AI Reasoning Failures: The Hidden Risk in Energy AI

A large language model can give you the right answer for the wrong reason—and that’s the kind of “success” that quietly breaks critical operations.

That distinction matters a lot more in 2025 than it did even a year ago, because AI isn’t just summarizing documents anymore. It’s being asked to coordinate work: triage incidents, propose switching plans, draft procurement recommendations, reconcile asset data, and even participate in multi-step operational workflows as an “agent.” In energy and utilities, those workflows touch safety, reliability, and compliance. And in supply chain and procurement, they decide what gets bought, when, and from whom—often under tight constraints.

Here’s the stance I’ll take: wrong answers are manageable; wrong reasoning is a systemic risk. If you’re using AI for grid operations, maintenance planning, or energy procurement, you should treat reasoning quality as a first-class requirement—right alongside cybersecurity and data governance.

Wrong reasoning is more dangerous than wrong answers

Wrong answers are visible. They can be caught with spot checks, KPI monitoring, or an operator’s intuition. Wrong reasoning is sneakier: it can produce outputs that look coherent, pass basic sanity checks, and still embed flawed assumptions that later cascade into outages, safety events, or costly procurement mistakes.

The recent wave of research on AI reasoning failures highlights a key point: models often fail in the “middle” of the work—during the multi-step process that leads to a conclusion. Two findings are especially relevant for energy workflows:

• Models struggle to separate facts from a user’s beliefs, especially when those beliefs are stated in first person.
• Multi-agent systems can converge on confident, incorrect consensus, where the “majority” drowns out a correct minority view.

Energy teams are adopting agent-like AI patterns fast: chat-based copilots embedded in outage management, procurement analytics assistants, and “multi-agent” setups that simulate different roles (operations, reliability, finance, compliance). Those patterns map directly onto the failure modes described above.

Why this shows up in energy and utilities faster than other industries

Energy is unusually exposed because it combines:

• High-consequence decisions (safety, grid stability)
• Complex constraints (network topology, protection, NERC-style compliance expectations, interconnection rules)
• Fast-changing conditions (renewable intermittency, extreme weather, dynamic tariffs)
• Messy enterprise reality (asset records that don’t match field conditions, conflicting “sources of truth”)

That’s exactly where brittle reasoning breaks.

The “facts vs beliefs” problem hits operations and procurement

One study introduced a benchmark to test whether models can distinguish objective facts from someone’s belief about those facts. Models did well verifying factual statements (newer reasoning-focused models scored above 90% on straightforward verification), but they struggled when false beliefs were stated in the first person, scoring around 62% (and older models around 52%).

In plain terms: when a user says “I believe X,” models often behave as if X is more likely to be true than it is.

Where this shows up on the grid

In control rooms and field operations, people constantly express beliefs:

• “I think this feeder is back-fed.”
• “I’m pretty sure the regulator is stuck.”
• “We already replaced that breaker last quarter.”

A helpful assistant should respond like a good engineer: separate hypothesis from evidence, ask for confirmation, and point to the data needed to verify. A sycophantic or belief-confusing model may instead accept the premise and build a plan on top of it.

That can lead to operationally plausible—but unsafe—recommendations.

Where this shows up in supply chain and procurement

In the “AI in Supply Chain & Procurement” context, belief/fact confusion is everywhere:

• “Supplier A is the only qualified vendor for this transformer.”
• “We can expedite in 10 days if we pay the premium.”
• “That part is interchangeable with the older model.”

If an AI assistant treats those as facts without verification, it can:

• funnel spend to a single supplier unnecessarily (and reduce resilience)
• miss alternate qualified sources during an emergency
• approve substitutions that violate engineering standards
• underestimate lead times and create downstream outage risk

Procurement isn’t just cost optimization in energy. It’s reliability engineering by other means.

Practical control: force the model to label claims

One tactic I’ve found effective: require the assistant to output three buckets in any recommendation:

• Verified facts (with internal source references)
• Unverified claims / assumptions (explicitly marked)
• Next verification steps (what data would change the decision)

This isn’t fluff. It directly counters the belief/fact failure mode.

Multi-agent AI can fail like a bad meeting—only faster

The second research thread tested multi-agent medical advice systems and found performance that collapsed on complex cases: around 90% accuracy on simpler datasets, falling to about 27% on harder cases requiring specialist knowledge.

Energy is full of “hard cases.” Protection coordination. Inverter-dominated dynamics. Interactions between switching plans and SCADA telemetry quality. Complex outages with partial restoration. Battery behavior under real market dispatch.

