Many organizations default to generative AI for every use case without understanding that established technologies like machine learning may be better suited, leading to misaligned deployments and eroded executive confidence.
Barbara Cresti, Founder of StratEdge, argues that AI cannot compensate for missing business strategy or poor data governance, and that scaling requires top-down alignment with clear strategic objectives.
Long-term resilience depends on embedding technology into business continuity planning, including digital sovereignty, dependency risk on external platforms, and structured feedback loops that protect workforce judgment.
Most organizations experimenting with AI are stuck. They run pilots, announce initiatives, and invest in tools. But the results rarely scale. The problem is not the models but the absence of a clear business strategy, clean data foundations, and executive leadership willing to define where AI fits and where it does not.
Barbara Cresti is the Founder of StratEdge, an advisory firm that helps boards and executives lead AI strategy and governance. Previously, she held marketing leadership roles at Amazon Web Services and Orange. With over 20 years of experience across regulated and tech-driven industries, Cresti now advises midsize and enterprise organizations working to close the gap between AI experimentation and strategic execution.
"AI cannot be adopted just because it's available. It has to be aligned with a clear business purpose, otherwise it remains an experiment instead of becoming a strategic capability," Cresti says. The confusion often starts with the technology itself. Cresti describes advising a manufacturing company that wanted to improve its supply chain planning. The leadership team asked which generative AI model to use, when in fact, generative AI was not the right tool at all.
The wrong AI for the job: "We told them that maybe generative AI is not the best AI for that. Maybe you need machine learning," Cresti recalls. "They didn't realize that other technologies could be better suited for their use case." For tasks like demand forecasting and logistics optimization, machine learning has a longer track record, greater reliability, and stronger auditability than generative alternatives. When the team understood that, confidence went up and perceived risk went down.
Strategy before tools: "The issue today is that AI is seen as a tool instead of an enabler to the business strategy. If you don't have a strategy, you don't know where you should focus first," she explains. In many midsize companies, the strategy itself is poorly defined. Leaders know they want to grow revenue or cut costs, but they lack the dashboards, indicators, and visibility. That structural gap makes identifying viable AI use cases almost impossible.
Garbage in, nothing out: "If the data is messy, there is nothing you can do. AI cannot fix garbage data, and without governance and visibility into what data is strategic, you cannot scale," Cresti says. She points to her time at AWS, where the team wanted to personalize outreach to small and midsize businesses but could not segment by industry because the CRM data was unreliable. If that problem exists inside a major cloud provider, she adds, it is significantly worse inside a typical midsize enterprise.
The organizational side of the problem is equally unresolved. Cresti says adoption in many organizations has been bottom-up, with individual teams experimenting without executive sponsorship or strategic alignment. She points to AWS as a counterexample, where implementation was driven top-down with clear definitions of where each function should and should not apply AI.
Safe spaces for pushback: One underestimated risk is that employees hesitate to challenge flawed AI outputs. Cresti argues that leadership, not individuals, must create that permission. "Leadership should install safe spaces and request mandatory feedback from employees on how it is going with AI," she says. At AWS, designated champions collected concerns by team and reported them to the group managing the rollout. Some organizations, she notes, are investing in reskilling programs that help employees whose roles are most affected by automation transition into new positions rather than lose them entirely.
Technology now sits inside business continuity, and Cresti says most companies have not absorbed that reality. She draws a parallel to Europe's growing dependency on foreign digital infrastructure. If a model provider goes down or a platform changes its terms, organizations without a contingency plan face an operational crisis. Cresti cites European payments as an example: most transactions run through Visa and Mastercard. If those systems were disrupted for any reason, the consequences would be immediate and structural. The same logic applies to any company building core operations on a single AI vendor without a fallback.
"Think strategically about your competitiveness and your unique advantages," Cresti says. "How can you protect and augment them over the medium to long term? These are the considerations that enable smart strategic choices, and they start now."
