AI Is Ready for Prime Time but the Org Chart Is Not

Artificial intelligence is doing exactly what it promised.

The technology is improving efficiency, sharpening forecasts, accelerating workflows and, in many cases, outperforming expectations across business functions. For corporate leaders who spent years waiting for the technology to mature, that moment has arrived.

And yet, inside many large organizations, AI is going nowhere fast.

Findings from the April edition of “The Enterprise AI Benchmark Report” from PYMNTS Intelligence highlight a rising paradox: even as AI delivers results, most companies are struggling to scale it. More than 70% of executives say the primary obstacles are internal, rooted in fragmented data, unclear ownership and budget friction.

On average, companies are grappling with four to five of these barriers simultaneously. And while companies can point to isolated wins, such as a chatbot that reduces call-center volume, a model that improves demand forecasting, a tool that automates internal workflows, and so forth, those wins often remain siloed.

AI, by contrast, thrives on integration. It requires companies to rethink how data flows, how teams collaborate and how decisions are governed. It demands changes that are often slower, more complex and more politically fraught than adopting new tools.

The Enterprise AI Problem and Its Many Parts

At its inception, enterprise AI adoption was initially mostly held back by doubts about capability. Would the models work? Could they deliver reliable results? Would the investment pay off?

Those questions are now fading, but AI’s success at the task level has not always translated into success at scale. That gap between performance and scale is now one of the defining features of enterprise AI.

Still, only 11% of executives surveyed blame AI itself for their problems scaling it internally.

In many cases, it is the very structure of large organizations that works against scaling. The modern business, after all, is one that is increasingly defined by departments that operate independently, data which is owned locally, and incentives that are rarely aligned.

The real differentiator then, is organizational readiness and the ability to align data, clarify ownership, allocate resources and integrate AI into core processes.

Data remains the most persistent issue, per the report. In many organizations, information is scattered across systems, business units and legacy infrastructure, making it difficult to feed AI models with consistent, high-quality inputs.

At the same time, ownership of AI initiatives is often unclear. Is it the domain of IT? Data science? Individual business units? Without defined accountability, projects stall or fragment.

Taken together, these dynamics form what might be called the AI readiness gap, or the distance between what companies want AI to do and what they are actually prepared to support.

Read the report: The Enterprise AI Readiness Gap: What Company Data Reveals About the Real Barrier to Scale

Against that backdrop, executives are coming to realize that scaling AI looks less like installing software and more like reengineering an organization. While traditional enterprise technology could often be deployed in a relatively contained way, AI frequently cuts across the entire enterprise.

Organizations that may appear “AI-active” on the surface are, in reality, likely to be running a constellation of disconnected experiments. The technology performs; the organization does not.

Compounding the issue is the report’s findings that many companies lack clear workforce transformation plans. Skill gaps, employee resistance and organizational complexity are major blockers to AI scaling.

The result is a new kind of inefficiency: AI that exists but does not matter.

Until that changes, the promise of enterprise AI will likely remain constrained, held back not by what the technology can do, but by what organizations are able to absorb.

At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.