Study Finds Enterprises Rethink Work to Get More From AI

Enterprises have spent substantial sums on artificial intelligence (AI), including models, copilots and capabilities. However, the next dollar invested in a more advanced model may yield less value than the next dollar spent on addressing data, permissions, workflows, and organizational infrastructure issues that hinder AI's productivity.

A study by PYMNTS Intelligence reveals a significant disconnect between AI adoption and the value generated from it. Companies with AI embedded in one or two functions utilize it across an average of 41 tasks out of 75, while those with AI in three or more functions employ it across 40 tasks – a virtually negligible difference. Yet, 55% of the former group reported generating returns from their AI investments, compared to a staggering 93% of the latter group.

It appears that AI could be reaching a familiar technology inflection point. The bottleneck has shifted from infrastructure to the enterprise's ability to support AI's operations. Companies may not possess an enterprise designed to accommodate powerful AI systems; this realization becomes increasingly critical as AI transitions from answering questions to executing tasks.

An AI assistant drafting a memo can function within a disorganized organization, but an agent approving invoices, modifying customer accounts, initiating procurement workflows, or recommending significant financial movements requires a more robust setup. This insight underscores the need for operational model enhancements alongside AI advancements.

Companies at the forefront of AI adoption confront an average of 5.6 barriers to adoption, as opposed to just three barriers among those with no AI integration. The leading AI companies are not discovering fewer problems but rather uncovering more of the company's underlying issues, such as incompatible databases, unclear ownership, inconsistent policies, fragmented workflows, and approval structures tailored for manual human information transfer.

Consequently, future AI spending trends may appear as investments in cybersecurity, identity management, cloud infrastructure, data management, consulting, integration, or workflow software. Economically, however, much of this expenditure could be attributed to AI-enablement rather than AI acquisition itself. As powerful models become more accessible, they may not offer a competitive edge, as competitors can easily acquire similar intelligence.

The true differentiator will be the organizational architecture capable of leveraging this AI. This transformation turns essential elements like clean data, interoperable systems, clear decision rights, and mature governance from mere corporate maintenance tasks into strategic assets. The focus is shifting from identifying which company possesses the most sophisticated model to determining which company can effectively deploy it.

In the rapidly evolving landscape of AI, the question is no longer which enterprise possesses the smartest model – the answer is accessible to all – but rather which enterprise can harness the power of this model. For comprehensive AI insights, subscribers can access PYMNTS Intelligence's daily AI newsletter. As PYMNTS Intelligence partners, businesses gain access to a team of PhDs, researchers, data analysts, subject matter experts, and editorial professionals dedicated to delivering rigorous research and dependable data to drive intelligent, data-driven discussions on evolving customer expectations, a more interconnected economy, and strategic shifts essential for achieving organizational outcomes.