Search: The Digital Transformation Project Quietly Hurting Your Customers

If you are a director or VP accountable for a digital transformation, you already know the projects that keep slipping. The re-platform that's a quarter behind. The AI initiative the board keeps asking about. The customer experience work that never quite makes it above the line. And somewhere further down that list, unstaffed and unloved, sits the one project that is quietly costing you customers right now: fixing search.

Not the AI search war with Google in the headlines. We mean the search box on your own website and inside your own company — the one that returns nothing useful, sends people back to Google, and bleeds perception, acquisition, retention, and revenue in ways no dashboard ever attributes to it. Over nearly two decades, our team has shaped digital experiences for organizations like Microsoft, Spotify, BC Ferries, Simon Fraser University, and dozens more. Across all of them, search has been the most consistently underfunded problem in the building — and in the AI era, it has quietly become the most consequential.

This is not a scolding. You are under real pressure to ship the visible things. What follows is the case that search is the highest-leverage transformation project you can accelerate this year, the data on what it is costing you today, and where to start.

Search is the use case your customers already voted for

You don't have to guess which digital investment your customers value most — they have already told you with their behaviour. 37% of consumers now begin searches with an AI tool, and 60% read AI-generated summaries instead of clicking through. Search is the behaviour AI actually changed.

The business case is even harder. Searchers are only 15% of ecommerce visitors but generate 45% of revenue, converting at roughly 2.5 times the rate of everyone else. Retailers know it: in Algolia's 2026 survey, search ranked as retail's single top digital priority, ahead of every other place they could put a budget. Whatever transformation work is being debated in your roadmap, a high-intent slice of your own customers is already proving with their wallets that search is where monetization concentrates.

The threat isn't Google — it's the search you own

It's natural to look at Google's grip on query volume and conclude the AI search race is the only search problem worth a strategy meeting. For most organizations, that instinct points at the wrong risk. The real bleeding is on your own website and inside your own company's data, where search has failed customers and employees for years with nobody senior enough noticing.

94% of consumers globally report getting irrelevant results when they search a retailer's own site, and nearly half simply buy from someone else after a failed search — an acquisition and monetization loss in a single moment. Seventy-seven percent avoid websites where they've had a bad search experience, and that failure costs US retailers an estimated $300 billion a year. Inside the walls, it's worse: enterprise search tools succeed on a user's first attempt only about 10% of the time, against roughly 95% first-page accuracy from Google. Employees have learned not to trust the tools their own company built for them.

Two decades in, the lesson never changes: search is underfunded

The pattern repeats at nearly every organization we work with. Budget pours into the homepage, the checkout flow, the onboarding sequence — the parts of the experience that show up in a board deck. Search gets a line item and a part-time owner, treated as plumbing rather than the feature customers reach for the moment your information architecture fails them.

Across products used by hundreds of millions of people, that underinvestment has been the most consistent gap we've seen — a structural pattern, not a rounding error. The person who built the most valuable answer to it agrees: Arvind Jain, who spent a decade scaling Google Search before founding Glean, calls enterprise search a neglected problem, not a hard one. The technology to fix it existed. The will to fund it didn't. That is precisely the kind of high-impact, low-glamour work that stalls inside a transformation program — and precisely where acceleration pays back fastest.

Even flawless UX can't stop people defaulting to search

If your transformation is investing heavily in navigation and information architecture, this is the uncomfortable part: a large share of your users will ignore all of it. This isn't a hunch — it's one of the most durable findings in usability research. Nielsen Norman Group finds the split lopsided: more than half of all users are "search-dominant," heading straight for the search box, while only about a fifth are "link-dominant" browsers. Baymard Institute's ecommerce testing puts it even higher, with roughly 69% of shoppers going directly to site search. No matter how good your information architecture or interface, most users skip navigation and reach for the search box.

