Circle’s chief executive painted a brisk picture at Davos this week: autonomous software agents that act for people could be using stablecoins to pay for everyday things within three to five years.

He said these agents will need a money system that is stable, fast, and programmable. That, he argued, points to stablecoins as the likely choice.

AI Agents And Money

According to reports, Jeremy Allaire of Circle said “literally billions” of AI agents may be transacting on behalf of users in the near term.

“Three years, five years from now, one can expect that there will be billions, literally billions of AI agents conducting economic activity in the world on a continuous basis,” Allaire said during the World Economic Forum in Davos, Switzerland.

He described work on new networks and tools aimed at letting software act like small businesses or helpers that buy services, settle bills, and tip content creators.

This idea is simple on the surface: software needs a reliable unit of account when it spends, and tokenized dollars can fit that role.

Building The Tools

Reports say companies across the crypto and tech world are racing to build the plumbing for this future. Circle is pitching USDC as a neutral payments layer that software can plug into.

Other firms are testing protocols that let a machine sign off on a payment when certain conditions are met. Some large tech groups are also exploring ways for their platforms to let software pay for services automatically. Progress is visible, but the path is not yet clear.

What Regulators Might Ask

Regulators will have questions. Reports note concerns about money flow, consumer protections, and where bank deposits sit if stablecoins grow rapidly.

At Davos, the CEO pushed back on the idea that stablecoins would drain bank deposits the way some fear, saying comparisons to other financial instruments are more fitting.

Still, lawmakers in the US and elsewhere are watching closely. Rules could move faster if policy makers see real volume coming from so-called agentic commerce.

New Networks, New Risks

Based on reports, the technical choices will shape both convenience and danger. If agents can move value at scale, fraud and theft risks may rise too.

Systems will need clear identity checks, fault handling, and ways to stop runaway payments. Some safety work is already under way, but much remains to be designed and tested.

Featured image from Pexels, chart from TradingView

On Tuesday, eBay updated its User Agreement to explicitly ban third-party "buy for me" agents and AI chatbots from interacting with its platform without permission, first spotted by Value Added Resource. On its face, a one-line terms of service update doesn't seem like major news, but what it implies is more significant: The change reflects the rapid emergence of what some are calling "agentic commerce," a new category of AI tools designed to browse, compare, and purchase products on behalf of users.

eBay's updated terms, which go into effect on February 20, 2026, specifically prohibit users from employing "buy-for-me agents, LLM-driven bots, or any end-to-end flow that attempts to place orders without human review" to access eBay's services without the site's permission. The previous version of the agreement contained a general prohibition on robots, spiders, scrapers, and automated data gathering tools but did not mention AI agents or LLMs by name.

At first glance, the phrase "agentic commerce" may sound like aspirational marketing jargon, but the tools are already here, and people are apparently using them. While fitting loosely under one label, these tools come in many forms.

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Bicycles for the mind. … Information at your fingertips. … Managers of infinite minds?

Microsoft CEO Satya Nadella riffed on some famous lines from tech leaders past this week in an appearance at the World Economic Forum in Davos, Switzerland, and offered up his own trippy candidate to join the canon of computing metaphors. 

Nadella traced the lineage in a conversation with former UK Prime Minister Rishi Sunak.

• “Computers are like a bicycle for the mind” was the famous line from Apple’s Steve Jobs.
• “Information at your fingertips” was Bill Gates’ classic Microsoft refrain back in the day.

And now? “All of us are going to be managers of infinite minds,” Nadella said. “And so if we have that as the theory, then the question is, what can we do with it?”

He was referring to AI agents — the autonomous software that can take on tasks, work through problems, and keep going while you sleep. Microsoft and others have been talking for the better part of a year now about people starting to oversee large fleets of them. 

Nadella said it’s already reshaping how teams are structured. At Microsoft-owned LinkedIn, the company has merged design, program management, product management, and front-end engineering into a single new role: full-stack builders. Overall, he called it the biggest structural change to software teams he’s seen in a career that started at Microsoft in the 1990s.

“The jobs of the future are here,” Nadella said, putting his own spin on a famous line often attributed to sci-fi writer William Gibson. “They’re just not evenly distributed.”

Nadella’s comments came during a live stream for LinkedIn Premium members, hosted from Davos by LinkedIn VP and Editor in Chief Daniel Roth, after Sunak mentioned his two teenage daughters, and the world they’ll enter. Young people may not manage lots of people at age 20 or 21, he said, “but they will be managing a team of agents.” 

Sunak was referencing an essay by Goldman Sachs CIO Marco Argenti in Time. 

The agentic shift, Argenti wrote, requires “moving from being a sole performer to an orchestra conductor” — your team now includes AI agents that “must be guided and supervised with the same approach you would apply to a new, junior colleague.”

Nadella agreed, saying “we do need a new theory of the mind” to navigate what’s coming, before he offered up his new metaphor about managing infinite minds.

In other remarks at Davos, Nadella made headlines with his warning that AI’s massive energy demands risk eroding its “social permission” unless it delivers tangible benefits in health, education, and productivity. Energy costs, he added, will decide the AI race’s winners, with GDP growth tied to cheap power for processing AI tokens.

Whether “infinite minds” catches on like “bicycles” and “fingertips” remains to be seen. But it’s definitely more psychedelic. And if this shift is stranger than what came before, maybe we do need a mind-expanding metaphor to make sense of it all.

Misaligned agents are just one layer of the AI security challenge that startup Witness AI is trying to solve. It detects employee use of unapproved tools, blocks attacks, and ensures compliance. 

The integration of agentic AI in enterprises could revolutionize compliance by enhancing governance, accountability, and operational efficiency.

The post IBM and e& launch agentic AI for enterprise compliance appeared first on Crypto Briefing.

PromptArmor threat researchers uncovered a vulnerability in Anthropic's new Cowork that already was detected in the AI company's Claude Code developer tool, and which allows a threat actor to trick the agent into uploading a victim's sensitive files to their own Anthropic account.

The post Vulnerability in Anthropic’s Claude Code Shows Up in Cowork appeared first on Security Boulevard.

CIOs are moving beyond AI copilots to autonomous agents. Learn where agents deliver value, how to govern them, and how to integrate with legacy tech.

The post Why Autonomous AI Agents Will Redefine Enterprise IT Strategy appeared first on TechRepublic.

CIOs are moving beyond AI copilots to autonomous agents. Learn where agents deliver value, how to govern them, and how to integrate with legacy tech.

The post Why Autonomous AI Agents Will Redefine Enterprise IT Strategy appeared first on TechRepublic.

As companies deploy AI-powered chatbots, agents, and copilots across their operations, they’re facing a new risk: how do you let employees and AI agents use powerful AI tools without accidentally leaking sensitive data, violating compliance rules, or opening the door to prompt-based injections? Witness AI just raised $58 million to find a solution, building what they call “the […]

We’re excited to announce a new GeekWire event for 2026: “Agents of Transformation: Inside the AI Shift.” This half-day summit will be held the afternoon of Tuesday, March 24, in Seattle, exploring how agentic AI is reshaping work, creativity, and leadership.

