The dominant story of AI in 2025 was "one capable assistant". The dominant story of 2026 is already different: most useful AI deployments look like a team of specialists, not a single polymath.
Nowhere is this more obvious than in the enterprise. And nowhere is this more invisible to Silicon Valley.
The warehouse floor is not a chat box
Spend a morning on the floor of a large distribution centre. You will not see a single person asking a general-purpose assistant to "improve our operations". You will see a shift lead tracing one specific stuck delivery. You will see a supervisor chasing a wave that failed overnight. You will see a putaway operator trying to figure out why their RF gun is refusing a bin.
Each of these is a precise, bounded question. Each one has an answer buried somewhere in SAP EWM, a human process, and a quirk of that specific site. The person asking doesn't need a polymath — they need an expert in one thing.
The enterprise has always looked like this
Enterprise software has spent three decades splitting the world into narrow, opinionated modules: a module for warehousing, one for transport, one for finance, one for procurement. The reason isn't architectural fashion. It's that a module that tries to do everything ends up serving no-one well enough to replace the software the business was already using.
Enterprise AI is now walking down the same road. The agents that work in real operations are the ones with a bounded job, a clear audit trail, and the right level of access to the systems that matter. The agents that fail are the ones trying to reason about everything at once.
Specialisation buys trust
There is a more practical reason to build narrow.
A general agent that can "help with anything" is impossible to evaluate. You cannot write regression tests for a surface area that is infinite. You cannot promise a compliance officer that it will never do a thing you haven't characterised. You cannot, as a buyer, put it on a change management plan.
A narrow agent — one that handles, say, wave failures in SAP EWM — is testable. It has a defined set of inputs, a defined set of tools, and a defined set of outcomes. You can write evals for it. You can measure accuracy. You can hold it accountable.
That's not a limitation. That's the price of admission into a real enterprise.
What this means for us
This is why Futuryntix builds named agents with defined scopes:
- Smart Warehouse does warehouse diagnostics on SAP EWM. Nothing else.
- CircleIQ handles EPR compliance for Indian producers. Nothing else.
- Logistics IQ watches SAP TM freight execution. Nothing else.
Each of them is boring in isolation. Together, they become a fleet — and a fleet of trustworthy specialists will outlast any one omniscient assistant.
The future of enterprise AI is narrow. Deliberately, insistently narrow. And that, quietly, is why it will win.