The Data-to-Decision Gap: Why Most Enterprise AI Still Isn't Closing It

A framework for understanding why organizations keep building data infrastructure without building decision capability — and what a different architecture makes possible.

Enterprise organizations have never had more data. They have also, paradoxically, never spent more time searching for it.

Forrester Consulting found that knowledge workers spend approximately 30% of their workweek finding, reconciling, and debating data before any decision gets made. A separate Forrester estimate puts the proportion of enterprise data that goes unused for analytics at between 60% and 73%. These are not infrastructure failures. They are symptoms of a structural gap that more data, more dashboards, and more AI features have so far not closed.

At Amoeba AI, we've run systematic market intelligence analyses across the industries our customers operate in — incentive compensation, mobile attribution, revenue operations, enterprise SaaS analytics, and more. The signal processing engine we built for this work applies neuro-symbolic reasoning to external market data: customer reviews, analyst research, competitive positioning signals. What we expected were category-specific findings. What we consistently found was the same structural problem, repeated across every domain.

The data is trusted. The decision doesn't get made.

Why the Gap Exists

To understand why the data-to-decision gap persists, it helps to distinguish between three architecturally distinct layers of enterprise intelligence — layers that are often conflated, and whose conflation is the root of the problem.

The reporting layer answers: What happened? Business intelligence platforms — dashboards, standard reports, visualization tools — have delivered this well for decades. They aggregate historical data and present it in digestible form. They are, structurally, designed for human interpretation after the fact.

The analytics layer answers: Why did it happen? Attribution platforms, customer success health scores, sales forecasting models, and cohort analysis tools operate here. They apply statistical models to find causes, correlations, and trends. They are more powerful than reporting, but they still produce outputs that require a human to close the loop to action.

The decision layer answers: What should we do about it — and what happens if we do? This is the layer that consistently does not exist in the enterprise stack. Organizations routinely invest heavily in the first two layers and then attempt to bridge to the third through manual analysis, analyst headcount, and executive judgment applied to incomplete information under time pressure.

Gartner's framing of this is direct: decision intelligence is "a practical domain that combines multiple traditional and advanced disciplines to design, model, align, execute, monitor and tune decision models and processes." Critically, Gartner distinguishes this from analytics augmentation — the decision layer is not a faster reporting layer. It is an architecturally different function. As Gartner noted in its 2025 AI research, neurosymbolic approaches specifically "can augment and automate decision making with less risk of unintended consequences" — language that points to why the underlying architecture matters, not just the capability.

The market is beginning to reflect this distinction. The global decision intelligence market was valued at approximately $16.8 billion in 2024 and is projected to reach $57.75 billion by 2032 — a compound annual growth rate of 16.9%. That growth rate is not driven by more dashboards. It reflects genuine demand for the third layer.

What We Found Across Our Customer Base

Running our engine across organizations in several data-intensive categories, the same pattern cluster surfaced in the review signal, the product feedback, and the behavioral data across all of them.

In incentive compensation — one of the most data-rich categories in sales technology — organizations have built platforms that calculate commission with precision and surface attainment data in real time. The gap that consistently appeared: the platforms accurately captured what happened (who attained quota, who didn't) but stopped well short of why the compensation design was producing those outcomes, or which structural changes would improve durable performance, not just quarterly numbers. One study estimated that 91% of SaaS sales teams miss quota despite having performance visibility tools. The data quality was not the problem.

In mobile attribution — another category with extraordinary data infrastructure — platforms processing billions of user events captured channel-level attribution with high fidelity. The consistent gap across review data: teams could answer where did this user come from but could not systematically answer which channels produce users who are still active at 90 days or which cohort signals predict retention vs. churn before it surfaces in revenue. Independent review signals from G2, TrustRadius, and Capterra across multiple platforms in this category showed 23 separate instances of the same ceiling: teams exporting data to supplementary tools because the primary platform stopped before the decision.

In revenue operations more broadly — across CRM, customer success, and analytics platforms serving organizations at scale — the pattern repeated. Pipeline visibility was high. Decision quality, measured by forecast accuracy, expansion predictability, and churn anticipation, remained dependent on manual analyst processes operating on exported data.

The structural signature of the gap is consistent across categories: multi-variable interaction effects are invisible to the reporting and analytics layers, and the decisions that matter are almost always driven by the interaction, not by any single metric.

Pipeline can increase while late-stage concentration rises and the quarter becomes fragile. Customer acquisition cost can hold steady while payback period elongates because retention softens. Quota attainment can be reported accurately while the compensation structure is systematically producing the wrong behavioral incentives. Standard analytics surfaces each variable. It cannot reason about how they combine.

Why Current AI Doesn't Close It

The current generation of AI features in enterprise software has largely addressed the interface to the reporting and analytics layers — not the decision layer itself.

