Why there is no such thing as a “universal AI assistant”

Why is there no such thing as a “universal AI assistant”?

Because organizations are not universal.

The same question inside company X can mean something completely different to an HR specialist, an operations manager and a system administrator. “What does the expense approval process look like?” – for one person it means step-by-step instructions, for another a view of the budget status, for a third a compliance check against the finance policy.

The same procedure carries a different meaning depending on the role, the accountability and the moment at which someone is doing their work.

A universal AI assistant would have to simplify those differences or ignore them. And in an organization it is precisely the nuances that determine the quality of decisions.

McKinsey & Company’s report “The State of AI 2025” shows that companies achieving the greatest value from AI rollouts personalise their systems around specific roles and business processes. Where AI works in a general way, detached from the organizational structure, the impact on financial results and productivity is markedly lower.

Deloitte’s “Tech Trends 2025” report reaches similar conclusions, stressing that organizational context – including the structure of accountability and the specifics of processes – is a key success factor for AI projects. Technologies based on general models need an adaptation layer that grounds them in the realities of a specific company.

In practice this means one thing: the problem is not the absence of an answer. The problem is the absence of the right answer at a given moment – an answer matched to the person filling a particular role in a particular context.

A manager does not need the same information as an operations specialist. A system administrator should not see or interpret data in the same way as a front-line employee. If AI answers everyone the same way, it introduces a simplification that can lead to wrong decisions.

Gartner’s 2025 report additionally indicates that one of the main challenges in generative AI rollouts is the failure to match answers to the user’s context and level of accountability. Organizations that do not account for role differences when designing AI systems more often report a decline in trust in the tools.

A good AI assistant in a company should not be universal.
It should be context-aware.

It should understand:
– who is asking,
– in what role they are working,
– what permissions they have,
– what stage of the process they are at,
– what the purpose of their action is.

This means moving away from thinking of AI as an “all-knowing adviser” and towards a model of an assistant embedded in the organizational structure.

That is exactly the assumption askee was built on.

It is not a system that answers every user identically. askee works in the context of roles, projects, tasks and the rules that apply in the company. It understands that the same question can mean different things depending on where you sit in the structure.

As a result the answer is not merely linguistically correct. It is operationally adequate.

In a world where AI is becoming ever more available, the real advantage is not universality. The advantage is fit.

A universal AI assistant sounds attractive in marketing terms.
But in a real organization, value is created by a system that understands the differences – and acts accordingly.