How to avoid the AI complexity trap

Developing, deploying, and supporting artificial intelligence can be a daunting venture that calls for an often-confusing array of new skills and technologies. Yet, ostensibly, it’s supposed to reduce complexity. Can we have it both ways?

Magical or a lot of work?

AI can’t just be dropped into an organization to start churning out insights — among many other things, it requires budgeting, rollout, and performance measurement, Chris Howard, global chief of research, explained in a recent video. “AI seems like this magical, really easy thing, and it can do all kinds of amazing things,” he said. “But once you start to work with it, you realize that it’s actually hard, and there are aspects of it that are really complicated.”

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Adding to the complexity is the fact that “the technologies themselves are constantly evolving,” Howard added. “So they haven’t reached a point of stability, at least in the generative AI space, where it’s really easy to understand how you would fit different pieces together. And so because that’s changing, it causes confusion — it’s super complex.” Add data to that complexity equation. “You need to bring it together into a place where you can actually operate on it and get better results. What appeared to be magical actually is a lot of work.”

Of course, AI itself offers a way to automate and abstract away this complexity. “AI has great potential to help resolve complexity in the workplace and expand productivity and employee and customer happiness,” Smita Hashim, chief product officer at Zoom, told ZDNET.

When done right, AI enables simplicity, cutting across layers of complexity — but with limits. “AI is not a silver bullet,” said Richard Demeny, a software development consultant, formerly with Arm. “LLMs under the hood actually use probabilities, not understanding, to give answers. It’s humans who design, build, and implement systems, and while AI may automate some entry-level roles and certainly bring significant productivity gains, it cannot replace the amount of practical experience IT decision-makers need to make the right trade-offs.”

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For an AI tool to give the best answer, “it would need to know every little detail that’s in the decision-maker’s head,” he added. “It’s simply more practical to come up with the decision oneself, with some AI assistance.”

“Your users work across many different applications,” said Hashim. “Choose platform solutions that are open and enable seamless integrations and workflows. This flexibility is crucial for reducing complexity in today’s multi-vendor environment.”

How AI can benefit IT operations

With the growing complexity of IT systems, “businesses are up against a conundrum like never before,” said Bill Lobig, vice president of product management and observability for IBM Automation. “Teams are managing massive amounts of applications, leveraging different clouds and on-premises environments — and applications need to stay up and running. Right now, over 1,000 applications are used by organizations, and 82% of enterprise leaders say IT complexity impedes success.”

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This creates challenges, “especially from siloed apps, to potential outages, to resource and energy waste, and a lack of performance,” Lobig added. Here’s where AI steps in. “How can IT leaders manage the risk of these potential issues and get ahead of looming situations of downtime? The answer is observability and application resource management — all made possible through AI-powered automation.”

“Using real-time, AI-powered automation and performance analytics, teams can now proactively optimize the allocation of compute, storage, and network resources at every layer of the stack,” Lobig said. “This capability eliminates the need for reactive measures and overprovisioning, ultimately saving time and money.”

When it comes to understanding how AI can benefit IT operations, it’s important to keep everyone up to date with new developments, Lobig adds. “Adapt and scale with hybrid architecture, while keeping a holistic view of performance, cost, and value across applications and networks.”

AI deployment needs to be thoughtful

To keep both AI and IT complexity at bay, “deployment of AI needs to be thoughtful,” said Hashim. “Focus on the simplicity of user experience, quality of AI, and its ability to get things done,” she said. “Uplevel all your employees with AI so that your organization as a whole can be more productive and happy.”

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Consistency is the key to managing complexity, Howard said. Platforms, for example, “make things consistent. So you’re able to do things — sometimes very complicated things — in consistent ways and standard ways that everybody knows how to use them. Even something as simple as definitions or taxonomy. If everybody is speaking the same language, so a simplified taxonomy, then it’s much easier to communicate.”

At the end of the day, “AI might offer informed suggestions, but it is still humans who make the final decisions and bear the consequences,” Demeny said. “Every product, every AI infrastructure, is different, and the complexities of each require human insight. AI’s role should be seen as a tool to assist, not a replacement for the judgment and expertise that comes with experience.”

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