Navigating the AI Hype Cycle

As I observe my fellow IT leaders vigorously researching the current wave of AI developments, I wanted to share some thoughts on what I see as the primary AI market forces and how to chart a way forward amid some of the hysteria that accompanies early stage technologies.

The current groundswell of AI interest and adoption, which accelerated tremendously as awareness of ChatGPT spread amongst our customers and my executive peers, is the result of a convergence of multiple AI-enabled capabilities coming to market with potential value for many organizations. There’s no denying that AI offers potential opportunities for transformational efficiency and productivity gains. And there is the feeling that companies must figure out how to either harness these capabilities or be disrupted by them.

But early AI adoption comes with substantial risks as well. The challenge comes in balancing the two. It makes me think of what Peter Parker’s Uncle Ben told him in the Spider-Man comic books: "With great power comes great responsibility." Chat GPT confirmed that for me (noting that some sources suggest that a young Winston Churchill said something similar in a 1906 speech) — another simple illustration of the growing value of AI assistance that could ultimately revolutionize our productivity.

Unpacking the AI Hype Cycle

Before realizing any transformational gains from AI, we have to unpack what is undoubtedly the most extensive and complicated technology hype cycle I have seen during my 30 years as an IT leader and CIO. Looking closer, I see four market forces at work that IT leaders should understand as they develop their own strategies.

1. This is a “hyper” hype cycle. Everyone is talking about AI. It’s not just your CEO and your board; it’s your kid’s teachers and your mother. That’s because the impact of AI extends well beyond our business borders to our families, our communities, and our societies. I don’t think I’ve ever seen an IT technology conversation as wide-reaching as this one. In terms of hype cycles, it reminds me of the emergence of the internet. I remember my first “e-Business Czar” meeting in the early 2000s, as we grappled with whether the world wide web would disrupt our business or how we might create a competitive advantage by leveraging it. Over time, e-business became just business, but the transformations that led to ubiquitous two-day shipping, widespread remote work, and hundreds of other innovations had a starting point — and the value and risks at the time were far from clear. Right now, AI hype continues to grow as media cycles, fears, and misunderstandings fuel mythmaking. My best advice to IT leaders is to remain vigilant and hyper-focused on business strategy as they assess the benefits of risk and AI for their organizations. 
   
   
2. The consultants are circling. As a former consultant myself, I say this without judgment. There are enormous opportunities here for advisors and consultants who truly understand the emerging business applications of AI to help enterprises develop their strategies and execute. But right now, it may be hard to tell who’s the right partner for your organization. Early in my career, I was a consultant during the lead up to the impending “Y2K disaster”. There was a similar massive hype cycle with one definite difference. In the Y2K days, we had a singular focus: implement a modern ERP system or get left behind — maybe permanently. With AI, there are hundreds (perhaps thousands) of potential use cases and new software and development techniques emerging daily. It's boom times for advisory services that can focus on understanding a quickly changing market and how it might benefit companies. But CIOs should take care to balance first-mover advantages with the long-term strategic outcomes they’re seeking in order to prevent failed pilots from becoming barriers to broader enterprise adoption.  
   
   
3. The cloud ecosystem wants YOU. The big cloud players – Microsoft, AWS, and Google — are making massive investments in AI. And the hardware that feeds the cloud, especially the Graphic Processor(GPU) capabilities needed to train AI models are fueling growth for AI chip makers and other network, computing, and storage providers. The companies that make up this cloud ecosystem are in an aggressive market battle to win your company's AI business. The hype from this quarter is that whatever you spend will be easily offset by the productivity gains or customer experience differentiation yielded from AI. But CIOs should proceed with caution. It's highly likely that, wherever you start building your AI footprint, your costs will grow. And the returns aren’t guaranteed. The economics of the data you expose, the proprietary AI models you leverage to protect your sensitive data, and provider-based AI assistants are all expected to drive accelerated IT spending.
   
   
4. AI is everywhere. The list of software products with AI built into them is vast and growing daily. What is unique, in my experience, about this hype cycle is that every software product in our IT environment today has an “AI story”. Getting beyond the hype means sorting through all these AI narratives to find what is real and has an actual proven ROI versus what is some AI-washing done by a marketing organization to catch customers' attention while product engineers work overtime to catch up with the sales pitch. Just like the war for market share among cloud providers, software companies know they’d better be leading with AI or risk of losing share to AI-enabled competitors or startups with niche solutions. For IT leaders, wading through this will require patience and substantial diligence. It’s important to ask vendors about real implementations, achieved ROIs, and customer references.

There are multiple business use cases emerging for AI that will be early wins for a lot of businesses. Companies with customer service models who previously adopted various chatbots solutions will see value in an improved AI-driven bot model. This isn't really disruptive innovation, but instead follows a pattern of incremental automation improvements in the area of customer service. Software development, because it’s a language-driven effort, will also derive early value from Large Language Model (LLM) AI to automate mundane tasks like code conversion and documentation, also providing incremental productivity and quality improvements.

Beyond these types of lower-risk, early-pay-off options, the direction may seem less clear. But IT leaders who step back from the hype will find themselves in a familiar place: making a build vs. buy decision. We know every software product company is building AI into its products. Do you wait and trust your existing partners to deliver AI innovation? Is there a more compelling opportunity in working with a startup that has built an AI solution already? Or do you build it yourself to capture a singular advantage ahead of your competition?

There is much to consider as we apply our traditional IT architecture decision-making to AI. The four market forces I described above are swirling, the investment in AI is massive and growing, and these changes are coming whether we’re ready or not. I choose to be ready.

Early adoption is no guarantee of success. There will be winners who come out ahead and early losers, whose early investments fail to deliver.

What’s certain is that it’s an exciting time to be in IT. The opportunities and potential value AI offers to create efficiency, improve product velocity, transform the customer experience, and more are all out there. Our companies look to us, as IT leaders, to dispassionately assess the marketplace to develop our AI strategies. Learning from our experiences with prior technology hype cycles, we are in the best position to build solid business cases for AI investments that yield demonstrable results. If we stay agile, keep connected to our peers and strategic partners, and remain aware of the market forces driving the hype cycle, we can harness this AI energy to drive our IT architectures forward.