There’s no point building your own agentic AI stack for a non-core function when a vendor already has a product ready to go that meets your needs. For non-tech companies in particular, building from scratch can take time, money, and expertise they don’t have.
Take for example, Fertitta Entertainment, with more than 600 Landry’s restaurants and the Golden Nugget hotel and casino. After the pandemic, the company had problems adequately staffing its call centers, says its director of contact center operations Brian Jeppesen. “The agents didn’t want to come back,” he says.
The virtual assistants didn’t work because the user experience was bad, and with the incoming call volume, 40 to 50% of them were abandoned.
“Everybody wants AI, but nobody wants BS,” Jeppesen says. “I talked to my CFO. This is what we’re abandoning. This is the potential revenue we’re losing. I can’t throw bodies at it anymore.”
Agentic AI has upped their game for the virtual assistants. Instead of only answering simple questions like check-in hours, it allows the company to connect APIs and carry out more complicated tasks, like making reservations.
“My abandonments fell to under 10%,” says Jeppesen. “From waiting 45 minutes to an hour, they can get their questions immediately answered, or immediately transferred and taken care of.”
And turnover went down. Call centers typically see 100% turnover, says Jeppesen. Not because everyone quits, but because the bottom 25% quit and need to be replaced over and over again.
Now that bottom tier doesn’t have to be rehired, while the people who stay are the most knowledgeable. “They give a better experience for my customers,” he says. “And it reduced my attrition by 50%.”
Forrester did a total economic impact study for the specific vendor that Fertitta used, PolyAI, and the ROI came back at 391% over the course of three years, with a payback period of less than six months.
Jeppesen says he reviewed his own numbers and found that the automated reservations system alone brings in 3,000 reservations a month. “That’s $600,000 in revenue a month for us without the need to speak to an agent,” he says.
Next, Fertitta plans to expand it to handle to-go orders and web chats. There are also opportunities for agentic AI on the technical help desk, Jeppesen says.
Agentic AI on the menu
Fogo de Chão, a Brazilian restaurant chain with around 120 locations, is also deploying agentic AI in small, focused use cases, and sees ROI with some of the projects.
For instance, it deployed virtual call center agents from PolyAI, and saw positive ROI from day one since there were no significant up-front costs or usage-based pricing. After several months of testing and small-scale pilots in 2025, the system has been rolled out across the US, with 100% of calls now being answered instead of up to 25% being dropped, and a 10% increase in total bookings that wouldn’t have happened otherwise, says Michael Breed, the chain’s chief marketing officer.
“If we compared it to a media channel, it’d be twice the ROI from other media channels,” he says. “It’s almost like another customer acquisition vehicle.”
The company is also building agentic workflows for its team members.
“We’ve identified jobs that are very repeatable and time-consuming, and we’re training agents to take those tasks,” he says. These are low-risk jobs, like proofing menus or other text, or replacing manual processes, such as pulling data from one system into another that had no automatic integrations.
“The agent does a great job bringing in the data, then the human in the loop can inspect it,” says Breed. “Is it getting classified right? Is it getting to the right places? And when guests are dissatisfied, can we make it better?”
Built for SMBs
For smaller companies in particular, off-the-shelf agents make the most sense. AltaClaro, for example, builds training simulations for lawyers, and counts more than 100 of the top 200 US law firms as customers, all with just 10 employees.
One product is a deposition simulator, says CEO Abdi Shayesteh, a completely agentic, voice-based platform.
“It’s a fully simulated environment that creates the experience of doing the deposition,” he says. “Different agents play the role of the witness, opposing counsel, and the court reporter.”
He says it’s kind of like a Zoom call. The product is built on top of technology from Verbit, a legal tech company with a custom agentic platform.
“They’re taking care of the technical infrastructure and we have the material to ground the AI.”
A different vendor, Credal, provides a secure AI platform that allows AltaClaro to switch between different models. It can obfuscate names and keep other sensitive data from leaking out, Shayesteh says. “It ensures everything is anonymized and does secure calls,” he says. “We standardized on Credal because it’s highly flexible and secure. We serve law firms and we’re as invested in security as they are.”
But security isn’t the only issue that enterprises face when deploying agentic AI.
