C-Suite AI Dilemma: Getting From “Pilot” to “Takeoff”

Moving forward with artificial intelligence is more complicated than many executives think.

A recent IBM sudy said 60% of U.S. companies using AI are “accelerating” their technology strategies in early 2024.

That’s likely good news for AI-deploying companies; those expansion efforts are being hampered by “limited” staff AI skills, “too much” data complexity, and ethical and regulatory concerns, IBM reported.

“We’re seeing that the early adopters who overcame barriers to deploy AI are making further investments, proving to me that they are already experiencing the benefits from AI,” said Rob Thomas, senior vice president of IBM Software. “More accessible AI tools, the drive for automation of key processes, and increasing amounts of AI embedded into off-the-shelf business applications are top factors driving the expansion of AI at the enterprise level.”

“We see organizations leveraging AI for use cases where the technology can most quickly have a profound impact like IT automation, digital labor, and customer care,” Thomas added. “For the 40% of companies surveyed stuck in the sandbox, I am confident 2024 will be the year of tackling and overcoming barriers to entry like the skills gap and data complexity.”

Now, a new study by the Dallas, Tex.-based business consultancy Yates Ltd. shows AI-minded companies are still having difficulty getting out of that sandbox.

The study tracks the sentiments of 50 global chief information officers whose companies are rolling out new generative AI programs. In it, Yates analysts found that many companies are mired in “Wave 1” of their overall artificial intelligence strategies, primarily due to the same growth barriers listed in the IBM study.

“Nearly 83% of global enterprises are either actively testing their capabilities through pilot programs or have already adopted gen AI for one or more production-grade use cases,” Yates noted.

Most of those companies are leveraging Gen AI tools to accomplish three major tasks, the study noted:

o Accelerating consumption of existing digital tools
o Reduce the latency of knowledge-sharing
o Shortening the product development lifecycle.

A Trio of Challenges

On the other side of the coin, three significant challenges are slowing the shift from Wave I to Wave 2 down.

“CIOs identifying their top three challenges to scaling gen AI initiatives most often named lack of clarity on success metrics (73%), budget/cost concerns (68%) and the fast-evolving technology landscape (64%),” the report stated. “Additionally, 55% named data security and privacy concerns, while 41% cited talent shortage.”

Getting up to speed with a data-driven Gen AI action plan should be “job one” for the C-suite, Yates said. Doing so should speed up those wave phases (Yates calls that “the velocity of existing operations.”)

“Successful transitions” should also come by weaving technological advances, organizational readiness, and ethical considerations into a company’s overall AI wave strategy.

Thinking ahead using a carefully crafted battle plan should also be front and center for executives.

“Gen AI is transforming senior executives’ perspectives on efficiency, growth, and competitive advantage and will revolutionize their operational strategies,” said Charlotte Yates, the founder and CEO of Yates Ltd.

“This blueprint should address a wide range of opportunities, risks, and investments in platforms, operating models, organization design, governance, strategic partnerships, and culture,” Yates added.




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