The Silent Threat to Retailers' Biggest Quarter: Outages and AI Blind Spots

Nic Benders
New Relic
Learn more about New Relic

AI continues to be the top story across the industry, but a big test is coming up as retailers make the final preparations before the holiday season starts. Will new AI powered features help load up Santa's sleigh this year? Or are early adopters in for unpleasant surprises in the form of unexpected high costs, poor performance, or even service outages?

Every year shoppers spend more money online, this year it could top $300 billion, and every year their expectations go up. People also expect fast, flawless experiences, and even a small hiccup — a checkout freeze, a payment error, or a laggy app can immediately lose revenue and trust.

Every retailer knows this, and knows the hard work that goes into preventing those problems. Decades of experience have shown us that testing, pre-scaling, and careful change management can help control the chaos. But this year, AI may add an unpredictable element.

Where AI Complicates Things

AI is everywhere in retail — powering recommendations, forecasting demand, personalizing experiences. But AI also makes systems more complicated. And because every answer is different, it doesn't always follow the usual rules for software. That can create blind spots, allowing problems to hide in places no one is looking until customers feel it first.

AI systems also scale differently than other software. It's not just CPU and RAM anymore. Suddenly GPU and memory bandwidth matter too. Cloud instances that can be used for AI are in constant demand.

What happens when everyone scales up at once? Do you spend more to win the bidding war for resources? Switch to smaller models that might make more mistakes? Live with slow responses?

The need to answer these questions is driving the rapidly growing AI monitoring tool space. AI monitoring lets teams see into the AI layer — keeping an eye on how those models perform, watching the usual usage and speed, but also watching costs and spotting weird outputs or failures before customers do.

The numbers tell the story. Last year, according to our research just over a third of retailers used AI monitoring. Now, more than half do. Another quarter say they'll add it in the next year. The use of predictive analytics, which enables them to anticipate and prevent potential system issues before they happen, is rising too, because guessing wrong about demand or pricing can be just as damaging as a crash.

Getting Intelligent About Observability

Let's start simple: observability is how you see what's happening inside your digital store. Think of it like the cameras, sensors, and dashboards that tell you what's happening in a physical one. You wouldn't run a store blind — same goes online. Just like AI is creating problems, it is also creating new solutions, making observability more proactive and intelligent.

Before the rise of AI and intelligent observability, IT teams would piece together logs and metrics, follow alerts, and chase down customer complaints to figure out where there were issues in the system. It worked, but it was slow and messy. Problems were usually found after the fact, and every minute of delay meant money lost.

Intelligent observability flips that script. By unifying all the data and adding predictive smarts, it helps teams catch problems before they turn into outages. It shrinks detection and resolution times, and in many cases, it can prevent issues entirely. For retailers heading into peak season, that's not just useful — it's survival.

The Time Machine

The financial hit from IT outages is brutal, but it might not be the worst cost. Nearly half of retailers deal with at least one major outage every month — each one pulling engineers off innovation. In fact, retail leaders say their teams spend an average of 25% of their time managing disruptions instead of building new features that drive growth.

Observability gives that time back. Retailers using it report finding and fixing issues about twice as fast. Add AI monitoring, and you also get AI-assisted troubleshooting, automated fixes, and quicker reviews after incidents. That's time, money, and customer trust saved.

Getting It Right

When you are preparing AI powered features for peak, make sure that you understand their scaling modes, and are monitoring the cost and quality of output, not just the response time and error rate.

And when you are looking for an AI monitoring solution, look for an Observability platform that gives you visibility across everything — checkout, inventory, cloud infrastructure, even the third-party services you rely on. It should unify your data, scale with peak demand, and plug into the tools your teams already use.

And it's not just about the tools. The first step is knowing your critical customer paths —  hop, buy, fulfill. Every one of those needs to be watched end-to-end. Intelligent observability handles the repetitive monitoring so your teams can spend their time on judgment calls, not dashboards.

The Safety Net You Can't Skip

Human-only monitoring isn't enough anymore. And rolling out AI without intelligent observability is asking for trouble.

Intelligent observability acts like a safety net. It surfaces issues you didn't even know to look for and can even fix them before anyone notices. AI monitoring adds an extra layer, making sure the AI apps you're betting on don't quietly erode your revenue or customer trust.

For retailers, that's the difference between a record-breaking quarter and one derailed by glitches and abandoned carts. When the dust (and snowflakes) have settled after this year's peak season, the winners are going to be the companies who weren't afraid to take risks, because they knew they had the right safety net.