The hidden costs of buying AI
The temptation to jump into agentic AI is high, says Dana Simberkoff, chief risk, privacy, and information security officer at AvePoint Inc., a data security company. And off-the-shelf agentic AI can be so easy to adopt that companies deploy it before they’re prepared for it.
Enterprise vendors will often add the technology at no extra cost, just to get people using it, and companies turn it on for their employees before truly understanding what it does, and before systems and workflows are ready. “Then it immediately exposes problems that were already there,” she says.
Say, for example, an agent helps with research, normally considered a low-risk task.
“AI will follow sharing links across documents,” she says. “So if you haven’t taught your users how to properly use sharing links, the AI agent is just going to follow that path, and it could expose information that might have been obscured in the past. It’ll gobble up everything it can.”
The result is it could expose HR data, personal information, or proprietary information to people who shouldn’t have access to it. One company she knows had to shut down their AI project immediately and go back, classify, and label all their information, put governance controls in place, and teach people how to apply metadata.
According to Gartner, by next year, 40% of enterprises will demote or decommission autonomous AI agents due to governance problems that only show up after they’ve gone into production.
Token management
Another hidden cost of off-the-shelf AI? Token use. When agentic AI is embedded into a system that a company is already using, then the cost of switching it on can be as low as zero. But someone has to pay for all that token use.
When agentic AI comes out of pilots and goes into production, usage can change dramatically, and token costs can quickly spiral out of control. It doesn’t help that agentic AI use typically requires much more tokens than AI chatbots.
“A lot of the embedded AI should be utilized,” says Doug Vargo, head of the national AI and alliances team at consulting firm CGI. “If big parts of your business processes are living and breathing in ServiceNow, Salesforce, or another application, you should leverage that, but you need to understand the cost.”
This is playing out in the marketplace right now, he adds, as underlying model providers change their token costs.
“That changes the economics of the decisions,” he says. “And six months from now, it’s a different discussion, because they’re going to continue to move the needle.”
The employer’s prerogative
Another hidden cost that companies might miss is they need to put training and support systems in place for employees. Many off-the-shelf agentic AI tools might seem easy to use, but actual adoption still requires change management. CGI did offer training to employees when it rolled out GitHub Copilot, a popular coding platform with agentic functionality. “And we expected them to adopt and leverage it on day one,” says Vargo. But people quickly went back to their old ways of working, he says, and CGI had to pivot its strategy.
“It’s going from training programs to habit formation or transformation programs,” he adds. “And we’ve seen adoption skyrocket.”
The change isn’t in just how individuals adopt AI to improve their own work, though. “You’re now a manager of 10 to 20 specialized agents that are driving that work for you,” he says.
The error is when companies put the technology first. “They fall in love with the tech or a vendor, but they’re not really thinking about how they’re truly integrating these tools and technologies into the business.”
Embracing change
Change management is a particular challenge with agentic AI because the tools are changing at such a fast pace.
“It’s technology that’s constantly changed almost every month, or maybe every week,” says Scott Likens, chief AI engineer at PricewaterhouseCoopers. “The challenge I see is the timescale of change is much faster than what we’re used to.”
We’re still in the early stages of agentic AI, he adds, and waiting isn’t a good strategy. “You have to start, but keep flexibility over longevity in mind,” he says.
For Luis Huerta, group head of gen AI and agentic automation at International Airlines Group (IAG), change management is one of the most complex parts of agentic AI adoption, as it uses agentic AI to improve customer experience, operations, and efficiency.
“The technology is powerful, but the bigger shift is organizational,” he says. So the company isn’t just focusing on building agents, but also on business ownership, adoption, and governance, and helping colleagues understand how these capabilities change the way work gets done and how it can support them, rather than replace them.
Huerta says IAG also uses agentic capabilities embedded in enterprise platforms, but also builds its own agents, too.
They’re not alone. According to Anthropic’s 2026 State of AI Agents report, 47% of organizations take a hybrid approach to AI agents, while 21% rely entirely on pre-built agents, and 20% build their own.
“We take a technology-agnostic approach,” says Huerta. “Our priority is to select the right solution for each use case.”