The event, presented by Accenture, features fireside chats, expert panels, and real-world stories from technology leaders, business execs, and others navigating how AI is changing the way we work and lead, from copilots and automation to the rise of intelligent agents.

Tickets are available now, with discounted early bird rates set to end Feb. 24. Speakers will be announced in the coming weeks.

AI agents is the tech industry’s obsession right now, but there can be a big gap between the pitch and the reality. We’re bringing together people who are in the thick of it to talk candidly about what they’re seeing: breakthroughs, challenges, and what comes next.

The event is part of GeekWire’s longstanding tradition of convening tech, business, and policy leaders for insights and new connections. Hosted at one of our favorite Seattle venues, Block 41, the afternoon will include networking opportunities before, during, and after the program, bringing together founders, executives, and technologists from across the region.

It builds on an ongoing GeekWire editorial series, underwritten by Accenture, spotlighting how startups, developers and tech giants are using intelligent agents to innovate. 

For sponsorship opportunities, contact events@geekwire.com.

Details:

• When: Tuesday, March 24, 2026, 1:30–5:30 p.m.
• Where: Block 41, 115 Bell St., Seattle
• Tickets: Early bird pricing is $145 through Feb. 24. Register here or below.

[Editor’s Note: Agents of Transformation is an independent GeekWire series and March 24, 2026 event, underwritten by Accenture, exploring the people, companies, and ideas behind AI agents.]

Microsoft is making its own bid to turn AI conversations into agentic commerce, announcing a new feature called Copilot Checkout that lets users complete purchases directly within its AI chatbot, without being redirected to an external website.

The company is betting that its existing enterprise technology footprint and established relationships with large retailers will give it an edge over OpenAI, Google, and Amazon in winning over merchants wary of giving up control to retail rivals or AI intermediaries.

“We’ve designed it in such a way that retailers own those relationships with the customers,” said Kathleen Mitford, corporate vice president of global industry marketing at Microsoft. “It is their data, it is their relationship, and that’s something that’s really important to us.”

It’s part of a broader AI rollout by Microsoft at NRF 2026, the retail industry’s annual conference in New York. Microsoft is also launching Brand Agents, pitched as a complete solution for Shopify merchants to add AI assistants to their websites, along with new AI tools to assist store employees and help retailers enhance their online product listings and metadata.

Copilot Checkout works by surfacing products from partner retailers within Copilot search results. Purchases can be completed without leaving the conversation. Microsoft says the retailer remains the merchant of record, handling fulfillment and customer service.

But will people buy in chat?

The bigger question for the tech industry is whether chat-based commerce is actually the next big thing. Forrester analyst Sucharita Kodali, for example, previously told GeekWire that “e-commerce isn’t a problem that needs to be fixed.” She added that it’s unclear what value chat-based commerce is bringing to retailers, “other than disintermediating Google.”

Microsoft’s Mitford offered a different take in an interview this week, saying that consumer behavior is shifting faster than it may seem. She drew a parallel to how quickly businesses moved from experimenting with AI to putting it into operation over the past year.

“I see the same thing happening with consumers … it just takes a little bit of time,” Mitford said, predicting that the speed of consumer adoption will eventually match the rapid uptake seen in the business world.

Copilot Checkout is rolling out now in the U.S. on Copilot.com, with PayPal, Shopify, and Stripe handling payment processing. Etsy sellers will be among the first available on the platform. Shopify merchants are set to be automatically enrolled following an opt-out window.

That last detail is notable given the backlash Amazon has faced over its “Buy for Me” feature, where brands complained about being included without consent and seeing inaccurate listings. 

Microsoft’s approach is more tightly connected to its partners — the company said Shopify will management the opt-out process for its merchants — but automatic enrollment seems to raise the potential for some of the same concerns. (We’ve contacted Shopify for more information.)

The competitive landscape

More broadly, Microsoft is playing catch-up on the consumer side.

OpenAI launched Instant Checkout in ChatGPT last September, partnering with Shopify and Stripe to let users buy from more than a million merchants. Google followed in November with its own “Buy for Me” feature which lets its Gemini assistant purchase products on a user’s behalf.

Despite its inroads with businesses, Copilot has a fraction of ChatGPT’s market share with consumers. Recent data from Similarweb’s Global AI Tracker showed ChatGPT with about 68% of AI chatbot web traffic, with Google Gemini at 18% and Copilot in the single digits.

But Microsoft has its advantages: Unlike Amazon and Google, which compete directly with retailers through their own marketplaces, it isn’t a retailer. And retail has long been a major vertical for its enterprise cloud and software business, with large chains running on Azure and Microsoft 365.

Mitford said Microsoft is leaning on its existing trust and long-standing relationships with retailers, along with a commitment to responsible AI, to help differentiate itself from rivals.

Microsoft is making the broader case for AI to retailers based on return on investment. A Microsoft-commissioned study from IDC, released in November, found that retail and consumer packaged goods companies are seeing a 2.7x return on every dollar spent on generative AI.

Mitford, a former fashion designer who has been in the technology industry for most of her career, said she sees the retail sector among the leaders in AI uptake across the business world.

The technology, she said, is being “adopted at a pace that I’ve never seen.”

Windows helped launch the PC era. Now, Microsoft wants to launch the age of AI agents.

The post Inside Microsoft’s Plan to Embed AI Agents Deep Into Windows appeared first on TechRepublic.

Windows helped launch the PC era. Now, Microsoft wants to launch the age of AI agents.

The post Inside Microsoft’s Plan to Embed AI Agents Deep Into Windows appeared first on TechRepublic.

[Editor’s Note: Agents of Transformation is an independent GeekWire series and March 24, 2026 event, underwritten by Accenture, exploring the people, companies, and ideas behind AI agents.]

It was “like bringing a Porsche into a world of Model Ts.” 

That’s what Microsoft said in its 1990 annual report about the shift from MS-DOS to Windows. But the bigger breakthrough for the company wasn’t the graphical interface. It was Windows’ ability to serve as a platform for applications made by others.

Windows 3.0, released that year, made third-party software easier to find and launch, and offered developers a clear bargain: build to Microsoft’s specs, and your software would become a first-class citizen on the computers that were arriving “on every desk and in every home,” as the company’s original mission statement put it. 

Thirty-five years later, AI feels less like a car and more like a rocket ship. But Microsoft is hoping that Windows can once again serve as the platform where it all takes off.

A new framework called Agent Launchers, introduced earlier this month as a preview in the latest Windows Insider build, lets developers register agents directly with the operating system. They can describe an agent through what’s known as a manifest, which then lets the agent show up in the Windows taskbar, inside Microsoft Copilot, and across other apps.

