From Search to Answers: Rethinking Search in the Age of AI

March 04, 2026 Data & AI, Digital Experience, Sitefinity, Agentic RAG

Your website search returns links. Lots of them.

A visitor types a full question into the search bar, gets a page of keyword-matched blue links, opens a few tabs, scans for relevance and either gives up or opens a support ticket. What they don't realize is the content they needed was likely there all along, they just couldn't find it in a way that matched how they thought through their question.

This isn't a content problem. It's a findability problem.

And it's happening everywhere: on public websites, customer portals, internal knowledge bases, SharePoint, Google Drive and beyond. Organizations don't struggle with a lack of content; they struggle with a lack of usable knowledge.

Search Expectations Are Evolving

Users aren't just searching anymore-they are asking questions.

Traditional search engines were designed around keyword matching and blue links. But increasingly, people expect a conversation instead of a list of results. The rise of generative AI (GenAI) tools like ChatGPT, Gemini and Copilot has fundamentally changed how people look for information. And it's not just in tech circles, but in everyday users' digital behavior.

GenAI adoption has happened at a pace rarely seen in tech:

These adoption curves show a real shift: People are comfortable engaging with GenAI and they're doing it at scale. This normalization has implications for search behavior.

People no longer type two or three keywords into a box, they type full questions the same way they would into ChatGPT, such as:

  • "How do I reset my device after a firmware update?"
  • "What's covered under the extended warranty?"
  • "How do I integrate this with Salesforce?"

And they expect a direct answer.

But traditional search was designed to help people navigate to documents. It was never designed to deliver outcomes. It matches keywords, ranks pages and makes the user do the work of reading, interpreting and deciding what's relevant.

Results make users work. Answers do the work.

The Big Shift for CMOs, CIOs and IT Leaders

This shift in behavior doesn't just impact user experience. It has real business consequences. CMOs and Digital Experience leaders have heavily invested in content. Over the past two decades, a tremendous amount of content ranging from product pages to help articles and videos has been produced to power digital experiences. But if users can't find what they need, that content isn't working for them.

Poor search leads to:

  • Lower engagement
  • Missed conversions
  • Frustrated visitors who leave your site

For CIOs and IT leaders, the same problem exists internally. Employees search across SharePoint, Google Drive, portals and knowledge bases and open multiple documents to piece together answers or ask a colleague for help.

The knowledge exists, but it's trapped across systems and formats.

What AI Search Should Look Like

Traditional Website SearchAI Search with Answers
A list of linksOne direct, conversational answer
Keyword matchingContextual understanding of the question
Multiple pages the user has to readContent read by AI for them
No indication of reliabilityClear citations for source content
A navigation toolAn answer experience

Instead of returning ten links, AI search reads your content, understands the question and responds with a direct answer. AI search can pull a specific sentence from a PDF, a paragraph from a help article or a timestamp from a video.

And critically, it shows exactly where the answer came from.

Trust: The Missing Piece in AI Search

One of the biggest problems with AI implementations today is that generic AI tools often guess or hallucinate. General-purpose large language model (LLM) solutions pull from their training data, the internet and generally provide answers with no or limited traceability. That might be fine for casual use, but it's not acceptable for organizations where accuracy, compliance and trust matter.

For AI search to be useful in a business context, it must be:

  • Grounded in your actual content
  • Able to cite its sources
  • Governed and auditable
  • Evaluated continuously for answer quality

When a visitor or employee asks a question, the response should say, in effect:

"Here's the answer, and here's exactly where it came from."

That transparency is what turns AI from an interesting demo into a reliable part of your digital experience.

Site Search: A Practical Entry Point

Site searches are often treated as small utility features, a box in the header meant to help users navigate. But, in reality they are some of the most revealing indicators of how well your organization makes knowledge accessible. When someone uses search on your website or customer portal, they are no longer browsing. They are signaling intent. They have a question, a problem or a task they are trying to complete. They don't want to explore; they want an answer.

Traditional search was never designed for that moment. It was built to return pages ranked by keyword relevance. It assumes the user will read, interpret and determine what is useful. It assumes the user has the time and patience to open multiple tabs, scan content and piece together what they need.

This is why website or internal portal search has become such a powerful starting point for AI initiatives.

For Digital Experience and marketing teams, this shift makes existing content far more discoverable and useful. Instead of relying on keywords and navigation paths, AI understands the meaning behind a user's questions and surfaces the exact content that answers it. Pages, documents and assets that were previously buried now become part of real user journeys, increasing the impact of content you've already invested in creating.

This also improves the experience metrics that matter. Visitors find answers faster, bounce less, engage more and move through conversion paths with less friction. An AI-driven search experience feels modern and helpful, turning your website or portal from a place users browse into a place that actively solves their problems.

Why This Is Bigger Than Site Search

Once your website content is connected, indexed and understood well enough to power AI search, you've done something more significant than improving search.

You've built a knowledge layer.

The same foundation that powers AI answers on your website can also power:

  • Internal AI assistants for employees
  • Support for deflection and smarter help centers
  • Knowledge management across systems
  • Future AI agents and automation initiatives

When you've built a knowledge layer, it becomes a reusable foundation for countless experiences across the organization. Instead of knowledge living in pages and files, it becomes something AI can reason over, surface and apply wherever it's needed. It also turns scattered information into an active, strategic asset that supports how people work and how your business scales.

Fix Search Today. Enable AI for Tomorrow

This is where the Progress Agentic RAG solution becomes a natural fit for modern site search. Instead of relying on keyword matching and page ranking, it connects directly to your content, understands it through multi-layer indexing and advanced retrieval strategies. This generates clear answers grounded in the exact sources your users trust. What users experience is simple and intuitive: they ask a question and receive a precise answer with a citation. Behind the scenes, the solution is turning scattered pages, documents and media into a unified knowledge base that AI can reason over.

Because the answers are traceable back to their source, this isn't a chatbot guessing or summarizing. It's your organization's own content being surfaced in a smarter way. That transparency builds trust with users while giving teams confidence that the information being delivered is accurate, current and defensible. At the same time, it requires no overhaul of your existing site or portal content, simply making what you already have dramatically more usable.

For many organizations, improving site search is the first place they see the true potential of the Progress Agentic RAG solution. It delivers an immediate upgrade to the digital experience while quietly establishing the knowledge foundation that can power future AI initiatives across the business. What starts as better search quickly becomes a smarter way to activate all of your content.

To try out the Progress Agentic RAG solution for yourself, start with a 14-day free trial today.

Michael Marolda

Michael Marolda is a seasoned product marketer with deep expertise in data, analytics and AI-driven solutions. He is currently the lead product marketer for the Progress Agentic RAG solution. Previously, he held product marketing roles at Qlik, Starburst Data and Tellius, where he helped craft compelling narratives across analytics, data management and business intelligence product areas. Michael specializes in translating advanced technology concepts into clear, practical business terms, such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and modern data platforms.

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