Find the work that repeats often enough to matter.
Ask your team where it spends time reading incoming information, copying details, categorizing requests, preparing summaries or updating systems. Look for tasks that recur regularly and consume meaningful effort. An occasional inconvenience may be a weaker first project than a modest task performed many times each week. Write down a few candidates before choosing one.
Describe the beginning, the end and the exceptions.
For each candidate, identify what starts the work, what information is needed and what a completed result looks like. Then ask what makes an instance unusual. Missing information, inconsistent documents and uncertain instructions can require additional handling. Understanding those cases helps you choose where AI can assist and where a person should make the decision.
- What information enters the workflow, and where does it come from?
- Which tools and people participate in completing the task?
- What makes an output useful enough to accept?
- Which situations need review or a different route?
Establish a baseline before making a change.
Record current volume, typical processing time, time spent correcting work and the people involved. Include waiting time if it affects the outcome. These observations do not need to be elaborate, but they should represent normal operations. Without a starting point, a faster-looking demonstration can be mistaken for a meaningful business improvement.
Match the method to the task.
Some steps need a reliable rule, such as routing an approved request to a defined owner. Others may benefit from AI interpreting text or preparing a draft. The complete workflow may need both, together with integrations and human review. Choosing tools after understanding the work helps keep the design focused on the result.
Measure completed work, including review effort.
Test the workflow against realistic examples, including the awkward ones. Compare total effort, useful output and quality with the baseline. Count time spent reviewing and fixing results, as well as ongoing operating costs. Those findings tell you whether to refine the workflow, expand it or reconsider the approach.
Use the first project to make the next decision better.
A focused first project gives your business evidence about where AI helps and what it needs to work well. Paramount Global Marketing combines AI workflow development with more than 10 years of complex cloud infrastructure experience. Tell us which task your team wants to improve and our sales representatives will follow up soon to discuss the opportunity.