As artificial intelligence (AI) evolves, new opportunities are opening for managed service providers (MSPs). Two of the biggest areas are context engineering and AI model routing.
Why context engineering matters
Context engineering includes the tools and processes that optimize everything a large language model (LLM) sees before generating a response. The goal is to improve output quality by giving AI agents access to trusted data while reducing the memory and tokens required to complete a task.
Organizations can build these capabilities on a variety of platforms, including databases, knowledge graphs, and indexing engines. These technologies help AI agents access the right information at the right time instead of relying on less reliable sources.
The rise of AI model routing
At the same time, AI model routing technologies are gaining traction. These tools direct prompts to the model best suited for a specific task.
Instead of sending every request to the same model, organizations can route simpler tasks to lower-cost options and reserve advanced models for more complex work. This approach reduces costs while maintaining performance.
A growing opportunity for MSPs
Together, context engineering and model routing are becoming key ways organizations apply FinOps principles to AI. However, few businesses have the expertise needed to optimize AI consumption on their own.
That gap creates a clear opportunity for MSPs.
The challenge, as always, is developing the required skills. Demand for AI expertise continues to outpace supply, forcing many MSPs to invest in training existing employees. They must also compete with organizations actively recruiting professionals with proven AI experience.
The next wave of AI is here
Regardless of how MSPs acquire these skills, one thing is clear: the next wave of AI has arrived.
The first wave exposed a common problem. Many organizations underestimated the importance of strong data management practices, which led to inaccurate or inconsistent results.
The next phase will focus on helping AI agents interact with data more effectively. Organizations are already moving in that direction, leaving MSPs with a narrowing window to build the expertise needed to stay relevant.
Many businesses will likely turn to service providers rather than build everything internally. After all, context engineering platforms and AI routing tools are only part of a larger strategy.
Every dollar spent managing AI infrastructure is a dollar that can’t be invested in building applications that deliver business value. MSPs that can help organizations bridge that gap will be well positioned as AI adoption continues to mature.
Photo: Cagkan Sayin / Shutterstock
This post originally appeared on Smarter MSP.