Drive Digital Transformation Success With Enterprise Search That Empowers Employees and Accelerates Innovation

Global AI spending is expected to top $2.5 trillion in 2026, according to Gartner. Yet professionals still spend up to five hours searching for a single piece of information.

In many cases, information is scattered across apps, shared drives, ticketing systems, intranets, and disconnected legacy systems, creating friction for both people and the AI models that depend on this information. 

Traditional search tools usually rely on keyword-based retrieval. So, if employees don't type the exact phrase stored in a system, those tools often fail — missing critical context, synonyms, and intent. 

Disconnected systems, data silos, and inconsistent knowledge access can significantly slow down AI adoption and limit the impact of digital transformation initiatives. Instead of data flowing smoothly between systems, making work more efficient, your employees keep running into the same roadblocks they always have.

That's why strategic organizations are turning to enterprise search to unify knowledge across their business systems. When information is securely accessible, both AI and your employees can better understand, interpret, and act on it. That naturally leads to:

• Faster support and decision-making
• More effective automation
• Higher employee trust and adoption of digital tools

In other words, modern AI enterprise search can help you build the foundation for real digital transformation that delivers the outcomes you've been looking for.

The growing pressure to deliver meaningful transformation

If you're an enterprise IT or digital leader, you're facing some challenging expectations.

You're supposed to automate workflows, increase speed, and improve employee experience. At the same time, you're expected to reduce costs, raise productivity, and ultimately get a better ROI from existing digital and AI investments.

Adding more tools doesn't automatically solve these problems. Too many disconnected tools could slow you down and make your team less productive, especially if their workflows force them to switch between tools multiple times to complete a single task.

So, before trying to bolt on more solutions, digital leaders should consider taking a step back and assessing how knowledge is shared across the enterprise. 

If information is siloed across apps, shared drives, wikis, and department-specific knowledge bases, employees waste time trying to locate and piece it all together. And without a single source of truth, there's no way to know which is accurate when sources conflict. 

AI alone isn't enough to solve fragmentation. A unified search layer can help break down these silos so employees get more out of digital tools — and it's becoming foundational to scalable agentic AI (AI that can reason and act), automation, and productivity gains.

Learn how to unblock productivity and empower your workforce with Moveworks Enterprise Search.

Why traditional search tools fail to support modern AI initiatives

Traditional or legacy enterprise search typically looks like:

• Keyword-based indexing with limited semantic understanding: That means it may not always comprehend language intent and nuance, so if you search for "2FA," it might not pull up results for "two-factor authentication" or "multi-factor authentication."
• Static ranking that doesn't adapt to user intent, role, or context: An employee in the U.S. vs. the U.K. likely wants to see region-specific HR policies, but traditional search typically can't accommodate that personalization.
• Reliance on manually curated or siloed content sources: Traditional search is often limited to a single, departmental knowledge base that teams have to continuously update and maintain over time.

Since these solutions are largely keyword-dependent and static, with limited to no natural language comprehension or cross-system access, legacy search tends to lead to:

• Inaccurate or incomplete search results that require follow-up searches and reduce trust in digital tools
• Poor search rankings that make it hard for employees to find relevant information
• No prioritization or summarization of search results, making it hard to digest information

When you try to build AI on top of fragmented, knowledge-based search systems, AI models struggle to deliver value because they lack context, connected knowledge, and reliable source grounding. It can limit adoption and slow digital transformation efforts. 

Modern enterprise search solutions, especially those with agentic AI, can help you create a single source of truth, pulling relevant information from across systems without forcing employees to dig through documents across platforms or navigate conflicting sources. 

Learn why digital transformation fails and what it takes to succeed.

Enterprise search unifies knowledge and accelerates digital transformation

Enterprise search can connect information scattered across apps, knowledge bases, ticketing systems, intranets, and shared drives into a single, AI-readable layer, creating a more reliable foundation fordigital transformation initiatives.

Instead of forcing employees to hunt through multiple systems, unified search can deliver speed, accuracy, and relevance, helping reduce friction, lower support load, and give employees the clarity they need to work faster.

By treating enterprise search as critical infrastructure for digital transformation, instead of just an add-on, you can start to overcome gaps like:

• AI without context: AI initiatives struggle to deliver value when models lack access to unified, reliable, and up-to-date knowledge. Without modern enterprise search, it may be difficult for AI to consistently understand intent, reason over information, or support real work beyond surface-level answers.
• Fragmented employee experiences: Without a unified search layer, employees have to search across multiple tools, systems, and repositories, leading to slower resolutions, duplicated effort, and declining trust in digital tools.
• Limited automation impact: A lack of unified search can also stall automation efforts, because workflows depend on accurate, contextual information. Without that data, AI-driven automation often remains brittle, manual, or narrowly scoped.
• Lower ROI on digital investments: It's hard to see a return on AI and transformation initiatives if employees can't easily access or act on the information those systems depend on.

