RevOps Leaders Roundtable: AI, Efficiency, and Revenue Operations Priorities for 2026

Revenue Operations leaders are navigating a rapidly shifting landscape in 2026. Artificial intelligence adoption is accelerating, revenue technology stacks are evolving, and expectations for efficiency and predictable growth are rising.

During SBI’s recent Q1 Revenue Operations Leader Roundtable, RevOps and Sales Operations leaders from across software, infrastructure, healthcare, and distribution discussed the operational priorities shaping their revenue engines this year.

While each organization faces unique market dynamics, several common themes emerged around artificial intelligence adoption, sales productivity, revenue intelligence, and the evolving role of Revenue Operations.

Demand Signals Are Improving but Buying Cycles Remain Slow

Recent SBI research shows improving sentiment across several commercial indicators including pipeline quality, deal size, and overall deal volume. Leaders participating in the roundtable confirmed that they are seeing similar signals within their own organizations.

However, one metric continues to lag: deal velocity.

Multiple participants noted that while pipeline health has improved, deals are taking longer to close as buyers increase scrutiny and purchasing processes grow more complex. Procurement requirements, expanded buying committees, and increased financial oversight are contributing to longer sales cycles across many B2B markets.

For Revenue Operations leaders, this reinforces the importance of improved forecasting accuracy, stronger pipeline management, and greater visibility into deal progression.

Organizations that can better diagnose stalled deals and identify friction points in the sales process will have a significant advantage as markets continue to recover.

Operational Efficiency Is the Top Priority for Revenue Operations in 2026

Despite improving demand signals, most organizations are not responding with aggressive hiring or large scale expansion of sales teams.

Instead, RevOps leaders are focusing on operational efficiency and revenue productivity.

Many roundtable participants described initiatives designed to increase the productivity of existing teams rather than expanding headcount. These efforts include more targeted account segmentation, improved use of buyer intent data, stronger prioritization of high value opportunities, and a greater focus on expansion within existing accounts.

Account intelligence is becoming increasingly important, particularly for organizations operating with smaller hunting teams.

Rather than casting a wide net across thousands of accounts, revenue teams are prioritizing accounts with the highest probability of conversion based on firmographic data, behavioral signals, and historical purchasing patterns.

This approach allows sellers to focus their time and effort on opportunities with the highest potential return.

Artificial Intelligence in Revenue Operations Is Delivering Productivity Gains

Artificial intelligence was a central topic throughout the roundtable conversation.

Nearly every organization represented is currently piloting or deploying AI enabled tools across their revenue technology stack. These tools support a range of use cases including conversational intelligence, automated forecasting support, CRM data enrichment, and sales productivity automation.

Most leaders agreed that the clearest value from artificial intelligence today is productivity improvement.

AI is proving particularly effective at reducing administrative workload for sellers. Tools that automate tasks such as meeting summaries, CRM updates, call transcription, and email drafting are helping revenue teams reclaim time that can be redirected toward customer engagement and selling activities.

For frontline sellers, the key question is straightforward: will this tool give me time back in my day?

While productivity improvements represent the most immediate value, Revenue Operations teams are increasingly exploring more advanced applications of artificial intelligence including predictive forecasting models, customer health monitoring, churn prediction, and identification of expansion opportunities within the installed base.

Several organizations reported that AI assisted forecasting models are beginning to rival traditional judgment based forecasting methods in accuracy.

AI Sales Agents Are Emerging but the Market Is Still Early

Another major topic of discussion was the rise of AI agents within sales development functions.

Several organizations are experimenting with AI driven tools that can automate elements of SDR workflows including outbound prospecting, lead follow up, and inbound qualification.

However, results remain mixed.

Some leaders reported early challenges with agent driven outreach including low response rates, inconsistent message quality, and operational oversight requirements. Compliance considerations and brand risk were also cited as potential concerns when automating outbound communication.

Other organizations are approaching the technology more cautiously by using AI agents to augment SDR productivity rather than replace roles entirely.

The consensus among the group was that AI powered sales agents represent a promising technology category, but the market is still maturing.

Technology Adoption Is the Real Barrier to AI ROI

One of the most important insights from the roundtable discussion was that technology adoption remains the largest barrier to achieving meaningful return on artificial intelligence investments.

Many organizations now operate with multiple AI enabled tools across their revenue technology stack. Despite this investment, adoption rates often remain inconsistent across sales teams.

The problem is rarely the technology itself.

Instead, the challenge lies in integrating these tools into the daily workflows of revenue teams.

Successful implementations require clear guidance on when and how tools should be used, integration with the existing sales process, and structured enablement programs that go beyond basic feature training.

Organizations that treat artificial intelligence tools as plug and play solutions often struggle to achieve measurable outcomes.

RevOps leaders must play a central role in ensuring that new technology capabilities are embedded into the operating rhythm of the revenue organization.

RevOps Leaders Are Taking a Disciplined Approach to AI Investment

Given the rapid pace of innovation in artificial intelligence and revenue technology, many organizations are adopting a more structured approach to evaluating new tools.

Rather than deploying new technology broadly across the entire organization, several participants described running controlled pilot programs within specific regions, teams, or sales motions.

This approach allows Revenue Operations leaders to measure performance impact more clearly, isolate variables affecting adoption, and evaluate return on investment before scaling deployment across the broader organization.

Some organizations are also partnering closely with emerging technology vendors during pilot phases, helping shape product capabilities while testing solutions within real operating environments.

This collaborative approach allows companies to influence product development while minimizing implementation risk.

The Revenue Technology Landscape Is Changing

Another emerging topic during the discussion was the evolving economics of revenue technology platforms.

Some vendors are beginning to explore pricing models tied to data usage, API access, and artificial intelligence processing requirements. As AI driven tools depend more heavily on cross platform data integration, the cost structures associated with revenue technology may begin to shift.

If this trend accelerates, it could reshape the revenue technology ecosystem by encouraging vendor consolidation, platform first technology strategies, and shorter technology contract commitments.

Several leaders emphasized the importance of maintaining flexibility in vendor agreements as the market continues to evolve rapidly.

What Revenue Operations Leaders Should Focus on Next

As the roundtable discussion concluded, one theme stood out clearly.

The organizations seeing the most progress with artificial intelligence are not necessarily those deploying the largest number of tools.

Instead, they are focusing on high impact use cases where technology can clearly improve existing processes.

These no regret opportunities include administrative automation, forecasting accuracy improvements, customer health insights, and revenue intelligence that improves decision making across sales and marketing teams.

The artificial intelligence landscape will continue to evolve rapidly. However, the fundamentals of Revenue Operations remain consistent.

RevOps leaders who apply new technologies deliberately, while maintaining operational discipline and clear measurement of outcomes, will be best positioned to translate experimentation into measurable growth.