While many leaders are debating where AI drives the most business value, some have already used it to save millions of dollars and thousands of hours.
Sydney Scott
Editorial Strategist, AI
Workday
At this point, every company, big or small, has experimented with AI. Everyone agrees AI will fundamentally transform the enterprise, but in reality many companies haven’t figured out how to leverage it to its full potential.
But, some have made real strides, figuring out ways to implement AI that improve workflows, drive efficiency, and save millions of dollars annually. The key isn’t just adopting the technology but knowing strategically where it will drive the most value for your business.
The following 10 examples give a little insight into how smart organizations are using AI.
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When tariff policies shift, the risk is often hidden in contract language: force majeure clauses, tax provisions, government action terms, and notice deadlines that determine who carries the financial liability.
FlexGen used AI to analyze supplier and contractor agreements for hidden tariff exposure, turning what would have taken weeks of manual review into answers in just two-and-a-half days.
The result: more than $35 million in avoided tariff costs and over $10,000 saved in direct wages.
To keep contract and administrative work from piling up, 44% of chief legal officers say they're leaning on AI.
To keep contract and administrative work from piling up, 44% of chief legal officers say they're leaning on AI.
Otsuka acted on that early.
The pharmaceutical company centralized document intake and automated the routing behind it, cutting 2-3 business days off power of attorney turnaround. That time now goes back into moving contracts forward instead of chasing signatures.
Fraud examiners estimate organizations lose about 5% of revenue every year to duplicate payouts and outright fraud. Expense reports are where most of it hides.
Washington State University deployed AI in its reimbursement review process and caught $20,000 in potential duplicate spend before a single dollar went out the door. It also freed up 160 staff hours and shaved six days off reimbursement time, without slowing down the review itself.
Imagine 90,000 contracts piled up on your desk. Corporate entity changes, post-merger cleanup, and data privacy reviews, all delivered to the legal team at the same time. No one reads through that volume fast enough alone.
NetApp used an AI-powered contract review tool to analyze, sort, and categorize those agreements at scale. Instead of relying on manual review for tens of thousands of documents, the team automated the work and saved more than $2.5 million in review and categorization costs.
Fraud examiners estimate organizations lose about 5% of revenue every year to duplicate payouts and outright fraud.
Field teams at Cushman & Wakefield were submitting weekly time sheets that needed to show where and how every working hour was used. When that data had to be corrected, journal line entries could take up to 45 minutes each.
Cushman & Wakefield used AI to streamline the process, cutting journal line updates from 45 minutes to 4 minutes. The change saved $1 million annually, reduced update time by 91%, and cut error tickets by 75% within 75 days of going live.
Forecasting demand across more than 50,000 SKUs on a spreadsheet is where accuracy usually falls apart. Terumo replaced that process with an AI-powered planning tool to improve forecast accuracy and had forecast P&L statements running within two months of kickoff.
The finance team now runs rolling forecasts monthly instead of quarterly, giving leaders more accurate forecasts and faster answers when a product line shifts and leadership wants a decision this week, not next.
A striking 77% of enterprise employees say only a few people in their organization know who owns contracts, creating confusion that can hide missed deadlines, unexpected costs, and unfulfilled commitments.
It’s exactly the problem Keller Williams was facing. Ten years of vendor contracts sat buried across legacy systems nobody wanted to open, let alone audit.
Keller Williams built that mess into a searchable, intelligent repository in 30 days using AI. In the process, the team flagged unused contracts draining money and surfaced 15 agreements tied to an active M&A deal that an outsourced review team had missed entirely. Return on the project took about a month.
A striking 77% of enterprise employees say only a few people in their organization know who owns contracts.
Heartland Bank was running 25 entities and three general ledgers that didn't talk to each other, which turned month-end close into its own kind of monthly headache.
The bank brought people data and financial data onto one shared platform, and the close process now wraps three days sooner. That's three fewer days spent reconciling numbers before anyone can act on them.
Opening a resort from scratch means staffing an entire small city before a single guest checks in.
Fontainebleau used conversational AI to handle the flood of applicants, and saw direct impact: 6,500 hires in three months, a 93% candidate satisfaction rate, and a median time-to-apply of three minutes.
None of that happens with a stack of paper applications and a handful of exhausted recruiters.
Global hiring usually means recruiters toggling between candidate messages and translation software, with candidates falling through the cracks.
Johnson Controls closed that gap with an AI candidate experience agent fluent in 18 languages, no translator required. Hires climbed 14%, satisfaction hit 98%, and interviews now get booked in 15 minutes instead of days.
None of these fixes required a moonshot. Each one came from a team that found the specific workflow slowing them down and pointed AI directly at that problem instead of buying technology first and figuring out the use case later.
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