Thoughts on the Market: Special Encore – Who’s Disrupting — and Funding — the AI Boom

Podcast Date: December 29, 2025
Recording Date: December 5, 2025 (live at Morgan Stanley’s Global Consumer and Retail Conference)
Host: Morgan Stanley
Panelists:

• Aruna Masinha (Global and U.S. Economics Team)
• Simeon Gutman (U.S. Hardlines, Broadlines, and Food Retail Analyst)
• Megan Clapp (U.S. Food Producers and Leisure Analyst)

Episode Overview

This episode dives into the rapid evolution and adoption of artificial intelligence (AI) within consumer businesses, exploring its impact on company operations, the race for implementation, and broader economic consequences. Panelists share sector-specific insights, practical use cases, and forecasts for AI's influence on productivity, labor markets, and economic growth through 2026-2027.

Key Discussion Points & Insights

1. AI Implementation Across Consumer and Staples Sectors

(01:57–04:12)

• Assessment Framework: Simeon Gutman outlines the methodology for gauging AI adoption across covered companies, using a framework built around six functional categories:

  1. Personalization & Refined Search
  2. Customer Acquisition
  3. Product Innovation
  4. Labor Productivity
  5. Supply Chain & Logistics
  6. Inventory Management

• Ranking Methodology: Companies were evaluated on three dimensions—breadth, depth, and proprietary initiatives (e.g., partnerships with leading AI firms).

• Walmart as a Leader:

  • Full-scale integration of AI tools (e.g., Sparky shopping assistant, generative AI-powered search and checkout, augmented reality for shopping, computer vision for shelf monitoring).

  • Notable AI-driven ROI:

    “Driving a 25% increase in average shopper spend.”
    (Simeon Gutman, 03:38)

  • Partnerships with OpenAI for ChatGPT-powered shopping and operational innovations.

2. Real-World AI Use Cases & Impending Step Changes

(04:19–05:13)

• Product Cataloging & Personalization:
  • AI significantly reduces the manual effort behind staging products online and anticipates a leap forward in customer-tailored experiences.

  “It sounds like we're on the cusp of a step change in personalization… We didn't get practical use cases, but a lot of companies talked about deployment into 2026.”
  (Simeon Gutman, 04:46)

3. AI Adoption in Food & Staples Sectors: Early Innings with Promising Potential

(05:24–07:25)

• Adoption Status: Companies are still building data infrastructure and piloting early use cases, with efforts beginning to scale.

• Competitive Advantages:

  • Large, established companies hold a “tremendous amount of high-frequency consumption data.”
  • Scalability and balance-sheet strength will separate winners from the pack, though nimble smaller companies may also succeed.

• Revenue & Cost Perspectives:

  • Top-line: Marketing, innovation, and R&D as main AI opportunities.
  • Cost-side: Quantifiable supply chain savings and labor productivity gains.

  “I think these companies start with an advantage in that they sit on a tremendous amount of high-frequency consumption data. The question now is, can these large organizations move with speed and translate that data into action?”
  (Megan Clapp, 06:11)

4. Sector Examples: Success Stories in AI Implementation

(07:25–09:33)

• Hershey:

  • Uses algorithms to reallocate advertising spend by zip code in real-time, enabling targeted and efficient marketing.

• General Mills:

  • Deployed “digital twins” to improve operational forecasting, pushing productivity savings from 4% to 5% annually—a structural shift.

  “General Mills… deployed what they call digital twins across their network… improved forecast accuracy. They’ve taken historical productivity savings from 4% annually to 5%. That’s something structural.”
  (Megan Clapp, 08:18)

• Shark Ninja:

  • Optimizing their direct-to-consumer (DTC) website for large language models (LLMs) like ChatGPT and Gemini.
  • The CEO’s view:

    “By Christmas of next year, commerce via these AI platforms will be meaningful.”
    (Megan Clapp paraphrasing Shark Ninja CEO, 09:05)

5. Agentic AI Commerce: Growth or Cannibalization?

(09:33–11:53)

• Incremental vs. Cannibalized Sales:

  • Agentic AI may expand e-commerce, but could disintermediate retailers or reduce the importance of retail media.
  • Framework for defense:
    • Strength in infrastructure and forward-positioned inventory—agents (AIs) will favor retailers offering fast, reliable fulfillment.
    • The future structure of retail media, consumer data, and payment via agents remains uncertain.

  “The AI and the agent will still prioritize that retailer within that network. That business will likely not go elsewhere... Retail media is a different can of worms.”
  (Simeon Gutman, 10:49)

6. Macroeconomic Context: AI’s Impact on Growth and Labor Markets

(11:53–14:09)

• Growth Contributions:

  • Direct AI infrastructure investment plus productivity acceleration could add 40–45 basis points to GDP growth in 2026–27.
  • Projected U.S. GDP growth: 1.8% (2026) and 2.0% (2027).

• Labor Market Effects:

  • AI adoption remains modest (mid-teens % of companies).
  • Anticipated to complement labor in the near term, though certain demographics may be disproportionately affected in the longer term.

  “We think… over 2026–27, [AI] add[s] anywhere between 40–45 basis points to growth... For now, we think it is going to be a complement to labor, although there are some cohorts... probably going to be disproportionately impacted.”
  (Aruna Masinha, 12:28 & 13:31)

Notable Quotes & Memorable Moments

• “Walmart has full scale AI deployment... introducing GenAI tools… driving a 25% increase in average shopper spend.”
  – Simeon Gutman, [03:38]

• “I think these companies start with an advantage in that they sit on a tremendous amount of high-frequency consumption data.”
  – Megan Clapp, [06:11]

• “General Mills… improved forecast accuracy... taken productivity savings from 4% annually to 5%... real tangible benefits.”
  – Megan Clapp, [08:18]

• “By Christmas of next year, commerce via these AI platforms will be meaningful.”
  – Shark Ninja CEO (quoted by Megan Clapp), [09:05]

• “The AI and the agent will still prioritize that retailer within that network. That business will likely not go elsewhere...”
  – Simeon Gutman, [10:49]

• “For now, we think [AI] is going to be a complement to labor, although some cohorts… are probably going to be disproportionately impacted.”
  – Aruna Masinha, [13:31]

Key Timestamps

• 01:57 – Introduction of the AI assessment framework and company ranking method (Simeon Gutman)
• 03:38 – Walmart’s comprehensive AI strategy and partnerships
• 04:46 – Step change in personalization and cataloging functions with AI
• 05:24 – Early stage adoption and data advantages in food/staples sectors (Megan Clapp)
• 07:25 – Specific top-line and cost-saving AI examples: Hershey and General Mills
• 09:05 – Agentic AI commerce insights (Shark Ninja)
• 09:58 – Sales cannibalization vs. expansion debate
• 11:53 – Macro impacts of AI spending and labor market evolution (Aruna Masinha)

Podcast Tone and Language

The panel combines data-driven insights with practical examples, balancing cautious optimism about AI's business and macro potential with realism about challenges like data inertia, retail media disruption, and workforce shifting. The mood is analytical but lively, reflecting live conference energy and sector expertise.

Conclusion

This episode offers a comprehensive look at the fast-growing intersection of AI and consumer-oriented business, highlighting real-time developments, competitive dynamics, and the macroeconomic outlook for 2026 and beyond. AI promises to reshape personalization, supply chains, operational efficiency, and—eventually—commerce platforms, but also introduces new strategic dilemmas and labor market questions. Major players, from legacy retailers like Walmart to data-rich food companies and tech-savvy brands, are racing to capture the AI dividend. For investors and business leaders, vigilance and adaptability remain key as the AI tide rises.