# Before you buy more AI, diagnose the gap you actually have

DevFeed: [Before you buy more AI, diagnose the gap you actually have](<https://devfeed.tech/articles/before-you-buy-more-ai-diagnose-the-gap-you-actually-have-65471.md>)

Original publisher: [Read original article](<https://www.cio.com/article/4230822/before-you-buy-more-ai-diagnose-the-gap-you-actually-have.html>)

Author: Vivek Gupta

Published: 2026-10-06T10:00:00Z

Content type: opinion

Language: en

Sources: [CIO](<https://devfeed.tech/sources/cio.md>)

Topics: [AI Context Management](<https://devfeed.tech/topics/ai-context-management.md>), [dogfooding](<https://devfeed.tech/topics/dogfooding.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-adoption](<https://devfeed.tech/tags/ai-adoption.md>), [ai-roadmap](<https://devfeed.tech/tags/ai-roadmap.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [artificial-intelligence-business-operations-it-leadership-it-management](<https://devfeed.tech/tags/artificial-intelligence-business-operations-it-leadership-it-management.md>), [business-operations](<https://devfeed.tech/tags/business-operations.md>), [contributor](<https://devfeed.tech/tags/contributor.md>), [enterprise-ai](<https://devfeed.tech/tags/enterprise-ai.md>), [it-leadership](<https://devfeed.tech/tags/it-leadership.md>), [it-management](<https://devfeed.tech/tags/it-management.md>)

## AI overview

The article argues that enterprises should diagnose why AI is failing to improve important decisions before buying more tools. It distinguishes capability, design, delivery and connection gaps, and recommends starting with a consequential recurring decision to identify which gap is limiting better outcomes.

## Source excerpt

A senior executive at a large enterprise recently described a capital allocation review to me. The committee had spent two hours deciding where the next tranche of maintenance capital should go. The discussion drew on last year's budget, a regional presentation and the persuasive advocacy of a plant head who had been escalating the same request for three quarters. Somewhere in that same organization sat a predictive model estimating failure risk across the asset base. It was reasonably good. A competent team had built it sometime earlier. Nobody in the room referred to it or mentioned it. What bothered him was not that the model was wrong. It was not wrong. It wasn't late, and it wasn't hard to access. It simply had no relationship with the decision. I have heard enough versions of that story to believe it describes the central problem in enterprise AI right now -- and that we are consistently solving for the wrong thing. AI adoption is no longer the constraint Mckinsey's The State of AI in 2026 makes the point clearly. Almost nine in ten organizations report regular AI use in at least one business function, and around 44% say AI is scaling across the enterprise. Eight in ten report higher individual productivity. Yet only about 37% attribute any positive enterprise-level EBIT impact to AI. BCG's survey of large-company CEOs found nearly nine in ten reporting cost or revenue benefits in targeted areas -- while more than half cited a missing link between AI and P&L, and only 14% had clearly defined P&L impact across their AI initiatives. PwC's 2026 CEO survey put it even more starkly: 12% of CEOs said AI had delivered both revenue gains and cost reductions. Individual productivity is rising faster than enterprise performance. That gap cannot be explained by technology alone, and more tooling should not be the default response. Part of the cause is how we frame AI in the first place. I ran an informal poll among my own network recently -- not research, but directionally