Revenue Operations (RevOps) is the discipline of unifying sales, marketing and customer success to drive predictable growth. In 2025, it has become a C‑suite function: 73% of companies now have a senior leader responsible for RevOps. Artificial intelligence is a key enabler. In a recent survey, 97% of RevOps teams reported measurable return from AI, noting improvements in forecasting accuracy, predictive analytics and operational efficiency.
C‑suite adoption: 73% of companies have a dedicated RevOps leader.
AI impact: 97% of RevOps teams see ROI from AI initiatives.
Economic relevance: 48% of leaders say macroeconomic volatility has increased the strategic importance of RevOps.
Clarity gap: 89% report that RevOps still lacks clearly defined goals and investment priorities.
CFOs, CIOs and CROs are under pressure to do more with less. AI helps RevOps teams connect fragmented data sets, detect patterns in buyer behaviour and highlight opportunities for upsell or churn risk. Early adopters report gains in forecasting accuracy and operational efficiency. AI also surfaces leading indicators for cash flow and conversion, giving finance leaders greater control over revenue predictability.
Many organisations struggle to move from pilot projects to sustained value. Six & Flow’s FLAIR framework (Foundation, Leverage, Activation, Iteration, Realisation) provides a repeatable process to embed intelligence across teams. It emphasises data quality, clear ownership and feedback loops. When intelligence is treated as infrastructure rather than a bolt‑on, time to value falls and financial impact becomes visible.
Foundation: Stabilise data and systems before deploying AI.
Leverage: Prioritise high‑impact use cases based on feasibility and time to value.
Activation: Wire AI outputs into decision‑making with clear owners.
Iteration: Continually refine models and prompts using feedback.
Realisation: Scale what works and ensure adoption across the business.
According to a CIO Dive analysis, many enterprises “over‑index experimentation and under‑invest in foundational capabilities”. Isolated pilots with no unified data or metrics deliver little value. Leaders should prioritise use cases by business impact, fix the data layer, and embed measurement and governance from the start. This aligns with FLAIR’s focus on building a strong foundation and establishing rhythm rather than chasing novelty.
Audit and clean your data. Perform a readiness assessment to identify gaps in data quality, ownership and integration. Clean CRM and finance data before introducing AI.
Select high‑value use cases. Score potential projects by revenue impact, feasibility and time to value. Target problems such as predicting deal slippage or automating renewal risk alerts.
Define metrics and governance. Set success measures at the start, assign an owner and involve compliance teams. Review results regularly.
Integrate insights into routines. Bring AI outputs into forecasting and planning meetings. Adoption grows when teams see actionable insights in their workflow.
Iterate and scale. Version prompts and models, share learnings across teams and build an enablement network to compound gains.
RevOps and AI are converging quickly. Economic volatility and competitive pressure mean CFOs, CIOs and CROs need precise, timely insights. The data show that AI‑enabled RevOps can deliver those insights, but only when it is grounded in strong data, clear objectives and cross‑functional adoption. FLAIR offers a practical way to achieve this. By treating intelligence as infrastructure and focusing on execution over experimentation, mid‑size companies can turn RevOps into a strategic advantage.