The Smartest AI Business Cases Put Freed Capacity Back to Work

The template is familiar to anyone who has sat through an AI investment review: automate 40 percent of a team’s workload, remove 40 percent of the cost, book the savings. The slide is clean and the math looks persuasive. The problem emerges when it reaches the approval room, because the person best positioned to write that business case is usually the person who manages the team it would eliminate. Efficiency cases get built, then slow-walked by the people who built them.

Prashant Parida leads AI strategy, transformation, and portfolio management at IBM and has advised and operated inside Fortune 15 organizations. He has spoken with over 15 executives running AI adoption at large enterprises, including a pharmaceutical company north of $80 billion in revenue and a media company around $120 billion, each approaching the same problem from a different direction. He previously discussed why enterprise AI stalls as a systems problem rather than a mandate problem. On a recent episode of The Data Wire's Beyond the IT Headlines podcast, Parida turned to the document where most of those programs actually break.

The 40 people nobody releases

Parida pointed to an application integration team of roughly 100 people inside a large enterprise. AI-assisted coding and specification work cut the team's effort by about 40 percent, which is exactly the result the program was funded to produce. "That means you get rid of 40 people," said Parida. "The guy who is working on the business case will never release those people, because then you're firing your own team. Why would you do that?"

The arithmetic isn't wrong, which is part of why it's hard to dislodge. A savings case gets approved at the top, then meets a middle layer with every reason to keep the pilot small, the scope contained, and the headcount conversation permanently scheduled for next quarter. The aggregate result shows up in the spending data. The share of enterprises whose AI returns still haven't outpaced their investment is holding at 57 percent, a plateau that has now persisted across two years of improving production capability.

What IKEA did with the other 53 percent

Ingka Group, the largest IKEA franchisee, ran into the same fork and stopped somewhere else. Its customer service assistant, Billie, absorbed roughly 47 percent of inbound inquiries, the delivery tracking and order status volume that never needed a person. Around 8,500 call center workers sat on the other side of that number.

Rather than size a reduction, the company looked at what customers were asking for in the half the bot couldn't resolve. "Somebody has a question about furniture, about a carpet, about interiors. These guys have domain expertise because they answer calls day in, day out," said Parida. "So they retrain them in interior design."

The remote design channel those advisors now staff generated about 1.3 billion euros, against roughly 13 million euros in customer service operating savings from the automation itself. Nobody had to defend a headcount number to get there. "They produced $1.3 billion in revenue with these people. The capacity that you release can be repurposed to produce new revenue. That is where it becomes a safe place."

When everyone has the same kitchen

Redeployment only pays if the work people move into is worth more than the work they left behind, and Parida expects the tooling advantage that funds these programs to compress quickly.

"When people use AI tools ahead of others, they have this unfair advantage. But when the dust settles, everything becomes the same," he said. His comparison is two restaurants. "A chef is a better chef because you understand the recipe. More than that, you understand your audience. You may have a brilliant kitchen, but the kitchen doesn't make you a better chef," explained Parida. "When everybody has AI, then your differentiation comes back to being human."

Picking the first function

Getting to redeployable capacity means starting with a scope small enough to measure. Parida is working with an early-stage venture that took marketing as its first target, on the logic that content and media creation sit closest to what current models do well. "If you spend $100 in marketing, there are components that AI can do," he said. The venture isolated about $40 of that spend, covering intelligence gathering, campaign strategy, and media creation through to channel deployment, and now runs the sequence for closer to $20 with no human intervention in the middle.

From there the pattern repeats outward. Four or five contained pilots connect into a function, a function into a cross-functional chain from marketing through sales, delivery, and solutioning. "Then you could have a bigger impact across the enterprise." The sequencing matters for approvals as much as for delivery. A 30 percent cycle-time reduction inside one function is small enough that nobody has to litigate their own team's future to get it funded.

The version that gets signed

None of this makes the productivity math disappear. The 40 percent is still 40 percent and the capacity is still real. What changes is what the business case commits to doing with it, and whether anyone inside the building has to lose for the program to succeed.

"You start small, but then create a business case in terms of reinventing the business, as opposed to saying 'Let's release productivity and let's release people from their jobs.' That's not going to work," said Parida. "Look at a revenue upside while you're releasing productivity. That'll be a good way to scale while deploying AI and creating a win-win game for everyone."