But finding information is only the first step. Modern, agentic AI-powered enterprise search supports what comes next, helping employees move from "answers" to resolutions. Rather than just surfacing documents or snippets, it forms the layer that feeds the context required to interpret intent and execute appropriate next steps.

See how agentic AI turns search into action. 

What to look for in an enterprise search platform

If you want an enterprise search solution that sets the stage for scaling agentic AI adoption and digital transformation, keep these key capabilities in mind:

1. High-quality answers: Look for semantic search powered by natural language processing (NLP), so employees can search conversationally instead of using exact keywords. Strong contextual ranking means the system is designed to understand the searcher's context (role, location, department) to deliver personalized results. The platform should also synthesize information across multiple sources into clear, accurate summaries grounded in verifiable enterprise data.

2. Strong integrations at scale: Enterprise search is most effective when it can connect easily to multiple systems (SharePoint, Slack, Drive, ServiceNow), with distributed indexing and caching to maintain speed as data grows. Real-time or near-real-time retrieval helps you prevent the data silos typical of traditional search systems and support more reliable search results.

3. Enterprise-grade security and governance: You need precise permission handling across different types of data, with robust access controls and encryption. Granular permissions can help ensure employees only see what they’re authorized to access, while still maintaining a smooth search experience.

4. Great employee experience: Think about how the search experience aligns with how employees work. Features like multilingual support and entry points in chat, portals, and the web increase usability, while strong connections to knowledge bases, files, external sites, and other data sources maximize usefulness.

5. Structured and unstructured data handling: IT leaders need search capabilities that can feed both structured data (records from ServiceNow, Jira, HRIS, CRM, asset databases) and unstructured data (docs, PDFs, slides, emails, Slack messages, Confluence pages, tickets) to AI models. When employees can find answers from past issues, rather than opening new tickets every time, support demands may drop substantially.

But the real value comes from action-readiness. While reliable, context-aware answers are foundational, the ability to quickly move from search results into next steps is a differentiator. Agentic AI enables systems to reason over retrieved information, connecting understanding with execution. 

So instead of just providing search results, AI agents are able to execute next steps based on user intent and enterprise rules. That might look like automatically setting up new devices based on a specific user's access controls, rather than just pulling up answers on how to set up new devices. 

AI agents don't replace human judgement, but they can initiate requests or workflows that save time and reduce friction. And when full workflow automation isn't possible, agents can still guide employees through approved processes on their own (like basic troubleshooting) rather than having to loop in IT every time.

Ultimately, the right platform should scale effortlessly, integrate broadly, and support AI-powered workflows beyond simple answer retrieval. When enterprise search can trigger workflows, system updates, and guided task completion from the same interface, it creates more of the outcome-oriented productivity that enterprises need from AI and digital transformation tools.

How AI-powered enterprise search solves digital transformation challenges

Not only does a strong enterprise search system have the potential to help employees find relevant answers quickly, but it can also fuel further digital transformation across the enterprise. Employees can focus more on interpreting data rather than retrieving and verifying it, and you can develop more of a single source of truth that feeds into other systems. 

Turning natural language into precise, contextual answers

AI-driven semantic search is designed to understand the intent or goal of requests, instead of just relying on keyword matching. So you can ask "How do I request vacation days?", and the system's natural language capabilities enable it to pull up relevant information about the PTO submission process, even if those resources don't include that exact phrase.

Agentic AI can also provide contextual ranking and summarize information from disparate systems, surfacing the most relevant answers instantly, even when data lives across disconnected platforms. This makes it easier for employees to understand search results without having to sift through multiple documents or toggle between apps. 

Multilingual capabilities expand this accessibility, ensuring that global teams can interact with enterprise search in their preferred language, while still drawing from the same unified knowledge base.

Clear, accurate answers build employee trust in AI tools, making them far more likely to adopt AI as part of their daily workflow — 3.3x more likely, according to Deloitte.

Powering automation and workflow acceleration

To get high-quality outputs from AI models and tools, you need high-quality inputs. Think of it like an accounting tool — you'll only get the correct balance if the underlying numbers are accurate. Enterprise search helps provide that foundation by seeding AI tools with the right context and information from across your organization. 

