By Shaun Modi, CEO, Capitol AI

Recent discussion around the ‘AI boom’, for lack of a better term, argues the case that AI is being turned into a lifeline for management consultants, rather than the doom and gloom of its existential threat to the sector that many predicted. Consulting firms such as McKinsey, Boston Consulting Group, and Capgemini partnering with OpenAI and Anthropic is not surprising, and for now, makes strategic sense. As companies struggle to translate AI experimentation into operational value, consultancies partnering with AI model developers aim for fast and more scalable AI deployment at the enterprise level. The alignment, for the short term, is completely understandable. 

After all, enterprises want speed, and model companies want distribution. Consulting firms can provide both: they have deep domain expertise into industry specific workflows, performance metrics, and procurement cycles. Acting as the implementation layer in between is their natural habitat.

But as is often the case with short-term gains, they rarely translate into lasting advantage. 

AI differs from previous advances in technology because its real-world applications are so diverse, and its full potential is still to be discovered. So, when an organisation’s AI capability becomes tightly coupled to a single model provider, it also inherits that provider’s roadmap and operational decisions, giving little wiggle room for diversification on a case-by-case basis. In an early experimentation phase the dependency on a single model is manageable, but when AI is required to be embedded in core operations, it becomes far more problematic to be ‘locked in’.

This is particularly true in high-stakes sectors such as defense, financial services and other regulated industries.

In these environments, model choice is a requirement of operational integrity and security, without the luxury of a trial-and-error approach. In these arenas, organisations need the ability to switch providers simultaneously and run multiple models in parallel without rebuilding an entire framework.

That is why the next phase of the market will learn that it must shift away from model-centric partnerships and move toward model-agnostic infrastructure. The firms that separate orchestration from any single model will ultimately have more leverage. After all, AI models aren’t created equal, they all have unique strengths. The ability to plug different models into the same workflows, optimise for cost or performance, and adapt as the technology landscape evolves is significant. And the landscape will evolve quickly. Agnostic models are improving rapidly, competition is intensifying, and pricing dynamics are still unstable. In high-stakes industries, betting too heavily on a single provider risks locking organisations into a dependency that may become expensive or strategically constraining at best, and cause serious operational failure at worst.

The consulting firms that recognize this early will likely be more durable than those whose value is tied to a single model relationship. Already, we are seeing the likes of McKinsey adapt its usual team model to include more engineers in their offering to clients, but this is not enough. Infrastructure and governance tends to outlast tech hype cycles, and this distinction will be pivotal when AI moves from experimentation to mission-critical infrastructure.