Pre-Season Inspection: Is Your Infrastructure Ready for the AI Race?
Monday Morning Pit Stop - Week 1
Pavle Majerle
Marketing Operations Leader, NexusTek
Welcome to the starting line of the most important race your business will ever run.
Just like Formula 1 teams spend months before each season meticulously inspecting every bolt, sensor, and system on their cars, smart enterprises are conducting their own pre-season inspections on the infrastructure that will determine whether they win or lose in the age of artificial intelligence.
Everyone already knows AI is inevitable. The question is whether your infrastructure is ready for the race.
Don’t Go Pedal to the Metal with Flat Tires
43% of C-suite execs are worried their infrastructure won’t be able to keep up with new AI modalities,1 and that number should be higher. Here’s what we’re seeing: 75% of businesses are experimenting with AI tools, but less than 20% have the infrastructure foundation to scale those experiments to the winner’s circle. It’s like trying to run the Indianapolis 500, but most businesses are jumping off the starting line with flat tires.
The problem isn’t lack of ambition or investment. The problem is sequence. Too many organizations are bolting AI engines onto infrastructure that was designed for a different era, then wondering why they’re not seeing the performance gains they expect.
Digital transformation projects of the last decade focused on migrating to the cloud and enabling remote/hybrid workforces. AI was niche, but now it’s everywhere. If you want a real shot at winning the race, you need to meet AI’s specialized requirements. That means planning for AI-accelerated hardware, clean data pipelines, enhanced data security and governance, and teams that can support AI paired with teams that know how to use AI. It’s the same difference between the pill-shaped racecars of antiquity vs. the sleek, blade-like cars screaming around tracks today. Older infrastructure is going to get smoked in this new paradigm. But don’t worry—we’ve got a checklist to help you burn rubber.
The Four Critical Systems Every AI-Ready Enterprise Needs
Just as every F1 car must pass rigorous technical inspections before it’s allowed on the track, every enterprise needs to validate four critical systems before deploying AI agents at scale:
1. The Chassis: Hybrid Cloud Infrastructure
"Your foundation determines your ceiling"
If AI is the engine, hybrid cloud is the chassis. Your cloud architecture isn’t just where your applications live—it’s the platform where your AI agents will operate. Is it flexible enough to handle unpredictable AI workloads? Scalable enough to support agent swarms? Optimized for the split-second decision-making that defines intelligent operations? In other words, are you driving something that looks like a fighter jet, or a hot dog?
F1 cars have layers upon layers of complex, interconnected systems enabling peak performance, and your AI-ready infrastructure is no different. You need the right combination of private infrastructure paired with public cloud instances designed for AI workloads, along with the top-level orchestration tools that enable IT teams to monitor everything, automate workload placement, and keep AI tools running smoothly for end users. That may sound complex but we’re here to help you navigate implementing a rightsized chassis that works for you.
Inspection Checklist:
✅ Multi-cloud flexibility for diverse AI workloads
✅ Edge computing capabilities for real-time processing
✅ Auto-scaling infrastructure that matches AI demand
✅ Low-latency networking for agent communication
2. The Fuel System: Data Modernization
"Clean fuel powers peak performance"
The effectiveness of AI agents is on par with the quality of the data they consume. Dirty data, siloed information, and batch-processed insights are like contaminated fuel—they’ll kill your performance before you leave the starting line.
Setting up clean, converged data systems ensures that your AI tools are operating with the freshest data to boost AI accuracy and trustworthiness. Clean data like clean fuel helps prevent your AI engine from getting gunked up and producing sputters—like hallucinations, wrong answers, or responses that go against your brand identity, standards, and policies.
Inspection Checklist:
✅ Real-time data pipelines for instant decision-making
✅ Unified data architecture eliminating silos
✅ Data quality monitoring and automated cleansing
✅ Governance frameworks ensuring consistent standards
3. The Safety Systems: Cybersecurity
"Protection that enables performance, not limits it"
In F1, advanced safety systems don’t slow drivers down—they enable them to push harder with confidence. Similarly, enterprise security in the AI era isn’t about building walls; it’s about creating intelligent guardrails that let AI agents operate safely at maximum performance.
This effort speaks to cybersecurity protections against AI-specific threats such as data poisoning and prompt injection, as well as AI trustworthiness—do you trust the data going into AI models and the responses coming out? Do you trust that AI agents have access to only the data and systems they need access to? Once you have your bases covered, then you can really fly.
Inspection Checklist:
✅ Zero-trust architecture for agent-to-system access
✅ AI-powered threat detection and response
✅ Automated compliance monitoring and reporting
✅ Identity management for autonomous operations
4. The Pit Crew: Managed IT Services
"Expert support keeps you on track while others are stuck in the garage"
As F1 phenom Lewis Hamilton said, “We win and lose together.”2 The best F1 drivers don’t worry about tire pressure or fuel mixture—they focus on mastering every twist and turn in the track while their pit crew minds the machinery. With managed IT services at your back, you can redline your progress and clinch any hairpin turns the market throws at you.
Experts are on hand to make informed hardware and cloud instance recommendations, proactively monitor for issues, and enforce policies for efficient and effective AI. It’s the difference between having a team to replace burned-out tires in a few seconds flat or having to jump out of the vehicle and do it yourself.
Inspection Checklist:
✅ 24/7 monitoring and proactive issue resolution
✅ Performance optimization and continuous tuning
✅ Change management for AI deployments
✅ Skills development for AI-augmented operations
Rubber Meets Road: A Real-World Example
Last quarter, we worked with a mid-market manufacturing company that was struggling with their AI initiative. They’d invested heavily in machine learning tools for predictive maintenance but were seeing inconsistent results and growing frustrated with the technology.
The problem wasn’t the AI—it was the infrastructure.
Their data was locked in departmental silos, their cloud architecture couldn’t handle the computational demands of real-time analysis, and their security policies were blocking the AI agents from accessing the systems they needed to monitor.
We didn’t replace their AI tools. We rebuilt their foundation.
Six months later, their predictive maintenance system is preventing an average of 12 equipment failures per month, saving them $2.3 million annually. Same AI technology, championship-level infrastructure.
Your Pre-Season Inspection Starts Now
Here’s the truth: while you’re reading this, your competitors are either getting ready for the AI race or they’re already running it. The companies that win won’t necessarily be the ones with the most advanced AI—they’ll be the ones with the infrastructure to deploy, scale, and optimize intelligent operations faster than anyone else.
The good news? Most of your competition is still trying to figure out which AI tools to buy. You have time to build the foundation that will matter.
Take the Championship Challenge
This week, we challenge you to conduct your own pre-season inspection:
Audit your current infrastructure against our four-pillar checklist
Identify your biggest performance bottleneck—is it your cloud architecture, data quality, security complexity, or operational gaps?
Calculate the cost of delay—what’s it worth to be six months ahead of your competition in AI deployment?
Reference
1. IBM, 3 reasons why the right infrastructure support is essential for AI, October 2024.
2. F1 Experiences, 10 iconic quotes from the history of Formula 1, accessed June 2025.
About the Author
Pavle Majerle
Marketing Operations Leader, NexusTek
Pavle is a growth marketing professional with a proven track record of driving business development across diverse technology verticals and strategic partner ecosystems. As Marketing Leader at NexusTek, he manages the complete lead lifecycle from generation to conversion, developing go-to-market strategies that accelerate customer engagement and revenue growth. His expertise spans integrated marketing campaigns, partner collaboration, and demand generation, with achievements including the ITSMA Gold Award for Driving Partner Collaboration and successful marketing initiatives for major technology partners including Microsoft, Google Cloud, and AWS.