Supercharging AI Assistants and Agents with Smarter Enterprise Search

AI assistants, from Microsoft Copilot to Google Gemini, are transforming the way we work. But despite their growing presence, there’s a gap between the promise of these tools and the reality employees face in the workplace. Many still struggle to find the information they need to do their jobs effectively.

According to Gartner’s report, “Rethink Enterprise Search to Power AI Assistants and Agents” the issue isn’t with the AI assistants themselves; it’s the fragmented, underdeveloped enterprise search infrastructure that they rely on. While tools like Microsoft 365 Copilot and Google Gemini are gaining traction, one-third of users still report difficulties in locating relevant information. The core problem is clear: everyday AI is falling short, not due to flaws in the interfaces, but because the search and retrieval capabilities behind them are failing to deliver.

Let’s explore why enterprise search is more crucial than ever; not just as a standalone tool, but as the silent engine driving the next generation of AI assistants and agents.

AI Assistants Are Only as Good as the Search They Rely On

The excitement around generative AI tools, which can summarize documents, answer complex questions, and engage in natural conversations, is well-deserved. These assistants promise to revolutionize our workflows. But there’s one critical factor behind their success: access to the right information.

Even with the growing use of AI assistants, many employees continue to struggle with finding the information they need. It’s not that AI tools like Microsoft 365 Copilot are flawed; it’s that the systems they rely on to retrieve and synthesize data are often fragmented and poorly integrated.

The real issue isn’t with the assistant; it’s with the underlying enterprise search infrastructure. When information is scattered across systems, poorly indexed, or buried under outdated content, even the most advanced AI can’t deliver meaningful results.

AI assistants and agents don’t replace enterprise search; they depend on it. Without a solid foundation of structured, well-governed, and easily retrievable data, generative AI tools can’t reach their full potential.

From Reactive Retrieval to Proactive Synthesis

Gartner predicts a major shift in how employees interact with information: “By 2028, employees will be informed through proactive synthesis rather than reactive retrieval 80% of the time.” In other words, instead of searching for data when they need it, employees will increasingly be fed timely, synthesized insights by intelligent agents embedded in their workflows.

But for that to work, your underlying search layer must evolve to:

• Understand user intent
• Connect knowledge across silos
• Deliver contextually relevant, real-time results

Assistants and agents aren’t replacing search; they’re changing the way it’s experienced.

Why RAG Isn’t (Yet) the Silver Bullet

Retrieval-Augmented Generation (RAG) powers many of today’s advanced AI assistants by combining large language models (LLMs) with real-time access to enterprise data. In theory, RAG promises more accurate, context-aware responses tailored to an organization’s unique knowledge.

However, RAG often struggles to scale across the complexity of enterprise data. Why? Because most organizations face foundational issues:

• Poor content quality, filled with redundant, outdated, or trivial (ROT) information
• Fragmented knowledge stored in disconnected systems and siloed applications
• Limited governance, making it unclear what content should be retrieved, trusted, or excluded

These issues cause AI assistants to appear intelligent but often fail when real business context is required.

To truly unlock RAG’s power, organizations must go beyond the AI layer and invest in smarter, cleaner, and more connected knowledge infrastructures. This is where Sinequa comes in; our AI-powered search platform is designed to address these foundational challenges, ensuring that data is high-quality, well-governed, and connected, enabling RAG and generative AI to deliver smarter insights at scale.

The Hidden Hero: Enterprise Search Infrastructure

While enterprise search may not be the most glamorous part of an AI strategy, it is the backbone of any successful AI assistant deployment. Gartner emphasizes the importance of a strong search foundation, recommending that organizations:

• Audit and reconfigure existing search and synthesis touchpoints to align with current business needs and leverage modern AI capabilities
• Rationalize retrieval services across the organization to eliminate redundancies, optimize performance, and ensure that the right information is accessible in the right context
• Implement comprehensive content governance to prioritize Accurate, Pertinent, Trusted (APT) content over ROT data, ensuring that AI assistants are working with clean, reliable information

For instance, rather than launching a new chatbot and hoping for better outcomes, a company could improve its internal search strategy first. By refining how the chatbot accesses and synthesizes information through a more intelligent search layer, organizations can ensure employees get the right answers, not just any answers. This approach enhances user experience and operational efficiency, leading to more meaningful interactions.

Sinequa’s AI-powered search platform is uniquely positioned to deliver this transformation. It enables organizations to create a seamless, scalable search architecture that addresses critical issues, ensuring AI assistants like chatbots provide more accurate, context-aware responses. With Sinequa, businesses can enable smarter knowledge retrieval, making every AI-driven interaction based on trusted, real-time data; a foundational element for the long-term success of generative AI applications.