Nic Benders is Chief Technical Strategist at New Relic

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The Silent Threat to Retailers' Biggest Quarter: Outages and AI Blind Spots

Nic Benders
New Relic
Learn more about New Relic

AI continues to be the top story across the industry, but a big test is coming up as retailers make the final preparations before the holiday season starts. Will new AI powered features help load up Santa's sleigh this year? Or are early adopters in for unpleasant surprises in the form of unexpected high costs, poor performance, or even service outages?

Every year shoppers spend more money online, this year it could top $300 billion, and every year their expectations go up. People also expect fast, flawless experiences, and even a small hiccup — a checkout freeze, a payment error, or a laggy app can immediately lose revenue and trust.

Every retailer knows this, and knows the hard work that goes into preventing those problems. Decades of experience have shown us that testing, pre-scaling, and careful change management can help control the chaos. But this year, AI may add an unpredictable element.

Where AI Complicates Things

AI is everywhere in retail — powering recommendations, forecasting demand, personalizing experiences. But AI also makes systems more complicated. And because every answer is different, it doesn't always follow the usual rules for software. That can create blind spots, allowing problems to hide in places no one is looking until customers feel it first.

AI systems also scale differently than other software. It's not just CPU and RAM anymore. Suddenly GPU and memory bandwidth matter too. Cloud instances that can be used for AI are in constant demand.

What happens when everyone scales up at once? Do you spend more to win the bidding war for resources? Switch to smaller models that might make more mistakes? Live with slow responses?

The need to answer these questions is driving the rapidly growing AI monitoring tool space. AI monitoring lets teams see into the AI layer — keeping an eye on how those models perform, watching the usual usage and speed, but also watching costs and spotting weird outputs or failures before customers do.

The numbers tell the story. Last year, according to our research just over a third of retailers used AI monitoring. Now, more than half do. Another quarter say they'll add it in the next year. The use of predictive analytics, which enables them to anticipate and prevent potential system issues before they happen, is rising too, because guessing wrong about demand or pricing can be just as damaging as a crash.

Getting Intelligent About Observability

Let's start simple: observability is how you see what's happening inside your digital store. Think of it like the cameras, sensors, and dashboards that tell you what's happening in a physical one. You wouldn't run a store blind — same goes online. Just like AI is creating problems, it is also creating new solutions, making observability more proactive and intelligent.

Before the rise of AI and intelligent observability, IT teams would piece together logs and metrics, follow alerts, and chase down customer complaints to figure out where there were issues in the system. It worked, but it was slow and messy. Problems were usually found after the fact, and every minute of delay meant money lost.

Intelligent observability flips that script. By unifying all the data and adding predictive smarts, it helps teams catch problems before they turn into outages. It shrinks detection and resolution times, and in many cases, it can prevent issues entirely. For retailers heading into peak season, that's not just useful — it's survival.

The Time Machine

The financial hit from IT outages is brutal, but it might not be the worst cost. Nearly half of retailers deal with at least one major outage every month — each one pulling engineers off innovation. In fact, retail leaders say their teams spend an average of 25% of their time managing disruptions instead of building new features that drive growth.

Observability gives that time back. Retailers using it report finding and fixing issues about twice as fast. Add AI monitoring, and you also get AI-assisted troubleshooting, automated fixes, and quicker reviews after incidents. That's time, money, and customer trust saved.

Getting It Right

When you are preparing AI powered features for peak, make sure that you understand their scaling modes, and are monitoring the cost and quality of output, not just the response time and error rate.

And when you are looking for an AI monitoring solution, look for an Observability platform that gives you visibility across everything — checkout, inventory, cloud infrastructure, even the third-party services you rely on. It should unify your data, scale with peak demand, and plug into the tools your teams already use.

And it's not just about the tools. The first step is knowing your critical customer paths —  hop, buy, fulfill. Every one of those needs to be watched end-to-end. Intelligent observability handles the repetitive monitoring so your teams can spend their time on judgment calls, not dashboards.

The Safety Net You Can't Skip

Human-only monitoring isn't enough anymore. And rolling out AI without intelligent observability is asking for trouble.

Intelligent observability acts like a safety net. It surfaces issues you didn't even know to look for and can even fix them before anyone notices. AI monitoring adds an extra layer, making sure the AI apps you're betting on don't quietly erode your revenue or customer trust.

For retailers, that's the difference between a record-breaking quarter and one derailed by glitches and abandoned carts. When the dust (and snowflakes) have settled after this year's peak season, the winners are going to be the companies who weren't afraid to take risks, because they knew they had the right safety net.

Nic Benders is Chief Technical Strategist at New Relic

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Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...