Uri Knorovich, CEO and co-founder of Nimble, orchestrating thousands of web search agents to give you complete, accurate data in real time.
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For decades, platforms like Bloomberg Terminal dominated financial intelligence by aggregating data, analysis and news into a single interface.
Despite a strong affinity among its enthusiastic user base, that model is starting to break. The plain truth: the way we access and use data is fundamentally changing.
Enterprises are now deploying AI agents to analyze markets, monitor competitors, automate research and support decision-making at scale. Still, as these systems move into real workflows, a limitation is becoming clear: AI is powerful, but it doesn’t know how to reliably interact with the outside world.
Models can reason and generate, but they struggle with questions like “What’s actually happening in the market right now?”
This isn’t a model problem. It’s an infrastructure problem: getting the right external information at the right time.
When it comes to capturing relevant, external information, most of today’s AI systems operate on static knowledge or generic web search. They often work better in demos than in production.
For example, in financial services, agents must validate companies, monitor regulatory changes and track real-time signals. In retail or CPG, they must understand pricing shifts, competitor activity and supply chain disruptions as they happen.
Without reliable access to live information, most AI systems either hallucinate or fail silently. But neither is an acceptable outcome in an enterprise context.
Ultimately, the problem that most organizations are starting to run into is that AI agents are capable, but they’re not grounded in reality.
A new wave of AI web search tools has emerged, giving agents direct access to the world’s information.
But the problem is that most of these tools rely on generic search approaches, meaning the way information is retrieved is largely the same, no matter the agent’s purpose, industry or context. For example, agents performing financial research, monitoring competitor pricing or analyzing supply chain risks typically pull from an undifferentiated set of sources with limited control over what’s included (or excluded).
That lack of control creates accuracy issues. Agentic web search is about pulling in relevant context, without noise that can clutter and distract from the key information in the workflow. Without granular control over the data being retrieved, LLMs can quickly become overwhelmed with irrelevant information, which can cause accuracy issues.
The challenge isn’t access to information; it’s how that information is retrieved.
What enterprises actually need is a more controlled approach to AI web search, where retrieval can be aligned to the specific use case of the agent. You need the ability to define trusted sources and tailor how information is gathered based on the given task at hand.
Ultimately, the quality of an AI system is determined by the quality and relevance of the data it consumes.
This is where a new layer in the AI stack is emerging: customized intelligence infrastructure for the specific use case and vertical.
Unlike generic AI web search, this layer is designed specifically for enterprise use cases and operates between AI models and the external world.
Again, enterprises don’t just need access to the web; they’ve already got that. What they need is to define which sources are trusted, how information is prioritized and how it all aligns with the context of the business.
For example, a bank evaluating potential high-profile clients may want its AI systems to scour regulatory filings, financial disclosures and specific news outlets. Meanwhile, a retailer might have agents focus on competitor websites, marketplaces and real-time changes in pricing. The underlying model may be the same for each of these use cases, but the way it interacts with the web should be fundamentally different.
This is what separates generic AI capabilities from production-ready intelligence systems. At scale, enterprise AI is all about accessing the right information, in the right way, for the right decision.
The move toward AI agents is happening, but many organizations are still early in understanding what it takes to make them work at scale. When you’re figuring out how to deploy AI so it efficiently benefits your organization, there are three questions worth asking:
1. How will your AI systems access real-time information? It’s not enough to rely on model knowledge or basic integrations. If your use cases depend on external data, you need a clear strategy for how that data is retrieved and validated.
2. Can you control and govern that access? Enterprise environments require consistency to foster compliance and trust. Your AI systems shouldn’t be pulling from arbitrary sources without oversight.
3. Are you building workflows or intelligence systems? There’s a difference between automating tasks and creating systems that continuously generate insights. The latter requires infrastructure that can operate dynamically and not just execute predefined steps.
Every organization is different, and there will always be many moving parts. But thoroughly answering these questions gives you a much better chance at deploying AI agents in a way that’s smart for the business.
The world is becoming a place where AI agents interact directly with information, and that changes where value is created.
In the past, you had an advantage if you owned the interface and could aggregate data into a single platform. But going forward, the advantage belongs to those who effectively connect AI systems to the world’s information, provided those systems can reliably interpret it.
Think of it as another in a long line of strategic shifts that businesses make in their lifetime. Organizations that adapt will be able to move faster, make better decisions and operate with a level of responsiveness that wasn’t previously possible. Those who lag may find themselves relying on systems designed for a different era, where humans were the primary consumers of information.
That’s not the world we currently live in.
Change is happening. Is your organization building the infrastructure to support that change?
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