The long-term promise for Windows users is autonomous assistants that operate on their behalf, directly on their machines. Beyond routine tasks like assembling a PDF or organizing files, agents could monitor email and calendars to resolve scheduling conflicts, or scan documents across multiple apps to pull together a briefing for an upcoming meeting.

Achieving that level of autonomy requires more than just a clever interface. It will take deep, persistent memory that operates more like the human brain.

“We are now entering a phase where we build rich scaffolds that orchestrate multiple models and agents; account for memory and entitlements; enable rich and safe tools use,” Microsoft CEO Satya Nadella wrote in a blog post this week looking ahead to 2026. “This is the engineering sophistication we must continue to build to get value out of AI in the real world.”

Elements of this are already emerging elsewhere.

• Google’s Gemini and Anthropic’s Claude offer desktop-style agents through browsers and native apps, with extensions that can read pages, fill forms, and take limited actions on a user’s behalf.
• Amazon is developing “frontier agents” aimed at automating business processes in the cloud. 
• Startups like Seattle-based Vercept are building standalone agentic apps that coordinate work across tools. 

But Microsoft’s Windows team is betting that agents tightly linked to the operating system will win out over ones that merely run on top of it, just as a new class of Windows apps replaced a patchwork of DOS programs in the early days of the graphical operating system. 

Microsoft 365 Copilot is using the Agent Launchers framework for first-party agents like Analyst, which helps users dig into data, and Researcher, which builds detailed reports. Software developers will be able to register their own agents when an app is installed, or on the fly based on things like whether a user is signed in or paying for a subscription.

The risks posed by PC agents

The parallels to the past only go so far. Traditional PC applications ran in their own windows, worked with their own files, and didn’t touch the rest of the system for the most part.

“Agents are going to need to be able to scratchpad their work,” Microsoft CTO Kevin Scott said recently on the South Park Commons Minus 1 podcast, explaining that agents will need to retain a history of user interactions and tap into the necessary context to solve problems.

Agents are meant to maintain this context across apps, ask follow-up questions, and take actions on a user’s behalf. That requires a different level of trust than Windows has ever had to manage, which is already raising difficult questions for the company.

Microsoft acknowledges that agents introduce unique security risks. In a support document, the company warned that malicious content embedded in files or interface elements could override an agent’s instructions — potentially leading to stolen data or malware installation.

To address this, Microsoft says it has built a security framework that runs agents in their own contained workspace, with a dedicated user account that has limited access to user folders. The idea is to create a boundary between the agent and what the rest of the system can access.

The agentic features are off by default, and Microsoft is advising users to “understand the security implications of enabling an agent on your computer” before turning them on.

A different competitive landscape

Even if Microsoft executes perfectly, the landscape is different now. In the early 1990s, Windows became dominant because developers flocked to the platform, which attracted more users, which attracted more developers. It was a virtuous cycle, and Microsoft was at the heart of it.

But Windows isn’t the center of the computing world anymore. Smartphones, browsers, and cloud platforms have fragmented the landscape in ways that didn’t exist back then. Microsoft missed the mobile era almost entirely, and the PC is now one screen among many.

In the enterprise, Microsoft has better footing. Azure, Microsoft 365 Copilot, and a growing ecosystem of business-focused agents give the company a strong position, competing against Google, Amazon, OpenAI and others for cloud-based AI agents and services.

Agent Launchers is a different bet — an attempt to make Windows the home for agents that serve individual users on their own machines. That’s a harder sell when the PC is competing with phones, browsers, and cloud apps for people’s attention. Microsoft can build the platform, but it can’t guarantee that developers will show up the way they did 35 years ago.

And unlike in the 1990s, Microsoft can’t count on users to embrace what it’s building. There’s a growing sentiment that these AI capabilities are being pushed into Windows not because users want them, but because Microsoft needs to justify its massive AI investments. 

In October, for example, Microsoft announced new features including “Hey Copilot” voice activation, a redesigned taskbar with Copilot built in, and the expansion of “Copilot Actions” agentic capabilities beyond the browser to the PC itself. 

“They’re thinking about revenue first and foremost,” longtime tech journalist and Microsoft observer Ed Bott said on the GeekWire Podcast at the time. The more users rely on these AI features, he explained, the easier it becomes for the company to upsell them on premium services.

There is a business reality driving all of this. In Microsoft’s most recent fiscal year, Windows and Devices generated $17.3 billion in revenue — essentially flat for the past three years. 

That’s less than Gaming ($23.5 billion) and LinkedIn ($17.8 billion), and a fraction of the $98 billion in revenue from Azure and cloud services or the nearly $88 billion from Microsoft 365 commercial.

By comparison, in fiscal 1995, five years after the launch of Windows 3.0, Microsoft’s platforms group (which included MS-DOS and Windows) represented about 40% of its total revenue of $5.9 billion. Windows was the growth engine for the company.

Windows is unlikely to play that kind of outsized role again. But AI integration is the company’s best bet to return the OS to growth. Whether that ultimately looks like a restored Porsche or a rocket ship on the launchpad probably doesn’t matter as much as keeping it out of the junkyard.

Microsoft CTO Kevin Scott has some advice for AI startups waiting for the next breakthrough model: the technology can already do far more than most people are getting out of it, so stop waiting and start building. 

Also: real customer traction still matters more than online buzz.

Speaking at a recent South Park Commons event with the organization’s general partner, former Dropbox CTO and Facebook engineer Aditya Agarwal, Scott said founders are sitting on a “gigantic capability overhang” —  meaning that current AI systems can do far more than most apps built on top of them. 

He cited ChatGPT itself as a past example: the underlying model was “pretty old” when it launched, as he put it, and nobody (including Scott and his peers) predicted at the time it would become a potential trillion-dollar product.

“The cost of doing the experiments has never been cheaper,” Scott said. “So do the damned experiments. Try things.”

The barrier isn’t model capability, he said, but the unglamorous integration work needed to put it to practical use.

“Some of the things that you need to do to squeeze the capability out of these systems is just ugly-looking plumbing stuff, or grungy product building,” he said. “But you’re in a startup, that’s kind of your life. It’s more about the grind.”

Scott also cautioned founders against mistaking online attention for real traction. The current environment, he said, is flooded with “false signal” — from media coverage to investor interest — that doesn’t really correlate with whether you’ve built something useful.

“You’ve got a bunch of people whose business model is getting clicks on articles online or getting people to subscribe to their Substack,” he said. “If you believe the things that particular part of the ecosystem is sending to you in terms of feedback, it could be that you’re steering yourself in exactly the wrong direction.”

The real signal, he said, comes from building something customers actually love.

Other topics included:

• Open-source vs. closed-source models (he effectively framed this as a toolbox, not a battle, and said Microsoft uses both).
• The importance of expert feedback in AI training, which he views as a potential startup advantage. 
• The infrastructure challenge of building memory systems for AI agents, a problem he said won’t be solved by simply training bigger models.

