The Agile Brand with Greg Kihlström®: Expert Mode

Episode #821: From eTail: CommerceIQ's Himanshu Jain and Bill Schneider on Delaying the Gap Between Strategy and Execution

Date: March 3, 2026
Guests:

• Himanshu Jain, Co-Founder & Head of Product, CommerceIQ
• Bill Schneider, VP Product Marketing, CommerceIQ
  Location: eTail Palm Springs

Episode Overview

This episode examines a critical bottleneck in commerce: the lag between strategic insight and operational execution. Host Greg Kihlström is joined by Himanshu Jain and Bill Schneider of CommerceIQ to discuss how agentic AI (automated agents) is transforming the way brands respond to market opportunities, shifting execution away from the slow, manual processes of traditional agency models. The conversation digs into research findings, tangible AI use cases, impacts on organizational roles, ROI measurement, change management, and the evolving skill sets needed in the AI-augmented workplace.

Key Discussion Points & Insights

1. The Core Bottleneck: From Insight to Execution

• Main Problem: It's not the lack of strategic insight that holds brands back, but the time and complexity involved in executing those insights across multiple systems and teams.
• Insight Overwhelm: 80% of commerce leaders feel overwhelmed by data, not by insights themselves, but by limited ability to act on them quickly. ([03:19], Bill Schneider)
• Execution Challenges: Complex processes, siloed systems, and coordination hurdles make it "manually impossible" to act on all the data in real time, especially for high-volume SKUs and diverse retailers.
  • Quote: "They need, brands need a 24/7 agent to help them manage all those processes effectively." ([06:43], Bill Schneider)

2. Traditional Agencies vs. Agentic AI

• Shift in Approach: Brands are reallocating budgets from agencies to AI. While cost savings are a factor, it’s fundamentally about speed, scale, and keeping up with algorithmic retail.
• Agency Constraints: Agencies cannot keep up with 24/7, high-frequency, high-volume operational demands of modern commerce.
• Transparency and Control: 80% of surveyed leaders are open to agentic AI if humans retain transparency and final decision-making.

3. Tangible Use Cases for Agentic AI

• Beyond Automation: AI agents bring planning and intelligence—they can analyze context, goals, and multiple data points, not just execute simple tasks.

  • Example: Competitive Pricing Response ([07:43], Himanshu Jain):

    • Traditional: See a competitor price drop, copy it.
    • Agentic AI:
      • Evaluates: Is this the right competitive set? Did previous changes matter? What’s the inventory situation? What’s the business objective (margin vs. share gain)?
      • Simulates scenarios before recommending a nuanced response.
      • Gives recommendations for human approval—"an agent can do 90% of the work and a human is applying 10% judgment." ([09:53], Himanshu Jain)

  • Example: Content Optimization

    • AI scans product data, retailer guidelines and analytics, then updates content at scale—work that previously took hours now takes less than a minute.

4. Unlocking the Long Tail

• Scalability and Reach: AI agents unlock opportunities by making it feasible to regularly update and optimize the "long tail" of SKUs and secondary retailers—areas often neglected by human teams and agencies for time and resource reasons.
  • Quote: "There’s not a 9-to-5 that they have to worry about... you can apply that to your whole SKU catalog and make updates and adjustments." ([11:24], Bill Schneider)
  • Retailer Strategy: Secondary retailers may offer more cost-effective opportunities (e.g., lower ad spend) that go untapped.

5. ROI & Business KPIs for Agentic AI

• Key Metrics:
  • Output: Sales, share, margins.
  • Input: Improved content quality, higher rankings on search/recommendation engines, inventory availability, reduced time out-of-stock. ([16:18], Himanshu Jain)
• Human Upskilling: With agents handling routine execution, employees can focus on strategy, creative work, and retailer relationship management.

