AI can deliver substantial ROI
ROI with AI isn’t always about splashy revenue growth. Sometimes, the biggest wins are found in less obvious places, like saving time, reducing overhead, and staying ahead when things scale fast. That’s exactly what happened at Oura.
In October 2024, Oura launched the Oura Ring 4. Product was solid. Customers lined up. But then came the pressure, support requests skyrocketed, and the company didn’t have the agents to handle them. Most companies would scramble to hire. But here’s the problem: recruiting, onboarding, and training hundreds of agents in a few weeks? Not realistic. That’s operational drag. So they did what smart leaders do when speed matters, they turned to AI.
Oura brought in Decagon’s conversational AI. In four weeks, the AI system was live, handling incoming support tickets, answering common questions, and easing the load across customer service. Support quality stayed strong. No massive hiring spree needed.
Tom Hale, CEO of Oura, put it plainly, avoiding the need to hire and train 500 new agents during the heavy window saved them $15 million. That’s measurable impact. They didn’t need to delay, dilute user experience, or overextend the team. They just moved fast, used AI as a lever, and kept things running at scale.
For any executive, this matters. The ability to scale on demand, without increasing cost and complexity, is valuable. Especially when timing drives customer satisfaction. This is heavy-lift ROI, compressed timelines, lower costs, and stability under pressure.
The move was practical and it gives us a key line of thinking: use AI where it creates immediate structural advantage. Build now, optimize later.
AI ROI is context-dependent
Too many businesses still treat AI like a broad solution for general productivity. That’s where the disconnect happens. You won’t see real return from giving every employee access to AI for the sake of it and hoping something clicks. ROI from AI doesn’t show up in one-off uses, it demands a real problem, at the right scale, with a clear path to value.
Oura understood this. They weren’t chasing trend. They faced volume pressure, at a mission-critical time, and acted with speed and focus. The difference is intent. They used AI to solve a tangible operational gap, not some abstract goal of “being more AI-enabled.”
Tom Hale, CEO of Oura, summed it up: “Necessity was the mother of invention.” Faced with a binary problem, fall short on service or scale effectively, they identified where AI could do the job fast, and made it work. That urgency created clarity around implementation. And that clarity led to ROI.
Cristina Mancini, CEO of Black Girls Code, added an important point: every industry defines ROI differently. And they should. The right AI deployment in healthcare won’t look like one in eCommerce or logistics. Smart leaders must ask: What exactly are we solving? Is it valuable? Is it safe? And will it scale?
This is the mindset that matters now. Broad productivity frameworks won’t cut it. Enterprises need to be specific, outcome-focused, and honest about where AI truly helps. The more urgent the problem, the easier it is to measure value. Timing makes the difference.
If you’re waiting to prove ROI without locking it to a real business challenge, you’re likely wasting your time. Push AI into clear friction points. Streamline decisions. And move fast when the opportunity shows up.
Healthcare AI delivers ROI through quality of service rather than by replacing humans
AI isn’t designed to replace people in healthcare. That’s not realistic, and more importantly, it’s not useful. What it does well is take on the repetitive, low-impact tasks that slow teams down, things like follow-up calls, appointment reminders, or simple administrative interactions. Those tasks don’t make up the core of a doctor or nurse’s job, but they eat up valuable time. AI clears that space.
Daniel Barchi, CIO at CommonSpirit Health, explained this clearly. AI doesn’t replace full-time professionals in healthcare, it replaces fragments of their workload. That can be 10% or 12% of their day. It’s not about eliminating roles. It’s about increasing the time they spend on high-value work, like direct patient care, which improves outcomes across the board.
The challenge? These improvements don’t always show up on a budget line. You’re investing in technology but not “removing” full-time equivalents. From a finance perspective, you’re increasing cost. From a care perspective, you’re increasing value.
What matters here is alignment. Outcomes improve because AI lets healthcare professionals do what they’re trained to do, focus on the patient. Quality goes up. Efficiency gets a real lift. Even if it’s hard to show on paper, the compound benefit is there. It’s operational, clinical, and reputational.
Executives in healthcare should stop measuring AI success only in terms of headcount reduction. That’s not where the real return is. The return is in net time recovered and reinvested in care, which leads to better results, stronger patient trust, and more resilient systems. That’s the long-term value.
Empowering employees with AI
The clearest impact of AI isn’t always found in headcount or short-term profit. It’s in mindset shifts, how people perform their work, how they make decisions, and how fast they solve problems. If your team starts using AI to enhance their thinking and automate useless complexity, you’ll see value, even if it’s not visible in a spreadsheet.
Sydney Klein, SVP, Chief Information Security Officer and Head of IT Core Services at Bristol Myers Squibb, explained this well. When employees begin to treat AI as a core capability, not a side tool, they become sharper, faster, and more effective. It’s about increasing leverage. You don’t need to track every result to understand the impact. The macro effect on performance speaks for itself.
But let’s be clear, this value isn’t always easy to isolate. It doesn’t show up as a clearly labeled ROI function in your quarterly rollover. It builds across time, across teams. Leaders who understand this will invest more deliberately in AI training, access, and integration. Not to check a box, but to fundamentally improve how their people work.
That’s where the real advantage kicks in. AI can make teams more autonomous and more strategic. It helps people focus less on routine and more on impact. The ROI here is alignment, agility, and better signal across the business.
For executives, here’s what this means: stop holding AI to traditional cost-based KPIs alone. Look at how it’s changing the culture of execution. Track your team’s speed, precision, and responsiveness. Incentivize adoption at scale. The measurable returns will follow, and they’ll show up in resilience, not just savings.
Key executive takeaways
- Use AI to scale without adding headcount: Executives should deploy AI in high-impact operational areas to handle surges in demand, reduce hiring pressure, and maintain service quality under tight timelines, Oura’s $15M in savings by avoiding 500 hires is a clear proof point.
- Define ROI by urgent, not abstract, impact: Leaders should align AI initiatives with urgent business challenges where value is most visible, rather than diffuse productivity goals that lack measurable outcomes.
- Target partial task automation in complex roles: In sectors like healthcare, AI proves most effective when it handles routine tasks, not full roles, freeing skilled staff to focus on high-value work while indirectly improving performance and outcomes.
- Invest in AI empowerment across the workforce: Executives should embed AI tools across teams to amplify employee efficiency and agility, over time, this shifts culture and output quality even when short-term ROI isn’t immediately trackable.
