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Fact: overhyped business expectations almost always lead to bursting bubbles. From dot-coms to crypto, history is littered with examples of technologies that promised to change everything overnight — and then didn’t. Artificial intelligence (AI) could be next if companies don’t ground their ambitions in clear, sustainable business opportunities.

That’s not to say AI isn’t transformative. It absolutely is. But to keep your organization from becoming a prospective AI casualty, leaders must treat this powerful technology with both excitement and discipline. For example, Walmart is using predictive AI to sort produce. Here are three ways to ensure your company’s AI bubble doesn’t burst.

1. Avoid waste by identifying clear business cases

The fastest way to waste money on AI is to deploy it without a purpose. Adopting AI “just because competitors are doing it” or to appear innovative is a one-way ticket to disappointment. Instead, the best organizations start by identifying specific, measurable problems worth solving.

For instance, a retail chain might use AI to optimize inventory based on seasonal trends — a tangible use case that directly impacts profitability. What they shouldn’t do is chase vague ambitions like “revolutionizing customer experience” without a clear plan or success metric.

Once those business cases are defined, companies must invest in the right foundations. Data is the fuel for AI — and without clean, accessible, compliant, and traceable data, no algorithm can deliver trustworthy results. That means auditing your data sources, ensuring they’re accurate, and creating a system where models can be monitored, explained, and maintained over time.

It’s also worth noting that most enterprise data (roughly 70%) still lives on mainframes. That makes mainframes critical to AI success. Ignoring them is like trying to power a rocket without jet fuel. It can be easy to do a demo with AI, but scaling adoption at the enterprise level to address real business challenges is a where core issues are uncovered. Companies that unify their data infrastructure across hybrid architectures are far better positioned to build durable, high-impact AI solutions.

2. Get the most from your employees with new mindsets

AI is not just a technology shift, it’s a cultural one. Successful adoption requires new ways of thinking, working, and leading. That starts with bringing everyone to the table: data scientists, yes, but also domain experts, legal, compliance, and frontline workers who understand the real business context.

Too often, AI initiatives mistakenly stay siloed in IT departments, detached from operational realities. Every AI decision should connect to a clear business proposition, whether that’s improving innovation, agility, or resilience.

Training plays a huge role here. Leaders and employees alike need to understand what AI is and isn’t. Indeed, many organizations underestimate the cultural and organizational adaptation required for AI success. The fix: invest in reskilling and upskilling. Empower your teams to work with AI as a trusted assistant, not fear it as a replacement.

3. Maximize AI benefits with uniform adoption

If the dot-com bubble taught us anything, it’s that hype without discipline leads to collapse. The same principle applies to AI. Chasing flashy capabilities or inflated expectations without clearly defined business outcomes is a recipe for failure.

 This is not achieved by treating AI as a separate initiative. It should be an extension of your organization’s broader modernization and standardization journey. Building blocks like multi-factor authentication, continuous monitoring, and role-based access control aren’t optional — they’re prerequisites. Similarly, standardizing on development tools and CI/CD pipelines ensures you can scale AI safely and efficiently across the enterprise.

Before rolling out AI projects, ensure your data governance, quality, and security practices are rock-solid. That means having frameworks for explainability, fairness, and compliance from day one. Conduct audits. Monitor for bias. Track ROI early and often.

Looking ahead, getting help

There’s no single formula to prevent an “AI bubble” from bursting. Market forces, innovation cycles, and human behavior are all variables. But organizations that build on solid data foundations, invest in human expertise, and secure their guardrails are far less likely to burst and far more likely to reap the technology’s full value.

AI may be overhyped in some corners, but it’s also one of the most promising technologies of our time. With clarity, discipline, and humility, CIOs can harness that promise without getting caught in the pop.

Want to learn more? Get in touch with Venkat Balabhadrapatruni, Broadcom’s resident AI expert. 

Venkat Balabhadrapatruni is a Distinguished Engineer, AI and DevX, at Broadcom