6. Evolving Roles, Skillsets & Change Management

• Human Talent Shift: The rise of agentic AI means employees become more focused on judgment, expertise, and agent management—training, monitoring, and providing context/feedback.
  • Quote: "Expertise in a particular area matters a lot more because you are teaching an agent to do that." ([19:16], Himanshu Jain)
• Analogy: Agents are like “junior analysts” or interns needing onboarding, oversight, and feedback loops.
• Change Management: Success depends on blending technical and human transitions—deploying engineers within client organizations to support adoption and customize agents for unique business contexts.
  • Quote: "...most successful brands and retailers would be the one who would embrace this change and are agile enough rather than getting caught in the red tape..." ([22:12], Himanshu Jain)

7. The Industry’s Next Phase

• Market Readiness: The enterprise AI conversation has shifted from pilots and chatbots to execution-at-scale.
  • Quote: "This year is really going to be about execution and rolling out these types of agents." ([21:40], Bill Schneider)
• Biggest Hurdles: Change management and grounding AI in business context remain top obstacles; a hybrid of rapid development and thoughtful governance is needed.

Notable Quotes & Memorable Moments

• On AI Agencies:
  "Traditionally agency teams are not able to keep pace with that amount of staff... They need, brands need a 24/7 agent..."
  – Bill Schneider ([06:30–06:43])

• On AI Agents’ ‘Intelligence’:
  "Intelligence and planning is the most fundamental shift that is happening in the automation world. And that is why agents can mimic human judgment to the cyber risk stuff."
  – Himanshu Jain ([10:44–10:54])

• On Employee Impact:
  "Most employees are now spending, with agents, they can now spend 80% of their time on strategic activities that agents are not good at."
  – Himanshu Jain ([17:23])

• On Human Skillsets:
  "Five years back a generalist was considered... the vogue in town. Now it is depth in a particular area, expertise in a particular area matters a lot more because you are teaching an agent to do that."
  – Himanshu Jain ([19:16])

• On Agility as a Mindset:
  "Agility is a mindset... things that used to take me days or maybe a week to complete are now done in less than a day."
  – Bill Schneider ([24:27])

• On Practical Upskilling:
  "Start to build things and not be afraid... and start to automate just one or two processes... That would be the best learning experience that you can get..."
  – Himanshu Jain ([25:18])

Important Segment Timestamps

• [00:49] – Bottleneck between insight and execution introduced.
• [03:19] – Research findings: 80% leaders overwhelmed by data, not strategy.
• [04:24] – The operational challenge of updating content for moments like Valentine’s Day.
• [06:22] – The shift from agencies to AI is foundational, not just cost-driven.
• [07:43] – What’s different about agentic AI vs. traditional automation (competitive pricing example).
• [11:24] – How agents unlock ‘the long tail’ and make scale practical.
• [15:52] – Key KPIs for agentic transformation.
• [18:20] – Evolving human roles: depth, onboarding, agent management.
• [21:00] – The market is moving from pilots to mainstream execution.
• [22:12] – Change management as a critical success factor.
• [24:27] – Rapid technology shift requires adopting an agility mindset.
• [25:18] – Concrete advice for personal upskilling in an agentic future.

Episode Tone & Takeaways

Energized, pragmatic, and optimistic, the conversation emphasizes both the opportunity and the operational realities of agentic AI. The speakers are candid about the complexity, but focus on actionable ways brands, teams, and individuals can stay ahead—by combining technology with a continuous learning and agile mindset.

Summary for Non-Listeners

This episode explores how brands can overcome the operational bottleneck between knowing what to do (insight) and actually doing it (execution), by leveraging AI-powered agents—a leap beyond traditional automation. The discussion provides clear, real-world examples of how agentic AI removes scaling constraints, improves responsiveness, and changes the roles and skill requirements of commerce teams. Listeners will come away understanding why the agentic model matters now, what KPIs to measure, how to approach change management, and how to prepare as individuals and teams for this fundamental shift in digital commerce.