Natural language querying is the clearest example. The ability to ask a data platform a question in plain English and receive an immediate answer is a genuine improvement in accessibility. It is not, however, a decision intelligence capability. The answer it produces is still bounded by what the underlying platform was designed to surface: where the install came from, what the commission calculation is, what the pipeline number looks like today.

The question which acquisition channels produce users who remain active at 90 days requires reasoning across attribution data, cohort behavior, retention curves, and lifetime value trajectories simultaneously — with explicit logic connecting each step. Natural language access to a reporting layer does not produce this. It produces a faster answer to the same class of question the reporting layer was already designed for.

This distinction matters because of how organizations currently evaluate AI investments. A 2024 study found that while 78% of global companies are already using AI technologies, only 5% of private corporate AI tools have been successfully integrated into production business processes. The adoption chasm is not a capability gap — it is an architecture gap. Tools that improve access to the existing reporting layer get adopted rapidly. Tools that require different reasoning infrastructure encounter friction because the infrastructure itself hasn't changed.

This echoes a deeper critique. Cognitive scientist Gary Marcus has argued that robust AI systems require "the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning" — that pure pattern-matching systems, however fluent, hit a structural ceiling when the task requires structured inference over multiple interacting variables. The enterprise decision problem is precisely this kind of task.

A Different Architectural Approach

The architecture Amoeba AI is built on takes this structural diagnosis seriously.

Neuro-symbolic AI integrates two fundamentally different computational paradigms: neural networks, which excel at pattern recognition across large, unstructured, or noisy datasets; and symbolic reasoning systems, which apply explicit logic, typed relationships, and formal inference rules to produce conclusions that are traceable and challengeable.

The distinction between these paradigms matters for the decision use case in a specific way. As the MIT-IBM Watson AI Lab has framed it, neural systems operate as the sensory layer — finding what isn't obvious in the signal volume — while symbolic reasoning operates as the cognitive layer — applying structured inference to what the sensory layer found. Neither is sufficient alone for the decision problem. A neural system alone produces confident outputs without transparent reasoning — the black box problem that is well documented in enterprise AI adoption literature, where 78% of financial services executives cite transparency concerns as the primary barrier to AI deployment. A symbolic system alone lacks the capacity to find non-obvious patterns in complex, high-dimensional data.

The neuro-symbolic combination enables what neither can do independently: pattern extraction at scale, followed by structured inference over those patterns, producing outputs that are both sensitive to complex signal interactions and transparent about how the conclusion was reached.

In practice, this means the output of an Amoeba analysis is not a prediction — it is a reasoned inference. It is structured around explicit premises, connected through formal logic, and constrained by domain-specific rules. A leader receiving this output can interrogate the premises, challenge the logic, and trace every conclusion back to the signals that produced it. This is the difference between an answer you can act on and an answer you have to trust blindly.

The Three Conditions for Closing the Gap

Based on our work across multiple enterprise categories, closing the data-to-decision gap consistently requires three conditions working in combination — not in sequence, not independently.

Structured multi-variable reasoning. The decisions that matter in enterprise contexts are almost never single-variable. A compensation design decision involves attainment, tenure distribution, territory equity, plan structure, and ramp timeline interacting simultaneously. A retention decision involves usage signals, support engagement, expansion motion, and cohort age combining in ways no single metric represents. An engine that reasons about variables in isolation cannot produce the inferences these decisions require.

Signal extraction that finds what isn't already being watched. The signals that precede consequential outcomes — churn, deal slippage, cohort health degradation, compensation-driven attrition — consistently appear in the data before they surface in the headline metrics organizations are monitoring. Finding them requires processing signal volume at a scale and speed that human analysis cannot match consistently. This is where the neural layer of a neuro-symbolic architecture earns its role: not to replace reasoning, but to find what reasoning should be applied to.

Explainable output connected to a specific decision. An inference that a leader cannot interrogate will not be acted on. This is not merely a trust issue — it is a governance issue, a regulatory issue in many industries, and a fundamental requirement for building organizational capability around AI-assisted decision-making rather than AI-dependent decision-making. The symbolic layer of the architecture is what makes explainability structural rather than bolted-on: the output is explainable because it was produced by a system that reasoned explicitly, not because a post-hoc explanation was generated after the fact.

What This Means for the Category

Decision intelligence as a discipline is at an early but accelerating inflection point. Gartner's 2027 prediction — that 50% of business decisions will be augmented or automated by AI agents for decision intelligence — establishes a direction of travel that is increasingly treated as an operational requirement rather than a strategic aspiration.

What that trajectory means in practice: the organizations that build the decision layer now, while the infrastructure category is still maturing, will compound the advantage. The organizations that continue to invest in reporting and analytics improvements while treating the decision layer as a manual process will face increasing friction as the volume and velocity of data requiring interpretation grows beyond human analytical bandwidth.

The data-to-decision gap is not a data problem. It is not an analytics problem. It is an architecture problem — and it requires an architecture built for it.

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