Balaji Sreenivasan is the founder and CEO of Aurigo Software.
In a recent Wall Street Journal article, Melissa Schilling, a professor at NYU’s Stern School of Business, put it simply: “Google had this seemingly insurmountable position in search, until AI came around, and now AI is to search what e-commerce was to Walmart.”
She’s right. History is full of moments where industry giants have failed to see the next big thing coming. Walmart underestimated the power of e-commerce when Amazon launched its online bookstore in the late ’90s. Microsoft and Blackberry were caught off guard when Apple reinvented the smartphone in 2007.
Now, in 2025, we’re at another tipping point. This time, AI-powered search is redefining the way we work, discover and make decisions. We’re moving beyond basic keyword searches and static results pages toward a more intuitive, context-rich process that doesn’t only give results but offers answers.
And this isn’t just about consumer search engines like Google or new players like ChatGPT and Perplexity AI. It’s about a fundamental change in how businesses analyze, manage and apply their data. This change making its way into enterprise SaaS solutions isn’t a matter of “if,” but “when,” and businesses unprepared to take advantage risk falling behind.
Stop searching for data. Start talking to it.
Think about how search works today, especially in enterprise software. You type in a keyword or phrase, hit enter and get a long list of results. Then, it’s up to you to sift through everything, hoping to find what you need. If you’re fortunate, it may be on the first page or two, or you may have to go back and refine your search or use filters to pin down what it is you’re looking for.
That made sense when data was simpler. But today? The amount of information businesses generate is staggering. Even small companies have mountains of structured and unstructured data scattered across spreadsheets, emails, reports and internal systems. A basic search bar isn’t enough anymore.
This is where AI-powered search can help, although calling it “search” doesn’t quite do it justice. Instead of just finding documents or reports, AI can better understand what you’re looking for. Using natural language processing (NLP) and large language models (LLMs), it can extract insights across multiple data sources and present them in a way that’s useful and actionable.
Picture this: You’re a project manager starting a new infrastructure initiative. Instead of digging through thousands of reports, spreadsheets and meeting notes, you simply ask, “What risks did we face in similar projects over the past five years?” AI-powered search can instantly pull together key insights from past data—spanning structured and unstructured sources—and present them in a way that makes sense.
Want to refine your search? Ask a follow-up question: “What strategies were most successful in mitigating these risks?” AI won’t just surface a document—it can provide an intelligent, contextual answer, making the act of searching feel more like a real conversation.
To make AI-powered search a reality, here’s where to begin.
For businesses eager to embrace AI-powered search, the first step is understanding whether they’re ready for this transformation. Many organizations still rely on outdated search methods, forcing employees to sift through scattered, siloed data across multiple systems. If finding relevant information is a slow, frustrating process that impacts productivity and decision-making, it’s a clear sign that traditional search tools are no longer sufficient.
But implementing AI-powered search requires more than just adopting new software; it starts with preparing the data itself. Companies must first assess the quality and accessibility of their information, ensuring it is well-organized, up to date and free from inconsistencies. AI thrives on connected data, so integrating information from various sources—documents, databases, reports and even emails—is essential to unlocking its full potential.
Once the groundwork is laid, businesses should focus on selecting an AI search solution that aligns with their needs. The most effective tools go beyond simple keyword matching, using NLP to understand user intent and retrieve meaningful insights.
Security, compliance and ease of use should also be key considerations, ensuring that employees across all levels can leverage AI search without extensive training. Instead of overhauling their entire system at once, companies should start small—perhaps by introducing AI search within a specific department or workflow to gauge its effectiveness before scaling across the organization.
By taking a phased approach, organizations can move beyond static search bars and into a future where employees don’t just search for data; they interact with it.
Democratized data is powerful.
This is more than just a linear upgrade. It’s a complete search overhaul. For decades, enterprise SaaS applications have relied on traditional search functions, reports and dashboards. AI-powered search is breaking those limitations, making software far more intuitive, powerful and accessible to employees regardless of their level of skill or experience.
This is the real impact: It democratizes data analytics. No longer do companies need to rely solely on data analysts and programmers to extract insights with intelligent queries. AI-powered search makes advanced data analysis accessible to everyone, turning project managers, engineers and executives into nimble, data-driven decision-makers.
If AI is to search what e-commerce was to Walmart, then enterprise SaaS is on the verge of its own revolution. The companies that embrace AI-powered search—by adopting the right technology partnerships—could gain a massive competitive advantage.
We’re not talking about some distant future. The shift is happening now. The only question is: Is your business ready for it?
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