The business world is preoccupied with gauging the return on investment for AI projects as the technology expands across operations.
The concerns stem from rising enterprise spend to support the deployment of AI tools. More than half of organizations allocate between 21% and 50% of their digital initiative budgets to AI, according to a Deloitte Insights survey. That equates to about $700 million for a company with $13 billion in annual revenue, according to the firm’s estimates.
Yet before companies can measure the success of their AI efforts, they must test their solutions and scale them into production — a tricky step for most businesses
A willingness for CEOs and their boards to swing big is a prerequisite for such endeavors. Committed partners from human resources, sales, marketing and other business units are critical, too.
AI magic for HR, payment leasing
When fintech firm Prog Holdings was migrating to Workday’s human resources software, leaders wondered if they could leverage AI to help employees get answers to questions on benefits and IT issues.
CTO Sridhar Nallani partnered with leaders across HR, R&D and other business functions to build Piper, a chatbot that uses a large language model to provide access to information.
The software has resolved more than 18,000 employee questions, with 58% answered correctly on the first interaction, according to Nallani. The resulting adoption spread like wildfire, Nallani told CIO Dive.
While the uptake was rapid, it wasn’t an overnight success. To gauge the chatbot’s competency, Nallani’s team started with a proof-of-concept based on ChatGPT, testing the chatbot with a small number of users before rolling it out across broader segments of the business, Nallani said.
Piper’s success emboldened Nallani to create an application that helps Prog set lease terms, pricing and eligibility to generate instant offers for customers. Nallani said the software drove a 75% reduction in decision time while improving direct-to-consumer conversions by 10%.
Prog also built a generative AI tool that helps customers pay for products, as well as software that produces content for marketing campaigns. The company has since standardized on Microsoft Copilot for most of its chat-oriented AI services.
Although the global obsession over ROI isn’t lost on Nallani, he said that not every AI implementation needs to deliver a calculable return. Building digital services that reduce friction and time creates happier employees and loyal customers.
“You don’t need to put a number in a spreadsheet and say, ‘I’m going to get $10 million or $12 million out of this thing,’” in order for it to create value, Nallani said.
AI agents keep freezers stocked
The cost-cutting potential of AI agents quickly became apparent to Arctic Glacier Premium Ice, CIO Doug Saunders told CIO Dive.
Deploying agents to automatically field inquiries about ice shipments, the packaged ice provider shaved tens of millions of dollars by reducing customer call center and shipping operations, said Saunders, who joined the company in 2023. These operations were the result of trial and error, including a platform switch before revving to production, said Saunders.
Arctic Glacier started with an agentic AI system built on Microsoft Copilot that helped answer customer requests to order ice or track their shipment locations. With promising early returns, Saunders expanded the program.
The company eventually migrated to Salesforce’s Agentforce platform, connected to an IoT system that includes lidar sensors in the ice merchandise boxes at each customer site.
When the system senses that ice is running low, it fires off an order to AI agents, which begin routing trucks to fulfill orders. The system also takes into account historical sales trends, weather analytics and other factors to anticipate each route’s ice requirements.
Saunders said automated ice deliveries represent a competitive advantage — at least for now. The company is exploring dynamic pricing for businesses in warmer climates who may run out of ice earlier than expected.
Iteration is guiding the technology strategy. “You start with one tiny business problem, you prove it, you get a really good ROI and you see the different ways you can expand it and make that EBITDA growth even bigger,” Saunders said.
The path to value is rarely linear
Despite positive outcomes, CIOs would do well to remember that AI is still a tool, not magical fairy dust. That’s the perspective of Mihai Strusievici, who founded Axsion Digital Evolution to consult SMBs on their IT strategies.
Strusievici said the relentless pursuit for ROI happening at some companies and consultancies misses the bigger point, which is that AI can help users tackle tasks better, faster and potentially cheaper.
“Did they calculate the ROI of the word processor or spreadsheet?” Strusievici asked. “I think it’s a fantastic tool, but it’s still a tool.”
What’s clear is that AI pilots are scaling from exploration into production.
While only 25% of senior leaders said their organization moved 40% or more of their AI experiments into production to date, 54% expect to reach that level over the next several months, Deloitte said in a recent report tracking AI adoption and impact.
In this period of AI exploration, IT leaders must search for solutions that deliver genuine value to the business, said Jason James, CIO of retail tech provider Aptos Retail. That path is rarely linear.
“We’re in an R&D era,” said James, who is experimenting with AI agents. “We need to figure out what works. We’re going to spend money on stuff that we’ll find out doesn’t work and has low value.”