The failure shows up when that box lets them down. When on-site search doesn't deliver, people don't blame the search bar — they conclude your website can't be trusted, open a new tab, and let Google route them to the exact page you already had. Seventy-seven percent of US consumers view a brand differently after an unsuccessful search, and nearly as many say they're less loyal to it. That is perception and retention damage compounding at once — and it's every marketing dollar working to push people through a front door that teaches them not to trust you. A website modernized for the AI era treats that search moment as a core experience, not an afterthought.

The problems search solves are the ones that decide retention

Here's where it gets deeper than information architecture. UX only makes the paths to a known destination easier. Search is what's left for everything else — and "everything else" is where the highest-stakes moments in the relationship live:

• The technical issue nobody wrote a landing page for.

• The billing change that's unique to one account.

• The data request that doesn't fit a menu or a nav item.

• Learning the product — figuring out what it can actually do for them and whether it's worth keeping.

These aren't edge cases — they decide whether someone stays or leaves, and search is the only tool built to catch them. It's also what people want to do on their own: 67% of customers prefer self-service to talking to a representative, and self-service is only as good as the search under it. Break that and you push your cheapest-to-serve customers into your most expensive support channel, or out the door.

I saw this most clearly on a support centre I worked on for Mozilla. Its visitors ranged from evangelists who'd defend the brand unprompted to people comparing offers and ready to leave. A search failure wasn't a minor inconvenience for either — it eroded the loyalists' trust and gave the switchers their reason to go. And nobody could see it happening, because it never showed up as a single dramatic failure. It was a slow leak in brand equity that never got its own line on a dashboard. Mapping those real journeys — where intent forms, where it breaks — is the heart of customer journey mapping, and it's usually where the hidden search failures finally become visible.

Why the bleed stays invisible — even when you have the data

Search failure rarely looks like an emergency. It looks like nothing — a slightly lower conversion number, a higher support queue, a customer who quietly left. But the scale of that "nothing" is enormous: 76% of US consumers say an unsuccessful search directly cost a retailer a sale. Smaller organizations miss it because no one owns "diagnose the search bar" as a job. Larger ones have the opposite problem: plenty of telemetry, no advocate.

Internally, the loss is staggering and almost entirely unmeasured. McKinsey found knowledge workers spend the equivalent of nearly a full day every week just searching for and gathering information, and 73% of organizations still don't have a real enterprise search tool to cut that number down. The data usually isn't the gap — the gap is organizational. Search touches product, support, IT, and marketing at once, so it belongs to all of them and therefore none of them. Everyone sees the problem, but nobody's targets depend on fixing it, so it survives quarter after quarter with no advocate. Giving that problem a mandate and a plan is exactly what a digital transformation acceleration engagement is for.

Glean's rise from $0 to $200M ARR is the evidence

For proof the need is real and enormous, look at what happened the moment someone built a credible answer. Glean doubled from $100M to $200M in ARR in nine months — a jump Fortune confirmed independently — then hit $300M by May 2026. The company has raised roughly $768 million and was valued at $7.2 billion in its most recent round. None of that is an accident. Enterprise buyers finally had a tool for a problem they'd quietly bled from for a decade, and they wrote checks the moment it existed.

AI agents raise the stakes, in both directions

AI makes the fundamental fix more achievable than ever — it can stitch together disparate systems, reconcile inconsistent formats, and surface the right answer from a mess no human team was going to clean up by hand. That is genuine leverage, and it's why the moment is right to prioritize search rather than defer it again.

The risk is deferring it while shipping agents on top of it. Every RAG pipeline, copilot, and agent you deploy is a search system with a language model bolted on top, and the model can only reason over what retrieval hands it. An agent built on weak search inherits every one of its failures, now with more confidence and less visibility into where it went wrong. Devi Parikh, CEO of Yutori and former Meta AI leader, explored just how far agents are set to reshape search and shopping on the Product Impact Podcast — and the throughline is that the agent is only ever as good as the retrieval and context beneath it. Fix the foundation first, and every agent you ship afterward inherits the fix instead of the failure.

