3 Priorities Retail Leaders Set at NRF 2026 and How Intelligent Observability Delivers

Last week, 40,000+ retail professionals gathered in New York City for NRF 2026: Retail's Big Show. From Google announcing a new agentic commerce protocol to retailers like Best Buy, Target, and Nordstrom sharing how they're reimagining their digital strategies, one theme dominated: the "shiny object" phase of technology is over.

At our booth, retail CTOs and IT leaders from companies like Nordstrom, Dick's Sporting Goods, and BJ’s Wholesale were focused on execution over experimentation. The conversations centered on operationalizing existing investments and building systems that deliver when it matters most.

Here are the three clear priorities that emerged and how intelligent observability helps retailers deliver on them.

1. "Black Friday Readiness" Becomes a Year-Round Discipline

Our booth headline, "BLACK FRIDAY. ALL YEAR.", captured a shift happening across retail. Technology leaders need systems that can handle Black Friday-level traffic and complexity every single day.

Retailers managing global operations asked how to quickly isolate whether an outage stems from network issues, POS software, or third-party integrations. Leaders from luxury brands discussed the challenge of treating every product launch with the same reliability standards they expect during their biggest sales events. For many, the traditional "peak season" mindset no longer applies, every day demands peak readiness.

The data backs up this urgency. Nearly 1 in 3 retailers experience critical outages weekly, with a median cost of $1 million per hour. With mobile commerce representing 60% of retail sales, there's zero margin for error. A slow-loading page, a payment gateway hiccup, or an inventory sync failure sends customers straight to competitors.

New Relic was recently named a Leader in the 2025 Gartner® Magic Quadrant™ for Digital Experience Monitoring, a recognition that reflects our comprehensive approach to monitoring the complete customer journey.

New Relic helps retailers shift from reactive firefighting to predictive prevention with Digital Experience Monitoring (DEM):

• AI-driven anomaly detection spots patterns before they become outages, letting teams simulate peak traffic loads to find breaking points 
• Mobile Session Replay shows exactly what customers experienced when they abandoned carts
• Browser RUM tracks Core Web Vitals like Interaction to Next Paint to catch third-party scripts blocking the main thread and killing mobile conversions
• Real-time payment monitoring tracks payment gateways, inventory APIs, and third-party integrations to catch failures instantly
• Synthetics test critical user flows 24/7 so teams know checkout breaks before customers do

Domino's uses New Relic to maintain 99.6% availability during peak order periods, and Mercado Libre achieved zero error rate during peak traffic, proving that year-round peak readiness is achievable.

2. Tool Consolidation Moves from "Nice to Have" to Mission-Critical

One of the most revealing sessions at NRF featured executives from Best Buy, Nordstrom, and Target discussing their marketplace strategies. The real insight came from Nordstrom's President of Digital: "Our marketplace wasn't a digital initiative. It was an enterprise initiative."

Retailers are done treating digital as a separate channel. They're consolidating systems, unifying data, and eliminating the tool sprawl that makes omnichannel operations fragile.

Our booth conversations reinforced this urgency. Technology leaders described the operational friction of managing multiple monitoring tools and the time wasted during critical incidents trying to correlate data across disconnected tools. The question we heard repeatedly: can you trace a single transaction from cart through fulfillment in one view?

Retailers have reduced their observability stack from 5.9 tools in 2022 down to 3.9 tools in 2025, with 51% planning further consolidation in the next year. With outages costing a median of $1 million per hour, every second spent jumping between tools is revenue at risk.

New Relic's Intelligent Observability Platform connects the entire retail stack in one view:

• APM for e-commerce platforms, Browser and Mobile RUM for customer-facing apps, Infrastructure Monitoring for POS systems, and Log Management for inventory sync—all correlated automatically
• End-to-end distributed tracing follows a single transaction ID from cart through the orchestration layer to warehouse WMS, showing exactly where delays happen
• Business observability with Pathpoint models the complete customer journey from discovery through checkout to fulfillment, connecting technical metrics to revenue impact
• SAP Monitoring catches "stuck" inventory reservations and sync failures before they cause order cancellations

Retailers who consolidate onto a single platform reduce MTTR by eliminating tool sprawl. One platform, one source of truth, working from the same data.

3. AI Shifts from Experimentation to Operational Reality

AI dominated conversations at NRF 2026—but not in the way it has at previous shows. This year, the focus shifted from demos and pilots to operational deployment and risk management.

Christian Beckner, NRF's Vice President of Retail Technology, summarized the shift: "Agentic AI continues to transform all aspects of retail, including supply chains, warehouse operations, marketing and customer engagement. Agentic AI also creates new risks that retailers need to understand and manage…companies will need to develop strong governance to address these challenges." 

Across the show floor, AI was in production. AI chatbot usage has increased, with customers who engage converting at higher rates. But as retailers deploy AI at scale, they're discovering new operational challenges: hallucinations in customer service responses, variable token costs that spike unexpectedly, and unpredictable latency from retrieval-augmented generation (RAG) adding 800ms or more to response times.

This aligns with our research showing that 50% of retailers cite AI as their primary driver for observability adoption. As AI agents proliferate and interact with each other, the governance and risk management require real-time visibility into the entire AI stack.

New Relic AI Monitoring provides visibility into the entire AI stack, whether it’s LLMs, vector databases, or agentic workflows:

• Model performance tracks throughput, latency, and error rates, and model inventory auto-discovers models in production detecting “shadow AI” usage
• Token cost analysis automatically calculates cost per request, while token efficiency ratios reveal successful outcomes versus tokens consumed, helping to identify wasteful prompt patterns
• Response tracing visualizes the entire chain of thought, showing the prompt, retrieved context (RAG), and final tool execution in a single view.

When retailers can see where their AI workloads are burning budget on unnecessary tokens or delivering inconsistent experiences, they can optimize before customers notice or before CFOs and CTOs start asking about AI ROI.

What This Means for Your 2026 Strategy

The contrast with previous NRF shows was striking. There were more conversations about MTTR, cost efficiency, and customer impact. Retailers have moved past asking "what's possible?" to demanding "what actually works?"

The retailers winning in 2026 are building for peak performance every day, consolidating tools to eliminate friction, and instrumenting AI to ensure it delivers value.

If you left NRF with a long list of priorities, here's where to start:

• Build for peak season, every season. Use load testing and predictive analytics today to find bottlenecks you'd normally discover later. Monitor the complete customer journey so you can prevent issues instead of explaining them.
• Consolidate your tools. If your engineers are touching more than three tools during an incident, you're burning time and money. A unified platform reduces MTTR and gives you the complete picture when seconds matter.

Instrument your AI stack now. Track model performance, token costs, and user feedback to understand unit economics before they spiral. Know which models are running in production and catch hallucinations before customers do.