In their effort to meet rising expectations for digital experiences — e.g., flashy designs for websites, ecommerce experiences, and customer self-service journeys — too many enterprises ignore the elephant in the room: outdated and siloed search systems.

Updated touchpoints may look and feel good, but don’t deliver what people really want: fast, intuitive, highly relevant search experiences. In other words:

Old wine in new wineskin.

The truth is that not integrating AI search comes with invisible costs that hinder innovation and impact your bottom line. Why? What is it about integrated AI-powered search that makes it so critical to the modern digital experience?

What Is Unified AI Search Integration?

Search is a traditional component of many point solutions — almost any and every system has its own search functionality. The problem? Each search is a cul-de-sac, connected only to the information stored in that system. This means users have to both know what they’re looking for and where it lives. 

If they don’t, they’re then tasked with looking through multiple repositories. And mercy on them if they don’t know the exact file name, because Google-Fu is a thing. 

By integrating unified AI search, users get a singular interface where they only need to ask once to find what they’re looking for (no query permutations required). Unified search connects with content wherever it lives, without needing to migrate or replatform data. Because data access is centralized in one index, ranking factors are applied equally across everything regardless of source. Plus, security and permissions are enforced, meaning searchers can only see what they have permission to see. 

Unified AI search combines both traditional keyword or lexical search with modern techniques like semantic or vector search to achieve hybrid search. When hybrid search receives a query, it balances precision and recall to retrieve a broad result set of relevant information. And since unified search can be connected with multiple touchpoints, that information becomes available wherever the user prefers. 

Think of this form of AI integration as the difference between asking a filing clerk to find a document versus asking a knowledgeable colleague. The clerk can find matching documents that contain the words you asked about. On the other hand, your colleague understands your work, knows what you’re trying to accomplish, and can anticipate what you’ll need next.

Successful unified AI search integration relies on three core components: 

  1. Centralized content foundation that allows for comprehensive indexing, ranking, and search results (i.e., bringing outdated or siloed search systems together) without data migration or re-platforming).
  2. Intelligent processing layer that uses machine learning algorithms, natural language processing, behavioral analytics, and contextual understanding to adapt search experiences to individual users and situations.
  3. Self-optimizing system that eliminates the need for constant manual search tuning. Learn how the Coveo index self-optimizes.

6 Ways Lack of AI Search Integration Hurts Business

For modern users, AI integration has become table stakes, whether they’re aware of the underlying technology or not. A slick, intuitive user interface only goes so far. If that interface is disconnected from the information that the user is looking for, people don’t just take notice; they tend to abandon the experience altogether.

It’s the new reality of digital business: users compare every digital experience to the best they’ve encountered anywhere. And the best digital experiences typically integrate AI to unlock considerable business upsides:

Without AI search integration, achieving these benefits is difficult, especially when hampered by siloed or outdated systems. 

The consequences can be significant:

1. Poor User Experience = Lost Conversions

Convenience is the watchword of 2025, but our research revealed that most users find digital experiences wanting. Eighty-four percent of 4,000 surveyed consumers felt they had to put in a moderate to a lot of effort to get help or find information with any company (Coveo CX Relevance Report 2025). Many challenges contributed to this sentiment, the topmost being ‘not being able to easily search for and find the information I’m looking for on my own’ (53%).

Bad news for conversion rates. Here’s why:

  • Siloed search frustrates users with irrelevant results. We’ve all experienced it by now: you try a company’s search function and quickly conclude that their website, ecommerce experience, or self-service portal is a dead end. At best, you press on to find what you need by different means (using category filters, for example); at worst, like 72% of consumers, you move on to a competitor or Google to find what you need.
  • Bounce rates increase and session lengths decrease. Relevance begets engagement. A digital experience that quickly surfaces what a user is looking for and recommends next steps shows that the company understands who they are and what they need. This is why digital marketing leaders pay such close attention to bounce rate and session length: adverse metrics may indicate inadequate AI integration, or total lack thereof.
  • Missed opportunities across touchpoints. Keeping digital visitors engaged goes beyond one-to-one conversions (e.g., “I want this pair of shoes, I find them, I buy them”). Without AI search integration, you may miss lucrative opportunities for upsells, cross-sells, and improved average order value (AOV). You may also miss the opportunity to help people help themselves (digital customer self-service).

Beyond delivering a better user experience, AI search uses machine learning models to continually adapt that experience to user behavior. These models can learn from clickstream data, dwell time, and other intent signals. 

AI search also opens the door to advanced search features, such as query suggestions, smart snippets, and generative answering. 

This also includes automatic synonym detection and ranking feedback, two sought-after features among search architects.

Automatic synonym detection addresses a persistent challenge in enterprise search: the vocabulary gap. Rather than requiring search admins to manually maintain extensive synonym lists—a task that’s both time-consuming and perpetually incomplete—machine learning automatically discovers relationships between terms based on actual user behavior. 

