Corporate AI is experiencing growing pains
Aug 19, 2025
Despite soaring expectations and vast investments in enterprise AI, many organizations are now grappling with the “GenAI paradox.” Companies are deploying billions toward AI initiatives, only to find that measurable productivity gains remain elusive, as reported in a recently published New York Times analysis.
In fact, 42% of AI pilot projects were abandoned by late 2024, up sharply from 17% the previous year, signaling that promise alone doesn’t translate to sustained value.
Most companies are experimenting with AI, but many of these initiatives remain trapped in pilots, never advancing into production. In many cases, the tools generate excitement but fail to align with the practical realities of corporate workflows. Chatbots that hallucinate, employees wary of new systems, and gaps in data quality all contribute to stalled progress.
Corporate AI is still in the phase where enthusiasm outpaces proven business impact.
The delay is part of the innovation process
Early setbacks can feel discouraging, but they are also expected. Failure is actually part of the innovation cycle, according to Andrew McAfee, a principal research scientist and co-director of the MIT’s Initiative on the Digital Economy. Many organizations view failed AI experiments not as waste, but as valuable groundwork.
JPMorgan’s CIO Lori Beer indicates that closing failed prototypes can be smart, because each one yields code, lessons, and insights that serve future efforts.
This cycle mirrors the history of other major technological shifts. The introduction of personal computers in the 1980s did not immediately deliver productivity gains; in fact, economists described it as the “productivity paradox.”
The same was true of the internet in the 1990s, where early adoption was followed by a period of disillusionment before the technology transformed industries. AI appears to be on that same trajectory: initial overhype, followed by recalibration, then eventual payoff.
AI is proving useful in targeted use cases
Progress is most visible in well-defined use cases. USAA is using AI to support 16,000 customer service agents, helping them deliver faster and more accurate answers. Johnson Controls has developed a field app that trims minutes off repair calls by quickly summarizing problems and suggesting solutions.
At JPMorgan, 200,000 employees now have access to an AI assistant that saves hours of repetitive work and improves decision-making for wealth advisers.
These examples show how AI’s impact is strongest when tied to specific tasks and clear business needs. Rather than replacing workers outright, AI is augmenting them, cutting down on routine tasks and improving efficiency.
In these contexts, the technology delivers practical benefits that build trust and momentum. Even incremental improvements, when applied at scale, can add up to significant impact.
Enterprise integration is necessary for real payoff
The challenge for enterprises is moving from scattered successes to enterprise-wide transformation.
Achieving ROI requires time, workflow integration, and a willingness to embed AI into core processes rather than treating it as a side experiment. Experts estimate it could take at least five years for AI to deliver broad-based value across sales, procurement, manufacturing, finance, and customer service.
That future depends on scaling AI from pilots to production. It also requires mastering critical success factors like developing data that is trustworthy and integrating AI with existing infrastructure.
Innovation doesn’t stop at building a model; it continues through deployment, monitoring, and refinement. For AI to pay off, it must mature from isolated projects into embedded enterprise infrastructure.
Centific turns AI pilots into enterprise impact
Many companies already have model-building capabilities; the problem lies in making those models operational, adaptable, and reliable at scale. Centific can help.
Through the AI Data Foundry by Centific, we help enterprises bridge the gap between experimentation and impact. Our platform provides the data foundation needed to scale AI responsibly, delivering high-quality, contextualized, and versatile data that models require. And through our expertise in data as a service, we manage the ongoing operations that keep AI effective over time.
By focusing on scale, reliability, and seamless integration, Centific enables enterprises to move past the GenAI paradox. We help organizations turn promising pilots into lasting business outcomes, ensuring that investments in AI generate measurable performance improvements.
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Corporate AI
Enterprise AI
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Responsible AI
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