Good website search is central to a high-performing digital experience, but it’s also the foundation of AI readiness. Coveo’s recent Website Search Readiness Crisis report examines how well B2B leaders are aware of this fact, and the journey to adoption.
The 213 B2B decision makers surveyed span industries including technology, healthcare, financial services, and manufacturing, from C-level executives to directors and managers responsible for day-to-day implementation. The report exposes a gap: how organizations perceive website search versus the difficulty in maintaining it. Among those surveyed, 78% rate their website search as “good,” yet 80% expend moderate-to-high effort in manual upkeep.
Meanwhile, users complain that search feels outdated and acts more like “a document dump rather than a true assistant.” This pattern of “expensive mediocrity” is a hamster-wheel approach to maintaining site search that requires constant tuning.
The “Good Enough” Paradox
That such a high percentage of respondents rate their website search as “good” suggests they believe that their organizations have adequate AI foundations in place. But if you dig one level deeper, it becomes clear there are persistent issues inhibiting website search effectiveness.
Findings include:
- 80% require moderate to high manual effort to maintain their search results.
- Only 12% describe their search as low effort or largely automated.
- 97% of managers who maintain day-to-day search functionality rate search as outstanding or good, while directors make up 55% of those who rate search as bad.
That last point is interesting because it illuminates a core issue. Managers who work on search give it higher ratings because they are closer to the manual workarounds that keep the system running.
Directors see the resource drain and performance gaps across teams. While managers may believe their site search is adequate, the people responsible for strategic improvements recognize fundamental problems that undermine AI initiatives.
User feedback makes the gap concrete. When respondents described what their website visitors actually say about search, the pattern was consistent: “It feels like the 90s.” “Feels like a document dump rather than a true assistant.” “Irrelevant or incomplete search results.” “Slow or unresponsive searches.” What operators call “good” and what users experience are not the same thing.
The Measurement Disconnect
Many organizations are pursuing strategic AI initiatives as a means to achieve their goals, without measuring the right things. Case in point: self-service.
While 62% of respondents prioritize customer self-service as a primary website goal, only 21% measure case deflection—the metric that proves a user found an answer instead of opening a support ticket. This means enterprises are flying blind, tracking high-level outcomes like pageviews while ignoring the metrics that would tell them whether search is actually driving the business goals they’ve prioritized.
To effectively integrate AI into search experiences, you need baseline metrics for content discoverability. Without the right metrics, it’s impossible to know if a new generative interface is connecting users to the right information versus providing a more conversational way to deliver the same suboptimal results.
The Infrastructure Reality Check
An organization’s underlying content infrastructure is what becomes the foundation for sophisticated AI capabilities — and this is where most initiatives break down. Three-quarters of organizations are attempting to build AI experiences on platforms never designed to support them.
Nearly a third rely on native search bundled with content management systems, where search is treated as a secondary feature. Another 24% run outdated homegrown solutions that can’t handle natural language queries or keep content fresh and relevant across distributed sources.
Content fragmentation compounds the problem. When asked how many places their organizations create and store knowledge, 58% cited between three and ten locations, and 19% use more than 20. The infrastructure supporting these systems can’t unify content, provide analytics to train AI, or respect the complex permission boundaries enterprise settings require.
This combination of outdated platforms and fragmented content makes it nearly impossible to implement sophisticated AI capabilities successfully. The infrastructure can’t unify content across existing systems, provide analytics to train AI, or respect the complex permission boundaries needed in an enterprise setting.
The Grounding Paradox
If we could communicate just one key takeaway from our research it would be this: there is a fundamental misunderstanding about the importance of grounding AI in vetted enterprise content. This disconnect is why organizations are deprioritizing the steps needed to fix the issue.
Respondents express significant concern about hallucinations and inaccurate responses, but treat anchoring AI to verified organizational content as optional or negotiable.
Many leaders don’t recognize that grounding AI in verified content is the primary mechanism for controlling hallucinations. Without it, AI generates responses that are confidently wrong — eroding the user trust organizations are trying to build. For the 66% of organizations managing ten or more website properties, proper grounding is an immense infrastructure challenge.
and bad actors with strong retrieval and business rules.
Relevant reading: LLM Grounding: Preparing GenAI for the Enterprise
To work reliably, an AI system needs unified indexing across every content system, permission-aware retrieval that respects security boundaries, scheduled content freshness, and clear attribution back to source material. For the 66% of organizations managing ten or more website properties, achieving all four is a significant infrastructure challenge.
Without all of the above, organizations face two specific failure modes.
- First, the AI returns no answer because the content exists somewhere the system can’t access.
- Second, the AI hallucinates an answer because it has only partial information and fills the gaps with confidently wrong answers.
Comfort with AI tools acts as another barrier to effective enterprise implementation. Fully 93% of respondents expressed some level of comfort with AI from “comfortable” to “cautiously” or “very” interested. But enterprise AI requires more guardrails and oversight than consumer-facing tools. Directors were understandably more skeptical about deploying AI across their enterprise, which reflects their proximity to the practical realities of deploying AI on fragmented infrastructure.
The Critical Choice Organizations Face
The findings in this report make it clear that even as AI capabilities are accelerating and user expectations are rising, the gap between ambition and readiness is widening. As enterprises continue to adopt AI, the questions to ask are about readiness and why the fundamental issues that cause AI systems to fail aren’t being addressed.
This is important because so many B2B leaders are exploring conversational or generative interfaces right now. Many envision AI as central to self-service, content discovery, and customer support. What this data shows is that ambition alone does not create readiness.
The same organizations preparing to deploy advanced AI experiences are also operating on fragmented systems, outdated platforms, and search infrastructure that can’t reliably retrieve or unify content. This makes AI risky rather than transformative.
Throughout this research, leaders overestimated the strength of their search foundation because day-to-day manual tuning gives the impression of stability. Yet the data shows persistent issues, from high maintenance effort to limited measurement to disconnected systems.
These challenges slow AI adoption and shape whether AI surfaces accurate answers, respects security boundaries, and earns user trust.
A Moment of Recognition, Not Reinvention
The findings in this report make it clear that even as AI capabilities accelerate and user expectations rise, the gap between ambition and readiness is widening. The organizations best positioned to succeed with AI are those that accurately understand where their search infrastructure stands today — not because the problem is insurmountable, but because deploying AI on a foundation that can’t support it amplifies existing problems rather than solving them.
What the data reveals is a recognition gap more than a technology gap. The infrastructure decisions organizations make now will determine whether AI delivers on its promise or inherits the same limitations that already undermine search.
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