See the full talk above or on the South Park Commons Minus One Podcast.

At the GeekWire Gala this week, we spent time talking backstage with five of this year’s Uncommon Thinkers — the inventors, scientists, and entrepreneurs who were selected in partnership with Greater Seattle Partners for their work transforming industries and the world. 

You can hear the full conversations on this week’s episode of the GeekWire Podcast. As I mentioned at the end, I came away with an unexpected sense of optimism. 

Jeff Thornburg of Portal Space Systems spent years building rocket engines for Elon Musk at SpaceX and Paul Allen at Stratolaunch. Now he and his team are reviving a NASA concept from decades ago: spacecraft propelled by focused sunlight.

When I asked what the world will look like “if Portal succeeds,” he made a classic entrepreneurial pivot: “When we’re successful,” he said, “we become the backbone of Earth-Moon logistics.” 

From there, he said, it’s about protecting orbits for commerce, supporting human presence on the moon, and eventually pushing out to Jupiter’s moons.

[Read the profile.] 

Anindya Roy of Lila Biologics is using AI to design proteins from scratch — molecules that have never existed in nature — to fight cancer. He trained in David Baker’s Nobel Prize-winning lab at UW, so he saw the before and after of machine learning’s impact on the field.

Before: success rates below 1%, ordering hundreds of thousands of designs to find one that worked. Now: 5-20% success rates, ordering a few hundred designs to find a drug candidate. 

“If you told me a couple of years ago that we can design an antibody from a computer, I would not believe you,” he said.

[Read the profile.]

Jay Graber of Bluesky runs the decentralized social network that has  become a leading alternative to X. But while most tech CEOs build moats, she and her team are building a protocol designed to help users leave. 

She talks about Bluesky and the underlying AT Protocol as a “collective organism,” and describes her role as guiding and stewarding the ecosystem rather than controlling it.

The industry and the world would be better off, she says, if leaders would think about their role “more as guides and stewards, rather than just dictators or emperors as they like to style themselves.”

[Read the profile.]

Kiana Ehsani of Vercept came to Seattle from Iran for her PhD, spent four years at the Allen Institute for AI, and is now competing with OpenAI and Google in the AI agent space with a fraction of their resources.

The ultimate vision is to help people move beyond mouse, keyboard, and touchscreen, letting them interact with computers the way they’d talk to a coworker.

AI agents are still early, she cautions. “Think of ChatGPT three years ago. Don’t think of it today.” Her advice for getting started with AI agents: “Start small, start with simple tasks that you don’t want to do, and then slowly build on top of it to see the magic.”

[Read the profile.]

Brian Pinkard of Aquagga is tackling forever chemicals, the PFAS compounds that have spread through our water, food chain, and bloodstreams. The industry standard is to filter them out and then landfill or incinerate the waste, approaches that don’t truly solve the problem and can simply move it elsewhere.

Aquagga uses technology originally designed to destroy chemical weapons to break PFAS down into inert salts under extreme heat and pressure. Pinkard didn’t believe it was possible until he saw the data. “I’m a skeptic, I’m cynical, I’m a scientist,” he said. “I wanted to see proof.”

His bigger vision is to transform hazardous waste processing entirely. Today, huge volumes of wastewater are trucked to incinerators and burned — which he calls “thermodynamic insanity.”

[Read the profile.]

We’ll speak on a future episode with our sixth honoree, Chet Kittleson, co-founder and CEO of Tin Can, the startup making WiFi-enabled landline phones to help kids connect without screens.

Uncommon Thinkers is presented in partnership with Greater Seattle Partners.

Subscribe to GeekWire in Apple Podcasts, Spotify, or wherever you listen.

Audio editing by Curt Milton.

LAS VEGAS — Speaking this week on the Amazon Web Services re:Invent stage, AWS executive Colleen Aubrey delivered a prediction that doubled as a wake-up call for companies still thinking of AI as just another tool.

“I believe that over the next few years, agentic teammates can be essential to every team — as essential as the people sitting right next to you,” Aubrey said during the Wednesday keynote. “They will fundamentally transform how companies build and deliver for their customers.”

But what does that look like in practice? On her own team, for example, Aubrey says she challenged groups that once had 50 people taking nine months to deliver a new product to do the same with 10 people working for three months.

Meanwhile, non-engineers such as finance analysts are building working prototypes using AI tools, contributing code in Amazon’s Kiro agentic development tool alongside engineers, and feeding those prototypes into Amazon’s famous PR/FAQ planning process on weekly cycles.

Those are some of the details that Aubrey shared when we sat down with her after the keynote at the GeekWire Studios booth in the re:Invent expo hall to dig into the themes from her talk. Aubrey is senior vice president of Applied AI Solutions at AWS, overseeing the company’s push into business applications for call centers, supply chains, and other sectors.

Continue reading for takeaways from the conversation, watch the video below, and listen to the conversation starting in the second segment of this week’s GeekWire Podcast.

The ‘teammate’ mental model changes everything. Aubrey draws a clear line between single-purpose AI tools that do one thing well and the agentic teammates she sees emerging — systems that take responsibility for whole objectives, and require a different kind of management. 

“I think people will increasingly be managers of AI,” she said. “The days of having to do the individual keystrokes ourselves, I think, are fast fading. And in fact, everyone is going to be a manager now. You have to think about prioritization, delegation, and auditing. What’s the quality of our feedback, providing coaching. What are the guardrails?”

Amazon Connect crosses $1 billion. AWS’s call center platform reached $1 billion in annual revenue on a run rate basis, with Aubrey noting it has accelerated year-over-year growth for two consecutive years. 

This week at re:Invent, the team announced 29 new capabilities across four areas: Nova Sonic voice interaction that Aubrey says is “very close to being indistinguishable” from human conversation; agents that complete tasks on behalf of customers; clickstream intelligence for product recommendations; and observability tools for inspecting AI reasoning. 

One interesting detail: Aubrey said she’s often surprised by Nova Sonic’s sophistication and empathy in complex conversations — and equally surprised when it fails at basic tasks like spelling an address correctly. 

“There’s still work to do to really polish that,” she said.

The ROI question gets a “yes and no.” Asked whether companies are seeing the business value to justify AI agent investments, Aubrey offered a nuanced response. “I observe companies to struggle to realize the business impact,” she said. But she said the value often shows up as eliminating bottlenecks — clearing backlogs, erasing technical debt, accelerating security patching — rather than immediate revenue gains. 

“I’m not going to see the impact on my P&L today,” she said, “but if I fast forward a year, I’m going to have a product in market where real customers are using and getting real value, and we’re learning and iterating where I might not have even been halfway there in the past.” 

Her advice for companies still hesitating: “If you don’t start today, that’s a one way door decision… I think you have to start the journey today. I would suggest people get focused, they get moving, because if you don’t, I think that becomes existential.”

