For today’s CIOs, the promise of AI comes with a paradox: delivering transformative new applications while managing infrastructure complexity that few internal IT teams are equipped to handle.
AI workloads often include massive datasets and query loads. AI training requires processing terabytes or petabytes of data, which can be challenging for traditional infrastructure to manage across systems. The data must also be cleaned, labeled, normalized and structured—otherwise challenges can crop up. In fact, a recent MIT study found that 95% of AI projects fall short, often due to data problems.
Enterprises are accelerating AI adoption, which is putting pressure on CIOs to quickly deploy reliable, high-performance infrastructure. Much is riding on these infrastructure decisions, because organizations can’t afford to have AI applications underperform. Competitors are often moving fast to put their models into operation.
But AI systems need fine-tuning and constant adjustment so they do not slow down due to CPU, GPU, networking or I/O bottlenecks. Also, the complexity of AI infrastructure as well as hardware and software integration often requires custom configurations. Some are sold as “plug and play” but still require weeks or months of optimization.
Skills needed
Because of the specialized skills required to configure and manage AI environments, few enterprises can rely on internal IT teams. One study found that more than 50% of companies cited a lack of internal expertise or knowledge as the largest barrier to AI adoption. Many IT departments are already stretched managing existing infrastructure and lack the expertise on AI topics such as GPU/accelerator optimization, data pipeline design, model training orchestration or hardware-software integration. Unless you are Google, Meta or Netflix spending billions on custom engineering, you should focus on using AI to drive outcomes, rather than trying to master the infrastructure behind it.
There are multiple levels of complexity involved with tuning hardware and software to work properly, so many IT teams are not capable of handling those configurations. Without expert support, organizations can face delays, increased costs and underperforming systems.
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A better model
This combination of complexity and skills shortages is driving enterprises to trusted managed service providers, who take responsibility for performance through SLAs. The provider takes the burden from the customer so that infrastructure meets agreed-upon service levels. Enterprises receive the benefit of focusing resources on core products and applications as well as business outcomes, rather than building this expertise internally or troubleshooting backend issues.
Own vs outsource
With AI, today’s CIO faces a strategic choice of what to handle in-house and what to outsource.
Core product and service innovation, as well as customer-facing AI applications, such as revenue-driving product features, are typically best kept in-house. Companies want to maintain full control of these items that affect their brand, customer trust and competitive advantage.
Meanwhile, infrastructure, storage and backend AI systems require specialized skills, and constant maintenance and tuning that often don’t merit internal resources. For example, infrastructure support systems of record such as a general ledger, accounts payable or human resources can make sense to outsource. Enterprises can thereby focus internally on critical innovation while trusting partners for infrastructure management.
An airline, for example, may want to develop and control its customer-facing AI, because it is mission-critical, while outsourcing the development and maintenance of infrastructure supporting its reservation system to a partner who guarantees reliability and performance. This can enable hands-on ownership without managing the day-to-day complexity of infrastructure.”
Accountability
One of the major appeals of managed services is the clear boundaries of responsibility between providers and customers. For enterprises, this eliminates the headaches of having to chase down OEM vendors or troubleshooting issues themselves. Instead, one provider is contractually bound to deliver against expected outcomes.
As a result, CIOs can direct resources toward their core priorities such as innovation, while the provider makes sure that infrastructure is reliable and meets performance goals. This model is predictable, like a water or electricity utility, which is steady and reliable. But it does require a cultural adjustment: CIOs must let go of day-to-day control and trust providers to deliver under clearly defined accountability and roles.
Flexible consumption
AI workload demands can be unpredictable and vary widely over time as needs evolve. With traditional models, companies can overspend or under-prepare. But flexible consumption models offer a model akin to a utility that enables businesses to start small and scale with business needs at the right time.
As a result, customers only pay for what they use, and costs can be managed efficiently while growing to meet outcomes. Just as water customers expect their utilities to be always on, CIOs expect infrastructure to just work. The service and economics components of flexible consumption complement each other well. Think of tap water as steady, reliable base infrastructure, while specialty bottled water represents the unique differentiated AI capabilities that organizations keep in-house.
Unlike traditional IT, demands of AI workloads are data- and compute-intensive and sensitive to bottlenecks. These systems typically require specialized expertise for model orchestration, GPU tuning and hardware-software integration that many IT teams don’t have. Enterprises can offload accountability to providers through SLAs. The key for CIOs is striking the right balance: keep innovation and customer-facing AI applications in-house, while outsourcing infrastructure and other systems of record to trusted partners.
Managed services provide predictability, freeing up CIOs for other priorities, while flexible consumption ensures cost-effective usage. And together, they empower CIOs to simplify complexity, focus on innovation and accelerate outcomes. In a time when AI has become a must-have, balancing internal AI applications with outsourced infrastructure enables enterprises to devote their focus on what is mission critical for their businesses.
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