Ranking feedback ensures that synonym-matched results aren’t just found, but appropriately weighted based on user engagement. If users searching for “onboarding” consistently prefer the “new employee orientation” guide over other synonym-matched content, the system adjusts rankings accordingly.

2. Wasted Development Resources

Both incumbent search and custom solutions present their own obstacles. 

Incumbent search — those that come with the point solutions we mentioned earlier — typically offers basic functionality and little else. Adding other options or even connecting that search function to data that exists outside that point solution can result in huge headaches for your IT team, both in the form of building the requested function and in maintaining it. 

Custom solutions have similar challenges. In its analysis of the buy-vs.-build debate, Amazon Web Services emphasizes that the opportunity cost of using skilled developers for custom builds is frequently a multiple of their salary. In other words, keeping your engineering team focused on feature development, rather than basic infrastructure or API integrations, unlocks significantly more value.

Those infrastructural needs and API integrations are significant, as our own analysis of total cost of ownership reveals: 

“Features like dynamic faceting, federation, authenticated search, exclusion logic, and more are often seen as the baseline, although from a development cost angle they’ll quickly surpass most enterprise budgets if built from scratch.”

On the other hand, Coveo’s AI-Relevance™ platform is a fully managed solution that makes the process of AI integration within tools and systems far more efficient. This is thanks in large part to search connectors, which connect and integrate various data sources. 

Search connectors deliver on the promise of time and cost savings in five important ways: 

  • Straightforward setup process
  • Scheduled content refreshing
  • Advanced security features
  • Flexible indexing capabilities
  • Connects to virtually any system

3. Data Silos Hinder Performance

In every enterprise, organizational silos are the silent undertow pulling against progress. What looks like healthy specialization often hides a deeper problem: teams, departments, and business units working in isolation. The result? Collaboration grinds. Customer experience initiatives stall. And innovation quietly slips through the cracks.

McKinsey calls the signs unmistakable: information that doesn’t flow, coordination that never quite clicks, and accountability blurred to the point of invisibility. The fallout? Decisions that drag. Agility that disappears. Opportunities missed—including AI itself.

Make no mistake: this isn’t just operational friction. It’s a growth tax. McKinsey found that companies with fewer silos deliver nearly 3x the shareholder returns of their peers. That’s not trivia. That’s a flashing red light.

And while AI promises transformation, it can’t perform alchemy. Models don’t thrive on fractured, outdated, or inaccessible data. They need a single, enriched foundation. Without it, even the most ambitious AI projects collapse under their own weight.

That’s why integration matters. With Coveo, native connectors (Salesforce, ServiceNow, Adobe, and more) unify sources into one AI-ready index. Add semantic enrichment and normalization across structured and unstructured data, and the result is simple: AI that actually delivers.

Importance of Unified Index and Enrichment: Ecommerce Case Study

Even the most expansive, highest quality ecommerce catalogs benefit from augmentation. Ecommerce AI catalog enrichment makes the most out of a product catalog’s rich metadata, significantly improving performance through: 

  • Automated metadata generation for product images 
  • Adding missing product information extracted from image analysis
  • Standardized product attributes across the entire catalog
  • Enhancing search relevance and product recommendations
  • Providing more accurate and consistent filtering options

A unified, enterprise-ready, AI-enabled search index is how FleetPride, the largest distributor of aftermarket heavy-duty truck and trailer parts in the U.S., achieved a 27% increase in search engagement. Unifying 1M+ product SKUs helped the company increase conversion rate with search by 9.6%. 

Read the FleetPride Case Study

4. Slower Time-to-Insight

Many enterprise teams still need to address the gap between data availability and actionable insights. Nearly three-quarters of business and analytics leaders report frustration with sluggish analytics processes that fail to deliver insights when they’re needed most. The same research revealed that 90% of leaders point to data integration complexity as the primary obstacle. 

Disunified search experiences embody this persistent challenge. While search data abounds, without centralized analytics and A/B testing tools, it’s difficult to learn from user behavior. Teams shuttle data between analytics platforms, testing tools, and decision-making systems, often creating delays that lengthen time to insight.

As a result, they miss chances to iterate, personalize, and improve.

Business Case for Built-In Analytics and Experimentation

True AI search integration means a built-in analytics and experimentation layer. Data collection, analysis, and testing happen within a single unified platform. Search analytics, user behavior data, and A/B testing flow seamlessly together. Teams that can monitor, tune, and take action in one place tend to get more out of search. 

A lot more, as the Caleres case study makes clear. 

The $2.8 billion footwear retailer used to maintain thousands of static rules across 13 brand sites, each requiring separate optimization cycles. It sometimes took weeks between identifying problems and implementing fixes across 600,000+ SKUs.

With Coveo’s integrated AI search platform, Caleres now monitors search performance, analyzes user behavior, and adjusts the experience within a single system. The results speak for themselves:

  • 21% YoY revenue increase
  • 25% increase in search conversion rate
  • 2x conversion rate for facet users vs. nonusers

5. Increased Scalability and Security Risks

Inevitably, custom or legacy search solutions run into scalability issues. How long can an enterprise expect to run manual search rules for hundreds of thousands of product SKUs, for example? And how would they go about launching multiple sites, in a compressed timeframe, each with enhanced product search and discovery, filtering, and ranking capabilities?