Trust requires observability. Aubrey says companies won’t get full value from AI teammates if they can’t see how they’re reasoning. 

“If you don’t trust an AI teammate, then you’re never going to realize the full benefit,” she said. “You’re not going to give them the hard tasks, you’re not going to invest in their development.” 

The solution is treating AI inspection the same way you’d manage a human colleague: understand why it took an action, audit the quality, and iterate. 

“You can refine your knowledge bases. You can refine your workflows. You can refine your guardrails, and then confidently keep iterating… the same way we do with each other. We keep iterating, we keep learning, and we keep getting better,” she said.

Product updates: Beyond Connect, Aubrey offered updates on other parts of her portfolio of Amazon’s applied AI solutions. 

• Just Walk Out, Amazon’s cashierless checkout technology, deployed more than 150 new stores in 2025 and should accelerate next year.
• AWS Supply Chain, meanwhile, is getting a reset. “I’m going to declare that a pivot,” she said, with a Q1 announcement coming around agentic decision-making for supply and demand planning.
• Also coming in Q1: a life sciences product focused on antibody discovery, currently in beta. 

She teased “a few other new investment areas” expected to come in early 2026.

Amazon is experimenting again. This week on the GeekWire Podcast, we dig into our scoop on Amazon Now, the company’s new ultrafast delivery service. Plus, we recap the GeekWire team’s ride in a Zoox robotaxi on the Las Vegas Strip during Amazon Web Services re:Invent.

In our featured interview from the expo hall, AWS Senior Vice President Colleen Aubrey discusses Amazon’s push into applied AI, why the company sees AI agents as “teammates,” and how her team is rethinking product development in the age of agentic coding.

RELATED STORIES

• Stars on the ceiling, Cher on the speakers: Our first ride in Amazon’s Zoox robotaxi
• We tested ‘Amazon Now’ in Seattle — and got our delivery in 23 minutes
• The AWS CEO thought Amazon needed a million developers, until AI changed his mind

With GeekWire co-founders Todd Bishop and John Cook. Edited by Curt Milton.

Subscribe to GeekWire in Apple Podcasts, Spotify, or wherever you listen.

Artificial intelligence has evolved beyond general-purpose applications, giving rise to a new generation of specialized systems known as Vertical AI Agents. Unlike traditional AI models that serve broad and generic functions, these advanced solutions are designed to understand and operate within specific industries such as healthcare, finance, manufacturing, and retail. This targeted approach allows businesses to achieve faster automation, deeper insights, and higher efficiency tailored to their exact operational needs.

According to a report by Grand View Research, the global AI market is projected to grow at a compound annual growth rate (CAGR) of 36.6% from 2024 to 2030, reflecting the rapid adoption of specialized AI systems across industries.

Vertical AI Agents are redefining how global enterprises and professional service providers streamline processes. They combine domain-trained data models with real-time analytics to deliver quality, industry-ready automation. As businesses look for smarter, faster, and more professional AI tools, vertical agents have emerged as the top choice for achieving measurable results without the heavy customization required by general AI systems.

Whether it is optimizing supply chains, enhancing fraud detection, or delivering personalized customer experiences, Vertical AI Agents bring precision and adaptability to every business layer. In this guide, we will explore what these agents are, how they work, why they are gaining popularity, and the practical steps businesses can take to implement them effectively.

What Are Vertical AI Agents?

Vertical AI Agents are specialized artificial intelligence systems built to serve the distinct needs of specific industries or business domains. Unlike traditional or horizontal AI models that perform general tasks across sectors, these advanced agents are trained on domain-specific data and workflows, making them highly accurate, relevant, and ready for professional use.

A Vertical AI Agent understands the unique processes, challenges, and regulatory requirements of an industry, whether it is finance, healthcare, manufacturing, logistics, or retail. This deep contextual awareness allows the system to deliver faster insights, improve decision-making, and support business-critical functions with precision. As a result, modern enterprises are increasingly relying on leading Vertical AI Agents to achieve quality automation and measurable performance outcomes.

What makes Vertical AI Agents stand out is their ability to adapt quickly to evolving market conditions while maintaining a strong focus on efficiency and compliance. They are not built for generic problem-solving but are designed as expert systems that provide fast, professional, and scalable solutions. Businesses adopting these advanced AI tools experience improved operational agility and reduced dependency on manual intervention, giving them a strong competitive advantage in a fast-paced digital ecosystem.

How Do Vertical AI Agents Work?

Vertical AI Agents operate through a systematic and intelligent workflow designed to deliver fast, professional, and quality automation for specific industries. Their functioning involves a multi-stage process that combines data engineering, model training, and real-time analytics to create ready-to-deploy AI systems that align perfectly with business operations.

Step 1. Industry Data Identification and Preparation

The working process of Vertical AI Agents begins with the identification and preparation of industry-specific data. This includes detailed datasets from operational records, customer behavior, financial transactions, compliance reports, and product performance logs. The purpose of this stage is to ensure that the agent understands the language, context, and real-world patterns of the target industry.

Step 2. Domain-Centric Model Training

After collecting and cleaning the data, Vertical AI Agents undergo domain-centric training. This phase involves using advanced machine learning algorithms that learn the business rules, market patterns, and decision-making logic of a particular sector. For instance, a Vertical AI Agent built for the healthcare industry focuses on clinical diagnosis and patient insights, while one developed for finance emphasizes fraud detection, credit scoring, and compliance tracking.

Step 3. Integration into Enterprise Systems

Once the training phase is complete, the Vertical AI Agent is integrated into the company’s digital ecosystem. It connects seamlessly with tools like ERP systems, CRM platforms, and data analytics software. This integration allows businesses to automate workflows such as report generation, predictive maintenance, and customer engagement, ensuring fast and consistent delivery across departments.

Step 4. Real-Time Processing and Decision Intelligence

When deployed, Vertical AI Agents analyze live data, detect trends, and provide instant decision support to business leaders. They process information in real time to identify risks, predict outcomes, and recommend data-driven actions. This capability enables businesses to operate more efficiently, respond quickly to challenges, and maintain a competitive edge in their industry.

Step 5. Continuous Learning and Performance Optimization

The final stage of the process focuses on improvement. Top Vertical AI Agents continuously learn from new data, feedback, and performance metrics. Over time, they refine their analytical models and improve prediction accuracy, allowing enterprises to achieve long-term reliability, faster automation, and consistent quality output.

Best Vertical AI Agents work as intelligent, evolving systems that bring together domain expertise and modern automation to help businesses make smarter, faster, and more profitable decisions.

Why Vertical AI Agents Are Gaining Popularity?

Best Vertical AI Agents are quickly becoming one of the most trending innovations in enterprise automation. Their rise in popularity is driven by the growing need for industry-specific intelligence that delivers faster, more professional, and higher-quality results compared to traditional AI solutions. Businesses today demand AI systems that not only process information but also understand their sector’s operations, regulations, and customer behavior. Vertical AI Agents meet this need by combining precision, adaptability, and speed.

