How AI Agents Can Take Your Business Analytics To Another Level
In my last FTC piece, I provided a primer on the capabilities of agentic AI, the value of the tech and how it can be tested and tweaked to improve accuracy. AI agents can make a major impact in many ways, such as cybersecurity, robotic process automation and customer support. But there's one use case where I've seen them really shine: business analytics.
Back in November 2023, I discussed how generative AI is accelerating enterprise analytics. LLMs allow normal business users to tap into their organization's data and uncover critical new insights. Agentic AI is taking things to an exciting new level here, and I believe they will eventually make LLMs obsolete when it comes to driving value from business data.
Agentic AI Analytics: Fulfilling The Role Of An Expert Analyst
Traditional GenAI analytics is powerful, making enterprise data more accessible and yielding far more insights versus plain business intelligence (BI) analytics. It works within clearly defined guardrails, learns as you interact with it and provides precise answers. LLM analytics ultimately fulfills the role of a junior analyst for organizations.
Agentic AI plays the role of a manager or expert analyst. It teaches itself new things, researches things on its own and delivers insights autonomously.
The key differentiator for agentic AI analytics is its proactive nature—it delivers valuable insights without needing explicit requests or prompts. For instance, consider a consumer goods company specializing in beverages. An AI agent could proactively alert business users that sales of a seasonal product line, such as flavored seltzers, are projected to decline significantly over the next quarter due to shifting consumer preferences. At the same time, AI could highlight emerging trends, such as rising interest in non-alcoholic spirits, recommending that the company explore opportunities in this growing market segment within the upcoming year.
As is always the case with AI and analytics, the important thing is that insights support meaningful actions. In the first example, the liquor company might want to consider pivoting away early from the declining category before sales tank. In the second example, they would want to think about launching a new product to get ahead of their competitors.
Here are the features that define early-stage generative AI analytics solutions:
• Rule-Based: Performs only the tasks it's explicitly programmed to do
• Opaque: Offers answers without explaining how it reached them
• Tool-Limited: Can only operate within a fixed set of preloaded tools
• Inflexible: Needs manual corrections or instructions to adapt
• Requires Oversight: Relies heavily on expert oversight to function properly
Here's how agentic AI analytics contrasts in the same categories:
• Autonomous Decision Making: Weighs options and makes choices independently
• Explainable: Clearly shows how it reached its conclusions
• Tool-Agnostic: Can choose and use tools on its own as needed
• Self-Adaptive: Adjusts behavior in real time without external input
• Self-Monitoring: Performs built-in checks to stay compliant and accurate
Don't Fall For Regular GenAI Posing As Agentic AI
The AI market is evolving rapidly. It can be difficult for enterprises to make heads or tails of all the various moving parts. Complicating things further—and this is always the case with the rise of significant new technologies—there are a lot of vendors that cling to buzzwords even when they don't fit their offerings. Organizations looking to leverage agentic AI to accelerate their analytics efforts need to be careful not to fall for plain generative AI that rebrands itself as agentic.
This will become less of a problem as the agentic AI market matures and winners and losers emerge within the next two to three years. In the near term, organizations will just have to do a little research. The best place to start is with this checklist, reflecting the points I hit above.
Agentic AI analytics should:
1. Make decisions independently.
2. Explain reasoning.
3. Use tools autonomously.
4. Self-correct and adapt on its own.
5. Be overseen by verifiers to ensure optimal accuracy.
A Step Further: Multi-Agent Networks
Looking even further ahead, agentic AI gets even more groundbreaking. Eventually, singular AI agents will evolve into multi-agent networks. Here, several AI agents will connect into a network with broader access to enterprise datasets, tools, models and domain context. These agents will be highly goal-driven and capable of completing more complex tasks that span multiple systems within a business.
AI Agents: Transforming Enterprise Analytics In 2026 And Beyond
AI continues to develop at a breakneck pace. It wasn't long ago that LLMs were a brand new, cutting-edge way to support analytics.
It should be repeated that traditional GenAI is still a fantastic, powerful method to improve analytics workflows and uncover more insights. However, AI agents are going to raise the bar. The tech is still in its nascent stage, though the market will start to take shape in a year or so, delivering insights that will prove transformative for organizations across the spectrum.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Motor Trend
15 minutes ago
- Motor Trend
The Future Of Porsche's Racing Tech Transfer to Street Cars? Software.
