What it really takes to be AI model independent

Artificial intelligence is still the Wild West. Every organization adopting AI is, in a sense, operating on someone else’s ranch.

Models, platforms and providers are evolving rapidly, and today’s market leader may not hold that position tomorrow. At its core, model independence recognizes that AI models are becoming interchangeable tools with different strengths, rather than technologies organizations should feel obligated to build around. The competitive advantage comes from matching the right capability to the right work at any given moment — not becoming attached to a single model or platform.

Rather than chasing every new release or trying to predict which provider will come out on top, CIOs should focus on building the capability to evaluate, route and adopt models as the technology changes. That starts with understanding how different models perform, knowing when to trust automated model selection and creating processes that can evolve as AI continues to change.

Start with the work, not the model

Model selection starts with a simple question: What am I trying to accomplish?

Every AI model is designed for different types of work, and not every task requires the same level of capability. Some models prioritize speed, while others are built for deeper reasoning. A simple factual question doesn’t require the same computing power as drafting a board memo, synthesizing several documents or optimizing a week’s worth of meetings to make the best use of an executive’s time. Many employees don’t realize those distinctions and will default to the best-known model regardless of what the work actually requires.

Anthropic’s Claude family illustrates this well. Haiku is designed to deliver quick responses to relatively straightforward requests. Sonnet balances speed and reasoning for many everyday business tasks, while Opus is intended for more complex analysis. Recognizing those differences allows organizations to match the right capability to the right work.

The same principle applies inside organizations. Leaders don’t assign every project to their most senior employee. They match the complexity of the work to the appropriate level of expertise. AI should be treated the same way.

This approach also has a direct impact on cost. Brown & Brown applies it in one of its own AI workflows. A lower-cost model orchestrates the agents that scan code to identify potential issues, while a more advanced reasoning model evaluates the highest-risk findings. Using Opus, for example, across every step of that process would be unnecessarily expensive. Instead, the organization gets the level of analysis it needs without paying for the most powerful model at every step.

Technology leaders should encourage teams to think in terms of capabilities rather than favorite models or platforms. Define the complexity of the work first; default to the least expensive model that meets the accuracy, quality and performance requirements, escalating only when additional reasoning or increased accuracy is needed. Just as importantly, evaluate success based on the accuracy and quality of the output rather than assumptions about which model should perform best.

As AI gets better at choosing models, people need to get better at judging results

One of the biggest changes in enterprise AI is happening behind the scenes.

Platforms like Microsoft Copilot and Claude feature “harnesses” that continually improve in their ability to evaluate a user’s request, determine how much reasoning it requires and automatically route it to the model best suited for the task without requiring the user to make every decision manually.

That doesn’t diminish the importance of understanding how different models behave. It changes where employees add value.

Rather than manually selecting a model for every request, employees need to recognize when the platform has made the right choice and when it hasn’t. Auto mode works well for many routine tasks, but it isn’t infallible. Users still need to evaluate whether the response meets the objective, determine when additional reasoning is warranted and recognize when the AI has misunderstood the request.

The same judgment applies as organizations build reusable prompts, AI skills and agents. For example, one of our employees learned this while using Claude to create a presentation slide. The instructions specified using a particular template, and the AI followed them exactly. The result technically met the request but produced a weaker slide than if it had been allowed to choose the format itself. The lesson was about recognizing when instructions — or assumptions — are limiting the quality of the output, not the model itself.

Organizations should also expect workflows to evolve. Model updates can change how AI responds, and accuracy, hallucinations, consistency and output quality still vary across models. Regularly comparing the same task across models, refining prompts and revisiting AI skills and agents help ensure the technology continues to produce the desired results.

As more of the model-selection process becomes automated, organizations should spend less time debating which model to use and more time developing employees who can evaluate AI output critically,recognize when intervention is needed and continually improve how AI is used.

Build an AI strategy that evolves with the technology

Periodically running the same workflow across multiple models allows technology leaders to compare output accuracy, quality, consistency, speed and cost and determine whether another model has become a better fit for a particular step in the process.

Those evaluations should extend beyond general-purpose foundation models. Industry-specific AI platforms, fine-tuned models and specialized SaaS providers may offer stronger performance for common business use cases because they have already configured model selection, data and workflows around a particular industry or function.

CIOs should look beyond the name of the foundation model and understand how vendors select, route and tweak models, how they evaluate new releases and how easily they can introduce another option. Model evaluation should remain an ongoing process, with critical workflows benchmarked regularly rather than only during the initial technology selection.

An AI roadmap should enable the adoption of stronger or more cost-effective models without rebuilding the workflows that depend on them.

Choose partners that think beyond today’s model

Most organizations — particularly small and midsized businesses — won’t build sophisticated model-routing systems themselves. They’ll rely on technology partners, SaaS providers and systems integrators instead.

When you’re evaluating an AI partner, know that the expertise behind the technology often matters as much as the technology itself. Some key ideas:

• Look for partners that already support organizations larger than your own and ask how they’re approaching model independence, model selection and resiliency. Those capabilities shouldn’t be nice-to-haves; they should be requirements.
• Don’t stop at asking which model they use. Ask what happens when that model changes — or when it’s unavailable.
• If an AI provider experiences an outage or releases an update that affects performance, can the partner route work to another model and keep critical business processes running?

Their answers to the above may tell you far more about the resilience of your AI strategy than the name of the model they’re recommending.