I’ve had the privilege of working through three technology revolutions: cloud at Salesforce, collaboration at Slack, and now AI at LogicMonitor. One thing has never changed—big promises fall apart when infrastructure is treated like an afterthought.
And right now, that’s exactly what’s happening in AI.
Generative AI is capturing boardroom attention and investment like nothing since the rise of the internet. But most companies are missing a massive blind spot: the infrastructure required to make AI fast, reliable, and cost-effective at scale. When AI fails, it doesn’t just crash quietly. It fails loudly, expensively, and publicly.
This is our moment to lead. But we won’t win with AI models alone. We’ll win with resilience, visibility, and speed.
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YOUR AI AMBITIONS ARE ONLY AS STRONG AS YOUR INFRASTRUCTURE
AI is redefining what’s possible, but it’s also redefining what’s required.
Training a single LLM like GPT-4 can cost tens of millions of dollars in compute. And every hour of AI system downtime can cost a Fortune 500 company up to $5 million in lost revenue and trust. Worse? Most companies can’t answer basic questions about their AI environments:
Is our infrastructure optimized?
Are we forecasting cloud spend accurately?
Are we sustainable?
We’re seeing organizations scramble. Seventy-two percent of IT and financial leaders say that GenAI cloud spend is already unmanageable. Some teams spend months troubleshooting issues that could be resolved in hours with the right observability.
Even more damaging is the erosion of trust. When AI systems misfire, customers don’t just experience lag—they experience brand betrayal. In healthcare, outages can lead to canceled procedures. In financial services, latency can break customer confidence. These aren’t technical problems—they’re business risks.
DATA CENTERS ARE THE HIGHWAYS OF AI
Imagine cities without highways: slowed, disconnected, impossible to scale. That’s what your AI looks like without a modern data center strategy.
Hybrid infrastructure is now the norm, not the exception. On-prem, cloud, edge—it’s all connected. And demand is only growing. As of last year, 86% of enterprises had already adopted hybrid infrastructure and global demand for data center capacity is climbing 22% year over year.
AI thrives on speed, scale, and resilience. But most infrastructures were built for a different era, one where uptime was enough. Today, what matters is real-time insight, automated action, and the ability to pivot fast when systems are strained.
In this new reality, the real race isn’t just to build smarter AI models. It’s to build the intelligent infrastructure that can actually support them.
THREE MOVES BUSINESS LEADERS MUST MAKE NOW
This next wave of AI leadership won’t be defined by who ships the most models. It’ll be defined by who scales them with speed, resilience, and purpose. That starts with infrastructure strategy.
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Here are three shifts business leaders need to make:
1. Anticipate, Don’t React
This is about predictive resilience. The future belongs to those who see around corners. Infrastructure observability is evolving from alerting to prediction, giving teams the ability to identify and resolve anomalies before they impact customers.
2. Unify Hybrid Environments
This is about hybrid agility. Enterprises today span cloud, on-prem, and edge, but they often operate in silos. Leaders need a single, intelligent view of their entire environment. Without it, costs multiply and operational agility disappears.
3. Operate Sustainably and Strategically
This is about sustainable growth. AI’s energy demands are soaring. Data centers already consume about 1% of global electricity, and GenAI will increase that footprint. Smart leaders are now optimizing for performance, cost, and sustainability together, not separately.
THE REAL COMPETITIVE EDGE? TIME
Every company wants to move faster. But speed alone doesn’t create advantage—time does. Time to think, time to pivot, time to lead. And if your teams are buried in troubleshooting and latency issues, that time disappears.
The companies winning this moment are the ones building infrastructure that gives their people time back—not more dashboards, not more alerts, but trust in their systems and space to move with confidence.
That’s the real story behind AI success. Not the code, but the capability. Not the model, but the momentum.
Because in this next chapter, the smartest AI strategy is the one built on a foundation that’s ready to scale, sustain, and lead.