In this post, I’m looking beyond the standard NRF recap of product launches and vendor highlights to share three key strategic observations from this year’s event, and what they mean for retail operations, staffing, and transformation in 2026.

NRF made one thing very clear: the complex challenges facing today’s retailers won’t be solved by a single new product or breakthrough technology. Instead, the focus needs to shift to more fundamental questions:

  1. How should AI augment human judgment?
  2. How do you make smart technology decisions in an increasingly crowded landscape?
  3. How can you harness data in ways that strengthen trust rather than erode it?

Rethinking human + AI collaboration as “AI in the human loop”

Shopper needs, behaviors, and preferences evolve rapidly and profoundly, especially as new digital technologies challenge the very foundation of the retail experience. But the one area that remains consistent, no matter how advanced or embedded technology becomes, is the importance of human engagement.

Our most recent consumer trends research, What matters to today’s consumer, reveals that 74% of shoppers value human interaction during in-store shopping, underscoring how essential people are when it comes to serving other people.

At the same time, there are areas where work can be meaningfully augmented by AI, particularly in high-volume, data-intensive, repeatable activities such as demand forecasting, replenishment, pricing optimization, and anomaly detection. In these domains, AI capabilities have matured  over several years already and are now delivering greater speed, accuracy and consistency, exceeding human performance – freeing human teams to focus on higher-value creation that more directly impact the end consumer.   

As AI increasingly takes on background processing and analytical effort, and as shoppers continue to expect more personal, high-touch experiences, retailers must intentionally design how humans and AI work together. In practice, this means solving for two distinct — but complementary — variations of the human-AI equation:

  1. Humans in the AI loop: Here, AI is the primary driver, with humans providing oversight, validation, and governance. Teams review and refine AI outputs as confidence builds, gradually reducing manual intervention where appropriate, such as demand forecasting tools that become largely touchless once trained and trusted.
  2. AI in the human loop:  Here, the human is the primary driver, with AI serving as an enabler. AI tools augment frontline and operational teams by putting timely insights and guidance at their fingertips—helping store associates resolve infrequent tasks like maintenance or respond more effectively to customer inquiries without replacing human judgment or interaction.

Ultimately, Human-AI chemistry isn’t about taking the human in or out of any particular area, it’s about designing the right balance so AI handles what it does best, while people focus on what only humans can do. Done well, this ensures retailers’ greatest resource — their people — is consistently applied where it creates the most value.

Making AI and tech decisions in a landscape with no clear winners

When speaking with Chap Achen from Gartner at NRF, he started the conversation by saying that he has a lot of empathy for the position retailers find themselves in today. I share that perspective.

Retailers are navigating a growing set of pressures: rising costs, shrinking margins and increasingly demanding consumers. Chap added another challenge to the list: the uncertainty created by the praid expansion of the technology landscape itself.

The market is experiencing a surge of AI solutions, platforms, and LLM-enabled tools, many of which deliver impressive out-of-the-box capabilities with limited differentiation. As a result, it has become relatively easy for organizations to appear capable, quickly.  At the same time, this abunanace of choice makes it harder to determine which platforms platforms to invest in based on which will become the market leaders.

There were several major announcements from enterprise platforms and hyperscalers, most notably from Google and Shopify with the Universal Commerce Protocol (UCP), a new open standard for agentic commerce that works across the entire shopping journey from discovery and buying to post-purchase support. Despite these major announcements, it is clear that no-one has ‘won’ the AI race yet and it is unlikely that we will be able to identify many clear winners in the near term.

With this in mind, retailers need to take a balanced, risk-aware approach: avoiding lock-in by spreading investment across multiple platforms and treating AI adoption as a portfolio strategy rather than a single-vendor decision. Enterprises will benefit most from interoperable, open ecosystems that support multiple models and technologies — enabling greater flexibility, resilience, and innovation.

Using data to contextualize the experience — without overstepping

Our consumer research reveals an interesting correlation: as shoppers use AI more, their trust in the technology declines.

According to our 2026 trends report, 71% of shoppers are concerned by how Gen AI collects and uses personal data and 76% want to be able to set strict boundaries for a digital assistant. Meanwhile, two-thirds of shoppers trust a digital assistant more when it provides clear explanations for its recommendations and actions.

The implication is clear: retailers must strike the right balance in how they use data and AI. When new experiences feel natural, timely, and seamlessly integrated, they land on the “cool” side and create real value for customers. When they feel intrusive or overreaching, they quickly cross into “creepy,” eroding trust instead of building it.

As brands and retailers incorporate AI in new ways within the shopper experience across many touchpoints, they need to ensure its use is transparent, responsible, and explainable. The cost of not doing so will be a massive opt-out on the part of shoppers (what does massive opt-out look like? Think about the AI version of the unsubscribe button from email marketing).

This is what purpose-driven retail really looks like.

Taking these 3 takeaways in combination, winning in Retail will not be defined simply by who adopts the newest technology first, but by who applies it most thoughtfully as part of a cohesive strategy. Competitive advantage and business value will come from putting AI in service of people (both your employees and consumers), building interoperable foundations that can adapt as the landscape shifts, and using data in ways that deepen trust rather than undermine it.

Are you ready to embrace the future of purpose-driven retail? Capgemini can help. Reach out to me to learn more about how our team can help yours navigate a challenging and uncertain landscape.