Tailored for Industry-Specific Needs

One of the most powerful advantages of Vertical AI Agents is their ability to focus exclusively on a particular domain. Whether it is healthcare, logistics, finance, or retail, each agent is designed to handle the distinct challenges and workflows of that industry. This specialization allows enterprises to achieve better accuracy, faster deployment, and measurable performance improvements without the extensive customization required by general AI systems.

Real-Time Decision-Making and Efficiency

Businesses are increasingly relying on Vertical AI Agents to make instant decisions based on real-time data. These agents analyze transactions, customer interactions, and operational metrics in seconds, enabling companies to respond quickly to market changes and customer demands. The result is a fast and efficient decision-making environment that minimizes human errors and reduces operational costs.

Alignment with Digital Transformation Goals

Modern enterprises are focusing on digital transformation strategies that emphasize automation, scalability, and agility. Vertical AI Agents align perfectly with these goals by offering ready-to-use, domain-specific solutions that enhance productivity and streamline complex operations. Their ability to integrate easily into existing systems makes them one of the best choices for business leaders looking to implement smart automation without disrupting workflows.

Expanding Use Across Key Industries

The adoption of Vertical AI Agents is spreading rapidly across professional sectors.

• In finance, they help identify fraud patterns and improve risk analysis.
• In healthcare, they support diagnostics and patient engagement.
• In retail, they optimize inventory, pricing, and customer personalization.

Each of these applications demonstrates how these top AI systems are creating new standards for efficiency and business growth.

As more organizations recognize the measurable value of domain-trained intelligence, top Vertical AI Agents are set to become an essential part of the modern business ecosystem, offering fast, scalable, and quality-driven automation across industries.

Key Benefits of Vertical AI Agents for Businesses

Vertical AI Agents are transforming how modern enterprises operate by combining industry knowledge with intelligent automation. They are designed to help businesses achieve fast, quality, and measurable results by reducing inefficiencies, strengthening decision-making, and increasing operational performance. Below are the most significant benefits that make Vertical AI Agents a top choice for professional and forward-thinking companies.

Enhanced Productivity and Process Optimization

Popular Vertical AI Agents streamline time-consuming and repetitive workflows that once required manual effort. By automating domain-specific operations such as financial analysis, logistics planning, or medical reporting, they help enterprises achieve faster turnaround and higher productivity. The ability to process large volumes of data with precision ensures that teams can focus on strategic and creative tasks, driving overall business efficiency.

Reduced Costs and Manual Dependencies

One of the best advantages of implementing Vertical AI Agents is the reduction in operational costs. These agents lower human resource dependency by automating data processing, analytics, and routine decision-making. Businesses experience fewer errors, reduced downtime, and better resource utilization. Over time, this leads to measurable cost savings and a faster return on investment.

Competitive Advantage Through Specialized Intelligence

Latest Vertical AI Agents provide industry-specific intelligence that general AI systems cannot match. Their deep understanding of business workflows allows them to predict trends, identify risks, and offer quick solutions tailored to the company’s environment. This gives enterprises a strategic advantage, helping them innovate faster and maintain leadership in competitive markets.

Faster Time-to-Market with Ready AI Solutions

Vertical AI Agents are pre-trained on domain data, which allows businesses to deploy them quickly without extensive customization or long development cycles. This readiness accelerates the adoption of AI and shortens the time needed to launch new products or services. As a result, companies gain agility and can respond quickly to new opportunities or changing customer demands.

Reliable and Scalable Performance

Unlike traditional AI models that require ongoing manual adjustments, Vertical AI Agents continuously learn from real business data. Their adaptive capabilities improve accuracy and scalability as business operations grow. This ensures that enterprises enjoy long-term reliability, consistent performance, and a professional-grade automation system that evolves with their needs.

The growing adoption of popular Vertical AI Agents across industries proves their ability to deliver fast, specialized, and quality-driven automation that enhances productivity, reduces costs, and supports sustainable business growth.

Comparison — Vertical AI Agents vs. General AI Models

As artificial intelligence continues to evolve, enterprises often face the question of whether to adopt Vertical AI Agents or rely on general AI models. While both approaches harness the power of machine learning and automation, their objectives, capabilities, and outcomes differ significantly. Understanding this distinction helps business leaders choose the best technology for their specific needs.

Focus and Purpose

Vertical AI Agents are developed to serve a single industry or business domain. They are designed to handle the unique language, regulations, and workflows of a sector such as healthcare, finance, logistics, or retail. In contrast, general AI models are broad systems that can perform various generic tasks, such as text analysis, image recognition, or language processing, without deep contextual understanding. The focused design of Vertical AI Agents allows businesses to achieve faster and more accurate outcomes tailored to their field.

Customization and Adaptability

General AI solutions often require extensive customization before they can be effectively used in an enterprise environment. They need to be trained, tuned, and integrated with sector-specific data, which consumes time and resources. Vertical AI Agents, on the other hand, are pre-trained with industry data and ready for immediate implementation. Their built-in adaptability allows companies to experience fast deployment and quick measurable results.

Performance and Accuracy

Latest Vertical AI Agents consistently outperform general models in terms of precision and operational efficiency within their specific domain. Since they understand industry-specific terminology and decision patterns, they deliver quality insights and reliable automation. General AI models provide versatility but may struggle with accuracy in specialized applications where contextual knowledge is crucial.

Integration and Scalability

Vertical AI Agents are built for seamless integration with business tools such as ERP, CRM, and analytics platforms. Their architecture supports easy scalability as the enterprise grows. General AI systems may require additional layers of configuration and manual updates to achieve similar compatibility. For enterprises aiming for professional-grade, scalable, and long-term automation, Vertical AI Agents represent a more sustainable investment.

Suitability for Businesses

For organizations seeking general intelligence to perform a wide variety of tasks, general AI models can serve as a useful foundation. However, for businesses looking to optimize performance, reduce costs, and gain a competitive edge in their specific domain, Vertical AI Agents are the superior choice. They provide fast implementation, industry expertise, and consistent, high-quality results.

In summary, while general AI models offer flexibility, Vertical AI Agents deliver the focused intelligence, speed, and professional accuracy that modern businesses need to stay ahead in competitive markets.

How to Implement Vertical AI Agents in Your Business?

Implementing Vertical AI Agents in a business requires a structured and strategic approach. The process should focus on aligning technology with real operational goals, ensuring smooth integration, and measuring long-term value. When executed properly, Vertical AI Agents can deliver fast, high-quality automation and measurable improvements in productivity, cost-efficiency, and decision accuracy.

Step 1. Assess Business Needs and Identify Use Cases

The first step is to identify the specific processes or functions that can benefit most from AI-driven automation. Business leaders should analyze areas such as customer service, logistics, financial management, or production operations where efficiency gaps exist. Selecting the right use cases ensures that the implementation of Vertical AI Agents directly supports core business objectives and generates clear value.