Agentic AI enables more productive action. When enterprise search retrieves relevant results, AI agents can then determine appropriate next steps and execute them according to pre-defined enterprise rules and permissions:

• Auto-resolving common tickets: Applying known solutions from past issues without requiring manual intervention.
• Generating knowledge base responses: Drawing from verified sources to provide accurate answers instantly.
• Triggering system actions: Provisioning access, updating records, or configuring settings based on role and context.
• Providing next-step recommendations: Guiding employees through approved workflows, like troubleshooting steps or request processes.

What matters most is that these actions happen within enterprise rules, permissions, and approval workflows. Agentic AI doesn’t bypass governance — it operates inside it.

Without a unified search foundation, automation often becomes brittle, inconsistent, or constrained to narrow use cases.

Enabling secure, organization-wide knowledge access

Agentic AI-powered enterprise search can interpret context and respect underlying role-based permissions, data governance policies, and access controls across systems. 

This means employees can find unified, relevant information without increasing  security or compliance risk. But it also lets you unlock the value of both your structured and unstructured data, turning records, tickets, chats, and files into a unified layer of knowledge that AI can reason over for insights and actions.

Instead of blocking entire systems to maintain security, you can connect data across a broad range of apps, shared drives, support tickets, chats, and more, confident that employees only see what they're authorized to access. This eliminates the need for multiple logins that slow down work, without sacrificing governance.

It's a strategic approach that directly supports digital transformation goals. When done right, employees can get instant answers without compromising security, and IT maintains control without creating friction. 

The more employees see AI reliably providing accurate information and supporting real workflows — safely and securely — the more likely they are to adopt digital solutions, creating momentum for organization-wide transformation.

Accelerating decision-making

Using semantic search and generative AI to instantly surface context-aware answers can also help accelerate decision-making, even when workflow automations aren't possible. 

For example, imagine you're a sales manager trying to find information on high-quality prospects. You can't automate judgment on how to best approach those prospects, but you could use enterprise search to quickly find relevant background information. 

Rather than digging through scattered documents and tools to find past interactions, relevant sales decks, and case studies that might help close these leads, you can locate them instantly with a unified search tool. That gives you the background knowledge to quickly decide on a strategy for closing those prospective deals.

Surfacing friction and reducing IT and support workloads 

With agentic AI, employees are able to resolve common questions and tasks without opening tickets or waiting on a specialist. Going directly from answer to action in one search interface can save time for the searcher and the supporting department, whether it's IT, HR, or any other department with high-volume requests. 

A strong enterprise search system can also provide analytics to help companies identify friction points and resolve root causes. By analyzing common searches, leaders gain visibility into what team members are searching for and where gaps exist.

If employees often ask about social media policies and best practices, your established guidelines might be inaccessible, outdated, or even nonexistent. Once you uncover the reason, you have an opportunity to address that friction at the source. 

Maybe that looks like implementing a new employee advocacy platform that supports compliant social media usage. And since it's something employees have already shown interest in, the risk of a failed investment may be lower.

Discover why enterprise search is the foundation of AI-driven transformation

It's difficult to achieve meaningful business value from your AI initiatives without unified, searchable knowledge, and traditional enterprise search can struggle to keep up at enterprise scale. Fragmented information makes it harder for AI to reason effectively, automate reliably, and deliver consistent outcomes.

Moveworks can help you solve this challenge by unifying systems, surfaces, and knowledge into an easy-to-use, intelligent search layer that grounds AI in enterprise-approved, permission-aware information. 

Moveworks Enterprise Search uses agentic AI and retrieval-augmented generation (RAG) to deliver fast, precise, contextual answers in natural language, reducing friction for employees and helping accelerate decision-making across your organization.

Enterprise Search is designed to retrieve and ground structured and unstructured information from across your existing systems, while agentic AI can interpret intent, context, and enterprise rules. The platform then supports appropriate actions within defined workflows and permissions, moving employees from answers to resolution without leaving the search interface.

The transformation impact is clear: 

• Fewer support tickets as employees resolve common issues through self-service
• Higher adoption of digital tools when people can easily access the knowledge they need
• Faster resolution of everyday employee needs, like benefit changes or IT troubleshooting
• Clearer ROI from AI and automation initiatives that are built on reliable, unified knowledge

With a solid foundation built on enterprise search, you can create an environment to support AI at scale — better position your organization to realize the productivity gains digital transformation promises.

Explore how Moveworks Enterprise Search can be the catalyst for true transformation.