact on enterprise EBIT from their use of gen AI.”
This reality exists even as Beth Kindig, CEO and Lead Tech Analyst for the I / O Fund, projects that Big Tech could spend around US $ 240 billion on AI in 2025.
As technology leaders, we face mounting pressure to implement AI solutions regardless of whether they address actual business problems. This disconnect is creating an expensive innovation gap where companies invest significant resources in AI initiatives without clear returns.
After nearly a decade as CTO and Vice President of Technology Development at Verra Mobility and from
When there’ s such a big technology innovation, and we’ ve seen it with cloud computing, mobile devices and now AI, organizations can latch on to a new tool without delving first into what they’ re trying to accomplish. Instead of asking‘ What problems need solving?’ they ask‘ How can we use AI to solve problems?’
This technology-first mindset leads to what I call‘ weaponizing’ AI – forcing artificial intelligence into business processes without clear purpose or value.
Learning from past technology cycles
This approach resembles past technology cycles. At 3M, we developed prismatic technologies, road paint and highway sheeting based on specific safety and visibility problems we identified. The innovations that succeeded weren’ t just novel technologies; they addressed specific market needs with measurable outcomes.
Creating new paint technologies that improved visibility was the scientific breakthrough but finding how this technology could improve public safety became the business solution that delivered ROI.
The distinction between invention and innovation is crucial here. Inventions create new technologies, while innovations solve real problems. AI without clear
Why AI Success Depends on Problem Discovery, Not Technology
24 INTELLIGENTCIO NORTH AMERICA www. intelligentcio. com