AI Orchestration in RevOps: Colin Gerber on Forecasting & Growth

Are you adding more tools but still struggling with clarity?

That’s the paradox many revenue teams are facing today. There is no shortage of data, dashboards, or AI tools promising better forecasting and productivity. Yet, despite all this, forecast accuracy remains inconsistent, sales teams continue to spend time on administrative work, and leadership still lacks a clear, unified view of what is actually happening across deals.

So what’s really going wrong?

In a recent conversation with Colin Gerber, VP of Revenue Operations & Strategy at Socure, one idea came up repeatedly: the issue is not the lack of data—it is the lack of orchestration. As Colin pointed out, most organizations are collecting vast amounts of information, but very few are actually designed to extract meaningful signal from it.

The real problem isn’t data—it’s signal

It’s common to assume that forecasting challenges stem from poor judgment in the field. But as Colin explained, that’s often a surface-level diagnosis. When you look deeper, the real issue is that systems are not built to highlight what truly matters.

Modern revenue teams operate with an overwhelming amount of context—CRM data, call recordings, emails, meeting notes, and multiple tools tracking every interaction. While this creates a rich dataset, it also introduces a critical challenge: more context does not automatically lead to more clarity. In many cases, it leads to noise.

Colin emphasized that the teams that consistently forecast well are not the ones gathering more data. Instead, they are the ones that have clearly identified the signals that actually influence deal outcomes and have built their systems around those signals. That distinction—between collecting information and extracting insight—is what separates high-performing revenue organizations from the rest.

Why adding more tools often makes things worse

When visibility becomes an issue, the instinctive response for many organizations is to invest in additional tools. Another AI platform, another dashboard, another layer of automation—it feels like progress. But in reality, this often leads to further fragmentation.

Each tool tends to operate within its own ecosystem, solving a narrow problem while introducing new workflows for teams to manage. Over time, this results in a disconnected stack where information is spread across systems and requires constant effort to piece together.

AI orchestration: the shift that actually matters

Rather than continuing to add tools, Colin described a different approach—one centered on AI orchestration. This is not about introducing more technology, but about making existing systems work together more intelligently.

AI orchestration focuses on connecting data across systems, extracting meaningful insights, and delivering those insights directly within existing workflows. The goal is not to create a separate destination for AI, but to embed intelligence into the tools that teams are already using.

Why CRM still sits at the center of the revenue engine

There has been ongoing discussion about whether traditional systems like CRM will remain relevant in an AI-driven world. However, as Colin noted, this perspective often overlooks the fundamental role these systems play.

Enterprises require a centralized system of record to maintain governance, ensure data consistency, and support reporting and accountability. CRM continues to fulfill this role, and rather than replacing it, leading organizations are strengthening its position at the center of their revenue operations.

The role of intentional friction in improving execution

Another important concept Colin discussed is the idea of intentional friction. While many organizations aim to eliminate friction entirely, doing so can often lead to ambiguity and inefficiency.

According to Colin, friction itself is not the problem—unclear processes are. When there is no structure or defined ownership, deals can move forward prematurely, resources can be misallocated, and teams may get involved at the wrong time.

Intentional friction, on the other hand, introduces clear checkpoints and accountability into the process. For example, ensuring that a proof of concept is only initiated when a deal meets specific criteria, or requiring proper deal modeling before entering formal approval workflows. These measures do not slow deals down unnecessarily; instead, they ensure that effort is focused where it has the highest impact.

Revenue doesn’t end at closing

One of the most significant shifts Colin highlighted is how revenue is defined, especially in consumption-based models. In traditional sales models, closing a deal was often seen as the finish line. Today, it is just the beginning.

Revenue realization now depends heavily on how customers adopt and use the product over time. This introduces a new layer of complexity into forecasting, as projections must account for ramp, usage patterns, and ongoing engagement.

Colin explained that this requires continuous alignment between revenue teams and finance, along with more granular tracking of how solutions are being used post-sale. The focus shifts from simply closing deals to ensuring that customers derive value and increase their usage over time.

RevOps as the architect of the revenue engine

All of these changes point to a broader transformation in the role of RevOps. It is no longer just a support function responsible for reporting or systems maintenance.

This shift requires a move away from tool-centric thinking toward a more holistic view of systems design. It is about understanding how different parts of the revenue engine interact and ensuring that they operate in a coordinated, efficient manner.

The best systems are the ones you don’t notice

Perhaps the most practical insight from the conversation is this: the best systems are the ones that reduce effort without drawing attention to themselves.

Sales teams should not have to spend time updating CRM fields, taking detailed notes during calls, or navigating multiple tools to complete simple tasks. Instead, these processes should be automated and integrated into their workflows.

Where should you start?

For organizations looking to improve their revenue operations, the path forward is not about expanding the tech stack. It is about refining how existing systems work together.

This begins with identifying the signals that truly drive outcomes, centralizing data within a reliable system of record, and using AI to connect workflows rather than complicate them. It also involves introducing structure where it improves clarity and focusing on long-term value creation rather than short-term bookings.

The future of RevOps is not defined by the number of tools in your stack. It is defined by how well those tools are orchestrated.

As Colin’s insights make clear, success comes from turning data into meaningful signal, embedding intelligence into everyday workflows, and ensuring that every part of the revenue engine operates in alignment.

Because ultimately, growth is not driven by more tools—it is driven by clarity, coordination, and the ability to act on the right insights at the right time.