Eventually, two scalability challenges in particular become nearly impossible to overcome: 

  • Increased website traffic
  • Evolving website architectures (e.g., headless, MACH)

With homegrown solutions come security concerns, as well. Patchy access control often leads to privacy risks and governance gaps. This leaves organizations (and their users) vulnerable to breaches, leaks, and other cybersecurity threats.

Integrated AI search platforms inherently address scalability and security issues. 

Multi-tenant cloud-native architecture and enterprise-grade security come built in. This includes compliance, data security, and document-level permissions. For instance, Coveo platform security covers:

  • Governance certified by ISO international data security standards
  • Maturity models based on CoBIT
  • Security processes defined by the ISM3

It’s ISO 27001 certified, HIPAA compliant, SOC2 compliant, and 99.999% SLA resilient. All possible with a homegrown search solution? Yes, but with far more effort (and far less scalability). 

6. Missed Competitive Advantage

Ultimately, a lack of innovation makes digital experiences feel outdated, even if the content or products are strong. And the truth is that competitors are already using AI search to deliver the personalized, next-gen conversational experiences users now expect.

An integrated AI search platform opens the door to next-generation conversational experiences that today’s users look for. Three experiences, in particular, come to mind, all enabled by Coveo:

Intent-Aware UX

The myth that you need big data to deliver personalized search results aligned to user intent is false. Even without big data, you can deliver relevant, personalized results perfectly aligned to user intent.

  • History-based: Comparable to shopping in a neighborhood store where they’ve known you and your family for years
  • In-session: Like going to a welcoming new store and interacting with a very intuitive and attentive seller who makes every effort to learn about your needs and preferences at that point in time

Read more about AI-Driven Search Personalization for Business Growth.

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Generative Answering

The traditional search flow goes: 

search query → list of results → click through to relevant resource

A generative search flow transforms this into: 

search query → AI synthesis → direct answer with cited sources

Generative answering eliminates the need to sift through multiple results by providing contextually relevant, comprehensive responses that directly address the user’s intent while maintaining transparency through source attribution.

United Airlines uses generative answering to make sure that visitors to its self-service experience can get what they need directly, often without having to click through to a dedicated knowledge base resource. 


Digital Customer Experience: How AI is Transforming CX Strategies for Fortune 100

Agentic AI

Agentic AI transforms a search experience into proactive, intelligent decision-making that anticipates needs and takes autonomous action to solve complex, multi-step problems. An agentic AI workflow might look like this:

user request → autonomous reasoning and planning → multi-step execution → complete solution delivery

At Coveo, we’re bringing this vision to life through deep integrations with leading commercial agentic AI platforms. Our approach ensures enterprises can meet people where they work—whether inside CRM, productivity suites, or commerce experiences—without adding friction or complexity.

  • Salesforce Agentforce: Together, Coveo and Salesforce Agentforce give service agents and customers AI-driven, context-aware experiences directly inside Service Cloud. Agentic AI workflows can automatically retrieve the right knowledge article, propose resolutions, and even execute next-best actions—slashing case resolution times.
  • Microsoft Copilot: By connecting Coveo’s AI-search and generative answering capabilities with Microsoft Copilot, enterprises get a more intelligent workplace assistant. Employees can ask Copilot complex questions and receive complete answers grounded in enterprise content, increasing productivity and reducing search time.
  • Amazon Bedrock: Our integration with Amazon Bedrock provides enterprises with scalable, relevance-augmented generative answering. Agentic AI doesn’t just generate text—it reasons with the most current, secure content to deliver trustworthy outcomes across customer service, commerce, and workplace.
  • SAP Joule (coming soon): We’re extending these same capabilities into SAP’s Joule assistant, ensuring SAP users benefit from the same relevance-first approach to AI-search and agentic workflows.

With these partnerships, agentic AI eliminates the productivity drain of juggling fragmented tools. Teams can focus on high-value strategic work while Coveo ensures AI agents act with precision, relevance, and trust.

Relevant viewing: Grounding AI Agents for Enterprise Success​

Building an AI Integration Action Plan: How to Get Started

While individual AI capabilities like intent-aware search, generative answering, and agentic decision-making each deliver significant value, the true hidden cost lies in deploying these as isolated point solutions. 

Organizations that treat AI as a collection of separate tools rather than an integrated intelligence layer face exponential inefficiencies from context switching, data silos, and duplicated learning cycles.

So how does your organization move toward integrated AI search? Start here:

  1. Conduct a relevance audit of your existing search experience.
  2. Identify key data sources and map their current integration state.
  3. Explore AI search platforms that support modular APIs and flexible deployment (like Coveo)

Integrating AI into your website is seamless with Coveo’s developer-friendly platform. Learn more about our solutions and how they work.

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