Assistants Need a Portfolio — Not Just a Plugin

Gartner recommends a portfolio approach for AI assistants; integrating multiple touchpoints for information retrieval and synthesis directly into employees’ everyday workflows. Instead of relying on a single AI assistant, organizations should embed AI across different points to ensure seamless, efficient access to data when needed most.

For example, this could include:

• An AI-powered assistant within your CRM to surface customer insights in real-time, helping teams make faster, better decisions
• A virtual onboarding agent to provide new employees with instant access to internal knowledge and streamline the onboarding process
• A smart assistant in your helpdesk platform to resolve customer issues faster by quickly pulling up relevant information and reducing resolution time

However, these solutions will only deliver value if they’re built on the right foundation. To ensure success, organizations need:

• A unified index that consolidates data from multiple sources, ensuring that all relevant information is easily accessible across different platforms
• Strong content governance to ensure the data being used is accurate, up-to-date, and trustworthy
• A search layer optimized for GenAI-powered synthesis, allowing AI to understand and pull together information in a way that’s context-aware and relevant, helping drive smarter outcomes

Sinequa is designed to address these challenges by providing a unified, intelligent search layer that enables better data access and AI-driven insights. With Sinequa, organizations can ensure that their AI assistants are embedded into workflows and supported by a robust infrastructure, delivering accurate, context-rich responses at scale.

Making the Shift: What You Can Do Today

1. Audit Your Current Landscape

Start by taking a close look at where search is already implemented across your organization. Ask yourself questions like:

• Where does search already exist, and how is it being used?
• Are your AI assistants providing useful, relevant answers, or are they struggling to meet expectations?
• How easily can your employees access the data they need through existing search tools?

Understanding your current setup will help you identify gaps and opportunities for improvement.

2. Clean and Govern Your Content

Content quality is crucial when it comes to powering AI assistants. Begin by identifying and removing or archiving any ROT (Redundant, Outdated, Trivial) content that could be confusing your AI. This will ensure that your assistants are only working with high-quality, relevant information.

• Promote and label APT (Accurate, Pertinent, Trusted) content to make it easily discoverable for AI assistants.
• Implement clear content governance to maintain consistency and accuracy across your organization’s data, helping AI assistants rely on trustworthy sources for their answers.

3. Invest in the Right Search Tech

The foundation of AI assistants lies in the search technology that supports them. When evaluating solutions, look for platforms that offer:

• Deep connectors to seamlessly integrate with your existing systems and data sources.
• Semantic understanding to allow AI to interpret and retrieve information based on context, not just keywords.
• GenAI support, ensuring that the search layer can support the latest advancements in generative AI, enhancing your assistants’ ability to provide context-aware responses.
• Technologies that combine search, discovery, and conversational interfaces, enabling a more fluid and interactive experience for users.

Choosing the right tech will set your AI assistants up for success.

4. Build for Scale

Modernizing search for AI assistants isn’t just about adding one new tool; it’s about creating a connected ecosystem. Think beyond a single assistant and consider developing an integrated portfolio of assistants and agents that can grow and evolve over time.

• Ensure your systems are scalable, so they can handle increasing volumes of data and adapt as your needs change.
• Build with flexibility in mind, so new assistants and AI-powered tools can be easily added as your company’s demands and AI capabilities grow.

By setting up a scalable, flexible foundation, you’ll position your enterprise to get the most out of your AI investments over the long term.

Sinequa’s platform helps organizations tackle each of these steps by offering an intelligent search layer that not only cleans and governs content but also integrates seamlessly with various systems, allowing AI assistants to access the right information. With Sinequa, companies can modernize their enterprise search to provide smarter, more relevant insights, and create an environment where AI assistants can thrive.

Conclusion: Search Is the Superpower Behind AI

AI Assistants and Agents may be the future of work, but without robust enterprise search, they’re just well-designed interfaces with no substance behind them. As Gartner makes clear, the real challenge isn’t building smarter bots, it’s rethinking how enterprise knowledge is retrieved, governed, and delivered. By investing in modern search infrastructure today, you’re not just improving search; you’re laying the foundation for a truly intelligent workplace.

Discover how Sinequa’s AI-powered search platform can help your organization deliver relevant, trusted, and actionable insights. See it in action in our latest demo or contact our team to explore your use case.

You can access the full report “Gartner® Rethink Enterprise Search to Power AI Assistants and Agents” and learn more on how Enterprise Search must evolve to support the demands of next-generation AI below: