GenAI Goes Mainstream in Marketing as 93% of CMOs Report ROI

GenAI has moved from pilot to profit: 93% of CMOs report ROI

GenAI is no longer a future consideration-it's a present-day imperative. A new study from SAS and Coleman Parkes reports that more than eight in ten marketers globally are already using GenAI, with 93% of CMOs and 83% of marketing teams seeing a return on investment. In EMEA, ROI sentiment averages 85%. Only 7% of CMOs say they don't see an ROI.

What marketers are actually doing with GenAI

• Core use cases: chatbots and content generation remain the most deployed.
• Rising use: trend analysis and customer journey mapping are gaining traction.
• Emerging bets: synthetic data, small language models, and digital twins are being tested to stretch capability and reduce model risk.

Reported outcomes and investment signals

• 94% cite better personalization.
• 91% see gains in processing large datasets.
• 90% report time and cost savings in operations.
• Nearly nine in ten report improvements in predictive accuracy, loyalty, and sales.
• 93% plan dedicated GenAI budgets through 2026, signaling sustained commitment.

"GenAI is no longer a future consideration, it is a present-day imperative," said Jenn Chase, CMO at SAS. Teams are weaving AI into daily workflows and building the stack for more autonomous marketing programs-while balancing risk and practical ROI.

Read the fine print

Survey-based adoption can be overstated, and self-reported wins don't always translate across every industry, data environment, or channel mix. Enthusiasm is high, but the gap between skeptics and realists is real-especially where data quality, governance, and tool sprawl are unresolved. Treat these results as momentum, not a guarantee.

How to turn GenAI into consistent ROI

• Define clear ROI models: tie GenAI efforts to a single metric per use case (e.g., CPA, AOV, retention) and set decision thresholds before launch.
• Instrument end-to-end: log prompts, responses, human edits, and downstream outcomes so you can attribute lift accurately.
• A/B everything: benchmark GenAI content, audiences, and journeys against strong human baselines, not strawmen.
• Move from pilot to program: standardize successful use cases with playbooks, SLAs, and retraining schedules.
• Control data risk: implement PII policies, synthetic data where appropriate, and approval flows for sensitive outputs.
• Right-size models: test small language models for speed, cost, and brand control; reserve larger models for complex tasks.
• Reduce tool sprawl: integrate GenAI into your core CRM, CDP, and analytics stack to cut handoffs and leakage.
• Guardrails for quality: use style guides, RAG or retrieval layers, and automated checks for bias, brand tone, and claims.
• Upskill the team: train marketers on prompt patterns, experiment design, and KPI attribution-not just tool buttons.

If you're formalizing AI skills across your team, see this focused program: AI Certification for Marketing Specialists.

Source study: SAS.