Why RevOps Fails Without Adoption (And How to Fix It)
— with Jelena Arnold

The real problem with RevOps isn’t what you think

Why do well-designed RevOps systems still fail to deliver results?

Most teams assume the issue lies in tools, data quality, or reporting gaps. So they invest in better dashboards, cleaner data, or new platforms—expecting that to fix forecasting, alignment, and execution.

But as Jelena Arnold explains, that’s rarely the root problem.

The real issue is adoption.

Organizations don’t struggle because they lack systems. They struggle because the systems they build don’t align with how people actually work. And when that happens, teams don’t resist openly—they simply work around it.

RevOps is not a systems role—it’s an internal sales role

One of the most important shifts Jelena highlights is how RevOps should be viewed.

Most organizations treat it as a technical or operational function—focused on building processes, maintaining systems, and ensuring reporting accuracy. But in reality, RevOps is much closer to sales than most people realize.

As Jelena puts it, RevOps is fundamentally an internal sales job.

You are introducing a new way of working to teams that didn’t ask for it, don’t necessarily need it, and won’t adopt it unless they clearly see the value. That means your success depends less on how well you design systems and more on how effectively you influence behavior.

This is where many RevOps initiatives fall short. They rely on compliance instead of buy-in. And compliance rarely leads to long-term adoption.

Why adoption fails (and why it’s not a user problem)

When systems don’t get used, the default reaction is to blame the users.

Sales isn’t updating CRM properly.
Customer success isn’t logging activities.
Teams aren’t following the process.

But Jelena challenges this thinking directly.

If people are not using your system, it is not a user problem—it is a system design failure.

Because in practice, teams are always using something to manage their work. If they are relying on spreadsheets, Notion, or personal trackers, it means those tools are delivering value that your system is not.

This is why forcing compliance does not work. Adoption happens when systems make work easier, not when they demand more effort.

How to drive adoption that actually sticks

Instead of rolling out systems top-down, Jelena takes a different approach.

She starts with the most influential people in the team—typically top performers—and focuses on solving a real problem for them. Not a large transformation, but a small, visible improvement that makes their day-to-day work easier.

This creates two things immediately: trust and proof.

Once others see that the system is helping, not hindering, adoption begins to spread organically. It is no longer about enforcing a process—it becomes something teams want to use.

This approach also shifts how RevOps teams think about their role. Instead of building for everyone at once, they focus on creating momentum through targeted wins.

Forecasting problems are actually alignment problems

Forecasting is another area where assumptions often lead teams in the wrong direction.

Many organizations believe forecasting issues stem from poor data quality. But as Jelena explains, the deeper issue is usually a lack of alignment.

Different teams define metrics differently. Ownership is unclear. And as a result, there is no single version of the truth.

She shares an example where multiple teams reported different versions of the same revenue metric, forcing finance to manually reconcile numbers. This not only slowed decision-making but also reduced confidence in the forecast.

Fixing this was not about adding new tools. It required clear definitions, ownership, and alignment across teams.

What actually counts as a revenue signal?

A related challenge is identifying what truly drives revenue.

Sales may prioritize meetings booked.
Post-sales teams may look at product usage.
Marketing may focus on pipeline growth.

Individually, these metrics are useful. But without agreement on which signals actually predict outcomes, forecasting becomes inconsistent and reactive.

This shift—from tracking everything to identifying meaningful signals—is critical for improving both forecasting and execution.

Designing systems for how people actually work

One of the most practical insights from the conversation is the need to design systems around real behavior.

It is easy to create a perfect process in theory. But if that process does not match how teams operate in reality, it will not be adopted.

Jelena’s experience across marketing, sales, and customer success allows her to understand these nuances. She knows what motivates each team, what they ignore, and what they prioritize.

This perspective helps her design systems that fit naturally into existing workflows instead of disrupting them. And that is what ultimately drives adoption.

When companies need to move beyond Sales Ops

In the early stages, many companies rely on effort to drive growth. Teams work harder, push deals through, and find ways to hit targets even without strong systems in place.

But this approach has limits.

At some point, the organization reaches a stage where effort can no longer compensate for a lack of structure. Revenue becomes unpredictable, targets are missed, and leadership cannot clearly explain why.

This is the inflection point where Sales Ops is no longer enough.

RevOps becomes essential to provide visibility across the entire revenue lifecycle—not just sales, but marketing and post-sales as well. However, success at this stage requires balancing two priorities: giving leadership the visibility they need while ensuring frontline teams can work more efficiently.

Ignoring either side creates friction and limits impact.

Why integrity is critical to scaling RevOps

Even well-designed systems can fail over time if they are not protected.

Small exceptions—special deals, one-off processes, or manual overrides—can gradually erode the system. Over time, these exceptions accumulate, leading teams back to spreadsheets, workarounds, and inconsistent data.

Maintaining discipline around processes ensures that the system remains reliable and scalable.

Where AI fits into RevOps today

AI is a major topic in RevOps, but Jelena’s perspective is grounded in practicality.

AI should not be implemented for the sake of it. It needs to serve a clear purpose—improving decisions, reducing time to action, or uncovering insights that would otherwise be difficult to identify.

One area where AI is particularly valuable is signal extraction. By analyzing large volumes of data, such as call transcripts, AI can surface patterns and insights much faster than humans.

However, she also cautions against adding AI to broken systems. If the underlying data and processes are not aligned, AI will only amplify existing issues rather than solve them.

The bigger shift in RevOps

Across all these themes, one broader shift becomes clear.

RevOps is no longer just about systems and reporting. It is about aligning teams, influencing behavior, and enabling better decision-making across the organization.

This requires a different mindset—one that prioritizes adoption, clarity, and real-world usability over theoretical perfection.

RevOps success is not defined by how advanced your systems are.

It is defined by whether your teams actually use them.

Because in the end, growth does not come from more tools. It comes from systems that work the way people do.