AI can play many roles in the technology stack of a modern enterprise. Its performance as a neutral, data-based, analytical advisor could allow businesses to use algorithms to predict whether a decision is the right one.
AI-based decisions are part of an arsenal of tools leveraged by technology high performers. Businesses led by digitally savvy leaders, those who champion emerging technologies such as AI, outperform other like-sized businesses by 48% on valuation and revenue growth, according to one MIT research study.
"The integration of traditional decisioning into AI is really just starting to hit its stride right now," said Rowan Curran, analyst at Forrester.
Market dynamics show more decision platforms integrating AI capabilities, as well as AI-based applications capable of using models for decision-making, Curran said.
When it comes to AI-based decision-making, the biggest challenge is seeing AI as a major cultural shift instead of an isolated tool in a kit.
"AI is not like an icemaker: bring it in, plug it in and you get ice," said Sreekar Krishna, head of AI for KPMG US. "That's not what AI is. AI is very custom-fit for the work that you're doing."
Solving for business priorities
Sam's Club is one example of a big, complex organization where AI insights contribute to improving business outcomes.
The Walmart division reported membership income increased 8.9% during the second quarter of its 2023 fiscal year, for the period ending July 29.
Member count, the company said, was at an all-time high, a milestone CTO Vinod Bidarkoppa attributes, at least in part, to the company's use of AI for decision-making.
"One of the things that we maximize for in any of our algorithms or AI models is: how do we drive member retention?" Bidarkoppa said. AI modeling helps the company determine price opportunities with membership renewal in mind.
But AI-based decisions that lead to positive business outcomes aren't immediate.
AI decision making requires some work — as well as attracting and retaining the people with the necessary skills. Businesses are also grappling with the fragmentation of AI. The technology is becoming the backbone of more products and services, Gartner data show.
"AI doesn't have any understanding of your enterprise goals, your cost-benefit trade-offs, and your capacities," said Arijit Sengupta, CEO and founder at AI company Aible. "It just knows what's in the data, and that is where the big disconnect comes."
Aible's pitch to the enterprise market is to automate away the upfront tasks associated with getting AI up-and-running. Instead of longer lead-times, the company takes an iterative approach to AI product generation, relying on collaboration with workers.
"Once you have confirmed that you can create value, then you keep iterating and improving it," said Sengupta.
Barriers to decision-making
One long-time concern with AI is explainability.
"There is some worry in the AI space that if these decisions are being made, you won't know why the decisions are being made and what the outcomes are," said Rowan.
What type of decision, and what outcomes a decision may yield, helps determine the course of action for business leaders. Highly regulated businesses could opt to rely on simpler decision trees than highly-convoluted neural networks, according to Rowan.
Organizations that spend time on responsible AI — an umbrella term for considering fairness and equity, social and environmental impact, and privacy and safety issues into AI use — can yield business benefits at a higher rate than those who don't, according to research from MIT Sloan and Boston Consulting Group.
Talent, much like in other areas of technology, emerges as another crucial hurdle.
"If you don't have a strategist who can come and tell you how to boil down the business process into a data process, and hence into an AI process, it won't work," said Krishna. "If you just start throwing AI at some random business unit, it will fail."
