As artificial intelligence continues to shape how consumers shop online, it’s also revolutionizing the back offices of the agencies that serve them.

Nowhere is this more evident than in the world of Amazon advertising, where agencies are adopting autonomous AI agents to dramatically cut down the time and resources spent on client reporting.

According to a survey of 60 Amazon ad agencies by Xnurta, an agentic AI advertising platform, the average Amazon agency spends 25% of their time building weekly client reports.

Reporting is one of the single most time-consuming elements of ad agency work.

On the consumer side, we’re seeing growing familiarity with and adoption of AI. For example, according to a recent Prosper Insights & Analytics survey, 39.5% of Amazon Prime members have already used generative AI tools, and another 18.4% are excited to try them.

On the agency side, adoption of AI might be growing even faster.

Enter AI agents.

"Agencies need to be efficient with their time, but in the Amazon Ads context, they also need to give their clients remarkably in-depth reporting to capture the complexity of their online business,” said Kashif Zafar, CEO of Xnurta, a leading agentic AI advertising platform that recently launched an AI reporting agent. "We wanted to build an AI reporting tool that allowed brands to not only get more efficient but could connect all the dots of a client’s data to capture this complexity.”

Getting more efficient is an understatement. One benchmark study found that marketing agencies spend an average of 3.5 hours per client per week on reporting. For an agency managing 50 clients, that equates to 175 hours a week—over 4 full-time employees.

Add that to an industry increasingly judged by the speed and quality of its insights, and you’ve got a serious efficiency problem.

After launching their AI reporting Agent, Xnurta has seen a reduction in the time spent generating client reports by 90%. Imagine going from spending 175 hours a week on reporting to 17.5 hours.

The autonomous agents are trained on advertising datasets and can write summaries, generate campaign insights, visualize performance, and even recommend optimizations in real time. These aren’t just dashboards. They’re proactive assistants that can comb through thousands of data points and deliver a human-readable report in minutes.

"It’s not just that agents are more efficient," Zafar noted. "It’s that they can catch tiny details across massive datasets. Client’s love seeing how their campaigns suddenly have access to every contextual data point. It gives them the bigger picture."

While Xnurta’s agent currently focuses on Amazon-specific ad signals such as return on ad spend (ROAS), advertising cost of sale (ACOS), click through rate (CTR), conversion rate (CVR), and cost per click (CPC), they plan to offer omnichannel insights as well.

I can imagine another avenue for this type of data: connecting brand and category-specific historical signals to forward-looking consumer trends.

For example, for an agency that’s generating a report for a men’s apparel brand, imagine connecting ad performance data to consumer spending forecasts in their category.

According to a recent Prosper Insights & Analytics survey, the consumer spending forecast for men’s apparel among Prime members regularly dips above and below the U.S. population at large. If you saw this forward-looking data in advance, you might ramp up Amazon ad spend to capture demand that wouldn’t necessarily show up in historical data.

You could also look at consumer confidence more broadly. While consumer confidence recently dipped—likely due to tariff uncertainty—it remains up 11.8 points since July 2022.

This increase in confidence correlates with higher projected spending. According to Prosper’s latest forecast, Amazon Prime members plan to spend more than the general population over the next 90 days. Agencies armed with real-time reporting can connect these macro trends directly to client performance, making their insights more strategic and contextual.

For now, Xnurta is focused on making reporting as interactive and actionable as possible.

“We’re designing the experience so users can actually ask follow-up questions, dive into visualizations, or test ‘what-if’ scenarios in the same interface,” said Zafar. “It’s about engaging with the data. That’s why we don’t just offer the ability to export the data into a Word or PDF file, but we also offer dynamic charts and tables so clients can actually interact with them.”

This is moving reporting beyond static dashboards and reports and into continuous intelligence systems.

"Think of it like ChatGPT for Amazon advertising," said Zafar. "You’re not just looking at what happened last week. You’re seeing what’s shifting today and what to do about it."

But according to Xnurta’s Head of Product, Matt Yu, the AI reporting Agent is more than a chatbot:

“The Xnurta AI Reporting Agent is a multi-step, deep research AI agent product that can build a full-funnel, in-depth report and analysis in tens of minutes that would take a human hours. Unlike a chatbot, Xnurta’s AI Reporting Agent is built to ‘think’ for a while, and it draws conclusions from rich datasets that include SP-API, Sponsored Ads, DSP, AMC, and first-party data.”

The next phase, according to Xnurta, includes integrating more sales and inventory data into its AI reporting agent, particularly from sources like Amazon Marketing Cloud and third-party marketplaces.

“The ability to synthesize full-funnel advertising data and different measurement methodologies is a prerequisite to adopting full-funnel advertising strategies,” said Yu. “Xnurta’s AI Reporting Agent removes the barrier of analysis and measurement, helping advertisers of all kinds to adopt full-funnel advertising at scale.”

In a world where consumer expectations are shaped by their own use of generative AI, full-funnel advertising at scale via AI Agents is particularly important for ad agencies. The firms that embrace automation in reporting will not only save time but gain a strategic edge in understanding Amazon's fast-moving ecosystem.

"The data doesn’t sleep," Zafar concluded. "With AI, neither do your insights."