企業向け AI エージェント：自社開発か購入かの選択を迫る現状

Markus McKay-Fleisch, SmartsheetEsther Shittu

With generative AI, and now agentic AI, enterprises and small and mid-sized businesses have long faced an important question that correlates with how they move forward with technology and also how cost-effective they aim to be: building in-house or buying from a vendor.

Four years ago, when OpenAI first introduced ChatGPT and companies began experimenting with generative AI, the answer appeared easy. For those who did not want to be left behind, buying large language models from OpenAI or Anthropic or trying to use open source models from Meta looked like a safe bet.

However, with the maturity of generative AI and the wide acceptance of agentic AI, the build versus buy dilemma is more nuanced, depending mainly on multiple factors such as the business size, application and also what an enterprise or SMB considers to be its “moat,” or defining value proposition that separates and protects it from the competition.

Related:AI's Impact: How Businesses Are Equipping the Future Workforce

Focus and Flexibility

For Home Depot, the home improvement giant sees CX as one of the most important core factors of the business, said Ningyu Chen, the retailer’s senior vice president of technology, during a session at the Ai4 2026 conference in Las Vegas.

“We would never outsource that piece to others,” Chen said.

However, Home Depot maintains partnerships with key AI technology vendors such as Anthropic, OpenAI, Google and Microsoft -- and uses their technology.

Despite this, Chen advises businesses considering the build-versus-buy question to focus more on the right use case and be flexible about whom they partner with and buy from.

“I will leave the partnership very open,” he said in an interview with AI Business. “We are still early, and there are a lot of things that can happen. Look at OpenAI, it was dominant; now it is not anymore.”

“You have to have an abstraction layer to prevent yourself from these changes,” he added.

Beyond that, businesses looking to build should consider the time and people they plan to invest in building generative or agentic AI technology into their platform, said Santhi Ramesh, CEO and chief AI strategy officer of Future Propel, an advisory firm.

As an adviser to enterprise leadership teams for AI transformation, Ramesh spent her early career leading AI transformation at organizations such as The Hershey Company and Ferrero, the Italian chocolate company.

She said the build-versus-buy decision also depends on a business's core competency.

“If your core competency is not a technology company, you are better off partnering and buying,” she said in an interview. “If you have intense data, security, massive amounts of data and a custom solution that you want, then you must build on your own. I have done both.”

Related:Alibaba Unveils Its ‘Most Powerful’ AI Model Yet

However, some enterprises and SMBs may also need more flexibility, in which case it is better for an organization to test, learn and buy a platform or ally with a vendor so that, if necessary, they can get out of the partnership, after, for example, trying it in beta, she continued.

The Cost of Buying or Building

Buying can lead to operational debt, said Rachel Ibarra, vice president of data and innovation at Cardinal Group Companies, a property management and real estate organization.

While Cardinal Group takes a hybrid approach, with an in-house AI agent called Stan Ibarra said that enterprises choosing between developing in-house and looking outside the company will need to consider “what a cohesive system looks like.”

“It’s not just the ecosystem of technology,” she said during a talk at the conference. “It’s the ecosystem of how the organization works.”

For businesses whose data provides a tangible advantage, it may be better to build, Ibarra said. It could also be a good idea for organizations to build when they have a fractional need.

“So, you only need a part of something that’s relatively small [and] where it’s more internal facing and gives you a lot of internal advantage,” she said in an interview. “That’s what I would say are better built for nontechnical companies that don’t necessarily have the resources to go and hire a huge engineering team.”

Related:Prompt: Enterprises Grapple With Cost of AI Scale

There is also the issue of infrastructure, said Markus McKay-Fleisch, professional services enablement director at Smartsheet.

“The real question for me is, do you feel like you have the right infrastructure to build yourself?” he said during an interview. “Is it a citizen development program where people in business are building, and if so, what are the guardrails you put around that?”

Businesses also need to ensure they have strong hosting infrastructure and can maintain the AI agent or system if it breaks or changes.

Moreover, building an AI agent or system requires navigating the nuances of choosing the best AI model for each use case.

Phenom is an HR technology and applied AI company that builds software to help organizations hire candidates and develop skills.

The vendor uses various models, including open models from Mistral and even the low-cost Kimi model from the Chinese vendor Moonshot AI. According to CEO Mahe Bayireddi, many organizations building AI agents or systems with AI agents sometimes need to switch models for appropriate use case. For example, some models are good for HR applications, and others for finance applications.

Companies are “not understanding the nuances of how to be able to switch, not at your company level, but at an industry level,” Bayireddi said. “Your models will switch on the fly.”

Just a Bite Better

Steve Toy, founder and creator of Just a Bite Better, an AI nutrition app, has chosen to build AI agents within his app rather than buy a system to implement them.

The app uses multiple agents working together to provide consumers with an overview of their nutrition.

“We don’t use any one provider or any one model,” Toy said in an interview. “We built our own model garden, if you will, which is merely just here is the code that accesses the APIs of all these things.”

For Toy, the decision to build rather than buy or use a model garden from Google, AWS or Microsoft is driven by his technical knowledge and his specific business application.

“I’m not dealing with passing lots of money back and forth,” he said, adding that consumers pay for the app through Apple, Google or Stripe. Moreover, consumers can delete any personally identifiable information, and their information is encoded so it does not specify a specific person.

“We’re low stakes in many respects, whereas IBM and PayPal have to care about different things,” Toy said.

He added that for businesses still exploring whether to buy or build, it is best to know what they need to buy, especially in a market saturated with AI vendors, products and services.

“To figure out how to buy, you need to know what problem you are trying to solve and remember that every business is decomposed into pieces,” he said. “When you’re buying, you’re buying to solve a problem, a part of your business.”

Building, though, can help with cost, Toy added. For Just a Bite Better, he has implemented a system that sets a hard cap on the number of tokens or the cost each model can use monthly.

“That’s how you can prevent disaster, and that’s really important because it’s really easy to wake up in the morning and find a $50,000 bill,” Toy said.

About the Author

News Writer, AI Business

Esther Shittu has covered AI technologies and industry trends since 2021. As co-host of the Targeting AI podcast, she talks with experts, thought leaders and practitioners exploring critical AI developments. Before AI Business, she wrote for SearchEnterpriseAI, the New York Daily News, Bklyner and the Brooklyn Daily Eagle. When she's not diving deep into the world of AI, she spends her time on passion projects and raising her three daughters.