Step 2. Choose the Right Vertical AI Partner or Platform

Selecting a trusted AI partner or solution provider is critical for success. Businesses should evaluate vendors based on domain expertise, quality of AI models, scalability, and post-deployment support. Leading enterprises often prefer ready-to-deploy Vertical AI Agents that are already trained on industry data and can integrate quickly with minimal configuration. This ensures fast implementation and reduces the burden on internal IT teams.

Step 3. Prepare and Integrate Data Sources

Accurate and comprehensive data is the foundation of every effective AI system. Companies must collect, clean, and organize data from internal systems such as CRMs, ERPs, or supply chain tools. The chosen Vertical AI Agent should then be integrated into these platforms to ensure continuous data flow and real-time insights. A well-connected system enables professional-grade automation across business departments.

Step 4. Pilot Implementation and Performance Evaluation

Before scaling AI across the enterprise, it is advisable to run a pilot project. This allows business leaders to evaluate the performance, accuracy, and efficiency of the Vertical AI Agent in real-world conditions. Feedback collected from the pilot phase can help fine-tune the system and ensure it meets quality and compliance standards.

Step 5. Scale, Monitor, and Continuously Improve

Once the pilot phase delivers positive results, businesses can expand the deployment across other departments or regions. Continuous monitoring is essential to track performance metrics, identify new opportunities, and ensure the AI system evolves alongside business needs. Vertical AI Agents are designed to learn and adapt, providing scalable intelligence that grows with the enterprise.

By following these steps, businesses can implement Vertical AI Agents in a structured and professional manner, ensuring a balance between technological innovation and operational stability. When aligned with strategic goals, these top AI solutions can help companies achieve faster decision-making, improved productivity, and a lasting competitive advantage.

Conclusion

Vertical AI Agents are transforming how modern businesses use artificial intelligence to achieve efficiency and precision. By combining industry-specific data, real-time analytics, and adaptive learning, they deliver fast, reliable, and professional automation that general AI systems cannot replicate. Their growing adoption among global enterprises highlights a shift toward focused, domain-driven intelligence that produces measurable results in productivity, cost reduction, and operational quality.

For forward-thinking businesses and AI companies, Vertical AI Agents represent the next stage of intelligent innovation. They integrate deep industry expertise with scalable, ready-to-use technology, enabling consistent performance without the need for complex customization. From improving workflow accuracy to accelerating data-driven decisions, Vertical AI Agents are shaping a future where automation aligns seamlessly with business goals.

As industries continue to evolve and data becomes more central to success, Vertical AI Agents will remain a core element of enterprise transformation. Their precision, adaptability, and efficiency make them essential for businesses and AI companies aiming to lead in the era of intelligent automation.

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The way people search for information is undergoing a game-changing transformation. Traditional search engines like Google are no longer the only gateway to online visibility. With the rise of AI-powered platforms, ChatGPT is becoming a trusted source where millions of users turn for instant, conversational answers.

For businesses, this shift presents both a challenge and an opportunity. If your website isn’t optimized for ChatGPT SEO, you risk missing out on a growing share of digital attention. On the other hand, those who adapt early with proven strategies can boost visibility, rank higher on ChatGPT, and establish a future-proof presence in the competitive online landscape.

According to a survey by Adobe Express, 77% of Americans who use ChatGPT treat it as a search engine, indicating that many are using it as a primary discovery tool rather than just as a chat assistant.

This blog is a step-by-step guide to rank your website on ChatGPT using reliable, actionable, and results-driven techniques.

Understanding ChatGPT and How to Rank Your Website on It

Understanding ChatGPT is essential before you start working on how to rank your website on it. Unlike traditional search engines that display a list of links, ChatGPT provides direct conversational answers based on context, relevance, and trustworthiness. This difference means that optimizing for ChatGPT SEO requires more than just standard keyword practices.

Here are the key points to understand about ChatGPT and how to rank your website on it:

• ChatGPT responds to natural language and conversational queries, not just short keywords.
• To rank higher on ChatGPT, your website must demonstrate topical authority by covering subjects in depth.
• ChatGPT SEO places strong emphasis on credibility and trust signals such as reviews, case studies, and citations.
• Unlike Google, ChatGPT does not display multiple search results, which makes visibility more competitive and valuable.
• Focusing on AI-powered search optimization ensures a future-proof strategy for website visibility.

By understanding ChatGPT and how to rank your website on it, businesses can begin to align their content strategies with the evolving way people search, ensuring reliable and proven visibility in the AI-driven digital landscape.

Step 1: Optimize Conversational Queries to Rank Your Website on ChatGPT

Optimizing conversational queries is the first step to rank your website on ChatGPT. Since this AI platform is designed to answer natural language questions, businesses must shift from traditional keyword stuffing to creating content that matches how users actually speak and search. This approach is critical for ChatGPT SEO and plays a proven role in boosting visibility.

Use Natural Language and Long-Tail Keywords

Long-tail keywords that reflect real questions make it easier to rank higher on ChatGPT. For example, instead of targeting “mobile app development,” create content that answers queries like “what are the best strategies to develop a mobile app for startups.”

Align Content With User Search Intent

Understanding intent is key to improving ChatGPT rankings. Content should directly address what users want to know, whether it’s informational, transactional, or problem-solving. A reliable way to achieve this is by structuring articles around common queries your audience is already asking.

Turn Short SEO Phrases Into Conversational Queries

Traditional SEO often relies on short, competitive phrases. To optimize your website for ChatGPT, reframe these phrases into natural questions. For example:

• Instead of “AI in business,” use “how is AI transforming business in 2025.”
• Instead of “SEO tools,” use “which SEO tools give the best results for small businesses.”

By following these proven methods, you can optimize conversational queries effectively, improve ChatGPT SEO, and establish a future-proof strategy to rank your website on ChatGPT.

Step 2: Build Topical Authority to Rank Higher on ChatGPT

Building topical authority is one of the most powerful ways to rank your website on ChatGPT. Unlike traditional SEO, where scattered keywords can sometimes bring results, ChatGPT prioritizes sources that demonstrate depth, expertise, and consistency across a subject area. Establishing yourself as an authoritative voice ensures your content is recognized as reliable and future-proof for AI-powered search optimization.

Create Comprehensive Content Clusters

To improve ChatGPT rankings, focus on creating interconnected content clusters. This means writing multiple articles, guides, and FAQs around a single core theme, linking them internally, and covering every related aspect in detail.

Publish In-Depth Guides, Blogs, and FAQs

Long-form and structured content gives ChatGPT more context to pull from. Publishing detailed blogs, case studies, and step-by-step tutorials shows both users and AI that your website offers proven and valuable insights.

Demonstrate Expertise Consistently Across Topics

Consistency matters when trying to rank higher on ChatGPT. Every article, guide, or resource you publish should reinforce your expertise within the same niche. Over time, this consistency builds trust, improves topical authority, and boosts visibility for related queries.