On April 12, 2025, Porsche made history, becoming the first automaker to win three classes of professional motorsports with three different powertrains on the same day. In California, a 963 hybrid won the LMDh class and took the overall win at the IMSA Long Beach Grand Prix while a 911 GT3 R won the GTD Pro class in the same race with pure combustion engine. Across the country, the 99X Electric won the Miami e-prix in Formula E on pure battery power. Different as they may be, each shares a common link with the Porsche road cars you can buy today and in the future. Porsche uses motorsport to drive tech innovation, focusing on software as the key to improving road cars. Software impacts drivability and efficiency, with lessons from racing series shared across projects. This approach is cost-effective and enhances both performance and development. This summary was generated by AI using content from this MotorTrend article Read Next Racing Technology for the Street 'We have a philosophy that, yes, motorsport is part of our DNA,' Porsche vice president of motorsport, Thomas Laudenbach, told MotorTrend , 'and I cannot imagine Porsche without motorsport, but we are not doing motorsport for the sake of its own. We do motorsport to give a contribution to the company, and this is exactly what we are talking about.' In the past, tech transfer from racing to the road consisted mostly of more power and better aerodynamics, but as everything on a car has become linked and controlled by computers, the next frontier is in the software that controls them. Hard parts like engines, suspension, and aerodynamics are fairly mature technologies, but automotive software is still in its relative infancy. 'It is still a very steep curve,' Laudenbach said. 'It's growing so fast, it's changing so fast…I mean, if you look at the combustion engine, obviously development [today] is slower like this because you know, it is more and more difficult to make the [next] step. If you look at the software, not only software itself, how we approach it, the tools…I would say [it is] still very steep, the curve, how fast it changes.' It's All in the Software In all three racing series, physical parts on the cars are heavily regulated, particularly when it comes to batteries and electric motors. Software, though, isn't and has become the most important factor in improving lap times and efficiency. 'Everything you can do on the software has a much bigger impact and a much bigger effect in the drivability,' Porsche Formula E driver and reigning champion, Pascal Wehrlein, told MotorTrend , 'because yeah, there's also software things in in a combustion engine, but the effect is just smaller than an electric car. We pay a lot of attention to the software and I would say that is our biggest toolbox for setting up the car and getting quicker and so on. And there's just so many more things you can do on the software compared to a combustion engine. 'How much we are going into the details,' he continued, 'into the smallest details, I would say on the software side is even more than what I did when I was in Formula One, just because there are so many different options on, you know, the four-wheel drive, how to set up the four-wheel drive. How much work do you want to have at the front? At which point in time in the corner you want to have more front torque or less? What you can do on the braking side, on the [energy] recuperation, setting it up for different corners? In certain corners, where it's high speed, you need something different than in the low-speed corner, but then also when the track is bumpy, or not bumpy. We are going so much into the details.' Lauderbach agrees. 'It's absolutely right. We always love about talking about hardware. And I did develop combustion engines for 18 years, so I'm a real mechanical guy, and it's absolutely right, probably the bigger part is software. And but this is the good thing about it because some things [physical] we are not allowed to touch [under the regulations]. But we have a big freedom of software, and I think that's good because racing should give freedom where it is beneficial also for your brand. And that's certainly in software and I think this is probably also the biggest change between the projects.' Because the applications are so different, it's not as simple as just sharing code between teams in different series or with the engineers working on the road cars. Instead, it's the exchange of knowledge and ideas which brings this tech to cars like the 911 GTS T-hybrid. 'It's for sure not a carryover part,' Laudenbach said, 'but it's from learning about the difficulties, about the weak points, about the solutions, for sure they benefit from each other. 