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Feb 04, 2026
The enterprise software industry has a habit of repeating itself. We often rush headlong into a promising new abstraction—ERP, BI, cloud, microservices – oversimplify it, overmarket it, and then act surprised when half…
The enterprise software industry has a habit of repeating itself. We often rush headlong into a promising new abstraction—ERP, BI, cloud, microservices – oversimplify it, overmarket it, and then act surprised when half of the implementations quietly fail.
Rest assured that AI agents will follow the same script.
Gartner predicts that 40% of enterprise applications will embed task-specific AI agents by 2026, yet in the same breath warns that more than 40% of agentic AI initiatives will be canceled by 2027 due to cost, complexity, and lack of business value. That’s not a contradiction. It’s a warning.
The uncomfortable truth for CIOs and CTOs is this: Most AI agents are being built for demos, not for enterprises.
The Real Problem Isn’t the Model. It’s Architecture.
When AI agents fail, the explanation is usually framed in technical shorthand:
But that diagnosis misses the point. Microsoft’s own guidance on hallucinations makes it clear that failures increase when models lack clear grounding, constrained context, and deterministic validation. In other words, when systems lack enterprise-grade data architecture (Microsoft Tech Community) they have a higher propensity to give confident yet incorrect guidance. And these hallucinations are not merely academic errors when made in the context of financial, merchandising, or supply chain planning. They can have immense implications on upstream and downstream decision-making, eroding trust in the whole planning process.
Put plainly: When your organization has 5 different ways to compute revenue and margin, with each department running their own excel files, your AI agent never stood a chance.
Hallucinations in AI aren’t simply a quirk of language or a semantic anomaly—they are symptomatic of deeper architectural and operational limitations in today’s AI systems. What practitioners label as “hallucinations” emerge when models generate plausible-sounding but ultimately incorrect outputs because they lack reliable, real-time access to trusted enterprise data, are constrained by inconsistent data definitions, and operate on probabilistic pattern-matching rather than structured reasoning grounded in authoritative sources. This problem isn’t merely linguistic; it’s fundamentally tied to how data is managed, accessed, integrated, and governed across the organization. (CIO+1)
Enterprise technology leaders are increasingly confronting this reality. CIOs report that inconsistent results and hallucinations are among the key drivers of declining generative AI enthusiasm because they undermine trust and reliability, particularly in business scenarios that demand precision and accountability. Moreover, many traditional data architectures were not designed to support the volume, velocity, and variety that data modern AI workloads require. Without modernized data access layers and governance frameworks, AI models are left to speculate rather than reason, amplifying risk in high stakes use cases.
Addressing these hallucinations therefore requires more than refining prompt wording or focusing on semantics alone. It calls for robust data foundations, consistent definitions, real-time data access, and governance practices that ensure outputs can be traced back to trusted sources. Only with these building blocks can enterprises shift generative AI from a creative assistant that “sounds right” to a dependable decision-support tool anchored in enterprise truth.
Why Most Enterprise Agents Are Architecturally Fragile
Most agent platforms today share the same flaw: they treat AI as a layer added on top of existing systems. Data is fetched broadly. Meaning is inferred probabilistically. Business logic is approximated. Validation is optional. Governance is bolted on later—if at all.
That approach works well enough for consumer tools, but it breaks quickly in mission-critical planning workflows for finance, operations, supply chain, and regulated environments. These are the very places CIOs are expected to deploy AI responsibly.
This is why Board made a different choice in how we architected our AI strategy. In a recent conversation with David Marmer, SVP of Product at Board, he articulated this design philosophy succinctly:
“The semantic layer defines a universe of data. Since our AI is not bolted on, it’s tied to the engine which gives us the ability to control the aperture of what the agent can see, analyze, optimize, and act on.”
That statement reflects a growing consensus among analysts. Both Gartner and Forrester increasingly point to semantic layers and governed metrics as a foundational imperative for building and implementing trustworthy AI. These are not optional enhancements. (Gartner, AtScale).
Said a different way, Board agents don’t “discover” meaning; they inherit it.
This dramatically reduces hallucinations because the agent is never asked to reason on ambiguous concepts in the first place.
Another quiet but critical design decision we have made: Board Agents execute analytics through Board’s native multi-dimensional and in-memory engines whenever possible. Why does this matter? Because probabilistic reasoning is a poor substitute for deterministic business logic.
As Marmer puts it, “When you build a calculation, it should be exactly how Board would have done it—not an approximation.” McKinsey has repeatedly warned that AI initiatives fail when organizations allow models to reinterpret core financial and operational logic rather than enforce it. In Board’s architecture, AI augments enterprise logic without replacing it, always favoring keeping the human-in-the-loop to ensure accuracy and accountability. For CIOs and CTOs, that distinction is the difference between insight and risk.
Why “Do Everything” Agents Are a Bad Idea
There is a growing fascination with fully autonomous, general-purpose AI agents. While there is certainly room for this as a “vision” for AI-driven continuous planning, the current truth should be more grounded: from an enterprise perspective, this is mostly a liability.
Gartner explicitly recommends task-specific agents over general autonomous systems because they are easier to govern, audit, and scale responsibly. This is a key reason that Board Agents are domain-specific, with clearly defined use cases that will deliver immediate value and ROI, out of the box.
Additionally, tightly scoped AI agents:
It’s key to remember that in enterprise AI, focus beats ambition every time.
Validation Isn’t Optional. It’s a Design Requirement
One of the most underappreciated aspects of agent design is output validation. Board incorporates validation steps that verify responses before they reach users. This is a practice strongly supported by academic research on hallucination mitigation (Springer).
To be clear, this is not about mistrusting AI. It’s about respecting enterprise risk and ensuring the decision-making expertise of human planners stay firmly embedded in the planning cycle.
Ultimately, CIOs are accountable not for clever answers, but for correct ones. Board Agents are here to take a planner’s job to the next level, not introduce confident yet inaccurate responses. This is not about being “more AI.” It’s about being more enterprise.
In the agentic AI era, architecture is no longer a technical detail. It is a strategy.
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