Some businesses also collaborate with AI SEO companies that specialize in building content clusters and strengthening authority across AI-powered search.

Step 3: Boost Trust Signals to Rank Your Website on ChatGPT

Trust is a critical factor in ChatGPT SEO. Since the platform is designed to deliver reliable and accurate answers, it favors websites that show credibility and authenticity. To rank your website on ChatGPT, boosting trust signals is a proven way to stand out and build long-term authority.

Add Reviews, Testimonials, and Case Studies

User-generated content such as verified reviews, client testimonials, and detailed case studies act as powerful trust signals. They demonstrate real-world experience and establish your website as a reliable source for AI-powered search optimization.

Strengthen E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness)

ChatGPT prioritizes content that reflects strong expertise and authoritativeness. Highlight credentials, show industry experience, and present content created by knowledgeable professionals to increase the chances of ranking higher on ChatGPT.

Cite Reputable Industry Sources

Linking to credible, third-party sources within your content reinforces reliability. By citing studies, research reports, or expert insights, you strengthen your content’s authority and give ChatGPT confidence to select your website for relevant answers.

By boosting these trust signals, you position your website as both credible and authoritative, which significantly improves ChatGPT rankings and ensures a future-proof digital presence.

Step 4: Create Structured Content to Improve ChatGPT Rankings

Structured content is one of the most effective ways to rank your website on ChatGPT. Since AI tools rely on context and clarity to generate answers, well-structured and organized content makes it easier for ChatGPT to recognize and deliver your information. Optimizing structure ensures reliable visibility and helps improve ChatGPT rankings over time.

Apply Schema Markup for AI-Friendly Results

Schema markup provides clear signals about your content to AI platforms. Adding structured data for FAQs, articles, reviews, and other elements makes your website easier for ChatGPT to interpret and use in responses.

Write Q&A and FAQ-Style Content

ChatGPT is built to answer questions, so creating content in a Q&A or FAQ style increases your chances of being selected. By directly matching conversational queries, you make it simpler for ChatGPT to find relevant answers from your website.

Keep Content Context-Rich and Well-Organized

Avoid scattered information and focus on clarity. Use headings, subheadings, and short paragraphs to keep content easy to follow. This structure allows ChatGPT to identify proven insights and deliver your website as a trusted result.

By creating structured content that combines schema, Q&A formats, and organized writing, you can optimize website performance, achieve stronger ChatGPT SEO, and establish a future-proof strategy to rank higher.

Businesses can also take support from trusted SEO companies to implement these strategies effectively.

Step 5: Leverage Backlinks and Mentions to Rank on ChatGPT

Backlinks and brand mentions remain powerful signals for credibility, even in the world of ChatGPT SEO. While traditional search engines have long valued links, ChatGPT also looks at external references to identify reliable and authoritative sources. To rank your website on ChatGPT, you need a proven strategy for building recognition across multiple trusted platforms.

Earn Brand Mentions Across Reputable Platforms

Brand mentions across blogs, news articles, and industry publications reinforce authority. Even without a link, a mention signals to AI that your business is recognized and relevant, which improves ChatGPT rankings.

Build Backlinks Through PR, Guest Posting, and Outreach

High-quality backlinks remain an essential trust factor. Securing links from top industry websites through guest articles, press releases, or digital PR campaigns can boost visibility and increase your chances to rank higher on ChatGPT.

Use Trusted Directories for Visibility

Getting listed on well-recognized directories and niche platforms provides additional proof of credibility. These listings act as reliable trust signals, making your website a stronger candidate to appear in conversational responses.

By leveraging backlinks and mentions, you create a broader web presence that validates your expertise. This proven approach strengthens authority, improves ChatGPT SEO, and establishes a future-proof foundation for long-term visibility.

Step 6: Strengthen Multi-Channel Presence to Rank Your Website on ChatGPT

To rank your website on ChatGPT, visibility must extend beyond your own domain. ChatGPT pulls information from multiple sources, and a strong multi-channel presence signals reliability and relevance. Expanding across platforms ensures a proven strategy to improve ChatGPT rankings and build a future-proof brand reputation.

Expand Visibility Through Social Media and LinkedIn

Consistent activity on social platforms like LinkedIn, Twitter, and industry communities increases your authority. When your content is shared and engaged with, it creates strong visibility signals that help you rank higher on ChatGPT.

Get Listed on Niche and Industry Directories

Directory listings provide trustworthy references that enhance credibility. Being featured on specialized industry platforms helps your website appear as a reliable resource that ChatGPT can confidently recommend.

Ensure Brand Consistency Across Platforms

Maintaining consistency in tone, messaging, and details across websites, profiles, and directories strengthens trust. A uniform presence assures both users and AI that your business is authentic and dependable.

By strengthening multi-channel presence, you create an ecosystem of recognition that helps optimize your website for ChatGPT SEO and improves long-term rankings.

Step 7: Track, Adapt, and Continuously Improve ChatGPT Rankings

Optimizing your website for ChatGPT is not a one-time effort. Since AI models evolve and user behaviors shift, you need to track performance, adapt strategies, and continuously improve to maintain strong visibility. This approach ensures a proven and future-proof way to rank your website on ChatGPT.

Monitor AI-Driven Traffic and Visibility

Use analytics tools to track how users are discovering your website. Look for patterns in conversational queries, referral traffic, and engagement to measure progress in ChatGPT SEO.

Use Analytics Tools to Optimize Performance

Tools such as Google Analytics, Search Console, and third-party AI-tracking platforms can help identify which pages are performing well and which need improvement. Regular analysis provides actionable insights to improve ChatGPT rankings.

Refresh Content to Align With AI Evolution

ChatGPT is updated and trained on evolving data. Refreshing blogs, guides, and resources with the latest information ensures your content stays relevant and reliable. Updated content is more likely to rank higher on ChatGPT and maintain authority over time.

By tracking, adapting, and continuously improving, you can ensure your website remains competitive in the AI-driven search landscape and steadily builds long-term authority.

Conclusion | Future-Proof Your Website by Ranking on ChatGPT

The rise of AI-powered platforms is reshaping how people search, discover, and engage with online content. Understanding ChatGPT and applying proven strategies is no longer optional; it is essential for any business that wants to improve visibility and stay competitive.

By following this step-by-step guide to rank your website on ChatGPT, you can optimize conversational queries, build topical authority, boost trust signals, create structured content, leverage backlinks and mentions, strengthen multi-channel presence, and continuously adapt for long-term success. Each step plays a critical role in ensuring reliable visibility and stronger ChatGPT SEO.

Businesses that adapt these strategies on their own or in partnership with proven ChatGPT companies will secure a future-proof advantage and improve their chances of ranking higher on ChatGPT.

Also read

• AI agent development with ChatGPT
• Deepseek vs ChatGPT
• ChatGPT for Shopify

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