The people building AI's frontier agree: search is the whole stack

The strongest case for prioritizing search isn't ours — it's the quiet consensus among the people building AI's frontier: the model is becoming a commodity, and the retrieval layer feeding it is where the value lives. Andrej Karpathy, who coined the term the field now uses, calls context engineering "the delicate art and science of filling the context window with just the right information for the next step." Strip the jargon and that is a description of search: getting the right information in front of the model at the right moment. A brilliant model handed the wrong document is worthless — the right one makes a mediocre model look like magic.

Nobody has bet harder on this than Jain, that same ex-Google-Search engineer. His whole Glean thesis is that context is the prerequisite for everything in enterprise AI: the model is generic and knows nothing about your company until something connects it to how your business actually runs. TechCrunch called what he's building "the layer beneath the interface". That layer is search, and every agent, copilot, and assistant your organization deploys will either stand on it or fall through it.

Fixing search forces the diagnosis worth doing

Search deserves priority for a reason beyond the leaked revenue and eroded trust. Fixing it forces you to diagnose why it broke, and that diagnosis is the one most organizations have avoided for years: disparate databases that were never meant to talk, no real data governance, and the fingerprints of successive leaders who each built their own system and never agreed with the last. It's the same rot that stalls everything else — MIT found 95% of enterprise generative-AI pilots never make it past the experimental phase, and the model is rarely the reason. The fragmented data it's pointed at is.

AI is the first real answer to that mess — not a smarter search box, but a system that can reason across the fragmentation instead of waiting for a decade-long cleanup that never comes. That is the reframe that turns search from a maintenance task into the anchor of a transformation.

Unlock the new business operating system

Solve search and you stop a leak — but you also unlock something most organizations have never had: knowledge and decisions interoperable across the entire enterprise. Once every system, document, and conversation is reachable through one layer that understands your business, the walls between tools stop mattering. A question doesn't care whether its answer lives in Salesforce, Slack, a contract PDF, or a half-finished Jira ticket, and neither does the agent asking it. Jochem van der Veer, CEO of TheyDo, made a version of this case on the Product Impact Podcast — the organizations that win build an intelligence layer that connects fragmented knowledge to the decisions that actually move the business.

That interoperable layer is the precondition for everything the industry is racing toward. Satya Nadella has said the era of standalone apps is fading, replaced by agents that sit on top of shared data rather than inside siloed tools. Foundation Capital calls the same shift "systems of agents" that collapse the enterprise stack. Every one of those futures rests on the same foundation: search. Get it right and agents stop being clever features stuck in one team's sandbox — they become a workforce operating across the whole stack, a support agent that can see billing, an analyst agent that pulls finance and CRM data in the same breath. That is the business operating system every vendor is promising you, and it runs on search.

How PH1 accelerates the search that's costing you

Fixing search is a customer experience problem, a data problem, and a change problem at the same time — which is exactly why it stalls, and exactly the intersection PH1 works at. We approach it through our Digital Acceleration Pillars: shifting from output to Value (measuring whether people find what they came for, not whether the feature shipped), from broadcast to Voice (designing search as a dialogue with intent, not a keyword lottery), from speed to Velocity (removing the friction that sends customers to Google), and from isolated KPIs to a unified Vision (giving a cross-functional problem a single owner and a plan).

In practice, that starts with the numbers almost nobody looks at — your site's zero-result rate, the share of sessions that search and then exit, the conversion gap between search-users and everyone else, the internal queries your employees give up on. Our CX AI research and strategy work turns that signal into a prioritized plan, and our AI product evaluation and UX evals tell you whether a fix — or an agent built on top of it — actually delivers value for a real user or just demos well.

The organizations that win the next few years won't be the ones with the flashiest agent. They'll be the ones whose experiences, and whose agents, were built on search that finally works. That is the work PH1 was built to accelerate — nearly two decades of turning stalled digital transformation into measurable customer and revenue outcomes.

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