Although agentic AI has taken the technology ecosystem by storm, one CIO believes that small to midsize businesses should prioritize actionable AI use cases to succeed.
“I feel like we haven’t really wrapped our heads around exactly what all we can get an agent to do,” said Matt Price, CIO of promotional products company Gold Bond Inc. “It’s still a gray area for us as to where we can really use it.” The real impact of AI for the Tennessee-based company has taken the shape of targeted use cases that do away with busy work.
He’s not alone in his evaluation of the technology. Many midsized organizations are working with a handful of AI use cases, deploying everyday tools like Gemini or Microsoft Copilot in an attempt to boost user adoption as they grapple with continuously unfolding developments, said Alys Woodward, senior director analyst at Gartner.
“Even in the midsized, people are definitely aware [AI] is happening and they are out there and implementing,” Woodward said of AI developments, including AI agents.
To scale its adoption of AI, Gold Bond tapped Promevo, a Google-focused partner, to lead workshops teaching employees how to use Gemini in 2024. Employee Gemini usage rose from 20% to 71% following the workshops, with nearly three-quarters of employees indicating the technology saved them up to an hour of time per day, according to Promevo.
Since then, the company has embarked on a number of AI projects, Price said.
Gold Bond collects around 120,000 invoices annually from its distributors. To help improve distributors’ marketing to end users, Gold Bond created a part-time role to categorize customer artwork orders and record the user data in its Oracle NetSuite ERP system.
“We said: We think we can use AI to automate this,” Price said.
Price built a pilot project using the Vertex AI platform in Google Cloud, powered by Gemini, and deployed a SuiteScript within Oracle NetSuite, automating the entire process. The part-time worker became a company representative as AI took over more administrative tasks, Price said.
“Every night, we throw 600 to 700 invoices at Gemini,” Price said. “It analyzes all the artwork and categorizes it properly for us.”
Another project where Gold Bond is seeing promising results is via image generating model Nano Banana Pro, which has allowed the company to automate some of the product design process, Price said.
“We’re all about image editing in our business, putting brands on drinkware, things like that,” Price said.
Finding the small wins
While invoice workflow automation and image generation mark significant AI wins for the company, Gold Bond has more projects underway for 2026, Price said.
Working with a telecommunications partner, Gold Bond plans to adopt an AI voice agent this year that will assist customers with order information, status and bill payment. The agent will launch for after-hours, weekend and holiday support to start and will eventually be incorporated during working hours, where it can escalate issues to employees and learn from interactions, Price said.
“We have about 500 orders a day coming through the building,” he said. “The AI voice agent, we feel like it’s going to be a great win for us.”
For SMBs to see success in their AI ventures, Price recommended executives pursue the targeted wins.
“If you can, take small use cases,” Price said. “Find where the company is paying X amount, or using this amount of resources, and see if you can get AI to automate that small workflow and get some small wins.”
As CIOs face high expectations to produce return on investment for their AI projects, Woodward said it’s critical for CIOs of midsized organizations to find sources of value including employee satisfaction, advancement along the AI maturity journey, faster customer service and improved efficiency.
AI agents beyond IT
Although Price has begun experimenting with agents and is identifying ways to make the technology work within Gold Bond’s operations, he said it’s not just the IT team looking at what the technology can do.
Price said he has what he calls “technology ninjas” in accounting, sales, customer support, manufacturing and marketing departments interested in what both generative and agentic AI tools can bring to the table.
Social media is one area the company’s marketing team is evaluating as a potential AI use case, Price said.
“The marketing team is working through that now, where an AI agent would post at certain times, different personas, things like that,” he said.
When it works well, using AI agents can take pressure off the CIO as different parts of the business automate some of their own workflows, Woodward said.
Data plays a key role in AI agent success, which is why they can be particularly effective when deployed within departments such as finance or marketing and used on that specific data, she added.
CIOs will need to prioritize cleaning up technical debt and improving data quality to enable a better foundation for supporting future AI use cases, including agents, said Mark Moccia, VP and research director at Forrester.
While one department might operate on a modern IT system, another might be functioning on a legacy system that won’t be able to scale AI the same way, Moccia said.
“It could create a mismatch of expectations with the C-suite, who just saw you do something quickly, and now this one’s taking longer,” Moccia said of AI projects in different departments. “You’ve always got to reframe back to the foundational investments to be successful.”
Governance comes into play
AI governance will be a big piece of the puzzle for successful, scaled adoption, Woodward said.
Business users are essentially constructing IT systems as they build AI agents, and they might be unaware of considerations IT departments inherently know — such as data access, privacy controls and security measures. CIOs must ensure only business users who have gone through AI literacy training can access such systems, Woodward said.
Less than half of IT decision-makers have formal AI governance policies in place, while only 47% make governance and compliance training available to employees, according to a Collibra survey.
“If you’re letting people build AI agents just for themselves, you don’t need to govern that,” she said. “But if you’re going to have agents shared between departments, that’s where you need to start thinking: 'Hold on, we need to make sure people are educated around this.'”
