CONTRIBUTOR

The mounting hype around artificial intelligence (AI), generative AI (GenAI) and the pressure to leverage the technology to achieve business objectives is causing many business leaders to take a more cautious view.

These were among the results of a Boston Consulting Group (BCG) survey of 1,406 C-level executives in 50 markets and 14 industries, which found the vast majority (90%) of survey respondents are taking a “wait and see” approach or moving forward in small steps.

Moreover, the report revealed two-thirds of executives surveyed are either ambivalent or dissatisfied with their company’s advancements in AI and GenAI.

Key factors contributing to this sentiment include a shortage of talent and skills (62%), unclear roadmaps and investment priorities for AI and GenAI (47%), and a lack of strategy regarding responsible AI and GenAI (42%).

Despite the wariness, 85% of surveyed executives said they plan to increase investment in AI and GenAI in 2024—an 11% increase from 2023—and the majority (54%) said they expect their investment in the technology to bear fruit this year.

Sesh Iyer, managing director and senior partner, North America co-chair of BCG X, says deploying GenAI is a table-stakes call in today’s competitive environment, so organizations should get started now.

“Business and functional leaders should steer the play,” he says. “These leaders will define a target vision for the company’s use of AI. They’ll establish guardrails and guide a series of pilots across multiple parts of the organization to identify what works.”

Survey respondents said they expect to see cost savings led by productivity gains the technologies bring to operations, customer service, and IT.

The report also indicated successful organizations exhibit vigilance toward GenAI’s cost of use.

Despite its long-term implications, only 19% of survey respondents prioritize cost as the primary concern when selecting AI and GenAI solutions. Furthermore, winners emphasize intentional relationship-building and responsible AI (RAI) principles.

“Our previous research on responsible AI has shown that organizations whose CEOs participate in responsible artificial intelligence initiatives realize 58% more business benefits than those whose CEOs are uninvolved,” Iyer adds.

More than a quarter (27%) of companies investing over $50 million in AI this year said they entrust the CEO with leading their RAI strategy, in contrast to the overall average of 14%.

Krishna Subramanian, co-founder and COO of Komprise, says GenAI is nascent and evolving rapidly – which compounds the challenge of using it in business contexts.

“Not only are companies trying to figure out where it fits and how best to use the technology, but the technology itself is quickly changing,” she says. “Predictive AI tools are more mature and there are best practices on when and where to use them.”

She explains AI has tremendous potential to deliver cost savings and productivity gains once it is integrated into business processes, but this process must be iterative and should not be rushed, especially since we are still learning about the risks and exposure from AI.

“Forcibly expecting and measuring tangible results too early can cause employees to rush the use of AI in production which may have unintended negative consequences,” Subramanian cautions.

She says iterative development with AI should be encouraged, and initial measurable milestones may have more to do with creating prototypes, testing in certain scenarios, documenting and checking data governance around its use, rather than tangible ROI savings.

Subramanian adds organizations can make progress on AI initiatives by identifying production use cases for predictive AI and start experimenting with GenAI in areas like chat that have less business risk.

“A key to systematically executing on AI is to have a timeline and goals of what an organization wants to achieve, and work backwards from that goal by creating milestones for employee retraining, internal knowledge sharing from prototyping with AI, and incrementally developing milestones to reach the goal,” she says.

Caroline Carruthers, CEO, Carruthers and Jackson, says a big part of the Generative AI learning curve is the language that organizations are using to talk about data.

“Overbearing policy guidance is making people fearful of embracing new tools such as AI and this is impeding natural adoption,” she explains.

By modifying the language and positioning AI as a new tool that will help employees do their job, and by providing lots of guidance on how they can safely use it, organizations will ensure people are more open to embracing these tools.

“Organizations need to identify what should be put in place to help people understand what will solve a certain problem, and then train specific people to use the tools they need,” Carruthers says. “This is about walking slow before you can run the race faster.”