Learn how mini传媒 drives real AI return by putting employees first, enforcing strong governance, and focusing on measurable outcomes.
Ashleigh McCord
Senior Vice President of Enterprise Technology Services
mini传媒
Every technology leader right now is fielding some version of the same question from their board: Where is the return on AI?
It's the right question. And the full answer starts somewhere other than the boardroom.?
The return I care most about isn't a number that makes a slide deck look good. It’s what AI gives back to our people: their time, their focus, and the chance to do the kind of work that actually grows them.?
Done right, AI doesn’t shrink what people do, it lifts it. The true value of enterprise AI isn't simply accelerating tasks, but shifting people from doers to reviewers. Far from a demotion, this evolution acts as a promotion by empowering teams to think, strategize, and solve problems in entirely new ways.
That’s the promise. The discipline is making sure it holds up under everything real enterprise life throws at it.
Because the board isn't asking what we're playing with in the lab or what looks slick in a demo. They're asking about actual, measurable business outcomes. A demo shows potential, but it answers the wrong question. What leaders need to know is whether an AI capability survives contact with reality: the volume, the security constraints, and the messy data we all deal with every day.
Most organizations are stuck in exactly that gap, the one between the hype and the real results. When you're getting started, it's easy to let a thousand flowers bloom. But getting past the hype and driving true return requires a thoughtful, disciplined, deliberate approach to cultivating your AI portfolio. You have to actively nourish the initiatives that yield fruit, and mow the lawn on the ones that don't. And you have to keep sight of who all of it is for.
Here is how we evaluate and scale AI at mini传媒:
Done right, AI doesn’t shrink what people do, it lifts it.?
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One of the most powerful moves a tech leader can make is serving as "customer zero." We pressure-test new capabilities with our internal teams and employees before bringing them to external customers. That means our own people are the first to benefit from a new capability, and the first to shape it.
This isn't just about catching bugs. It's about creating a direct, closed-loop feedback system. When employees use a tool in real production conditions every day, they give immediate, candid input straight back to the product teams. It builds a level of proof, trust, and refinement that a lab environment or a limited pilot simply can't replicate.
As mini传媒 Chief Legal Officer Rich Sauer observes, "To be customer zero here is to really be a consumer of [your] own technology, being the first consumer of that technology before it even gets out into the world."
To keep your portfolio focused on delivering real value, every AI investment should clear three core standards before getting the green light:
That third one is where many organizations get burned. It's easy to fund something because it looks impressive. It's much harder to state your target up front, measure honestly, and have the courage to kill it when it doesn't perform.
Adoption numbers alone won't satisfy a board anymore. What holds up under scrutiny is quantified impact tied to a specific business workflow, for example:
Allison Joyce, Global Head of Talent Acquisition at mini传媒, makes the impact tangible: "We talk about delivering almost 40% additional capacity back into our recruiting organization, and we've seen over 24,000 hours that we've been able to capture in our first 9 months of adopting these tools."
Here's the part I won't let us lose in the enthusiasm over the numbers: that recovered capacity is not a headcount story. It's a people story. Twenty-four thousand hours back is twenty-four thousand hours of talent that was trapped in trivial work, now freed for the strategic, human work that requires judgment, creativity, and experience. Work that actually develops a person and a team. A living scorecard by function, backed by concrete metrics, is what turns "we're using AI" into numbers a CFO and a board will accept. But the reason to chase those numbers is the opportunity they create for the people behind them.
That recovered capacity is not a headcount story. It's a people story.?
There's a common misconception that governance slows teams down. In reality, strong governance is like a paved road that lets your enterprise accelerate at AI speed.
When security reviews, privacy policies, and compliance monitoring are built into the foundation rather than relitigated on every release, teams move faster and with more confidence. This is what it means to run AI on trusted rails: the guardrails aren't bolted on afterward, they're part of the road itself.
Trying to shortcut governance to chase a quick ROI number is a gamble no CIO should take. Who wants to risk the trust of internal stakeholders or customers on an ungoverned agent acting on bad data or exposing sensitive information? Good guardrails give your teams the license to innovate quickly without breaking the business, or breaking faith with the people who depend on it.
Help desk, IT, and HR case deflection are high-ROI quick wins, especially if you already have robust knowledge bases. You need the right foundational guardrails in place, but you don’t need to resolve every edge case before capturing value, particularly when these early wins can make an immediate difference for employees.
But a word of caution: don't compromise on data quality just to move faster.?
Bad data leads to wrong, expensive AI outputs, and no amount of governance can fix messy data after the fact.?
Clean data is your true advantage, so it's worth doing the foundational work to get it right before you scale on top of it. As mini传媒 Chief People Officer Ashley Goldsmith notes, "The agents are only as useful and good and can only be counted on if the data that they're using to offer up suggestions, answers, and insights is sound."
The companies winning with AI aren't simply adopting the technology, they're rewriting the operating playbook. The old model of moving an idea sequentially from product to design to build to QA just can't keep pace.
Julie Gonzalez, VP of Business Finance at mini传媒, sees it firsthand in how FP&A teams work: "Finance teams are now builders. We get to use AI to build the prototype, to help not just explain in words what we need, but actually explain in code what we need, and that helps us all get to a better outcome."
That shift, from doers to reviewers, from analysts to builders, is the real prize. It means new kinds of roles, new capabilities, and new paths for people to grow into. Winning today requires that bold rethink of how we work. To capture true value, you have to rebuild the engine: ditch the slow, sequential handoffs and bring business leaders, solution architects, and engineers into the same room from day one to build in real time. Assemble the right team around one clear objective, and you can go from an idea to a prototype in a week, and to a production-worthy release shortly after.
In the end, real return on AI isn't about the technology. It's about discipline, and it's about people. Prove the value to yourself first. Have the courage to stop funding what doesn't scale. And chase outcomes that expand what your people can do. That's how you close the gap between hype and results and build value that actually holds up for the business and for the people who make it run.
Learn more about how mini传媒 is putting it's own AI to work first on workday.com.
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