Culture, Not Code, Blamed for Failed AI Deployments as $37B in Spending Falls Short of Expectations

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Companies poured $37 billion into artificial intelligence initiatives in 2025, according to a Menlo Ventures report, yet many executives are reporting negligible returns on investment.

Behind the staggering spending lies a deeper problem: organizations are treating AI as a simple software installation instead of a fundamental workforce and culture transformation, industry experts warn.

Why AI Rollouts Are Stalling

“Deploying AI is a workforce strategy that demands behavior change and a new operating model. It’s not a technology rollout; it’s a workforce and culture transformation,” said a spokesperson from West Monroe, a consultancy that has studied dozens of enterprise AI implementations.

Most companies make the critical error of automating existing, broken workflows rather than redesigning processes from the ground up, the firm found.

Background: The $37 Billion Wake-Up Call

The massive investment in AI—spanning industries from finance to healthcare—was expected to boost productivity and unlock new efficiencies. Instead, early adopters report low user adoption, no measurable productivity gains, and a projected ROI that remains theoretical.

“Organizations handed AI to their IT team like it was new software to install and called it a rollout,” the West Monroe spokesperson added. “The result is a system no one uses and an expense no one can justify.”

The core issue is a mismatch between how AI is deployed and how humans actually work. Companies ask the wrong questions, focusing on speed rather than true redesign.

“Instead of ‘How can we do this job faster with AI,’ leaders should be asking, ‘If we were building this from scratch today, what would humans do, what would AI do, and what should we not do at all?’” the expert said.

What This Means for Enterprise AI

Industry analysts predict that without a culture-first approach, the current wave of AI investment could become a cautionary tale similar to early digital transformation efforts that failed to deliver.

To succeed, companies must identify three to five high-impact workflows—not entire departments—and rebuild them around AI’s strengths, such as synthesizing data at scale.

“For example, M&A due diligence that used to take weeks can now be done in days when the workflow is redesigned around what AI does best,” the West Monroe spokesperson said.

Adoption Requires More Than Training

While centralized training programs remain important, they are too slow to keep pace with rapid AI evolution. The fastest path to adoption is activating an internal “champion network” of early adopters.

“Most organizations already have people leaning in—proactively learning, experimenting, and applying AI to their work,” the expert explained. “The best thing you can do is connect them and give them agency, time, and tools so they can inspire others.”

At West Monroe, this approach led to a corporate-wide leaderboard, AI challenges, and innovation bonuses that made learning both competitive and engaging.

Leadership buy-in is equally critical. “If leadership isn’t using AI, no one else will believe it matters. Leaders must model the behavior and hold people accountable,” the spokesperson emphasized.

Building a Culture of Continuous Learning

Organizations must shift from episodic training to continuous upskilling, ensuring employees remain employable both inside and outside the company.

“It’s our responsibility not only to employ people, but also to keep their skills current so they remain employable whether at our company or elsewhere,” the West Monroe representative said.

Experts conclude that the next successful AI rollout will be one that treats culture as the primary product, not the technology itself.