The Promise of AI in Retail — and the People Problem That Can Stall It

Last updated: Oct 31, 2025

AI is transforming retail and e-commerce. From massive brands rolling out chatbots that process millions of queries, to visual-search engines that let customers upload photos and find products instantly, the business case is compelling.

But as these technologies roll out, the internal reality is not always smooth: employees worry, feel left out, or resist. If organizations ignore that human dimension, they risk under-delivering on the promise.

In this post we’ll walk through:

Two retail success stories of AI adoption (large scale)
What employees actually feel about AI in the workplace (with data)
How to bridge the gap — making the technology work and making employees comfortable too.

1. Big Retail AI Success Stories

Here are two real-world examples from major, credible players that show what can be achieved when scale, data, and organizational buy-in align.

a) Alibaba Group – “AliMe Assist”

In a published paper, Alibaba’s AI assistant (AliMe Assist) processes millions of customer questions per day and is able to address ~85% of them without human intervention.¹

Why this matters: That’s a high automation rate for routine customer inquiries—in a large e-commerce context, freeing up human agents for more complex tasks.

Takeaway: When done well, AI can absorb a large part of the support workload at scale—especially in customer-facing retail settings.

b) Walmart Inc. – Generative AI for Operations & Customers

Walmart has publicly rolled out a suite of AI tools:

A new associate tool that reduced shift-planning time from 90 to 30 minutes in pilot stores.²
A broader strategy of generative AI/LLMs for customer support and immersive commerce.³
Why this matters: This is a large global retailer integrating AI not just for support but for both operations and customer-experience, at massive scale.
Takeaway: AI is shifting from “nice to have” to core operations—when it supports employees and customers, the impact potential grows.

What Retailers Should Focus On: Insights from McKinsey & Company

McKinsey’s “LLM to ROI: How to scale generative AI in retail” report highlights that:

Retail’s Gen AI opportunity is between US $240 billion and $390 billion (1.2–1.9 percentage points of margin)⁴
In a survey of 52 global retail executives: 90% say they’ve begun experimenting with Gen AI; 64% have conducted pilots; only 26% say they are scaling solutions.⁴
Two “must-haves” for success: choosing transformational use-cases and moving from pilot to scale (which requires rewiring org/data/tech).⁴
Implication: Success stories like Alibaba and Walmart illustrate what’s feasible—but the path to large-scale deployment remains challenging for many retailers.

2. What Employees Really Feel

Behind these successes, though, lies a significant under-current of employee concern. Research shows many workers feel anxious, uncertain or even resistant when AI enters the workplace. Here are key themes, supported by data.

Concern A: Job security & role obsolescence

52% of U.S. workers say they feel worried about how AI may be used in the workplace in the future.⁵
32% say it will lead to fewer job opportunities for them in the long run; only 6% believe it will lead to more.⁵
Implication: Even when organizations are excited about AI’s benefits, many employees don’t share that enthusiasm—they fear displacement or downgrading of their role.

Concern B: Skills gap and lack of training

63% of workers say they don’t use AI much or at all in their job.⁵
Only 35% of workers say AI skills are “extremely or very important” for success today—versus ~70%+ who say interpersonal/communication/critical-thinking skills are.⁵
Implication: If the workforce isn’t equipped or doesn’t feel equipped, AI adoption risks creating anxiety or resistance instead of momentum.

Concern C: Trust, transparency & fairness

Among workers who’ve used AI chatbots: 40% say the tools helped them do things more quickly, but only 29% say they improved the quality of their work.⁵
Implication: Employees may accept AI for speed/automation, but if they don’t perceive quality or fairness improvements (and if transparency is low), trust can erode.

Concern D: Poor implementation and unintended consequences

McKinsey notes many retailers remain stuck at pilot stage, indicating organizational/operational barriers far exceed purely technological ones.⁴
Implication: A rushed or disconnected rollout can cause frustration, low morale and poor ROI—even when the technology is sound.

**3. Bridging the Gap: Making AI Work For Employees and Business**

Given the promise of the retail AI case-studies and the real fears employees are harboring, here are strategies to ensure your AI deployment both delivers value and supports the workforce.

Strategy 1: Start with the human-in-the-loop

In the Alibaba and Walmart examples, the AI systems didn’t replace humans entirely—they handled the routine and supported the humans. That means:

Clarify what will change for employees, what stays the same.
Emphasize augmentation (“AI will help you”) not replacement (“AI will replace you”).
Build escalation paths: humans handle exceptions, complex queries, judgement calls.
Why it matters: Because data shows many workers still don’t believe AI will genuinely augment their roles; clarity builds trust.

Strategy 2: Upskill and train your people

Provide role-based training on how you expect tools to be used in day-to-day.
Create “learning time” for experimentation without penalty.
Make training ongoing; the tech and workflows evolve.
Involve employees early in selecting or designing the AI workflows so they feel agency.
Why it matters: The skills gap and feelings of being left behind are real risks to adoption.

Strategy 3: Be transparent about how AI works and affects roles

Communicate clearly: what AI is being used, what decisions it will assist or make, how roles may shift.
Explain safeguards: privacy, fairness, error-handling.
Make AI governance visible: ethics committees, human oversight, feedback loops.
Why it matters: Research shows lack of transparency and data governance are major barriers to scaling.⁴

Strategy 4: Align with measurable business benefits—and share them

From success-stories: Alibaba ~85% queries handled by AI¹; Walmart reduced shift-planning time from 90 ➜ 30 minutes in a pilot.²
Share metrics with employees: “Because of AI, we reduced resolution time by X %”, “You’ll spend less time doing X, more time doing Y”.
Highlight jobs evolving—not disappearing: “You’ll spend less time doing repetitive tasks and more time on high-value interactions”.
Why it matters: Employees are more likely to buy-in when they understand how changes benefit customers, business, and themselves.

Strategy 5: Roll out thoughtfully

Pilot one focused use-case first (e.g., associate task-management or customer chat-assistant) → learn → scale.
Measure both technical KPIs (accuracy, resolution time) and employee experience (satisfaction, stress, perceived fairness).
Show early wins, iterate, maintain human oversight to avoid AI mistakes and loss of trust.
Why it matters: McKinsey emphasizes many retailers struggle to scale because the org/data/tech/culture aren’t aligned.⁴

4. Conclusion

The business case for AI in retail & e-commerce is compelling. From large-scale deployments at Alibaba and Walmart to measurable shifts in employee expectations and increasing investment horizons, the examples show that well-designed AI systems can deliver value.

But success is not guaranteed simply by flipping the “AI switch”. The human side of the equation—employee training, trust, fairness, transparency—can make or break adoption. Employees who feel threatened, unprepared or out of the loop will either resist, under-utilize the technology, or even sabotage outcomes.

AI-enabled retail wins

when you treat the people side of change just as seriously as the technology side.

Move thoughtfully, involve your workforce, align the benefits and be transparent—and you’ll increase your odds of turning promise into performance.