The AI Daily Brief: Artificial Intelligence News and Analysis - Where AI Spend is Already ROI Positive

Episode Date: October 29, 2025

While most companies are still trying to measure the impact of their AI deployments, a growing number are already seeing returns. In today’s AI Daily Brief, NLW looks at new data showing where GenAI is ROI-positive — from marketing and media generation to enterprise productivity gains. Plus, updates on Amazon’s latest layoffs, Anthropic’s new Claude for Excel agent, and Qualcomm’s push into AI inference chips.Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? [e-mail]

Today on the AI Daily Brief, where organizations are already seeing ROI on their Gen.A.I. investments. And before then, in the headlines, how much are current layoffs about previous overhiring versus a direct result of AI? The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, robots and pencils, assembly AI, blitzie, and area. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief. or you can subscribe on Apple Podcasts. To get more information about the show, go to AIDailyBrief.A.I. And while you're there, please take a minute or two to fill out our AI ROI benchmarking study.

We're heading into a year where every organization is going to be focused on how to measure the performance of their AI deployments, and we are all going to be flying blind unless we start to have better baseline standards and benchmarks. This should take you no more than a couple minutes, and anyone who participates is going to get a survey highlights report as well as be entered to win an Amazon gift card or $200 in AI credits. And if you submit your use cases, there's a variety of other things you can get as well, up to an including a one-on-one meeting with me. You can find this at rhoysurvey.com. And thank you so much for checking it out. Welcome back to the AI Daily Brief Headlines edition, all the daily AI news you need in around five minutes. Today we kick off with a discussion about

the AI labor disruption narrative. On Monday, sources started leaking that Amazon was was about to let go a ton of workers. The number given by those sources was 30,000, about 10% of Amazon's corporate workforce. The question was, of course, going to be whether Amazon would blame AI or acknowledge other factors at play with their business. Amazon conducted a string of rolling headcount reductions across late 2022 and early 2023, obviously too early to be a result of AI disruption. Instead, those layoffs appeared to be a reversal of overhiring during the pandemic. That reduction in force total 27,000 corporate employees in total, so was also a very significant size. In addition, Amazon has been one of the weakest of the tech giants during the AI boom.

Growth in their cloud business slowed from 19% last year to a forecast 18% this year. Compare that to Microsoft Azure, which is growing at 39% in the most recent quarter. In any previous era, this would be an obvious cost-cutting measure to trim-blooded workforce, but CEO Andy Jassy has already laid the groundwork to blame layoffs on AI. In June, he wrote in a memo to staff that AI agents, quote, should change the way our work is done. He said, we will need fewer people doing some of the jobs that are being done today and more people doing other types of jobs. It's hard to know exactly where this nets out over time, but in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.

Bloomberg sources suggested that in reality it might be a little bit of both. Citing meetings and internal strategy documents, they said that Jassy has led a big push to do more to automate work using AI. At the same time, he's told colleagues explicitly that parts of the company are still unwieldy after a pandemic-era hiring binge. And indeed, when we got the official announcement on Tuesday, the number was lower than the 30,000 leaked, which feels to be very intentional. Instead, this number was 14,000. And while they hinted at AI, enough for it to show up in the title of news articles like this one, Amazon laying off about 14,000 corporate workers as it invests more in AI, when you go read the announcement post from Beth Goletti, the senior VP of People Experience and Technology,

they're sort of hedging a little bit and saying that this is about being nimble for the AI era, rather than blaming it explicitly on AI. She writes, This generation of AI is the most transformative technology we've seen since the Internet, and it's enabling companies to innovate much faster than ever before, in existing market segments and altogether new ones. We're convicted that we need to be organized more leanly, with fewer layers and more ownership, to move as quickly as possible for our customers in business.

The second layoff announcement comes from education tech company Chegg, and this one is definitely more directly aimed at AI. The company plans to lay off 388 workers, which is around 45% of their workforce. And indeed, they said that this was just part and parcel of the, quote, new realities of AI. Those realities are that Chegg was one of the first companies to get absolutely decimated by the introduction of ChatGBT. students were no longer seeking out online homework help in digital tutoring, instead using ChatGPT to access similar services for free.

