Despite significant investment in modern AI platforms, advanced models, and skilled data science talent, many business leaders have yet to recognize the full value of these initiatives. For all the promise AI holds, one unfortunate truth remains: too many models never make it past the proof-of-concept stage, especially in critical go-to-market (GTM) functions.

The problem isn’t the technology itself, but rather the gap between model development and business execution. Recent Alexander Group research found that 83% of companies cite a lack of relevant use cases as the top reason they’re not investing further in AI. This suggests that the AI’s ROI challenge might not be about data—it’s about strategic alignment.

Taking AI from experimental to operational requires support from all areas of a business, starting with revenue operations (RevOps). From defining use cases to ensuring deployment readiness, RevOps can help to bridge AI’s value gap and unlock a world of possibility.

RevOps + Data Science = AI Success

AI models don’t drive value on their own, and deploying them effectively requires more than just technical savvy. While data science teams focus on building models using standard frameworks like the Cross Industry Standard Process for Data Mining (CRISP-DM)— encompassing business understanding, data understanding, data preparation, modeling, evaluation, and deployment—RevOps is the function that ensures those models align with actual business priorities.

In fact, RevOps often owns more of the AI deployment lifecycle than a typical data science team. Acting as a translator between business strategy and technical execution, RevOps helps to define KPIs, clarify GTM objectives, and curate the right data inputs. Once a model is built, RevOps validates its outputs against real-world business logic, embeds it into existing GTM systems, automates sales and marketing workflows around it, and trains revenue teams on how to interpret and act on the resulting insights.

Without this connective function, AI models are at risk of continuing to serve as high-potential shelfware.

Strategic Alignment Drives Tangible ROI

To derive real value from AI, RevOps and data science teams must align across three key areas: use cases, data management, and role clarity.

There exists a relevant AI/ML use case for every stage of the customer lifecycle. Whether tackling demand generation, churn prediction, or customer expansion, AI can drive impact across the entire lifecycle, spanning basic machine learning models to advanced generative AI.

Data sharing is also critical for ensuring AI alignment between RevOps and data science teams. Together, these teams can build robust, unified datasets to drive AI success by aligning on shared data definitions and tapping into their combined organizational reach to access the information they need.

Clarified roles and swim lanes are key throughout these motions, with each team actively participating in tying AI to business outcomes. RevOps serves as the business translator by surfacing use cases, shaping KPIs, and ensuring model outputs are actionable. Meanwhile, data science teams stay closely engaged to ensure their work aligns with broader organizational goals to drive growth.

The Work Doesn’t Stop There

Ensuring alignment between RevOps and data science doesn’t end with holding collaborative meetings and exchanging emails. True team integration is dependent upon mutual, continuous learning and effort.

Top RevOps teams are increasingly enhancing their technical knowledge to improve their business translation capabilities, digging deeper into areas like business intelligence and data warehousing, self-service automation and analytics, system admin and configuration, and IT software development support. With in-depth knowledge of more technical topics, RevOps can draw even more insight with AI and speak the language of data science teams to drive success.

Meanwhile, top data science teams remain in lockstep with RevOps to understand evolving business needs and goals, including what the C-suite is talking about and prioritizing as market shifts occur. This means data science is spending more time in the field, participating in ride-alongs, conducting customer interviews, and taking a look at solutions from the end user perspective to gain a deeper, holistic understanding of value creation.

It’s Time to Operationalize AI with RevOps

Unlocking the full potential of AI isn’t a matter of more data, better models, or even bigger investments—it’s about bringing together core business functions to make a real impact. By acting as the bridge between technical capability and commercial execution, RevOps—in tandem with data science teams—ensures AI initiatives aren’t just experimental. From defining high impact use cases and shaping the right data foundation, to driving deployment and adoption across the GTM organization, RevOps has the ability to turn AI from a mere idea into a genuine growth driver.