And the failure modes observed in multi-agent setups map cleanly to energy AI deployments:

• Shared blind spots: if all agents run on the same underlying model, they share the same knowledge gaps.
• Circular discussions and contradictions: agents “talk” but don’t converge on truth.
• Lost information: key early details disappear by the time a final recommendation is produced.
• Wrong consensus: correct minority views get overruled by confident incorrect majority—reported in 24% to 38% of cases across datasets.

Translate that to an energy scenario

Imagine a multi-agent workflow for an emergency procurement and restoration plan:

• Agent 1: Reliability (needs to restore critical loads)
• Agent 2: Supply chain (checks stock, alternates)
• Agent 3: Finance (approvals, budgets)
• Agent 4: Compliance (approved vendors, standards)

If three agents confidently accept “Supplier A is the only qualified source,” and one agent flags a second qualified vendor but gets ignored, you’ve just created a digital version of the worst kind of meeting: fast consensus on a bad premise.

In a storm response or summer peak event, that can translate into longer outages and higher costs.

Practical control: add a “process supervisor” agent

A useful design pattern is to create an explicit role whose job isn’t to propose an answer, but to:

• detect groupthink (“everyone is repeating the same assumption”)
• force enumeration of alternatives
• require evidence for key claims
• preserve key facts in a running “case file” so nothing gets dropped

That supervisor should be rewarded for reasoning quality, not just final answer correctness.

Why training rewards outcomes, not reasoning (and why you should care)

Many modern models are trained with reinforcement-style methods that reward paths that end in correct outputs. That’s fine when problems have crisp right answers (math, code). It’s risky when the task is:

• diagnosing ambiguous conditions
• interpreting human intent
• negotiating tradeoffs (risk, cost, time)
• making decisions under uncertainty

Energy and utility work is heavy on the last two.

If a model occasionally gets the right answer by luck—or by mirroring a user’s assumptions—it won’t generalize reliably. That’s exactly the difference between a demo and a system you can trust in operations.

A practical playbook: building AI you can trust in energy supply chains

Teams asking “Can we use generative AI for procurement and grid operations?” are often really asking: How do we prevent confident nonsense from entering the workflow? Here’s a concrete approach.

1. Treat AI as a junior analyst with guardrails

Give it bounded authority:

• Draft recommendations, not approvals
• Propose switching sequences, but require operator confirmation
• Suggest suppliers, but require qualification checks

The goal is speed without surrendering control.

2. Make reasoning auditable by design

Require outputs to include:

• assumptions
• constraints used (standards, voltage class, environmental rating)
• alternatives considered
• what would falsify the recommendation

If it can’t explain those, it shouldn’t be driving a decision.

3. Build verification into the workflow, not as an afterthought

For supply chain and procurement, the model should be forced to verify (or flag as unverified):

• approved vendor lists
• part equivalency rules
• lead times by lane and season
• contract constraints (minimums, escalation clauses)
• inventory and reserved stock status

A strong pattern is “verify-first prompting,” where the assistant must call internal tools/APIs (ERP, EAM, CMMS, supplier portals) before it’s allowed to recommend.

4. Use disagreement on purpose

If you’re going to use multi-agent systems, don’t optimize them for agreement. Optimize them for productive dissent:

• assign agents different data sources
• force one agent to argue the opposite case
• score the group on whether it surfaced risks and uncertainties

Consensus is not a success metric. Decision quality is.

5. Monitor reasoning drift like you monitor equipment drift

You already do condition monitoring for transformers, breakers, and batteries. Do the same for AI:

• track how often outputs rely on unverified assumptions
• track how often humans override recommendations
• track repeated failure themes (e.g., persistent supplier lead-time optimism)

That becomes your “AI maintenance plan.”

What this means for the AI in Supply Chain & Procurement series

Most supply chain AI content focuses on forecasts, automation, and savings. In energy and utilities, the bigger win is resilience: fewer stockouts of critical spares, faster restoration, better vendor risk management, and fewer “single point of supplier failure” surprises.

But resilience requires trust. And trust requires reasoning you can inspect.

If your generative AI procurement assistant can’t reliably separate belief from fact, or if your multi-agent workflow tends to converge on the loudest wrong idea, you’re not automating procurement—you’re automating risk.

A reliable AI system doesn’t just produce answers. It produces checks you’d still run if the answer came from a human.

The next step is straightforward: evaluate your current AI pilots against reasoning failure modes, not just accuracy on a handful of prompts.

If you’re rolling out AI in outage response, maintenance planning, or energy procurement in 2026, what will matter most isn’t how often it sounds right—it’s how often it can prove it’s right.