AI Can Give You the Right Answer to the Wrong Question

In October 2025, Gartner introduced a term it expects to replace prompt engineering as the more important skill for getting useful output from AI: context engineering. Gartner's own framing is that this shift exists so AI systems can "understand intent, make better decisions and deliver contextual, enterprise-aligned outcomes." The distinction buried in that phrase is worth pulling out on its own. An AI system can produce an output that's correct, well-reasoned and fluent, and still miss what the organisation actually needed, because correctness and relevance aren't the same test.

Why correct and right aren't the same thing

A model given a well-posed question, with accurate data and sound logic, will generally produce a defensible answer to that question. The harder failure to catch isn't the model getting the arithmetic wrong. It's the question itself carrying assumptions the model was never told to check: which policy applies here, what tradeoff the business actually prioritises, which exception this particular case falls under. None of that shows up as an error in the output. It shows up as an answer that's internally consistent and still wrong for the business asking it.

That's harder to catch than a factual mistake, because nothing about the output looks broken. An obviously wrong number tends to get questioned. A right answer to a subtly wrong question is easier to wave through.

What Gartner's own research points to

Gartner's Hype Cycle commentary on AI agents points to the underlying difficulty. As the firm's Senior Director Analyst Haritha Khandabattu put it, organisations "need to determine the most relevant business contexts and use cases, which is challenging given no AI agent is the same and every situation is different." That's a direct statement of where the work sits: with the organisation deploying the system, deciding case by case what context actually matters, rather than with the model provider or the AI system itself.

That's a genuinely demanding requirement, and it's also why context engineering is emerging as its own discipline rather than a refinement of prompt writing. Prompt writing typically relies on whoever is asking the question to supply the relevant context themselves, each time, in the moment. Context engineering, in Gartner's sense, treats the accumulated set of business rules, priorities and constraints as something the organisation maintains deliberately, so it's there whether or not the person asking thought to include it.

A hypothetical illustration

Consider a hypothetical procurement team using an AI system to evaluate supplier bids. Asked to recommend the best offer, the system compares unit price, delivery time and stated quality metrics, and recommends the cheapest supplier that meets the stated requirements. The recommendation is defensible on its face.

What the system wasn't told is that the recommended supplier can't guarantee same-day resupply, and the procurement team knows same-day resupply is required to meet a service-level commitment to a major client. That requirement is real, and the team is aware of it, but it was never included in the information the AI system had access to. The system answered the question it was asked. It wasn't asked the question that actually mattered.

The point isn't how often a scenario like this occurs. It's the shape of the underlying problem: the distance between what a system is asked and what the organisation actually needs answered, rather than whether the system can compute a correct response to the question it received.

What this means for AI initiatives

The practical implication is that accuracy testing, on its own, isn't sufficient. It's still necessary: a system that gets facts wrong is a different problem, and one worth catching separately. But accuracy checks are generally built to test whether an answer matches the question asked rather than whether the question captured what the business actually needed, so passing them doesn't guarantee a recommendation the business would actually sign off on.

Context engineering, as Gartner frames it, is the discipline of closing that distance deliberately: identifying which business rules, exceptions and priorities need to travel with a given decision type, and maintaining that context as something the organisation actively owns rather than something each individual user is expected to supply from memory every time they ask a question.

This is where Context Intelligence comes in. It connects fragmented organisational knowledge, business rules and decision history so AI systems can work with the context that matters to the decision at hand. The goal isn't simply to give AI more information. It's to give it the right information, with the right meaning, at the right moment.

AinTools helps organisations turn fragmented information into trusted decisions through secure AI powered by Context Intelligence. Our why Context Matters page sets out how we approach it.

Sources

• Gartner, "Context engineering" (gartner.com, 6 October 2025): gartner.com
• Gartner, "Gartner Hype Cycle Identifies Top AI Innovations in 2025" (official press release, 5 August 2025), quoting Senior Director Analyst Haritha Khandabattu: gartner.com