Chegg acknowledged that the introduction of AI had led to reduced traffic from Google to content publishers, and consequently a significant decline in Chegg's traffic and revenue. The goal now is to restructure to support its academic learning products. This is going to increasingly become a policy conversation, and so it's important that we understand what's actually going on. You might have seen this chart flying around the internet, showing that since 2023, while senior employees have continued to be hired, junior employees have sort of fallen off a cliff. What's interesting to me about how this is being shared

is that while it's being presented as a chat GPT issue, there's clearly something else going on here as well. The downward trend in junior employees is both at non-adopting AI firms and at AI adopting firms, even if it is, yes, slightly worse at AI adopting firms, the trend in both is the same, which suggests to me that there are other things going on as well. This, by the way, comes from a Harvard working paper. What some people are grocking is that it doesn't matter what combination of post-pandemic reset and AI this is, it's going to demand a policy response. Vivek Ramoswamy, who's running for governor in Ohio, tweeted that chart and said, AI is making the job market a lot tougher for young graduates. This is a big deal, and ignoring

it will become untenable. Now, his solution is to give people a chance to participate in the equity upside of these companies, but that, frankly, is secondary to the fact that this is going to become absolutely, increasingly in every election cycle we have from here on out, a bigger and bigger issue. Moving over into new product and new feature land, Anthropic has expanded Claude for financial services with a new Excel agent. Claude for Excel is now available in beta as a research preview. It adds Claude in a sidebar for Microsoft Excel, allowing the agent to draw context from the application, as well as modify or create new worksheets. When working as an agent, Claude provides a line-by-line explanation of changes. This allows the user to follow a link.

long and check the work in real time. Anthropic has also added seven new connectors for Claude to provide access to relevant data and news feeds for work in the financial industry. The update also utilizes Claude's new skills feature with a set of preloaded workflows accessible to the agent. These include comparable company analysis, discounted cash flow models, due diligence data packs, and earnings analysis. Skills allow Claude to tap into these workflows as needed without requiring the user to build any agentic scaffolding our infrastructure. Norges Bank, the Norwegian Sovereign Wealth Fund is already seeing some dramatic results. Their CEO, Nikolai Tangan, reported 20% productivity gains equivalent to

213,000 hours in annual savings, or the work of around 100 full-time employees. He said the portfolio managers and risk departments are now able to, quote, seamlessly query our snowflake data warehouse and analyze earnings calls with unprecedented efficiency. AIG CEO, Peter Zafino, said that their use of Claude had compressed the timeline to review business by more than 5x in their early rollouts, while simultaneously improving their data accuracy from 75 to over 90%. Some once again thought that this was yet another indication of how hard it's going to be for junior employees. Hey, Builds, writes, this just instantly took out 80% of junior analyst jobs. Associates will take this, make their work 5x faster because they already have the expertise knowledge and can just edit.

The longest piece of a deal prep was to build the model in the presentation deck. Others were just excited to not have to go deeper in Excel. Bouchow writes, my decade of avoiding to learn Excel is about to pay off. In Chippland, Qualcomm is launching a new line of AI accelerators to take a slice of the rapid rapidly growing AI inference market. Qualcomm has dominated the market for mobile phone, CPUs, and plans to repurpose the integrated neural processing units from those chips into full-size AI accelerators. The new chips will be offered as standalone units as well as racks of up to 72 chips in a server, suitable for deployment in data centers. Interestingly, if supplied as a chip

only, the component could be installed in existing hardware supplied by Nvidia or others. The first release will be the AI 200 chip beginning next year, with a more efficient AI 250 model plan for release in 2027. Both chips are optimized for inference rather than model training, so won't compete in all aspects with Nvidia's leading GPUs, but the choice highlights are growing bifurcation in the chipmaking space. While Nvidia continues to be top of the tops when it comes to model training, and the choice for most large-scale data centers, demand for inference to serve models to end users is growing much more rapidly, which is opening up space for high-efficiency chips designed for that specific purpose. Qualcomm has already secured an anchor customer

to produce their first run, with Saudi Arabia's publicly owned AI company Humane planning to deploy 200 megawatts of data center capacity using the AI 200 chips. Unsurprisingly, the market cheered on the move to diversify away from the stagnant smartphone space into red-hot AI chip manufacturing, sending the stock soaring by 15% on Monday. That, however, is going to do it for the AI Daily Reef headlines. Next up, the main episode. AI changes fast. You need a partner built for the long game. Robots and pencils work side by side with organizations to turn AI ambition into real human impact. As an AWS-certified partner, they modernize infrastructure, design cloud native systems, and apply AI to create business value, and their partnerships don't end at launch.

