Information is abundant—but AI Confidence in the right information, at the right time, remains elusive

For legal and knowledge professionals, this challenge is not new. What’s changing is the growing magnitude of the problem and the scale of the opportunity. Search and findability are no longer just technical capabilities; they’re core enablers of effectiveness, insight, and competitive advantage. With the rise of generative AI and machine learning, we're entering a new era where search is not only more powerful—it is foundational.

Some fundamental questions need considered answers. Let’s unpack the following: why search remains central in legal knowledge work, how AI is transforming the way we find and use information, and why hybrid search approaches (keyword, semantic, and vector-based) are unlocking long-awaited outcomes for legal professionals.

The Stakes: Why Search Matters More Than Ever in Legal Work

In law, outcomes often hinge on nuanced information—whether it's precedent, a passage in an advice note, a regulation, or an internal memo. Poor findability means missed insights, additional risk, duplicated work, and lost time. For firms, this translates to inefficiencies, compliance issues, and reduced opportunities.

Key outcomes lawyers and knowledge professionals seek from content discovery:

• Contextual Understanding: Being able to situate a result within a broader legal or organizational context.
• Accuracy: Finding the most relevant, authoritative sources for a given legal question.
• Completeness: Uncovering all necessary documents or references to make informed decisions.
• Speed: Minimizing time spent sifting through irrelevant documents.
• Reuse: Finding and leveraging existing internal work product, templates, and expertise.

All of these hinge on a single question: Can we reliably find what we need, when we need it, with the right context, that I can trust?

The AI Hype—how to get to AI Confidence: Why Search Still Matters

Much of the excitement around generative AI assumes that large language models (LLMs) will magically produce the right answers. But even the most advanced LLMs rely on access to high-quality, contextually relevant data. That’s where search comes in.

In fact, many enterprise-grade AI systems today use a method called retrieval-augmented generation (RAG). Before the AI can generate a response, it must search for relevant content—usually from internal document repositories or external legal databases. Without good search, even the smartest AI fails, whether the RAG uses classical keywords (many do) or vector searches.

Why LLMs Rely on Search:

• Context windows are limited: LLMs can’t “know” everything. They need to be supplied with the right documents.
• Factual grounding: Search provides the factual substrate for AI to reason on.
• Accuracy and defensibility: Especially in law, AI needs verifiable sources. Retrieval ensures traceability.
• Good sources of knowledge: Often firms have not managed content well into knowledge so search is a substitute for good content.

So, while AI might transform how we interact with information, search is what ensures we’re interacting with the right information. Trust with transparency means accuracy with every answer.

The Evolution of Search: From Keywords to Vectors to Best of All Worlds

For decades, legal search has been dominated by keyword-based approaches. Boolean search, metadata filters, and taxonomies have served well—but they require users to know what they’re looking for and how to ask for it.

With the rise of AI and natural language processing, a new generation of search is emerging: hybrid search.

What is Hybrid Search?

Hybrid search blends three core technologies:

• Keyword Search: Matches based on exact terms—fast and precise, diverse results, pinpoint accuracy when desired.
• Semantic Search: Understands meaning and intent—helps match similar ideas phrased differently, leveraging taxonomies and ontologies.
• Vector Search: Uses machine learning to map documents and queries into high-dimensional space, enabling retrieval of conceptually related content, even without keyword overlap.

Together, these approaches create a more robust, context-aware, and user-friendly search experience. Clever UIs can access the right approach at the right time and start to understand the user's intent, leveraging personalisation.

The Transformation: From Search to Findability

Search is a tool. Findability is the outcome. Confidence is a feeling. It should be easy to capture knowledge, find it, and turn it into collective intelligence.

Legal professionals don’t care about “search precision metrics.” They care about outcomes like:

• Finding a contract clause drafted by a colleague two years ago
• Locating every regulatory filing relevant to a new jurisdiction
• Reusing the best precedent, not just any precedent
• Knowing the firm has a standard they should look at first
• Knowing who authored that first draft that they should talk to

With hybrid search and AI-powered retrieval, we’re finally approaching the long-sought ideal of true findability—where systems help you uncover what you should know, not just what you asked for.

This is especially important for tacit knowledge—expertise that’s buried in emails, slide decks, or outdated internal databases. Hybrid search is helping surface this hidden value.

What Legal Organizations Should Do Next

• Audit your current findability: how people find content? What’s being missed?
• Invest in hybrid search technologies: Look for tools that combine keyword, semantic, and vector search in a domain specific knowledge search.
• Prepare your content: Clean up, tag, and organize your documents so AI can retrieve and reason over them effectively (your #IAbeforeAI)
• Design for reuse: Create systems that promote reusing prior work—briefs, clauses, research memos—rather than reinventing the wheel.
• Integrate AI responsibly: Ensure that retrieval-augmented systems have traceability, algorithm version control, and are grounded in high-quality sources.

Final Thoughts: Findability is the Future

As AI becomes a co-pilot for legal professionals, the value of high-quality, discoverable information will only increase. In this new era, the winners will be those who don’t just have knowledge—but can find it, use it, and trust it. Search is no longer just a backend feature. It is a strategic capability.

Let’s invest in it accordingly.

Why iManage Insight+ Helps Deliver on This Vision

This is exactly where iManage Insight+ comes in.

Insight+ is built to meet the evolving needs of modern legal and knowledge professionals by enabling powerful, AI-enhanced search across your entire knowledge base. It combines keyword, semantic, and vector-based search into a single platform—helping users discover the most relevant work product, insights, and precedents, no matter how they were phrased or stored.

Here’s how Insight+ supports the findability outcomes legal teams are seeking:

• Smarter Search: Insight+ uses hybrid search powered by Microsoft Azure OpenAI to understand both the intent and context behind a query, going beyond simple keywords.
• Content Intelligence: It unlocks value across documents, emails, and prior work by automatically enriching content with metadata, concepts, and relationships—so knowledge is not just stored, but surfaced.
• Knowledge Reuse: Lawyers can quickly find and reuse prior work, reducing redundancy and enabling consistency in advice and output.
• Trusted, Secure, and Scalable: Built in the iManage Cloud, Insight+ ensures that your knowledge assets are discoverable and defensibly governed—essential in regulated legal environments.
• AI-Ready: It seamlessly integrates with retrieval-augmented AI workflows—so when you’re ready to bring AI copilots into your legal processes, your knowledge is ready too.

By bridging the gap between how people search and what they need to find, Insight+ turns search into strategy—and knowledge into a competitive asset.