​Andy Kohm is CEO of SCIP.gettyFor years, enterprise leaders have operated on the assumption that if you give people better data, they’ll make better decisions. That assumption is starting to crumble.​Organizations today have more data than ever. They have invested heavily in analytics platforms, dashboards and now AI tools layered on top of it all. And yet decision-making in many companies is still slow and reactive. Analysts spend 70% of their time collecting and reconciling data across systems rather than actually analyzing it. The tools keep improving, but the decisions are not getting faster.The AI wave was supposed to change that. Instead, it exposed how deep the problem goes. An MIT study of over 300 enterprise AI deployments found that 95% of pilots deliver zero measurable return. McKinsey’s 2025 survey of nearly 2,000 organizations found that almost two-thirds have not begun scaling AI across the enterprise, with data quality as the primary blocker. In supply chain, only 15% of professionals trust their own systems to produce clean data.The pattern is consistent: it is not the models that are failing. It is the foundation underneath them.​​The Next Enterprise CategoryEvery major wave of enterprise technology has been defined by a category. ERP systems became the systems of record for transactions. CRM systems became the systems of engagement for customer interactions.​Now, a new category is emerging: systems of intelligence. This is software that doesn’t just store what happened or display what’s going on. It tells you what to do next.​A system of record can tell you a component exists. A system of intelligence can tell you that the component is at risk, what alternatives are available, how supplier lead times affect your options and which action will keep disruption to a minimum.​Why Most AI Efforts Start In The Wrong Place​Most enterprise AI efforts stalled because they started with the wrong question. They asked “Where can we apply AI?” instead of “What decision do we need to make faster, cheaper, or more reliably?” The difference matters. One approach leads to copilots layered onto existing systems, answering questions about data trapped in a single platform. The other starts with a business outcome: reduce supply chain risk, lower component costs, shorten time to market, improve margins on a product line. Then it works backward to what intelligence is required to get there.​When companies start with the outcome, the architecture question answers itself. You need a system that synthesizes context across platforms. A procurement system cannot tell you how an engineering change affects supplier risk, inventory exposure and customer commitments simultaneously. That requires a layer that sits across all of them. When companies start with the tool instead of the outcome, they end up with faster access to the same fragmented data. That is a framing failure.​What A System Of Intelligence DoesOne of the biggest misconceptions in enterprise AI is that you need to fix your data before you can build intelligence on top of it. In practice, that often results in multiyear data cleansing initiatives that never fully deliver value.​A system of intelligence does not wait for clean data. It is the thing that validates data across sources, cleans the conflicts, aligns different systems that describe the same entities differently, and augments records with the attributes that actually drive decisions. If a platform cannot do that continuously and automatically, it is not a system of intelligence. It is just a reporting layer waiting for someone to feed it clean data. This is not preprocessing. This is intelligence.The Intelligence Loop​At the core of a system of intelligence is what I call the intelligence loop, a continuous cycle that turns raw signals into trusted action. Behind it is a specific architecture with six stages:​Sense: Ingest signals and events from systems of record and external sources.Align: Cleanse, deduplicate, normalize, and resolve entities across functions.Augment: Fill in missing context, infer constraints, attach policies.Prescribe: Generate options, quantify tradeoffs, recommend the next action.Execute safely: Integrate with workflows, approvals, and permissions so the recommendation actually gets acted on.Learn: Capture outcomes, measure regret, and improve future prescriptions.​Most enterprise AI today stops at Sense. Maybe Prescribe if the vendor is ambitious. The full loop, all six stages running continuously, is what separates a system of intelligence from AI features.A New Decision LayerNone of this makes systems of record obsolete. ERP, CRM and other core platforms are still essential, but their role is changing: They are becoming data sources, not decision-makers.​This shift also changes how leaders should evaluate their AI investments. Most companies are still measuring AI by adoption: how many users, how many queries, how many workflows touched. That is the wrong scorecard. The question is whether AI changed the outcome. Did decisions get faster? Did risk get identified sooner? Did component costs come down? Did the business do something it could not do six months ago? If the only answer is “people are using it,” that is activity, not intelligence.​The companies that recognize this shift early will build a compounding advantage. Better decisions generate better data, which feeds stronger intelligence, which produces better decisions.The flywheel favors the companies that move first. Most enterprises cannot build this layer internally, because the challenge is creating and maintaining a living data model that spans every function, validates continuously and improves with use. That is a product, not a project. The question for leadership is no longer which system of record to standardize on. It is what intelligence layer sits across all of them.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?