As AI changes, robots and pencils stays by your side, so you keep pace. The difference is close partnership that builds value and compounds over time. Plus, with delivery centers across the U.S., Canada, Europe, and Latin America, clients get local expertise and global scale. For AI that delivers progress, not promises, visit robots and pencils.com slash AASANPensuals.com. Daily Brief. If you're building anything with Voice AI, you need to know about Assembly AI. They've built the best speech-to-text and speech-understanding models in the industry, the quiet infrastructure behind products like Granola, Dovetail, Ashby, and Clule.

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today. This episode is brought to you by Blitzy, the Enterprise Autonomous Software Development Platform with Infinite Code Context. Blitzy uses thousands of specials. AI agents that think for hours to understand enterprise-scale code bases with millions of lines of code. Enterprise engineering leaders start every development sprint with the Blitzy platform, bringing in their development requirements. The Blitzy platform provides a plan, then generates and pre-compiles code for each task. Blitzy delivers 80% plus of the development work autonomously, while providing a guide for the final 20% of human development work required to complete the sprint. Public companies are achieving a 5x engineering velocity increase when incorporating Blitzy as their pre-IDE development tool,

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aggressively this year from a paradigm of pilots and experiments into much more extensive and large-scale deployments, which is, of course, not to say that there aren't still lots and lots of things that are still in that zero to one pilot stage. Even companies that are quite advanced in their AI and agent strategy still have tons of areas of their company where there's no AI or agents at all. That said, the companies that do have some number of months of full-scale deployments under their belt are increasingly going to care about how those deployments are performing in the real world when it comes to making decisions about how they do the rest of that zero-to-one AI deployment. Arvin Jay and the CEO of Glean posted about this on LinkedIn this week. He wrote,

lately I've seen a clear shift in my conversations with enterprise AI leaders, and with Glean, I can see it in the data too. I asked Glean to analyze our customer call notes from the past couple of years to track how the reasons for adopting Glean have evolved. From mid-20203 to 2024, improving general productivity was the top driver, behind 67% of Glean implementations. A year later, that dropped to 37%. Why? Because leaders have realized that productivity for its own sake doesn't move the business forward. It only matters when it shows up in measurable business outcomes. Over the past year, adoption drivers have shifted decisively towards outcome-based goals. Revenue growth, faster ship cycles, better customer support.

Accelerating sales revenue, for example, is now about five times more likely to be cited as the top reason for adopting Glean than one year ago. Every function has its own North Star metrics that tie AI efforts directly to business outcomes. The bar for AI has been raised. The new standard isn't general productivity, its measurable business outcomes. Now, what's interesting is that companies are not only optimistic but getting more optimistic about their anticipated AI ROI. In 2024, KPMG asked CEOs as part of their annual CEO survey

when they anticipated to see a return on their AI investment. The vast majority, 63% said that it was going to take three to five years. 16% said more than five years and 20% said one to three years. Only 1% said six months to a year. When they asked that same question this year, the number that said that they anticipated ROI in six months to a year had jumped to 19% and the number that thought it was going to be one to three years, represented a full two-thirds, 67% of respondents.

Those pull-forward ROI expectations are, I think, part of what's going to be driving such a focus on it in the next year. If you anticipate a return on your AI investment, in short order, you better believe you're going to spend a bunch of time. Well, you have to actually go out and figure out what your ROI is. And while generalist expectation surveys are super useful, the way that AI is, of course, actually going to make an impact is within specific domains and functions. Not all different types of AI deployments are at the same level of maturity and are going to have different types of dynamics surrounding their performance profile. Artificial Analysis recently released their State of Generative Media Survey Report.

This was based on a survey of personal and organizational users in Q3 of this year. The survey was meant to understand how organizations and individuals are adopting and using both image generation tools as well as video generation tools. Now, notably this happened before the release of SORA 2 and the OpenAI Video API API. but it's still a fairly contemporary study. Let's talk first a little bit about what they found in that sector, and then we'll get into the ROI part. When it comes to image generation,

Google Gemini is out ahead of OpenAI. This doesn't surprise me very much, although the embedded Chat Chatsubit image generation tool can be good under the right circumstances, it has some foibles, and just in general it's very feature incomplete relative to other image generation models. The fact that it's so high,

I think, reflects the fact that it's just embedded in Chat Chabit. What's interesting about this list is that while the models you would expect are right at the top, there is still a broad base of other tools that lots of different companies are using. Some might be surprised to see Mid Journey down at 17%. I'm not necessarily in the sense that while Mid Journey is artistically and aesthetically top of the tops for me and always has been, it still lags in a lot of the very practical areas you need when you're using it for a business purpose at least, like, for example, text reproduction. The one that is criminally underused on this list is, of course, ideogram at just 12%.

