How to alienate customers and undermine revenue

Big Tech has entered an arms race to dominate the artificial intelligence space, with Amazon, Google, and Microsoft investing more than $600 billion into AI development between 2023 and 2025. This massive surge in AI spending reflects their belief that AI will revolutionize the tech industry and reshape the global economy.

Executives and investors may be prioritizing AI at the expense of traditional systems that millions of people use every day. This unrelenting focus on AI risks undercutting providers’ ability to serve current customers and sustain stable revenue streams. It doesn’t require a crystal ball to foresee lower customer satisfaction, reduced revenues, and damaged reputations.

The cost of overinvesting in AI

The hyperscalers are financially strong, but AI spending leaves a noticeable dent on anyone’s balance sheet. We’re not just talking about an incremental investment—it’s a seismic shift in resource allocation. Google, Microsoft, and Amazon alone are spending astronomically to build AI infrastructure, train advanced AI models, and acquire talent or smaller AI-focused firms. They view AI as a transformative technology, but such heavy investment carries an inherent gamble.

To justify these costs, AI must deliver returns that match or exceed the opportunity cost of neglecting other technologies. However, this outcome is far from guaranteed. Many AI innovations are years from reaching full potential, especially at enterprise scale. Developing and deploying AI requires time and organizational change while cautious businesses weigh adopting unproven tech.

If AI fails to deliver quick, meaningful financial returns, the money spent on it could drag down profitability. The obvious path to recover the lost revenue would be to raise prices elsewhere or cut budgets for essential services, both of which would reduce customer satisfaction.

Ignoring today’s tech and its users

Both AWS and Microsoft Azure have redirected major parts of their R&D and leadership focus toward AI, prioritizing emerging technologies like machine learning and artificial intelligence. This shift risks neglecting the optimization of existing tools such as traditional cloud computing services, enterprise software, cybersecurity solutions, and infrastructure products that millions of businesses rely on every day. This could lead to lower performance, weaker security, and fewer customer service enhancements as companies pursue AI breakthroughs.

The risk here is obvious: Current customers who generate stable, predictable revenues might feel overlooked. Clients could start looking elsewhere if essential services decline or stagnate because resources were devoted to AI development. This isn’t hypothetical; businesses rely on reliable, well-supported tools to achieve their operational and financial goals. Any perception that the big providers are favoring moonshot AI projects over maintaining and improving core technologies will hurt customer relationships and weaken trust.

AI is not an immediate payoff

One of the biggest misconceptions driving this AI gold rush is that revolutionary outcomes are just around the corner. The tech industry loves to pitch rapid innovation cycles, but actual enterprise AI adoption is far slower. Implementing advanced AI in highly regulated, risk-averse sectors such as healthcare, government, or finance is a process measured in years, not quarters. Companies require rigorous testing, integration with legacy systems, and buy-in across multiple layers of leadership—none of which happens overnight.

Additionally, many businesses lack the expertise or infrastructure to fully leverage advanced AI capabilities today. Enterprises that have only recently transitioned to cloud computing, for example, are unlikely to have the technical infrastructure or highly skilled personnel to support cutting-edge AI systems. This presents a paradox for vendors. Even as they develop generational innovations in AI, the enterprises paying for these services may not be positioned to adopt them at scale. If that market inertia remains in place (and there’s little reason to assume it will vanish quickly), the revenue potential for AI in the near term may fall far short of the sky-high projections.

Ironically, the aggressive drive for AI dominance also reveals weaknesses in the business models of Microsoft, Google, Amazon, and others. These companies are signaling to investors that their futures depend on technological innovation rather than their historic successes. This poses a dilemma: Any slowdown in AI deployment or poor adoption rates could undermine their leadership strategies and shake investor confidence. Moreover, shifting priorities might allow smaller competitors to target market segments that feel neglected. For instance, mid-tier cloud providers or enterprise software companies that focus solely on high-quality, reliable services could attract customers frustrated by Big Tech’s AI-driven goals.

A balanced path forward

To avoid long-term fallout, the hyperscalers must strike a better balance between innovation and stability. While AI unquestionably represents an important step forward for the industry, it shouldn’t overshadow the bread-and-butter technologies that drive current income and build customer loyalty. Enterprise customers expect more than just cutting-edge technologies—they rely on consistent, supportive partnerships to accomplish their goals. The danger in ignoring this is alienating the very businesses that generate the revenue fueling these AI ambitions.

Investing wisely involves recognizing AI’s importance but not overemphasizing it. Maintaining open communication with clients, supporting their needs, and bridging current and future technologies are crucial. Slow progress may not generate headlines, but it’s less likely to disappoint or destabilize loyal customers.

The tech industry is all about pursuing the next big thing. It’s in our DNA. However, the balance between existing cloud services (the bird in the hand) and AI (the birds in the bushes) is where risk versus reward can quickly get off track. Tech leaders would be wise to remember that although new frontiers in technology are thrilling, they are meaningless without the support of the enterprises and systems that bring those possibilities to life.