Enterprise Information Systems in the Age of AI

An AI assistant can help draft a customer response or summarize a job record. Its usefulness in a business process depends on more than the quality of the draft. It also depends on whether the customer record is current, whether the job status is reliable, and whether the person reviewing the output has the information needed to act.

For organizations exploring AI, this creates a practical starting point: examine the operating system around the proposed use case. The records, responsibilities, and controls behind the work deserve as much attention as the assistant itself.

Start with an Operational Question

“We want to use AI” describes an interest. “Our coordinators spend too much time assembling the information needed to respond to a customer” describes a problem that can be investigated.

The second statement lets a team identify the records involved, observe the current effort, and define a useful output. It also makes it easier to decide whether the task needs AI, a simpler automation, or a change in the process.

Choose one recurring task with a clear owner. Document what starts it, what information it requires, and what a correct result looks like.

Decide Which Records Can Be Trusted

Suppose a customer asks when a service visit will occur. A calendar, a dispatcher’s message, and a work order may show different times. Generating a fluent response does not resolve that disagreement.

The business first needs a rule: which record is authoritative, who may update it, and how a change reaches the people who need to know? The same questions apply to prices, inventory availability, approved scope, and completion status.

For a pilot, create a short information map. List each required fact, its source, its owner, and the conditions that make it unsuitable for use. Missing or conflicting information should trigger a review rather than a confident guess.

Separate Assistance from Authorization

Drafting an explanation and approving a transaction are different responsibilities. A proposed customer message may be ready for a coordinator to review, while a price change may require a manager’s approval.

Define what the assistant may read, what it may propose, and which actions require a person to approve them. Keep those boundaries aligned with the user’s responsibilities. An assistant should not become an indirect route around the controls that apply to the underlying process.

This distinction is especially useful when a workflow includes external communications, changes to commercial terms, or updates to records other teams rely on.

Test the Difficult Cases

A useful evaluation includes more than complete, straightforward records. Try a canceled visit, two customers with similar names, missing authorization, an outdated document, and a record the user should not access.

For each scenario, agree on the expected behavior. Sometimes the correct output is a draft. Sometimes it is a request for clarification or a referral to the responsible person. Review whether the assistant identifies the relevant source and handles uncertainty appropriately.

Measure the effort of checking and correcting the output alongside the time spent producing it. Fast generation has limited value if review becomes more demanding than the original task.

Build the Next Improvement from Evidence

A pilot can reveal problems beyond the AI component: inconsistent records, unclear ownership, or missing steps in the workflow. Resolving those gaps can improve the operation even before the assistant becomes part of daily use.

InnovanSIS approaches applied AI as part of a connected business system. We begin with the process, the information, and the responsibilities, then evaluate where assistance or automation can contribute.