How to implement AI-powered website search without losing trust

In a previous post, our CEO Richard Chivers looked at why traditional keyword-based website search is no longer fit for purpose on large, complex digital estates.

User expectations have shifted. People increasingly arrive at organisational websites expecting to ask full questions and receive clear, direct answers – not to scan long lists of loosely related pages. Traditional search was never designed to meet that expectation at scale, particularly across universities, councils, and other organisations with fragmented, fast-changing content.

The temptation, understandably, is to “add AI” to bridge the gap.

But for organisations responsible for publishing accurate, authoritative information, the question isn’t whether AI-powered search is possible. It’s how it can be implemented without undermining trust.

AI-assisted search introduces a different class of risk to traditional site search.

Keyword search can be frustrating, but it is generally predictable. Results may be incomplete or poorly ranked, but users can see the source material and make their own judgement.

AI-generated answers change that dynamic.

When trust in AI breaks down, it’s rarely because an answer is slow or inelegant. It’s because the system appears confident while being wrong, incomplete, or opaque.

Common problems with using AI tools in a customer-facing environment include:

• Confident but incorrect answers
  Responses sound authoritative even when the underlying information is partial or outdated.
• Lack of transparency
  Users can’t see where an answer came from, and content teams can’t easily trace or audit outputs.
• Loss of editorial confidence
  If teams can’t predict what the system will say, or why, they lose trust in their own digital estate.
• Reputational and compliance risk
  In higher education and local government, surfacing the wrong information isn't just a UX issue. It can lead to misinformation, operational errors, or breaches of policy and guidance.

These risks aren’t an inherent characteristic of all artificial intelligence systems. Instead, they’re a direct consequence of applying consumer-grade AI approaches to enterprise environments where correctness matters more than novelty.

Most consumer AI tools are designed to maximise engagement. Tech startups need ever-growing user numbers to keep investor confidence high and funding flowing in. So, their services are optimised to keep users interacting – asking follow-up questions, exploring tangents, and extending conversations. In that context, producing a fluent response is often prioritised over determining whether a definitive, well-grounded answer exists.

These systems are trained to generate plausible language based on patterns across vast and opaque datasets, rather than to operate within a bounded, authoritative set of organisational content. When information is incomplete or unclear, they’re incentivised to continue the interaction rather than to stop.

That design makes sense for consumer use cases, where exploration and conversation are the goal. Organisational search serves a different purpose. Users are typically trying to complete a task such as understanding a requirement, following a process, or finding a specific piece of guidance.

In those contexts, an answer that’s confident but incorrect is worse than no answer at all.

It’s clear that if AI-powered search is to be used responsibly on organisational websites, it needs to meet a different standard from general-purpose AI tools.

In practice, that means an AI search approach that is:

• Grounded in authoritative content
  Answers must be derived strictly from known, published sources – not inferred, extrapolated, or “filled in” by a model. Without this constraint, systems can generate responses that sound correct but are impossible to verify or defend.
• Transparent by design
  Users should be able to see where answers come from, and teams should be able to trace responses back to specific pages or passages. Without transparency, errors are difficult to spot and trust erodes quickly.
• Predictable in behaviour
  The system should behave consistently, with clear boundaries around what it will and will not answer. Inconsistent or surprising responses make governance and accountability difficult.
• Aligned with existing governance models
  AI search should respect content ownership, review cycles, and publishing workflows, rather than bypassing them. If AI outputs are disconnected from governance processes, risk increases rather than decreases.
• Comfortable saying “I don’t know”
  In regulated or high-risk contexts, refusing to answer is often safer than guessing. A clear “no answer found” avoids presenting speculation as fact.

Taken together, these requirements change how AI-assisted search behaves in practice. Rather than extending a conversation for its own sake, the focus shifts to surfacing information the organisation already considers reliable and approved.

When search works this way, the state of the underlying content becomes much more visible. Gaps, ambiguity, and outdated material are harder to gloss over, which makes preparing your content estate a necessary step rather than a nice-to-have.

AI-assisted search doesn’t remove the need for good content governance. In many ways, it makes weaknesses or gaps in your content more obvious.

When search is expected to surface direct answers rather than lists of pages, the quality of the underlying content becomes much more visible. Rather than being hidden from navigation and buried deep in your site, ambiguous, duplicated, and outdated guidance is retrieved and used by the AI to generate answers.

We’ve been developing and testing our new Insytful AI Search product with partner organisations for several months now. When we sit down with teams, a consistent set of issues tends to surface very quickly. These aren’t problems with the AI itself, but with the content it is being asked to work with.

The most common challenges we see are:

• Content gaps
  Teams often test the AI search by asking realistic, task-focused questions. In many cases, there’s simply no single page – or combination of pages – that clearly answers those questions. The issue isn’t with the AI failing to retrieve the information, but the absence of content that was ever written to address the need directly.
• Outdated or incorrect content that is still live
  Content that has drifted out of date can still be indexed and surfaced by any search system. When answers are generated directly from published content, those inaccuracies become more immediately visible to users.
• Content that is difficult to crawl or interpret
  The same technical and structural choices that limit how traditional search engines discover and index content can also affect AI-assisted search. Information hidden in accordions, tabs, or poorly structured markup may be visible to users, but difficult for search systems to reliably interpret and use.

Addressing these issues is largely about getting content into good shape and keeping it that way – something most teams recognise as important, but often have to balance against many other priorities. Increasingly, it also means ensuring that content is structured and exposed in ways machines can reliably read and use, whether for AI-assisted search on your own site or elsewhere.

