B2B Demand Gen Leaders Can Learn from Kellanova’s ‘Test-and-Learn’ AI Strategy 

Artificial intelligence isn’t just reshaping how brands reach audiences; it’s redefining how organizations operate, learn, and grow. For marketing leaders accountable for ROI, the lessons from manufacturing and operations adoption cycles show that technology delivers value only when people trust and use it. Upskilling, change management, and continuous feedback are now as critical as model accuracy or automation. That’s why CMOs and demand generation leads must treat AI as a culture shift, not just a software install, as aligning budgets and incentives to reward learning and adaptability over short-term output will be key.

This operational lens is important because marketers face the same challenges: new tools, fragmented data, and uneven readiness across teams. The path to sustained performance lies in creating a “test-and-learn” culture that mirrors what forward-thinking manufacturers are doing today. It’s about building champions, training continuously, and ensuring data-driven insights translate into real action.

The following excerpt from “Teaching AI to Ops: A Continuous Education” by Andy Hanacek on FoodProcessing.com captures how industry leaders are teaching AI to work for humans, and not the other way around:

“The biggest challenge in teaching operations teams about AI isn’t training the technology—it’s leading the change, according to Jill Stuber, vice president and co-founder of Catalyst Food Leaders.

‘We have to focus on the organizational change management aspect,’ adds Mike Smith, reliability engineering manager for Life Cycle Engineering. ‘Show [employees] why it benefits them and the company.’ Kellanova is moving beyond isolated pilots to democratize AI access and invest in upskilling, creating what the company calls a “test-and-learn” culture. Smith cautions that some firms risk ‘buyer’s remorse,’ spending hundreds of thousands of dollars on sensors without turning data into planned work. The companies that succeed, he says, are those that turn AI insights into workflows—linking analytics to action through human training, communication, and reinforcement.” 

Continue reading “Teaching AI to Ops: A Continuous Education” by Andy Hanacek on FoodProcessing.com.

Why It Matters to You:

For marketing leaders navigating AI adoption, the lesson from plant-floor transformation is clear: Technology success depends on human readiness. Just as operations teams must continuously train to extract value from AI-driven systems, marketing organizations need structured upskilling to turn generative tools, analytics, and automation into measurable ROI. Without strong change management, campaigns risk becoming pilot projects that never scale, which can drain resources and confidence across teams.

The Food Processing article underscores a universal truth in digital transformation: AI isn’t plug-and-play; it’s teach-and-trust. CMOs and demand gen leads who invest in a “test-and-learn” culture — where teams iterate, measure outcomes, and share insights — will see faster innovation cycles and stronger accountability. The marketing parallel to predictive maintenance is creative optimization, as both hinge on feedback loops that turn data into smarter decisions, better performance, and sustained competitive advantage.

Next Steps:

• CMO: Launch a quarterly “AI enablement sprint” to train teams on real campaign use cases; track participation and time-to-implementation as key metrics. 
• Demand Generation Manager: Pilot one AI-assisted workflow, such as lead scoring or content personalization, and compare conversion or engagement lift versus control campaigns. 
• Marketing Analytics Team: Build feedback loops to evaluate which AI insights drive measurable outcomes (CTR, MQL, CAC); refine data models accordingly. 
• Creative and Content Leads: Adopt a “test-and-learn” approach mirroring Kellanova’s model—rotate AI tools through small projects, document results, and scale the top performers. 
• Marketing Operations Leader: Identify internal AI champions to sustain adoption momentum and ensure workflow alignment across teams; review adoption rates monthly.