Overwhelmed by AI? Why Managed Services Are Your Best Strategy

If you feel the ground moving beneath your feet, you aren't alone. We are currently living through a transitional era comparable to the dawn of the Industrial Revolution, a shift from a services-based economy to an AI-augmented one.

For IT leaders and executives, this transition is equally exhilarating and terrifying. You are likely experiencing growing demands from your organization. Departments want agentic AI for coding assistance yesterday. Marketing wants generative content tools. Operations wants computer vision for facility management. The pressure to adopt is immense, but the path forward is often complex. How do you integrate AI with enterprise-grade server equipment? Should you start on the cloud or build on-premises? Do you have the physical infrastructure to handle power and cooling requirements?

If you are navigating these questions, it is because the requirements for AI are orders of magnitude larger than typical IT systems. The gap between wanting AI and successfully deploying it is widening, and for many organizations, the bridge over that gap is managed services.

Before diving into strategy, let’s address the physical reality of AI. Many are often unfamiliar with the extraordinary infrastructure requirements of these systems. When organizations invest in high-performance hardware, it’s easy to assume that those resources will scale broadly across the workforce. In reality, capacity planning for AI workloads is far more complex.

Factors like workload intensity, concurrency and deployment models can significantly impact how far your investment goes. Without a clear strategy, organizations risk underestimating the resources required, which can lead to overspending or performance bottlenecks. Both cloud and on-premises approaches come with unique cost structures and scaling challenges, making expert guidance critical for long-term success.

Beyond the hardware, there is the human element. Your staff is likely already fully loaded. Asking them to learn, deploy and maintain complex AI systems is often a recipe for burnout. Many organizations attempt to solve this by launching small experiments, or "skunkworks projects.” While these might show initial promise, they rarely scale because they lack executive alignment, clearly defined business outcomes and the rigorous maintenance required to keep an AI system healthy.

Furthermore, relying solely on a direct pitch from a single original equipment manufacturer (OEM) may limit your perspective and lead to a solution that isn’t tailored to your specific needs. An unbiased view is essential to cut through the noise of the 20+ leading solutions currently on the market.

The most effective way to steer these high-stakes conversations toward a profitable outcome is to stop guessing. You cannot find the right solution for your organization without deeply understanding what your goals are. A good workshop can save CTOs and CIOs thousands of hours of research and unclear ROI later by helping them align their technology investments directly with business objectives.

The first phase of a successful AI strategy is about alignment. It includes stakeholder introductions, market context and in-depth discussions around both short- and long-term priorities. For example, consider a major sports franchise looking to adopt AI. Their needs may vary from marketing content generation to player recruiting analysis to complex play prediction. Each of these components could cost millions of dollars. Offering a "quick fix" solution without understanding what matters most to them would be irresponsible.

Once the goals are clear, the second phase moves to implementation. This is where you look at reference architectures, the real technical "bits and bytes" of how the pieces move together and what’s required to define a realistic bill of materials and scope of work.

By validating these needs upfront, you move from an experiment to a strategy. But once the strategy is built, who runs it?

If you are an executive thinking, "I need to create X in AI, but I don't want to overtax my people," managed services are the answer. This is how you offload the burden of the day-to-day so your team can focus on higher level work and innovation instead of maintenance.

Managed services for AI are not just about fixing things when they break; they are about continuous alignment.

1. Regular alignment: Imagine a monthly service contract involving engineers and project managers who meet with your stakeholder leads. They discuss issues, potential improvements and help ensure the system is actually delivering the value promised.
2. Technical responsiveness: For higher tiers of service, this involves strict service level agreements (SLAs). Whether it is a one-hour or four-hour response time, you have a remote technical support team dedicated to your AI services.
3. Hardware lifecycle management: If a component fails, for example, a GPU in a critical server, the managed service provider coordinates the return merchandise authorization (RMA) process and support with partners like NVIDIA or Cisco.

We are about to witness a jump in capabilities that can be difficult to comprehend and a team of professional consultants can help prepare organizations for these changes and guide them through this transition wisely.

LLMs are measured by "parameters," pieces of learning acquired through training. Today, the largest models consist of up to one trillion parameters. To run effectively, these require massive GPU clusters.

By early next year, we expect to see models released with two trillion parameters. Then three. Then four. We may see ten-trillion parameter models in the very near future.

To put this in perspective: Imagine the difference between the ChatGPT of 2022 and the tools we have today. Now, imagine leaping three years ahead of that capability, but it happens in the span of a few months. The speed of innovation is accelerating, and the time between capability jumps is shrinking.

This exponential growth curve where capabilities double every few months means the IT industry of 2027 will look nothing like the IT industry of today. We are moving toward a future where the top websites on the internet will likely be generative AI applications, creating content on the fly rather than just displaying static pages.

In this environment, "setting and forgetting" your infrastructure is impossible. You need a guide who is constantly watching the horizon, briefing you on the latest offerings from partners and helping you pivot your strategy to be cost-effective.

Finally, a trusted advisor plays a crucial role in safety. As models grow larger, they become more complex and, occasionally, unpredictable. There are valid concerns about "hallucinations" or models that misalign with human intent.

Navigating the legal, ethical and moral implications of these tools with the help of a managed services team provides a layer of governance and oversight, ensuring that as your AI capabilities grow, they remain aligned with your business values and safety protocols.

If this sounds overwhelming, that’s because it is. Even industry veterans are feeling the weight of this change. But discomfort is often a sign of growth. We can learn to be comfortable with rapid changes. We can support each other through this transitional era. By leveraging workshops to set a solid foundation and managed services to handle the execution, you can turn this overwhelming tide into a competitive advantage.