On May 22, 2025, RightWave hosted a power-packed webinar with three Marketing Operations leaders—Deirdre Mahon (VP Marketing, Super{set}), Tarun Arora (CEO, RevCrew), and Sanjiv Verma (Strategic MarTech & Ops Leader, Juniper Networks) and Moderated by RightWave CEO – Anurag Khemka —to demystify what it really takes to make AI work in Marketing Operations.
Here’s a round-up of the most valuable takeaways:
1. AI is Only as Good as Your Data
Sanjiv and Tarun emphasized a universal truth: bad data kills AI value. Whether you’re using AI for content, segmentation, or personalization, poor data quality will lead to poor decisions and misfired campaigns. If your systems are riddled with duplicates, inconsistent scoring, or incomplete ICP fields, AI will accelerate inefficiencies—not fix them.
Sanjiv’s advice: “AI is not a magic wand. You need clean, structured, and unified data foundations before implementing anything advanced.”
2. Start with the Problem, Not the Tool
Deirdre offered a clear mantra: “Don’t start with the AI tool. Start with your team’s biggest pain point.” Whether it’s cleaning your CRM, scaling campaign execution, or improving attribution—solve a business problem, not just chase AI hype.
Deirdre’s advice: “Document a use case, show results, and brag about them internally. It helps get org-wide buy-in.”
3. Use Cases That Work
The speakers shared real-world examples of AI delivering value:
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Content Generation: Rapid drafting of email copy, social posts, and SEO content—when guided by brand voice.
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Campaign Ops Automation: AI-powered agents managing campaign planning, execution, and response analysis.
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Email Response Management: Filtering replies to identify high-intent leads and unsubscribe requests.
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Data Enrichment & Normalization: Using AI to automatically categorize job titles, clean junk data, and deduplicate contacts.
Tarun’s insight: “AI agents can now handle end-to-end campaign workflows. That’s the busy work your team shouldn’t be doing.”
4. Pitfalls to Avoid
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Deploying AI without a clear process or change management plan.
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Ignoring data readiness before launching AI pilots.
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Over-relying on content tools without preserving human tone or brand consistency.
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Experimenting without governance—especially risky in large teams.
Deirdre cautioned: “Random experimentation with LLMs across teams can lead to shadow IT, cost overruns, and inconsistent outputs.”
5. Centralize Oversight, Encourage Bottom-Up Innovation
Sanjiv recommends a centralized AI task force, especially in enterprise settings, to handle risk, ethics, and integration. Tarun, meanwhile, suggests balancing that with bottom-up experiments—letting team members run micro-tests and share wins.
6. What’s Next? Agentic AI
The panelists agreed: the future of AI in MOPS is agentic. That means multi-step automation—AI not just answering prompts, but taking actions across platforms. Think: launching a campaign, syncing leads, and generating a report—all without human intervention.
Get AI-Ready with RightWave
The webinar concluded with an overview of RightWave’s AI Readiness Assessment—a structured audit to help you:
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Identify gaps in your data structure
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Evaluate segmentation and lead scoring practices
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Assess MAP/CRM integration health
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Get a clear roadmap for making your stack AI-ready
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