AI is like electricity when it was first introduced more than a century ago: People understood its promise, but didn’t know what to do with it.
That’s where enterprises are with AI technology today: IT leaders know it will transform businesses, but are not yet sure how to implement it safely and securely — or how to see ROI from its use.
That’s how Deepak Seth, a director analyst at Gartner, describes the current AI landscape. His advice: companies need to shed their fear around the use of AI and get their employees involved with it immediately.
“AI by itself is not going anywhere,” Seth said. “It will make this transition from people talking about what AI is to what AI can do.”
IT leaders want to make AI like electricity – flip a switch, and it turns on. But AI leaders face a long road ahead.
At the turn of the 20th century, companies often hired chief electricity officers to bring power to the workplace and revolutionize factories. Regulations emerged to protect workers from electrocution and other hazards. And over time a whole industry around electrical engineering flourished, Seth said.
Early AI hopes give way to real-world challenges
AI is going through similar growing pains involving the deployment of safe and secure models, and early results have been poor. A majority of experiments — up to 95%, according to one study — have failed, though successful projects in some places are now steering knowledge management, back-office functions and customer support.
“We still don’t understand how to best work with AI,” Seth said. “We still don’t understand how to build that team structure where AI is an equal member of the team.”
Companies are now moving beyond the hype and waking up to the consequences of AI slop, underperforming tools, fragmented systems, and wasted budgets, said Brooke Johnson, chief legal officer at Ivanti. “The early rush to adopt AI prioritized speed over strategy, leaving many organizations with little to show for their investments,” Johnson said.
Organizations now need to balance AI, workforce empowerment and cybersecurity at the same they’re still formulating strategies. That’s where people come in.
A human-centric approach will ensure that “AI complements human ingenuity, while also educating employees on what tools to avoid and why certain guardrails exist,” Johnson said.
For most organizations, the focus should be on applying AI effectively rather than building everything from first principles, said Matthew Blackford, vice president of engineering at RWS.
AI introduces new angles of exposure, and the people who already think carefully about those issues are often the best placed to work with it. “Strong engineers still think about privacy by design, security by design, and risk,” Blackford said.
Move past frustrations and failed projects
Despite their AI frustrations, many companies remain stuck in innovation theater, said Joe Depa, global chief innovation officer at Ernst & Young (EY). But others are finding real value in AI, especially in back-office functions.
EY, the global tax and advisory firm, has embraced the technology and now has 30 million documented processes internally and 41,000 agents in production. An AI agent called the EY tax assistant provides up-to-date tax knowledge to personnel and customers; that’s critical given that there are approximately 100 tax changes each day globally.
AI is becoming less a tech problem and more of an adoption hurdle, Depa said. “What we’re seeing now more and more is less of a technology challenge, more of a change management, people, and process challenge — and that’s going to continue as those technologies continue to evolve,” he said.
DXC Technology is taking a similar approach, designing tools where human insight, judgment, and collaboration create value that AI can’t do alone, said Dan Gray, vice president of global technical customer operations at the company.
DXC’s security operations center has an AI agent that functions as a junior analyst, handling entry-level work like classifying alerts and documenting findings. “This approach has helped us cut investigation times by 67.5% and reclaimed 224,000 analyst hours,” Gray said.
The company’s efforts have freed up human analysts up to redirect their expertise to higher-value work, such as complex investigations and fine-tuning systems to catch emerging cyberattacks, Gray said.
“The most successful enterprises moving through this transformation will be the ones embracing ‘good friction,’” he said.
Managing AI success — the paradox
As is often the case, early movers will have an advantage. But they will still need to figure out what to do with the productivity gains they see, Seth said. “For that competitive advantage to turn into reality, I would still say that the organization culture — the people, the incentive systems — have to change,” Seth said.
Companies might have to accept underutilizing some of the AI gains in the near term. AI could help workers complete their tasks in half the time and enjoy a leisurely pace. Alternately, employees might burn out quickly by getting more work.
“If you try to lay them off, you don’t have a good workforce left. If you let them be, why are you paying them? So that’s a paradox,” Seth said.
AI success will come only if companies care about the people, he said. “Because as you do that, you keep the morale of the team up and then they’re more willing to try new things, [to] go on that journey.”
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