The model is only half the story. Let’s talk about your institutional knowledge.

If an AI workflow produces polished, plausible work with the wrong institutional standards, is it still useful?

If a new employee did the same, the answer would probably be “no,” And more experienced colleagues would train them, review the work, and explain the exceptions and nuances of why certain decisions were made.

As models become better at building and maintaining agents and workflows, business leaders may assume much of that institutional effort is irrelevant. The engineering burden may fall dramatically, but the management burden does not.

Anthropic's own research on agent autonomy makes the distinction clear. Software engineering is comparatively amenable to autonomous work because code can be tested before release. In finance, law, and other domains where verifying an output may require the same expertise as producing it, the transition may be slower or take a different form.

This reveals a critical constraint that all leader should be assessing as they develop their AI strategies.

A specialist partner must do more than build

Models will increasingly write integrations, construct workflows, generate tests, diagnose failures and implement repairs. Model providers, cloud companies, and enterprise platforms will supply more of the routing, monitoring, and orchestration beneath them.

1. Enable the institution to define what “good” looks like.

A small group of senior business owners and subject-matter experts must ultimately define and approve what acceptable work means. A strong partner will not allow this responsibility to remain diffuse.

2. Turn that standard into an operating playbook.

The partner must translate examples, exceptions and institutional judgment into workflow logic, evaluations, escalation rules and evidence requirements. This is how knowledge held by a few experienced people becomes reusable operating capacity.

3. Remain accountable for performance over time.

The work does not end at first deployment. Models change, policies evolve and new edge cases appear. A specialist partner should operate the workflow, judge outputs against the institution's approved standards, investigate failures and keep the system reliable at a predictable cost.

Models will improve, but the context still matters

Perhaps the model will soon do most of this too.

It will interview subject-matter experts, infer rules from historical decisions, identify inconsistencies, generate evaluations and monitor its own outputs. Firms with strong internal leaders may be able to use those capabilities without a specialist partner.