5 RevOps Predictions for 2026

RevOps spent 2025 proving it belonged at the strategy table. The function moved from "the team that fixes Salesforce" to "the team that owns the revenue engine." Leadership started listening. Budgets followed.

2026 is where that influence gets tested.

AI agents are moving from demos to production. Data quality problems that used to be annoying are now blocking entire automation strategies. And the RevOps role itself is splitting - some teams are becoming more technical, others more strategic, and the ones stuck in the middle are getting squeezed.

Here's what we see coming for RevOps teams this year.

1. RevOps Becomes the AI Agent Orchestration Layer

2025 was the year of AI agent experimentation. Marketing tested content generation tools. Sales piloted AI SDRs. Customer success tried automated health scoring. Everyone ran their own experiments in isolation.

2026 is when someone has to make these agents actually work together.

That someone is RevOps.

Think about what happens when you have AI agents operating across the bow-tie: marketing agents qualifying inbound, sales agents doing research and personalization, CS agents monitoring usage patterns. Without coordination, these agents step on each other. They create duplicate records, trigger conflicting workflows, and generate insights nobody acts on.

RevOps becomes the orchestration layer, defining which agents own which processes, how data flows between them, and what governance ensures quality. This isn't a tools question, but a systems architecture question. And RevOps is the only function with visibility across the entire revenue process to answer it.

The teams that figure out agent orchestration early will compound their advantages. The ones still running disconnected experiments will fall further behind.

2. Data Quality Becomes the Biggest Revenue Lever

Here's the uncomfortable truth about AI in revenue operations: it makes your data problems worse, faster.

An AI agent fed bad data doesn't produce neutral results. It produces confidently wrong outputs at scale. Bad firmographics become bad routing decisions. Stale contact data becomes wasted outreach. Incomplete account records become flawed forecasts.

RevOps teams have always known data quality matters. In 2026, they'll finally get the budget to fix it (hopefully), because the AI investments everyone made in 2025 are failing without clean data foundations.

Expect serious investment in data governance, enrichment infrastructure, and pipeline integrity. Not as a "nice to have" cleanup project, but as the prerequisite for everything else working.

The companies that nailed data quality in 2024-2025 are now reaping compounding returns on their AI investments. The ones still arguing about who owns data hygiene are watching their agent implementations stall.

Platforms like Databar that aggregate multiple enrichment sources become critical infrastructure here - you can't maintain data quality with a single provider's coverage gaps. Waterfall enrichment that queries 90+ sources until finding matches is the difference between 50% data completeness and 85%+.

3. GTM Engineering Emerges as a Core RevOps Discipline

The traditional RevOps skillset, CRM administration, reporting, process documentation isn't enough anymore. The function needs builders.

GTM Engineers are the new role filling this gap. Part marketing ops, part sales ops, part systems architect. They build signal-based outbound workflows, automate enrichment pipelines, and create the technical infrastructure that makes AI agents actually work.

This isn't about replacing traditional RevOps skills. It's about adding a technical layer that most RevOps teams currently outsource to engineering (and then wait months for). GTM Engineers can ship automations in days.

Some organizations are evolving existing MarOps roles into this. Others are hiring specifically for it. Either way, the expectation is changing: RevOps needs people who can build, not just configure.

The challenge is finding this talent. GTM Engineering is a new enough discipline that there's no established career path. Companies are pulling from marketing ops, sales ops, data engineering, and even product management to assemble these teams.

4. AI Workflows Expand Beyond Top-of-Funnel

Marketing adopted AI workflows first. Content generation, ad optimization, lead scoring - these are now table stakes. The easy wins have been captured.

2026 is when AI workflows push deeper into sales and customer success.

Deal management gets automated assistance: AI that identifies stalled deals, suggests next actions, and flags when engagement patterns indicate risk. Pipeline inspection moves from quarterly reviews to continuous monitoring. Forecasting incorporates real-time signals instead of relying on rep gut-feel.

Customer success sees similar expansion. Usage pattern analysis. Churn prediction. Automated intervention triggers. The reactive "customer calls to complain" model shifts toward proactive "AI flagged declining engagement three weeks ago" motions.

But here's the reality check: AI workflows amplify your existing process. If your sales process is poorly defined, AI will execute that bad process faster and more consistently. If your handoffs between teams are broken, AI will automate the breakage.

The winners in 2026 won't just be the companies with the most AI workflows. They'll be the ones who fixed their foundational processes first, then automated.

5. RevOps Shifts from Reporting to Product Thinking

The old RevOps ask: "Build me a dashboard."

The new RevOps ask: "Own this system."

This shift is subtle but fundamental. RevOps is no longer being measured on report accuracy or dashboard aesthetics. It's being measured on whether the revenue engine actually works - whether leads route correctly, whether handoffs happen cleanly, whether data flows without manual intervention.

That requires product thinking: defining SLAs, monitoring system health, iterating based on user feedback, measuring outcomes rather than outputs.

The best RevOps teams in 2026 will operate more like internal product teams than support functions. They'll own workflows end-to-end, treat sales and marketing as their users, and continuously improve based on what's actually driving revenue, not what's theoretically best practice.

This also means RevOps gets accountability they've never had before. When the revenue engine breaks, it's no longer "sales didn't update their deals" or "marketing sent bad leads." It's "the system RevOps owns isn't performing."

More influence comes with more accountability. That's the trade.

What These Predictions Mean for Your Team

If you're running RevOps in 2026, here's the practical takeaway:

Invest in data quality infrastructure now. Everything else (AI agents, automation, forecasting accuracy) depends on clean, complete, current data. This isn't optional anymore.

Build or hire technical capability. Whether that's GTM Engineers specifically or upskilling existing team members, RevOps needs people who can build, not just configure off-the-shelf tools.

Take ownership of AI orchestration. If you don't coordinate agents across marketing, sales, and CS, nobody will. And disconnected agents create more problems than they solve.

Think like a product team. Own systems, not reports. Measure outcomes, not outputs. Treat revenue teams as your users and optimize for their success.

The RevOps function has earned its seat at the strategy table. 2026 is about proving it deserves to stay there. 

FAQ

What is the biggest RevOps trend for 2026?

AI agent orchestration. Individual departments ran AI experiments throughout 2025, but making those agents work together, sharing data, avoiding conflicts, maintaining quality, requires centralized coordination. RevOps is the natural owner of that orchestration layer because it's the only function with visibility across the entire revenue process.

How does data quality impact AI in RevOps?

Directly and significantly. AI agents trained on bad data produce bad outputs faster. Flawed firmographics become automated routing mistakes. Stale contact data becomes scaled outreach to wrong people. The AI investments companies made in 2025 are stalling because data foundations weren't addressed first. Clean, enriched, current data is now the prerequisite for AI to deliver value.

What is GTM Engineering?

GTM Engineering is an emerging RevOps discipline focused on building signal-based workflows, automations, and technical infrastructure. GTM Engineers combine skills from marketing ops, sales ops, and systems architecture to create the technical layer that makes modern RevOps work. They build things rather than just configuring them, shipping automations in days instead of waiting months for engineering resources.

How will RevOps roles change in 2026?

RevOps is splitting into more technical roles (GTM Engineers who build automations) and more strategic roles (leaders who own revenue system performance). Teams stuck doing only traditional CRM administration and reporting will get squeezed. The function is also gaining more accountability - owning system outcomes, not just delivering reports about them.

What should RevOps teams prioritize right now?

Data quality infrastructure first. It's the foundation everything else depends on. Then technical capability, either by hiring GTM Engineers or upskilling existing team members. Finally, take ownership of AI coordination before disconnected experiments create more problems than they solve.