Great Lakes Chennai paper flags agentic AI shift for Indian consumer-brand marketing
A Great Lakes Institute of Management Chennai white paper, launched with CavinKare founder C.K. Ranganathan, says brands are applying AI to customer profiling, campaign execution, ad bidding and inventory decisions while retaining human oversight for trust, cultural context and relationships.
What happened
Great Lakes Institute of Management, Chennai · Great Lakes Chennai’s AI marketing white paper, launched with CavinKare founder C.K. Ranganathan, says Indian
Key facts
- AI analysis time for 10,000–15,000 records reduced from about 4 hours to just over 1 hour
- AI-driven profiling achieved comparable campaign results by targeting 2 lakh customers instead of 12 lakh
- Great Lakes Institute of Management was founded in 2004
Why this matters
Corporate-development teams should evaluate acquisitions or partnerships in AI-native customer-data, campaign-orchestration and retail-forecasting platforms built for human-supervised deployment in India.
What to watch
- Published case studies showing sustained CAC or media-waste reductions from agent-led campaigns versus traditional segmentation.
- Adoption of agentic workflow tools by Indian FMCG, beauty, personal-care and direct-to-consumer leaders.
- Retail-media networks and marketplaces opening APIs for automated bidding, promotion and inventory actions.
- Evidence that smaller target pools maintain sales lift, validating the cited 2 lakh versus 12 lakh targeting efficiency.
- Consumer complaints, regulatory guidance or legal disputes involving AI targeting, consent, pricing or deceptive personalized claims.
- Increased hiring for AI product owners, marketing operations, data governance and regional-language AI quality roles.
- Build unified first-party customer, retailer, product and campaign data layers before deploying autonomous agents.
- Start with bounded use cases: audience suppression, creative variant testing, budget reallocation and demand-sensing alerts.
- Measure incrementality, conversion quality, repeat purchase and gross-margin impact rather than click-through rates alone.
- Create human approval thresholds for pricing, claims, culturally sensitive content, high-value customer communications and inventory commitments.
- Reallocate some agency and analyst spend toward AI orchestration, data governance, multilingual content QA and experimentation capability.
- Pressure media agencies and ad-tech vendors to disclose optimization logic, brand-safety controls and access to campaign-level data.