'When you have more than one project,' he said, 'you just work it on in a wider range and then you always find synergy. These two programs (Formula E and LMDh) benefit from each other in various areas. And at the same time, this is linked so close to our road car development. We work a lot on road cars as well in the motorsport department and do benefit from each other. If you tell your engineers, oh, please sit together with these guys from this program, then you know what they do? They sit together for an hour, they chat, and they go to it. If you sit side by side, if you meet each other with a coffee, this is the best way to benefit from each other. These two programs benefit, but also this is very beneficial to what we do on road cars, even inside the motorsport department.' It's not just about making the cars faster, either. Power makes heat, and heat needs to be dealt with before it breaks things, on a race car or a road car. Efficiency matters in racing because using less fuel or electricity allows you to go farther between pit stops, and it matters for the same reason on the street. 'Look at Formula E,' Laudenbach said. 'It's not our battery, but we control the thermal system [and] energy management. That's a lot of control systems. That's a lot of software. I mean, if we work with AI in the meantime and there you can learn a lot of the one side and transfer it to the other. Sometimes then you figure out that, okay, I can only take this because this [other technology] is not allowed. It's never carry over one to one. 'But you still learn a lot about how to handle it. It's software functions, it's control systems, it's sometimes also just the tools that we use, the approach that you take is not always [about] the final product. In the end, you have the product, there, no matter if it's software, hardware, but it's also, how do you approach it, because you're always looking for being most efficient. Especially Formula E, [where] we have a cost cap. It's a factor to say, okay, can I reach a certain goal with the smallest amount of money? These kind of things we always exchange because it's in the background.' Cheaper and Easier Not only are software learnings easier to transfer between programs, software is also easier to iterate on and less expensive to develop. 'Compared to hardware,' he said, 'it's not that cost intensive. Yes, you have you have the labor. But you know, you' not always having to change your bits and pieces. And don't forget, if you talk about bits and pieces, you always have to stop and throw parts away. So it's a lot more, let's say, cost efficient.' Whether in the office or trackside, the way data is managed and analyzed has changed a lot in the past decade. 'If we would do it like 10 years ago,' Lauderbach said, 'where the engineer himself goes through all the raw data, that doesn't work anymore. You got to feed your data through automatic analysis. It's just more [a question of], how do you analyze? How do you get something out in order to make the car quicker? This is a lot more or this is high sophisticated, a lot more automation and algorithms, than 10 years ago. 'It's a software basically to calculate what the car's doing because you got sensors. Obviously, you want to calculate some figures, you see what the car's doing in order to feature simulations. The simulation then gives you back again in which direction you have to go.'
Motor Trend
15 minutes ago
- Motor Trend
Rivian Is Integrating Google Maps Into Its Native Navigation Software
Fresh off major upgrades to its R1S SUV and R1T pickup, Rivian is now set to launch a software update of its onboard navigation system to include Google Maps data—both for the new vehicles it's producing and its existing customer cars. This isn't simply Google Maps like the app on your phone, however. Leveraging Google's Automotive SDK, Rivian has, in effect, overlaid its existing navigation experience over that of Google Maps, taking advantage of the internet giant's superior "routing, estimated time of arrivals, traffic updates, search capabilities, and satellite imagery," as Rivian put it, in order to further augment its own in-house "EV-friendly navigation features." Rivian is updating its R1S SUV and R1T pickup navigation systems to integrate Google Maps, blending Google's routing, traffic, and imagery with Rivian's EV-specific features. The update includes a new interface and extends to the Rivian Mobile App, rolling out starting tomorrow. This summary was generated by AI using content from this MotorTrend article Read Next In other words, Rivian's navigation system will now blend Google's superior mapping capabilities with its proprietary charging information, including estimated range and battery capacity remaining at your destination, route planning, real-time charging info, and more. In addition, Rivian skins the whole interface in its own design, a new version of which will also debut with the Google Maps update. Even better, Rivian says the update will extend to its Rivian Mobile App (a 2025 MotorTrend Best Tech award winner), which benefits from Google-like photos and descriptions of searched destinations, as well as satellite map views and real-time traffic data. As before, users can send trips and navigation destinations from the app to their Rivians, and now they'll be able to use the "share" function via Google Maps to do the same. The rollout of the new Google-augmented system is imminent—Rivian says it'll begin doing so as soon as tomorrow via an over-the-air update for every all-electric R1S SUV and R1T pickup it has sold to date, and it will come already equipped with every new model it sells.
