Redesigning Whole Processes Around AI Moves Companies From Quick Relief To Lasting Returns

The views and opinions expressed are those of Sanjay Choubey and do not represent the official policy or position of any organization.

Enterprise AI has produced plenty of pilots, but most organizations still haven't scaled it across the business. Many of those pilots make a task faster or easier without changing what the company spends or earns. Executive teams and boards are now asking what the spending has returned, and a tool that saves someone an hour rarely answers that question. Turning AI into a result on the P&L means redesigning a whole process around it. How quickly that happens depends on how far a company can trust its data.

Sanjay Choubey is Global Chief Digital and Information Officer at global paper manufacturer Sylvamo, where he leads its SAP S/4HANA, manufacturing systems and AI programs. He was previously VP and CIO for North America at Dr. Reddy's Laboratories, and led global SAP transformation programs at Johnson Controls, IBM and SAP. His career spans pharmaceutical, manufacturing, and industrial companies, a range that shapes how he judges where AI can pay off and how long it will take.

"If someone identifies one or two small things where AI can quickly give relief, one or two quarters is enough. But that isn't value creation, it's relief. If you're really looking at value creation, you have to change the value stream end to end, and that needs more time," says Choubey. In his view, most enterprise AI today falls on the relief side of that line. The work that moves a P&L starts with the process, not the tool.

Relief versus value

Choubey sees companies approving small proofs of concept because a tool looks impressive in a demo. His example is a robot that can lift a glass of water to someone's mouth, an impressive thing to watch that solves almost nothing. Enthusiasm for AI also carries prestige, since executives worry about looking behind if they aren't talking about it. "There's a lot of technology that looks very cool but doesn't help your top line or bottom line," notes Choubey. "How much of this AI is really creating value you can recognize in your P&L?"

His own board offers a clean example of relief. Outside directors often sit on several boards and receive hundreds of pages before each meeting, so his team gave them a language model they could query directly. The directors' preparation was simplified, and the company's costs stayed the same, with token spend added on top. "We made their lives easier, and it can give people confidence that it works," he explains. "But did I realize any value? Nothing. Maybe I added some more tokens to pay for."

Choubey measures every AI investment against the economics of the business. He asks the question of whether a tool lowers the cost of the same work or brings in more revenue. A capability that can't show either one doesn't count as a return, however useful it feels. "Our business is to sell paper at a price higher than the cost," he adds. "If a piece of technology can't show me that for the same work I was spending X dollars on last month, and now I'm spending X minus something, then it means nothing to me."

The cross-check problem

How fast AI pays off depends heavily on the data underneath it. Financial services, insurance, and consumer goods companies work mostly with customer and revenue data, much of which can be verified against an outside source. A customer's address, for example, can be checked and corrected automatically. "Even if I have bad data, I have a way to quickly cross-check it," says Choubey. "It is easy to correct."

Capital-intensive companies don't have that option. A paper machine carries sensors the company paid to install, and a sensor can start reporting bad readings with nothing external to check it against. Adding more devices to verify the originals is another capital expense, so data quality work takes longer in manufacturing than in a customer-facing business. "Sometimes a sensor can go wrong, and I'm getting bad data," he explains. "I have no way to cross-check that."

The timeline for returns follows the same split. Revenue-centric businesses can design and test AI into a process relatively quickly, because they can find and fix bad data before a decision rests on it. Heavy industry needs longer, since the cost of acting on a wrong reading is higher and the data is harder to trust. "If it's a front-end industry, where you're talking to customers and vendors, that's a shorter cycle because you have a method to check the bad data," Choubey notes.

Reimagining the process

Choubey's teams work from what he calls an AI-first method, which starts by redrawing a process with AI in it and only then choosing tools. He applies it to decisions as large as how his company sells, since it reaches customers through distributors today and could build a direct digital channel. AI can help with that design, but deciding whether to build it stays with the business. "AI can help me find a different way to sell, but it won't tell me how I should be thinking about selling," he says. "How you use it is a business decision."

How much autonomy a process gets depends on what's at stake. Invoice processing can run with a person reviewing exceptions for a quarter or two while the system learns, then scale back the review. Manufacturing and maintenance need more caution, and regulated industries need proof that a regulator will accept. Some agencies still require a wet signature over an electronic one. "Pharmaceutical companies, even if the AI works, need more reassurance because of FDA regulation," he explains. "You need to prove it out."

Choubey organized AI training for his senior leadership team and a separate session for the board, to set expectations at the top. Executives who use generative AI for daily tasks often expect the same speed from enterprise programs. He cites robotic process automation a decade ago, when companies expected bots to automate everything and ended up with bots that had their own costs to manage. "Unless you have a real plan for which process you're going to address and how you're going to do it, AI is an intervention, not a solution," Choubey concludes. "You don't take a pill and suddenly become Iron Man. It doesn't work that way."