Early AI Risers Are Thriving, But Speed Bumps Need Navigating

Data complexities and ethics issues cloud artificial intelligence acceleration.

In most instances, being the first out of the gate with a new technology is more curse than conquering.
Chief financial officers who’ve been there before with massive new technology investments (think Betamax, the Apple Newton, Web TV, and the Windows Phone, among other calamities) now know it’s usually smarter to come in at system 2.0.

That’s when the bugs are fixed, and the requisite number of canaries have survived the coal mine.

Artificial intelligence, however, may be an outlier on the “early adopter” list, a new study suggests.

About 42% of enterprise-scale companies surveyed (of at least 1,000 employees) report having actively deployed AI in their business. Of that number, 59% of those companies surveyed already exploring or deploying AI “say they have accelerated their rollout or investments in the technology,” according to IBM’s most recent Global AI Adoption Index.

“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. 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,” said Rob Thomas, senior vice president of IBM Software.

Thomas says IBM is seeing organizations leveraging AI for use cases where he believes the technology can most quickly have a profound impact, like IT automation, digital labor, and customer care.

“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,” he says.

Research and development (44%) and reskilling/workforce development (39%) are the top AI investments at organizations exploring or deploying AI, the report noted.

The AI use cases driving adoption for surveyed companies currently exploring or deploying AI include the following business operations, IBM reports.

• Automation of IT processes (33%)
• Security and threat detection (26%)
• AI monitoring or governance (25%)
• Business analytics or intelligence (24%)
• Automating processing, understanding, and flow of documents (24%)
• Automating customer or employee self-service answers and actions (23%)
• Automation of business processes (22%)
• Automation of network processes (22%)
• Digital labor (22%)
• Marketing and sales (22%)
• Fraud detection (22%)
• Search and knowledge discovery (21%)
• Human resources and talent acquisition (19%)
• Financial planning and analysis (18%)
• Supply chain intelligence (18%)

Still, Risks Abound

That’s not to say there aren’t challenges facing companies in the midst of AI rollouts.

The top barriers “hindering” robust AI adoption are limited AI skills and expertise (33%), too much data complexity (25%), ethical concerns (23%), AI projects that are too difficult to integrate and scale (22%), high price (21%), and lack of tools for AI model development (21%),” the IBM study notes.

Generative AI is something of a different animal, with its own unique challenges and risks, especially for companies new to AI.

“Data privacy (57%) and trust and transparency (43%) concerns are the biggest inhibitors of generative AI according to IT professionals at surveyed organizations not exploring or implementing generative AI,” the study notes. “35% also say that lack of skills for implementation is a big inhibitor.”

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