Businesses are rushing to adopt AI, but many deployments are creating more complexity than value.

On April 6, business and technology leaders gathered at an Expert Voices roundtable in San Francisco to discuss the challenges and opportunities that come with implementing AI in the workplace.

• The conversation touched on a range of topics, from the differences across industries to how generational divides impact AI adoption.

Why it's important: This year marked the first time that half of employed American adults reported using AI in their job at least a few times a year, up from 46%, according to Gallup.

• As adoption rises, leaders need practices that boost teams and workflows, not slow them down.
• AI scaling can be difficult, and costs can rise quickly, making outlining clear long-term benefits and budgeting essential to success.

The strategy: "We have found a secret sauce is to reach beyond traditional functional boundaries, we have to rethink how the organization comes together," says Sarah Kim, vice president of commercial marketing at Cox Business, who attended the roundtable.

An example: When Cox Business began building an AI agent to support sales, the company realized that sales enablement and marketing planning relied on the same data.

• Cox Business created a shared knowledge base that combined both, enabling targeting, marketing outreach, sales outreach and sales conversations in a single workflow.
• "In a pilot focused on one customer segment, we unified those steps into one workflow and saw the average deal size increase by 35%," says Kim.

Duane Barnes, president of RapidScale, a Cox Business company helping organizations use AI to improve business outcomes, says companies succeed most when they first define the problem that they want AI to address.

• "AI doesn't create value just because it's deployed — it has to be connected to clean, trusted data and integrated into the workflow where decisions actually happen," says Barnes.
• Next steps: Leverage the technology to complement humans and address that issue, rather than doing it for them.

RapidScale recently helped Barrett Financial Group, a family-owned mortgage company, eliminate manual limitations and scale its operations from a few hundred loans a month to more than 3,000 by letting AI do the initial processing.

• "The human value is in the context. AI can look at all the data, but if we don't understand how it correlates with other data, we don't know how all those things work together in a business," says Barnes.

Why now: Keeping strategy top of mind is especially important due to the rapid pace of AI adoption.

• "We're even seeing governments adopt AI faster than we would have predicted — in the previous world, government was often 10 to 15 years behind the private sector," says Zac Bookman, co-founder and chairman emeritus of OpenGov, a leader in AI and ERP solutions for local and state government.

A challenge: Even as companies push forward with AI, employee behavior can add complexity.

Generational divides play a role in how people are interacting with AI in the workplace. Research from Cox Business shows Gen Z and millennials are less enthusiastic about AI adoption.

• Nearly 50% of employees did not wish to disclose how much they rely on AI at work.
• 47% reported concern that AI could replace their jobs.
• 30% are either unfamiliar with, or claim their company has no defined policy or AI guidelines.

A solution: That gap is pushing companies to take a more deliberate, structured approach to AI adoption.

• "We've made AI adoption a non-negotiable expectation at Cox Business — it's written into goals, we measure usage, and we expect employees to show the value in their day-to-day work," says Kim.

The outlook: Leaders at the roundtable pointed to a few key takeaways on how to best integrate AI into their work:

1. Treat AI adoption as a controlled rollout, not an individual experiment.
2. Focus AI on business-critical processes, not individual tasks.
3. Reshape organizational structures with AI around outcomes, not functions.
4. Give direction centrally while encouraging teams to explore and build.

The takeaway: AI works best when companies rethink workflows, instead of adding tools on top of existing processes, and reshape teams around outcomes and customers. It's also important to remember that as adoption grows, inherently human skills like judgment, communication and trust are crucial.

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