Brakes aren’t there to make you go slow. They’re there so you can go fast with control.
In today’s AI race, security plays the same role.
When my team looked at a particular fintech startup’s situation recently, it was clear they were trying to drive a high-performance machine with a manual, fragmented braking system. On the surface, they were hitting every milestone: new features, a climbing customer base, and a rapidly expanding team. But underneath that success, the strain of growth was starting to show.
The startup was managing a sprawling fleet of developer laptops, sensitive financial data, and a complex cloud footprint. Despite having the tools and policies in place, the sheer scale was creating a “security debt.” Every new device or contractor login felt like another potential vulnerability to manage manually. Their fragmented procurement and inconsistent provisioning meant security was compensating after the fact. Instead of enabling speed, it was slowing it down.
What they needed to do wasn’t to slow down. They needed a system that was built with security by design. This was a leadership decision: move from experimentation to scale. By partnering with us, they shifted their entire philosophy, turning security into a foundational part of how they procure, deploy, and manage their infrastructure.
Bridging the AI ROI gap
We’re seeing what I call the “AI ROI gap,” whether AI investments can scale safely and profitably. While 96% of organizations are increasing AI investment, only 27% have established a full governance framework. That means most companies are funding AI faster than they can control, secure, or scale it responsibly.
As we move toward agentic AI that supports autonomous decision-making to help reduce human oversight, that gap becomes a leadership issue. Scaling autonomy without embedded security is a risk most enterprises cannot afford.
This is the leadership inflection point: the decision to move from isolated AI experimentation to true enterprise scale. The cost of not embedding security before reaching this level of agentic autonomy is now simply too high. And the best way to bridge that gap is to treat security as a lifecycle discipline. It’s about letting security guide how we buy, manage, and scale technology from day one.
Building trust into procurement
For our fintech client, the shift started with their hardware. They recognized that in a high-stakes environment, security is a systemic decision, not just product choice.
In the AI era, security is a lot more than just the software you install after the fact. A laptop travels thousands of miles before a developer opens the box. If you lack visibility into that journey, you lack confidence in what sits on that device.
By choosing ThinkPad L14 laptops, this startup opted for a Zero-Trust supply chain. This gave the team a clear, auditable trail from the factory floor to the employee’s desk. It meant that every component was verified before it ever reached their office. This kind of transparency lets them proactively address the data security and oversight challenges that 56% of organizations are still struggling to resolve, often after AI systems are already in production.
They invested in a foundation they trusted, rather than just buying laptops.
Secure provisioning for an AI workspace
As the startup grew to over 1,000 employees, they focused on establishing autonomous, always-on protection that scales effortlessly with their growth. This is a great setup for the Agentic AI era, where the environment needs to be secure by design so developers can stay focused on building financial products.
The team put this into practice by deploying 1,300 licenses for ThinkShield’s autonomous endpoint protection, which uses SentinelOne to find and stop threats in real-time. They chose it because it was integrated and operationally aligned. It gave them the confidence that their customer data was protected from malicious attacks at all times.
By using this autonomous system, the startup gained a level of resilience that allows for automatic or one-click ransomware fixes, which reduces downtime. This shift moved the daily task of “firefighting” security issues away from their IT team. Now, their experts were freed up to spend time on strategic work that helped the company stay agile and successful as it continues to evolve.
Infrastructure and AI accountability
The lifecycle approach naturally leads to the data center, the core of AI investments. Today, 84% of organizations are choosing on-premises or edge setups for their AI workloads. This is not because the cloud lacks innovation, but because accountability, data residency, and regulatory control are now non-negotiable at scale. We saw this when the fintech team added a ThinkSystem SR650 server to anchor their hybrid cloud strategy.
This was a strategic move to make sure they had total control over how their AI processes information. By keeping their most important AI tasks in-house, they can be sure they are following financial rules and protecting customer privacy. For a financial institution, control over where and how AI processes data is non-negotiable.
Expanding their footprint to the data center allowed the company to align their endpoint security with their server infrastructure. This created a unified cloud strategy where security standards are consistent from the developer’s laptop all the way to the core database. And this kind of infrastructure is essential in the AI age, as it gives leaders the control they need to scale responsibly while meeting strict industry guidelines.
From experimentation to scale
For the last year, many organizations have been in a “wait and see” mode, experimenting with AI in isolated pockets. But as we move toward agentic autonomy, the cost of waiting has changed.
Embracing this lifecycle model closes the AI ROI gap and builds the foundation of trust required to lead in the AI era. Scaling AI requires both financial investment and a commitment to deep-rooted security. When security is embedded from procurement to deployment to infrastructure, it stops being a cost center and becomes a growth enabler.
In the AI era, speed without trust is fragile. Speed with trust is sustainable.