Tools are only as good as their inputs
Automation and AI both run on information. If customer records are duplicated, product codes are inconsistent, or key details live only in someone’s head, even a well-built system will produce poor results. Usually the first improvement isn’t new technology at all. It’s getting the information in order.
The good news: data doesn’t need to be perfect. It needs to be good enough for the specific process you want to improve.
Five questions to ask
- Where does it live? List every place the information for this process is kept: software systems, spreadsheets, inboxes, paper.
- Is it consistent? Are the same things named the same way everywhere, such as customer names, part numbers, and job types?
- Is it complete? Are required fields reliably filled in, or are there frequent blanks and “see notes” entries?
- Can systems reach it? Do your tools offer exports or connections, or is the data locked inside?
- Who owns it? Is someone responsible for keeping it accurate going forward?
Structured, unstructured, and both
Structured data, such as fields in a database or columns in a spreadsheet, is ideal for traditional automation. Unstructured information, like emails, PDFs, and call notes, is where AI can help by reading it and turning it into structured fields. Many real processes have both, which is why a combined approach is often the best fit.
Quick wins before you automate
- Agree on standard names and codes for the things you track most.
- Make the truly required fields required.
- Retire duplicate spreadsheets that compete with your main system.
- Write down the unwritten rules your best people follow.
- Pick one source of truth for each type of information.
An afternoon spent cleaning up a process can save weeks of building around its problems.
Not sure where you stand?
Our free AI readiness check includes questions about your data and points out where to focus first. Or tell us about your process and we’ll help you size up what’s needed.
