Adopting frontier AI

Context

Start by evaluating a specific task. The architecture guide explains when one model is sufficient and when a more complex approach may be worth testing.

Adoption guidance

1. Identify processes that need flexible reasoning

Look for work requiring context and judgement that has been difficult to automate:

complex analysis drawing on several sources;
creative tasks with ambiguous briefs;
decisions without a complete set of explicit rules;
translation between professional domains, such as technical and business communication.

2. Evaluate total cost

Compare simple automation, one model and any proposed routing or multi-agent design on your own tasks. Measure the cost of a completed case, including review and corrections. There is no universal 80/20 split for smaller and larger models. Self-hosting costs must include administration and infrastructure utilisation.

Maintain the ability to replace models, and repeat quality, security and compatibility tests after a change.

3. Educate management

Explain the difference between ad hoc conversation with an AI tool and integration into a repeatable workflow with assigned responsibilities and measurable outcomes. Ad hoc use can also have significant consequences, so its controls should reflect the actual task.

Advanced models may help with complex or ambiguous tasks. Validate that benefit in a pilot: the model’s label alone does not establish suitability or return on investment.

Security considerations

Data residency

Where is data processed: the EU, the US or other locations?
What contractual and technical commitments cover data location?
What retention applies at the provider?

Access

Who can use the tools and connected systems?
How are identities and permissions managed, including SSO and role-based access where appropriate?
What audit trail is available?

Sensitive data

Before transfer, establish whether the data is permitted for the purpose and service, and use secure transport. Transport encryption does not prevent a provider from processing the content.
Assess the contract, processing roles, transfers, subprocessors, retention and data-use settings. An “enterprise” label alone guarantees none of these.
Limit input data, tool permissions and outbound communication. Data loss prevention complements these controls; it does not reliably detect every possible disclosure.

Monitoring advanced models

In addition to operational monitoring, pay attention to:

Area	Consideration
Model updates	Track provider changes and their effect on quality and compatibility
False outputs	Repeatedly evaluate factual accuracy on relevant tasks
Bias	Monitor systematic disparities
Cost	Track current charges and cost per completed, reviewed task
Supplier dependence	Test alternatives and maintain a realistic migration route

Gradual rollout

The durations below are planning examples. Advance only when agreed quality, security and operational readiness criteria are met.