Overview
This episode of the Retail Genic Podcast features Carl Howler, a partner at IBM Consulting, discussing the evolution of AI in retail, the emergence of AI native brands, and the transformative impact of agentic AI on shopping, operations, and personalization within retail and CPG sectors.
AI Adoption Trends in Retail and CPG
- AI integration in retail has shifted from experimentation to transforming core business workflows.
- Early AI discussions faded during COVID but have resurged strongly since 2022, now central to client strategy.
- Retail clients focus on targeted workflow transformation rather than broad experimentation with many use cases.
- Operational savings at IBM from AI and automation have reached $3.5B annually, improving efficiency.
- Scaling AI remains challenging; 60% of CEOs report being stuck in the pilot phase, mainly due to organizational and process hurdles rather than technology constraints.
Emergence of AI Native Brands
- "AI native brands" are expected to operate with dramatically fewer people (20–30 for $100M+) by automating functions traditionally requiring large teams.
- These brands will leverage AI first across product development, merchandising, supply chain, finance, HR, and legal.
- Analogous to digitally native brands, but with greater cost advantages due to automation and agentic workflows.
Impact on Store Operations and Customer Experience
- Applying AI to store operations requires managing workflows across potentially thousands of locations.
- AI enables event-driven task automation, increasing store staff effectiveness rather than just reducing labor costs.
- AI can facilitate a new level of in-store personalization, offering experiences previously reserved for top-tier customers.
- Integration of data from loyalty programs and digital interactions will enhance customer engagement and personalize offers.
Personalized and Dynamic Pricing
- Loyalty pricing will become more individualized, with variable discounts based on customer data and behaviors.
- Digital price tags will enable frequent store-level price updates, optimizing inventory and promotions.
- Retailers are wary of visible surge pricing; most personalization and dynamic adjustments will happen behind the scenes to avoid consumer backlash.
Agentic AI and Future Shopping Trends
- Agentic AI shopping is expected to reach a tipping point in the next two years, especially with advancements like GPT-5 and integrated checkouts.
- Early adopters and new AI native brands will lead, but mainstream adoption will follow as consumer behavior quickly shifts.
- AI agents will reshape how retailers approach digital shelf design, site content, and customer interactions.
Recommendations / Advice
- AI adoption strategies should be tailored to each client, considering their customer base, technology stack, and market readiness.
- Early adoption is advisable for brands with tech-savvy or early adopter consumers, while others may take a more cautious approach.
- Ensuring high-quality, accessible data infrastructure is a prerequisite for effective AI transformation.
- Retailers should prepare for increased automation in both customer-facing and back-office functions.
Decisions
- Focus on workflow transformation over broad AI experimentation.
- Anticipate AI-driven personalization in both online and physical retail environments.
Action Items
- TBD – Retail leaders: Assess readiness for AI workflow transformation and identify high-impact areas.
- TBD – Store operators: Explore event-driven task automation and personalized in-store experiences.
- TBD – Pricing teams: Consider pilots for individualized loyalty pricing and dynamic price updates.