AI as business infrastructure: when integration makes sense

AI becomes business infrastructure when a recurring workflow depends on it and someone is responsible for its operation. That is a design choice, not a reason to add a model to every process. A useful integration must justify its cost, handle bad inputs, and give people a clear way to intervene.

This guide presents a MyNew Technologies planning framework. The example is illustrative; it is not a report of measured customer savings or a promise that automation will outperform your current process.

Choose one workflow with a clear finish line

Start with a repeated task whose inputs and acceptable outputs can be described. Identify the person who owns the result and the system that holds the authoritative record. A workflow that changes every week may need process clarification before AI.

For example, an assistant might extract a requested date, business need, and missing details from an incoming enquiry. It could prepare a draft response for review. That is a narrower and more testable objective than “automate sales.” It should not invent prices, approve contracts, or promise availability.

Define the boundary between a suggestion and an action

• Read: which sources can the assistant access, and for what purpose?
• Draft: what may it suggest without changing the underlying system?
• Approve: which person reviews a proposed external action?
• Execute: which operations are permitted after that approval?
• Recover: what happens if a request times out, a person declines, or the result is wrong?

Use separate permissions for these stages. A generated instruction is not authorization to send an email, change a customer record, or spend money. Keep credentials outside prompts and logs; grant only the access the workflow needs.

Evaluate the whole task, including rework

Build a small evaluation set from representative, permitted examples. Include missing information, conflicting records, unusual formatting, and requests the system must refuse. Record whether each result was usable, required correction, or should have been escalated.

Compare total effort with the existing process. Include model usage, connected software, review time, exception handling, maintenance, and failed attempts. A faster draft is not a business improvement if checking it creates more work elsewhere.

Design for operation after the demonstration

Set usage limits, retain useful audit records, and define who responds to failures. Where actions can be retried, prevent duplicate messages or repeated updates. Give operators a way to disable the integration and continue manually. Review performance again when the model, source data, or connected application changes.

The NIST AI Risk Management Framework offers a broader reference for identifying and managing AI risks. This checklist does not establish regulatory compliance or certify an implementation.

Know when ordinary software is enough

If a fixed rule solves the task reliably, use that rule. If the data is unsuitable or nobody can review the output, pause the project. AI earns a place in infrastructure through a useful, supportable workflow—not through a claim that every organization must adopt it.

Explore our AI and software integration capabilities, or bring one workflow to a discovery discussion. Describe its inputs, owner, current difficulty, and the outcome you want to evaluate.