AI & Marketing : Episode 1 with Piet Loubser & Ashruti Singh
Gokul Anantha
This week got off to a great start as I moderated a discussion with 2 amazing marketing leaders – Piet Loubser and Ashruti Singh.
The topic? Building an AI ready marketing function
Ashruti Singh is a London-based marketing leader with 20 years of experience across product marketing and management roles at firms like Kantar and SAP. Piet Loubser is a former CMO with a background in software engineering. He specializes in product marketing, competitive intelligence, and demand generation. Piet brings a data led perspective to marketing.
We discussed about what makes their day in marketing, their perspective on AI in marketing, AI readiness and AI readiness audit/ benchmarking.
Highlights of the Discussion
AI in Practice: Ashruti shared how her team at Kantar experimented with agentic workflows to improve campaign design, using AI to simulate outcomes based on marketing goals.
Skepticism vs. Reality: Piet emphasized the challenges of “black box” AI platforms like 6sense, pointing out the need for transparency, training volume, and interpretable outputs.
Cultural Shift Required: Both leaders agreed that executive sponsorship and organizational understanding of marketing’s evolving role are crucial to make AI transformation successful.
Toward Collective Learning: I introduced the idea of evolving this discussion series into a “chain-letter” style collective—each participant inviting another marketing leader to grow the circle and diversify expertise.
Key Takeaways
Where marketing adds value to business is in terms of predictability. All actions – AI or otherwise – need to meet this test. In that sense, Predictive AI initiatives still corners the lions share of attention , but other AI initiatives are progressing from experimentation.
AI readiness Is multi-faceted
We discussed seven key levers for assessing AI readiness in marketing:
Data quality
Martech stack maturity,
Use case clarity,
Internal talent,
Governance
Leadership buy-in
Organizational culture.
Data remains #1 among the AI readiness levers
No surprises there! Without clean, unified, and accessible data, AI applications—especially predictive ones—struggle to deliver meaningful outcomes.
Use cases need to be goal oriented
For example, improving campaign effectiveness is a goal. For e.g. , “ if the goal is to increase (qualified) leads by X percent, then can AI help design the campaign? “
Often, AI initiatives are focused on lead scoring or attribution, but these are operational dimensions.
Audits can catalyze executive buy-in
Audits need to be scoped well and focused on specific marketing objective(s). An AI readiness audit can validate assumptions, highlight gaps, and build momentum for investment—especially when led by external partners or used during strategic transitions.
Lakehouse’s are promising but must be lightweight / marketer friendly. Not IT focused
Solutions that aggregate data from different sources are foundational for predictive modeling. In that sense, centralized data platforms that provide a 360-degree view with ease are critical. Marketing teams can’t afford long implementation cycles. Rapid, marketer-friendly integrations are critical.
Skill gaps remain a barrier
This is a top 3 pain . Despite AI tooling , lack of in-house data/AI skills makes implementation difficult without support
What’s Next?
Ashruti will host the next session in August, inviting a new marketing leader to join. The group agreed to build a short list of core themes—such as campaign intelligence, content ops, and buyer insights—to guide future discussions.
Stay tuned as we journey together to demystify AI in B2B marketing— one candid conversation at a time
