From CRMs to intelligence hubs: How go-to-market is being rebuilt

For years, customer relationship management (CRM) occupied the centre of the go-to-market (GTM) universe. It was positioned as the system where leads were captured, deals were tracked, and revenue forecasts were created. In 2026, however, many GTM leaders are acknowledging a gap between what CRMs were built to do and what modern GTM actually demands.

When human judgment was the intelligence layer

Software sales did not always operate in today’s layered and signal-driven environment. Earlier GTM motions relied heavily on human judgment. Sales and marketing teams manually reviewed engagement data, website visits, and account lists, then decided who to contact and how. Decisions were imperfect, but they were contextual. A human could interpret nuance across multiple systems and act accordingly.

That human decision layer is now being replaced.

Automation and AI have made it possible to run outbound motions at a massive scale. Emails, LinkedIn messages, and follow-ups can be triggered with little to no human involvement. While this has increased efficiency, it has also flooded buyers with undifferentiated outreach. Attention has become scarce, trust has eroded, and response rates have declined across industries.

This creates a contradiction at the heart of modern GTM. Teams are more automated than ever, yet outcomes are harder to achieve. Scale alone no longer translates into effectiveness. What increasingly matters is relevance.

As a result, GTM organisations are forced to rethink the foundation of how decisions are made.

The limits of traditional CRMs

Most CRMs were built with a clear purpose. They help teams log activity, manage pipelines, and maintain records of customer interactions. What they were not designed for is continuous intelligence generation.

Modern GTM teams need answers to questions that go beyond records and reporting. They need to know which accounts actually match their ideal customer profile today, which ones are showing buying signals now, and what specific problem is driving that interest. They also need guidance on what action to take next and through which channel.

CRMs struggle here not because they lack features, but because they are structurally rigid. Their data models assume predefined objects and workflows. Intelligence still depends heavily on humans interpreting scattered data and deciding what matters.

Meanwhile, the GTM ecosystem has expanded rapidly. Intent data, product usage signals, hiring trends, content engagement, community activity, and third-party behavioural data now live across dozens of tools. Each tool captures a slice of reality, but none provides a unified picture.

The result is fragmentation. Teams accumulate tools, dashboards, and alerts, but still lack clarity. Insight exists, but it is not consolidated into decision-ready intelligence.

The rise of the GTM intelligence hub

In response, leading teams are building what can best be described as a GTM intelligence hub.

This is not a replacement UI for a CRM, nor is it a single off-the-shelf product. It is an architectural layer that sits above tools and focuses on decision-making rather than data storage.

A well-structured intelligence hub typically consists of four core layers:

The first is the ICP layer. This defines the relevant universe of accounts and personas. Unlike static definitions, this layer evolves based on performance, market feedback, and outcomes.

The second layer is intent. This is the core of the hub. It pulls together clear signals from across first-party and external sources, that is, how people use the product, interact with documentation and repositories, move through the website, consume content, and engage with communities and the wider ecosystem, to show who is genuinely evaluating.

The third layer is the action layer, where signals are translated into decisions. The system determines which accounts deserve attention now and what message or channel is most relevant. For example, actions like sending emails, connecting on LinkedIn, making calls, or getting help from a person. Sales and marketing act on ranked, context-rich opportunities.

The fourth layer is the feedback layer. The results of actions taken return to the system, which helps make better decisions and increases accuracy over time. At its core, the intelligence hub turns scattered signals into clear guidance on who to engage, when to engage, and how to engage before anything reaches the CRM.

Where AI changes the equation

AI’s role here is not about adding surface-level personalisation. It is changing how teams build understanding in the first place. Signals that were once easy to miss, such as leadership comments, technical blogs, hiring activity, filings, or community conversations, can now be pulled together and understood in context. What earlier took hours of manual digging can surface much faster.

The second shift is in decision support. Instead of relying on instinct or incomplete data, teams can see patterns across usage and engagement that point to real problems buyers are trying to solve. This context improves timing and relevance, allowing GTM teams to act with confidence rather than guesswork.

However, AI only delivers value when it is grounded in structure. Without a central intelligence hub, AI simply adds another layer of output without coherence. Intelligence must be collected, combined, and routed intentionally.

Ownership and execution

As this model emerges, ownership is shifting toward GTM engineering. These teams operate at the intersection of data, systems, and revenue operations. Their focus is not on tool adoption, but decision architecture.

The most mature organisations build intelligence hubs using data warehouses, giving them flexibility and control. Smaller teams often start with spreadsheets. The scale differs, but the principle remains the same.

CRMs still play a role. They serve as execution and visibility layers for sales teams. What is changing is their position in the stack. They are no longer the central brain.

Not a trend, but a structural rebuild

This change will happen slowly. CRMs are a huge part of how GTM teams work, and it takes time for things to change at that level. Some platforms will change and grow their role, while others will have a hard time staying relevant.

It’s becoming clear that there is a steady shift away from making decisions based on volume to more on information. Teams that understand buyer intent earlier, interact in a more relevant way, and use automation wisely will be in a better position by 2026. The GTM intelligence hub is becoming the structure that will support this change.

Achintya Gupta is the Co-founder and CEO of Reo.Dev