In this segment of “Office Optional with Larry English,” Larry talks about how though AI ROI may be hard to measure, that doesn’t mean it’s not worth the investment.
AI has an ROI problem — or so many leaders fear as chatter about the AI bubble bursting gets louder. A recent Microsoft survey found that 79 percent of leaders feel AI is necessary for long-term success, yet 59 percent worry about quantifying AI’s impact on productivity.
If you’re one of those more skeptical leaders, here’s some good news: While it’s still early, companies are finding strong ROI with AI, particularly in the areas of efficiency gains, revenue growth, and cost reduction, says Stefani Quarles, director of Microsoft’s Azure Go-To-Market strategy. For instance, McKinsey found that 59 percent of companiessee increased revenue, and 42 percent have lower costs after implementing AI.
Real Life Stories Of Major AI ROI
Statistics like the one from McKinsey are great but don’t bring AI’s ROI benefits to life. For that, we need to look at some real-world examples of companies that have found success with AI. To cite just three notable examples:
Walmart recently announced 4.8 percent revenue growth and 21 percent growth in e-commerce. Company leaders cited generative AI as a contributing factor to the growth. Specifically, Walmart optimized its inventory management and supply chain with AI. It used LLMs (large language models) to create and improve upwards of 850 million pieces of data in its catalog, a task that would have taken a huge workforce to complete manually. As a result, customers have a better shopping experience, and Walmart has improved customer insights.
Visa provides another success story of AI ROI. The payment technology company used generative AI to identify and predict fraudulent activity more effectively. The tactic had a big impact: Visa prevented $40 billion in fraud from October 2022 to September 2023, double the amount of the previous year. “We look at over 500 attributes around [each] transaction, we score that and we create a score — that’s an AI model that will actually do that. We do about 300 billion transactions a year,” said James Mirfin, Visa’s global head of risk and identity solutions, in a CNBC article.
Finally, Coca-Cola used generative AI to supercharge its marketing. In 2023, the beverage company launched “Create Real Magic,” a marketing campaign enlisting consumers to create new Coke advertisements using AI platforms GPT-4 and DALL-E for a chance to have their creation featured on a digital billboard in New York City or London. The campaign received more than 120,000 submissions from 17 countries and generated 300 million social media impressions.
The Tricky Thing About AI ROI
That’s great for Walmart, Visa and Coca-Cola, three behemoth companies with lots of resources to throw at an AI implementation. But smaller organizations can also find ROI with AI. The first step is understanding why capturing AI’s ROI trips up so many leaders.
For starters, many of AI’s benefits aren’t easily quantifiable. Employee satisfaction, increased innovation, organizational agility, improved customer experience, and reduced error risk are all potential AI benefits that are difficult to quantify.
Secondly, AI models improve over time as they’re exposed to more data. In theory, the benefits should increase as the model gets smarter — leaders need a tolerance for longer-term investment payoff. “As AI models get better each year, organizations will need to think about improvements made by their AI implementation over time, which could positively impact their ROI,” says Edward Challis, head of AI strategy at UiPath.
Finally, the cost of delaying or not pursuing AI is an important consideration indirectly tied to ROI. By not acting now to integrate AI into your organization, you risk falling behind competitors. For example, Amazon Web Services found small and medium-sized organizations using AI are more likely to outperform competitors and more likely to attract the best talent. A 2024 LinkedIn study found that job listings mentioning AI receive 17 percent more applications.
“If you’re not AI-friendly or AI-enabled a year from now, you’re going to start losing folks to companies that are,” warns Quarles. “Leaders need to ask themselves how effectively they’re going to capture share within their industry if they’re not leveraging this technology to do work.”
How To Stack The Deck For AI ROI
The best practices for getting ROI out of AI are the same as for any technology implementation, advises Quarles: start with the business case, determine how you’ll measure success, compare data to baseline measurements, have an organization-wide change management plan, and reassess ROI regularly. Then, keep iterating.
“A data-driven approach that continuously measures and refines AI implementations will yield the greatest long-term value,” Quarles says.
ROI tends to go missing when leaders implement AI tools just for the sake of having AI, neglecting to align the technology with the business strategy, Challis notes. “Without understanding the pain points AI can solve and clearly identifying the business objectives they want to measure against, improved outcomes will be minimal — including ROI — and organizations will inherently see more AI barriers that prevent its scale and adoption,” he says.
So yes, AI implementation can be complex. But if you make sure to align the technology with your business strategy, have ROI in mind from the beginning, and put a robust measurement program in place, you’ll soon start reaping the rewards of AI.
This article was originally published on Forbes.com.
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