AI & Marketing : Episode 2 with Reet Mand
Gokul Anantha
We’re living through one of the most disruptive moments in marketing since the rise of marketing automation. When Marketo and Eloqua reshaped how we thought about lead scoring and nurture, it set off a wave of change. AI is different. It’s bigger, faster, and less forgiving to those who wait.
For me, this moment feels personal. I began my career in CRM implementations, moved into sales leadership, and later took on a product role at SAP—where I saw how even small improvements in marketing platforms (like smarter channel selection or better deliverability) could have outsized impact. That’s where I got hooked on marketing, and it sparked a passion I carry into my work today: helping teams bridge the gap between data, tools, and decision-making.
That’s why I launched Loglens Conversations—a space to bring marketing leaders together to make sense of AI’s disruption, learn from one another, and explore what readiness really means. In our latest session, I had the privilege of speaking with Reet, a veteran revenue marketer and lifelong learner.
Reet’s experience in marketing is vast. From Google, Okta to Honeycomb.io & Crowdstrike. She reminded us that marketing has always been shaped by waves of disruption—from the rise of marketing automation with Marketo and Eloqua, to ABM’s surge, and now to AI. However unlike past shifts, AI feels different. It is not just another tool in the stack—it’s a force capable of reshaping workflows, decision-making, and even the role of marketers themselves. What struck me most was how practical her perspective was.
Our discussion topic? From Hype to Real Impact of AI in marketing
The big takeaways
Beyond the hype, AI will succeed in marketing only if it helps us do the work that matters—better, faster, and with clarity.
AI will win by simplifying, not complicating
AI’s promise is not “another tool in the stack,” but the ability to unbundle complexity—stitching fragmented workflows into coherent journeys. This is critical. Data from accredited surveys suggest that , despite the martech explosion, <55% of marketing processes are managed within the martech stack.
Readiness Is a Culture, Not an OKR
In a recent CMO survey (Deloitte), nearly 60% of marketing leaders said they feel pressure to adopt AI, but fewer than 20% believe their teams are fully ready.
That gap is cultural, not technical. Reet put it well: “Good prompts come from knowing the outcome you want to drive.”
Readiness isn’t about adding an AI initiative into your OKRs—it’s about building a culture where teams experiment, question, and learn.
Use Cases Before Tools — Framed by Jobs-to-Be-Done
Too often, AI adoption starts with tools. But tools don’t create impact—use cases do. The most effective way to define those use cases is through the Jobs-to-Be-Done (JTBD) framework.
Instead of asking “Which AI tool should we try?” ask:
What “job” in marketing needs to be done that currently isn’t?
What tasks are consuming too much manual effort?
Where are the gaps in delivering pipeline and growth?
Data Moves from IT’s Problem to Marketing’s Mandate
AI raises the stakes: garbage in, garbage out has never been truer. As Reet put it ” Marketers need to have a clear hypothesis before they can use data , data science & AI”
What’s changing is ownership. Marketing leaders now have both the tools and the mandate to take control of their data destiny—shaping how first-party, third-party, and customer journey data is connected, cleansed, and activated.
👉 Data isn’t a technical project anymore. It’s a marketing business case.
The Future Is Collective Learning
No one team has all the answers. AI is evolving too fast, and every company is at a different stage of maturity. The path forward isn’t about individual experiments in silos—it’s about collective conversations
As Reet said: “The more conversations we have, the faster we move. Collectively, we rise up. There’s no reason marketing can’t lead this AI revolution.”
Final Word
AI in marketing is not about hype, tools, or vanity experiments. It’s about clarity of use cases, the courage to experiment, and the discipline to own our data. Most of all, it’s about marketers stepping into the driver’s seat of disruption—rather than waiting for it to happen to us.
The Jobs-to-Be-Done lens gives us the clarity to match AI with the problems that matter most. When marketers own the job list—and AI becomes the assistant—we unlock real leverage