ideogrammed by a factor of about 10 is my most used image generation tool. On video, once again, Google is in the lead. 69% of respondents were using V-O-3. A lot of the other top models, though, were from China, with runway all the way down and forth with just 30%. And when it comes to adoption patterns, as you might imagine, personal usage is leading organizational usage. Image generation has reached a pretty significant level of ubiquity for personal use at 89%

and is now used by over half of organizations at 57%. Video lags behind, although not as much as you might think. A little over 60% of individual users are using video generation compared to about a third of organizations. I would have actually bet that was closer to a 20% number, but I may be underestimating how quickly people have understood that AI-generated video could be really useful for some very discreet purposes like ad and social content.

Video is definitely more of an experimental stage, while 53% of respondents said that they had integrated, generative images into their creative workflow, a full 58% said that they were just experimenting with video. When it comes to organizations, the distribution between deployment, prototyping, and exploring was actually pretty similar across images and video. Deployed into production workflows was actually the leading category, again confirming what I was saying before, that we're quickly moving out of the pure experimentation phase. We'll race through a couple of other interesting nuggets when it comes to this category among organizations. It does appear that

marketing and advertising are the key use case for organizations with generative video at 55% of organizations using it for that purpose, but there's pretty good distribution across other areas, including creative storytelling, design, and educational and training content. When it comes to why people and companies are choosing a model, quality remains at the top, which is kind of what we've seen across a lot of different categories, but cost is a still large consideration, with 53 to 55% of organizations saying that lower total cost is a key factor for them. One other interesting note, it looks like organizations are pretty willing to design new workflows around these new tools, with only 27% saying that integration with tools and workflows was a major factor for

images and just 14% saying that it was a factor for video generation. This says to me that particularly video generation might be a new capability that is coming online for the first time because of AI, rather than it's just replacing some existing workflow. There's a lot more in here. I would encourage you to go check this out, but this is the big chart. When asked when you expect to see a return on investment from your organization's media generation initiatives, 34% said that they were already seeing ROI. Another 31% said that they expect ROI within the next 12 months, meaning that a full two-thirds of organizations are already seeing ROI

or anticipate it within a year. Another 23% said that it would be one to two years. Once again, we see ROI expectations being pulled forward in a pretty significant way. Zephyr on Twitter described this as AI-generated video and image having crossed the ROI Rubicon, and I think that's a pretty good way of putting it. Now, beyond this one study, you're also starting to see reports pop up pretty frequently indicating where organizations are seeing the actual practical benefits of AI. On Monday this week, CNBC published a story called AI is driving huge productivity gains for

large companies while small companies get left behind. This was based on a Wells Fargo research note that shared that productivity for the S&P 500 is up 5.5% since ChatGBT. For the smaller companies of the Russell 2000, real revenue per worker is actually down 12%. Now, this is that particularly vicious macro approach to measuring productivity that's not getting in there to do studies around how much faster people are doing their work, but is instead just looking at revenue compared to headcount. But still the narrative here is that at least for big companies, AI, is having a positive impact on productivity.

What's interesting is that another study, this time from Intuit QuickBooks, found that small companies were seeing productivity gains. In fact, more than 7 and 10, 75% of those organizations using AI said that it was boosting their productivity, up from 46% a year earlier in July of 2024. When you look at the chart, you can see a massive increase in organizations that say AI is very helpful or somewhat helpful and a very significant decrease in organizations who found it neutral. 56% said that they're more productive than three months ago. This survey, by the way, tried to do away with the complexity of productivity by simply defining it as, higher output for the same or lower input costs. There are some other interesting notes in that survey.

For example, 24% said that their work days are shorter thanks to AI, all of which seems to suggest that while market performance for small companies might be off relative to the big guys, small companies are certainly seeing the benefits of AI in practice. I think we are just at the very beginning of this conversation around AI's ROI, and I would expect real results over the following weeks, months, and year. I will use this as an excuse to tell you once again about the big AI ROI benchmarking study, that we're doing, we're having people share the use cases that are driving value and what specific value it's driving, be it time savings, cost savings, new revenue, new capabilities, or something

else entirely. It's been live for about one day and we've already had hundreds of use cases with their ROI shared. And I think that with your help, we could actually make this the biggest repository of AI ROI data that exists. Find it at ROISurvey.com.A.I. And for now, that's going to do it for today's AI Daily Brief. Appreciate you listening, as always. Until next time, peace.