CNET
17 minutes ago
- CNET
Your OnePlus 13 Will Get a Dedicated AI 'Mind Space' in Update Rolling Out Now
It's a non-negotiable right now that every phone-maker out there must have a plan for integrating AI into its devices. OnePlus is a little late to the party, but it's arrived nonetheless. Back in May, the company announced plans for bringing its own vision of personalized AI to OnePlus phones, and from this week, it's rolling out to the OnePlus 13 and 13R. At a launch event in London earlier this year I not only got to see the first AI features to land on OnePlus phones in action, but also learn about what the company's future plans are for bringing more complex and sophisticated AI features to its phones down the road. With all Android phone-makers increasingly making use of best-in-class Qualcomm chips and relying on Google's Gemini AI, having a strong AI strategy is one way they can set themselves apart from rivals. I was impressed with how far OnePlus seems to be thinking ahead and not rushing into going ham on AI. Its initial AI rollout will likely capture people's attention, even if its ideas aren't entirely original. The OnePlus 13's upcoming "Mind Space." Katie Collins/CNET OnePlus' statement AI tool is called Plus Mind, which can save, suggest, store and search based on what's currently on your phone screen, ultimately depositing the details in an app OnePlus is calling "Mind Space." Plus Mind can be activated at any time, either by a dedicated button (if your phone has one) or by a swipe-up gesture. If it spots details of an event or reservation, it will propose creating a calendar entry. Mind Space is a place to "organize your fragmented memories," said Arthur Lam, the company's director of OxygenOS and AI strategy. This is a hub where all of your most important content will live. AI search will allow you to find what you need without the information overload you may be used to, or it will automatically translate content into another language to make it accessible and searchable. Plus Mind will debut with the upcoming launch of the OnePlus 13S, a phone designed specifically for the Asian market, which comes with a dedicated AI button (the "Plus Key," as OnePlus is calling it) on the side of the phone. That means for those of us in the US and Europe, we'll have to wait a little longer to enjoy OnePlus' vision for AI ourselves. It will eventually be rolling out to the OnePlus 13 as an over-the-air update later this year, although the company is yet to confirm exactly when. The Plus Key on the OnePlus 13S. Katie Collins/CNET Plus Mind and Mind Space: My first impressions On the OnePlus 13, which shipped before the addition of the Plus Key, you instead have to use a three-fingered swipe up from the bottom edge of the screen to activate the AI features. When I tested this in person, it was hit or miss as to whether I could get it to work. There's definitely a knack to it -- you need to start from a couple of centimeters above the lower rim -- and there's a high chance of accidentally displacing what's on the screen. It's clear that OnePlus designed Plus Mind to be used with a dedicated button, and no doubt all future OnePlus phones will feature a Plus Key of their own. It is a shame in retrospect, though, that the key is missing from its most widely available 2025 flagship phone. After using Plus Mind to save a variety of content, I had mixed opinions on how useful it was. The process of capturing and creating events out of details displayed on screen was seamless, and I found that I was able to use natural language within Mind Space to pull up the details of these events after the fact. But when saving articles I thought were interesting, Mind Space wasn't able to provide a summary of the entirety of what I'd been reading -- only of the specific text that was on screen at the time I activated Plus Mind. OnePlus' natural language search within Mind Space worked well for me. Katie Collins/CNET I also struggled to organise the content into collections within Mind Space. This is a manual process, rather than a situation in which the AI takes over to categorize everything you've saved. This feels a little like a missed opportunity. Like other Android phone makers, OnePlus has the benefit of tapping into the best of Google's Gemini phone tools, while also choosing what additional features it wants to bring to its phones to make them stand apart from its competitors. That said, its initial foray into AI with Mind Space is bound to draw comparisons to what Nothing is doing with Essential Space -- its own dedicated hub for saving content, snippets, links and reminders. What's next for OnePlus AI? Plus Mind and Mind Space are just the first part of OnePlus' three-stage AI strategy. Next up is integrating a large language model into Plus Mind, allowing your phone to understand your habits to create a "persona" it uses to understand you. "It will help you understand yourself," said Lam, and could even help you discover something "surprising" or "enlightening" about yourself. Stage 3 is when OnePlus plans to go full AI agent, turning into a personal assistant that can know everything about you. But the company's not quite there just yet. In the meantime it has a few other ideas in the pipeline. Coming first to India (again, not the EU or the US), are AI VoiceScribe, which will provide you with a quick summary after your call on WhatsApp, Snapchat or Telegram, and AI Call Assistant, which provides you with in-call translation in both text and voice. On the more playful side, OnePlus is introducing two AI photo tools. The first, AI Best Face 2.0, will allow you to correct the faces of up 20 people in a group photo so that everyone is looking their best (if they have their eyes closed, for example, or what OnePlus describes as a "suboptimal expression"). AI Reframe, meanwhile, will analyze your carelessly shot holiday snaps and suggest creative cropping and framing to make it look like you weren't three cocktails deep when you shot them. These photo features will be come to OnePlus phones this summer, but for the major OnePlus AI tool rollout, you might have to wait a little longer.
