How to Handle “AI Stalls”

These four speed bumps could derail a chief financial officer’s best AI strategies.

Approximately 90% of U.S. CFOs say their AI budgets are on the rise in 2024, with 71% saying they’ll boost their AI budgets by more than 10% this year, according to a recent Gartner survey.

“As organizations venture further down the AI path, executives must agree on their ultimate goals for using this technology,” said Alexander Bant, chief of research at Gartner Finance. “CFOs should complement increased spending on AI with critical C-suite discussions about the organization’s AI ambition.”

A big part of that ambition should be avoiding potential landmines that could stop a company’s artificial intelligence plans in their tracks.

This week, Gartner pointed to several potential “AI stalls” that could curb or even cancel a firm’s adoption of artificial intelligence.

“Gartner has been working with 80,000 executives around the globe to figure out the right use cases, to improve data, and to get teams ready for the new era of AI,” said Clement Christensen, senior director analyst, research, in the Gartner Finance practice at the company’s CFO & Finance Executive Conference This week in National Harbor, Md. “However, as enterprises continue to pursue AI, we see cracks emerging: four enterprise-level organizational challenges in particular that we call the ‘AI Stalls’.”

At the keynote event, Christensen and Gartner vice president and senior analyst Nisha Bhandare said AI Stalls are process-oriented and not structural. “These are common problems with the ways organizations use AI rather than problems with the technology itself and can cause significant delays in the adoption and return on investment of AI,” Christensen said.

Gartner’s “Four AI Stalls” Explained

The AI stalls cited at the event cover a quadrant of key areas: cost overruns, misuse in decision making, loss of trust, and a rigid mindset.

“These stalls will be pervasive across most organizations of all sizes and industries from now through 2030. The time to course correct these stalls is now, and CFOs have a vital role in the enterprise in identifying and counteracting these stalls before they become a reality,” Bhandare said.

Cost Overruns. “There’s a uniqueness to AI costs. Given how new AI is, CFOs don’t really know how much it costs: they are learning as they go, driving cost estimates off by 500-1000%,” said Bhandare. “Initial rollout costs, such as infrastructure, user licenses, hiring new talent and implementation costs, are something CFOs are aware of and are not different to other technologies.”

Bhandare points to a pair of cost buckets that match up with company AI investments, and CFOs need to account for those costs.

— The cost of keeping AI running, compliant, and data-efficient. ‘Gen AI comes with its own uniqueness and usage cost per query, per employee,” Gartner noted. “This is where most of the volatility in cost projections arise – especially as organizations mature from basic to more advanced AI use cases.”

— The cost of experimentation (i.e., “sunk costs”). Unlike other technologies, AI follows an experimentation process: start small and keep training the AI model. “With experiments, there are failures due to low adoption or from choosing the wrong use case,” Gartner stated.

Misuse in Decision Making. Smart CFOs will “pace” their AI rollouts to ensure expectations are being met and that the company isn’t getting ahead of itself with its artificial intelligence initiative.

“Most of the CFO’s enterprise colleagues – such as business decision-makers in marketing, sales, and supply chain are excited about the benefits of automation, and they will likely overestimate AI’s intelligence,” said Bhandare. “They’ll want to go to an automation solution right away instead of a trial period using more of a decision support or augmentation approach.”

Loss of External Trust. Like any new technology, especially a groundbreaking one like AI, trust is a huge issue for senior executives, especially in finance, where the metrics are vital to a company’s story.

That’s why it’s important for CFOs to ensure that their company’s investments don’t “break” the trust built with external parties.

“When the data that AI is using to interact with external parties is biased or insecure, when the model is not updated to reflect current regulations, or when employees are not skilled to explain AI results to their customers: these failure points can lead to AI providing information that is incorrect, biased, or simply contrary to the company’s culture. This will erode the trust organizations have built with their stakeholders,” Christensen noted.

A ”Rigid” Mindset. There’s little doubt that AI will perform some tasks better than humans.

Establishing those tasks as a “set of lower value tasks” that staffers will no longer handle is frightening for employees.

“The mistake CFOs often make is that while they tell employees what they wanted them to stop doing, they don’t properly identify what they wanted them to start doing or provide any support for new ways of working,” said Christensen. “Rather than just asking ‘Is the tool easy to use?’ ask, ‘How will staff react to the use of AI, and how are we planning for their response?’”

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