This typically means:

• Being explicit about which content is authoritative and answer-worthy
• Identifying common questions that are not currently addressed clearly anywhere
• Reviewing high-risk content to ensure it is accurate and up-to-date
• Ensuring important information is structured and exposed in ways search systems can reliably access

None of this is new. What changes with AI-assisted search is how clearly these issues show up, and how directly they affect the answers users see.

The same content gaps, inaccuracies, and structural problems also apply beyond your own website. Public AI tools like ChatGPT, Copilot and Google’s AI Overviews increasingly rely on open web content when generating answers elsewhere, and the same weaknesses can surface there too – often without the context or caveats an organisation would want to apply. When that happens, it’s difficult to spot and harder still to correct.

Introducing AI-assisted search on your own site can make those issues more visible in a way teams can actually act on. Seeing the questions users are asking, and where no grounded answer can be provided, highlights gaps, outdated content, and structural problems that might otherwise go unnoticed.

In Insytful AI Search, for example, we record unanswered queries and surface them in the Insytful dashboard. Over time, those patterns can be used to prioritise fixes and improvements, turning search into a source of insight about the state of the content estate rather than just a delivery mechanism.

However, even with a well-prepared content estate, AI-assisted search won’t be the right answer for every type of query or every user journey. Some search tasks still benefit from the predictability and control of traditional keyword-based approaches.

AI-assisted search tools can synthesise information, interpret intent, and return fluent answers – but that doesn’t mean they should replace traditional site search everywhere. In practice, different search approaches serve fundamentally different user needs.

AI excels at helping users make sense of large, complex digital estates, particularly when they arrive with an open-ended question rather than a clear destination. In those contexts, it can provide fast, consolidated answers without requiring users to understand internal structures or terminology.

For example, AI-assisted search works well as an overarching search on the home page of a large organisation, where users might ask broad questions about how a process works or what applies to their situation. Here, the ability to interpret intent and draw together information from multiple sources can significantly reduce effort and time to completion.

That same approach isn’t always the best choice deeper within a site. When users are navigating a specific section and looking for a known item – such as a particular policy, article, or document – traditional keyword-based search often produces faster and more predictable results. In these cases, users already understand the domain and benefit more from precise matching, filtering, and browsing than from a synthesised answer.

The sections below outline where AI-assisted search adds the most value, and where traditional search continues to be the more effective option.

AI-assisted search adds the most value when:

• Users ask full, conversational questions
  Particularly when they are unfamiliar with organisational structures or terminology.
• Answers span multiple pages or content types
  For example, where guidance is distributed across policies, explanatory pages, and supporting documents.
• The goal is task completion, not navigation
  Users want to understand a requirement, follow a process, or get a clear answer with minimal effort.
• Cognitive effort should be reduced
  A grounded answer can remove the need to open and reconcile multiple search results, provided it is traceable to authoritative sources.

Traditional keyword-based search remains the better option when:

• Users are looking for a known item
  For example, staff searching for a specific policy, form, committee paper, or previously used document.
• Content is highly structured or list-driven
  Course catalogues, service directories, staff listings, and event calendars are often better served by filtering and sorting.
• Users are familiar with organisational terminology
  Internal teams or specialist users typically prefer precise keyword matching.
• The task involves browsing or comparison
  Some journeys require scanning options, comparing entries, or understanding what is available rather than receiving a single consolidated answer.

The most effective digital estates are introducing AI-assisted search to meet rising user expectations, while retaining traditional search where it continues to provide fast, predictable access to known information. Instead of simply replacing one approach with another, they’re looking to use each technology where it performs best.

In practice, that also means designing AI-assisted search experiences with a clear fallback to traditional search. When users want to explore source content, refine their query, or bypass a synthesised answer altogether, they should be able to do so easily. Providing this choice recognises that different users, and different tasks, benefit from different search experiences.

Taken together, this balanced approach allows organisations to introduce AI-assisted search in a way that supports user preference, builds confidence over time, and improves outcomes across a complex digital estate.

Before implementing any AI-powered search capability, it’s useful to be clear about what you’re actually asking it to do.

Many AI search tools address similar use cases on the surface, but the differences that matter tend to show up in how they behave in practice – particularly when content is missing, unclear, or subject to governance constraints.

Here are some questions your team can use to assess whether an AI-assisted search approach fits your environment and expectations:

• What content is the system allowed to use?
• How does it behave when information is missing, ambiguous, or outdated?
• How does it handle sensitive queries?
• Are answers traceable to specific sources?
• Can users see where information came from?
• How does this align with our organisation’s accessibility, compliance, and governance requirements?
• Does the system prioritise correctness over fluency?

These questions matter more than model size, speed, or novelty. They determine whether AI search strengthens trust – or quietly erodes it.

User expectations around search are not going backwards. People will continue to arrive at organisational websites expecting clear answers, not just lists of links.

Meeting that expectation responsibly requires more than adding an AI layer on top of existing search. It requires an approach designed around accuracy, transparency, and clear limits.

Insytful AI Search has been designed to meet the stringent requirements of enterprise organisations. It’s a reliability-first approach to AI-powered site search that helps people get clear, verifiable answers from your website, using only your published content. When an answer can’t be supported by that content, Insytful AI Search clearly says so.

If you’d like to see how it works in practice, get in touch to set up a personalised demo using your own site content.