LinkedIn·Tuesday, 18 August 2026·9d ago

Thinking about an AI Project? Here are 7 crucial data, workflow, and integration risks you need to evaluate before kicking off your next AI…

Sigli

13,192 followers

Thinking about an AI Project? Here are 7 crucial data, workflow, and integration risks you need to evaluate before kicking off your next AI initiative: 1. Messy, unreliable data The check: is your data actually accurate, complete, consistent, and up to date? The risk: garbage in, garbage out. If your foundation is weak, your AI will just produce highly confident (but entirely wrong) outputs. 2. Hard-to-access data The check: is the data you need actually available in the right systems and formats? The risk: your AI use case might sound brilliant in theory, but it will fall flat if the model can't practically access the information it needs to run. 3. Unclear workflows The check: is your process truly understood and followed consistently across all your teams? The risk: if your human teams are confused, your AI will be too. You’ll end up automating chaos instead of actually improving the process. 4. Disconnected systems The check: does information flow smoothly and reliably between all the tools involved? The risk: instead of upgrading your wider ecosystem, your fancy new AI just becomes another isolated, siloed tool. 5. Manual workarounds & duplicate data The check: are your teams constantly copying and pasting information between spreadsheets, tools, or systems? The risk: your AI is going to inherit all of those inconsistent inputs and broken handoffs, severely limiting its effectiveness. 6. Murky ownership & permissions The check: do you know exactly who owns the data, controls access, and is responsible for security and quality? The risk: your project stalls out completely, or worse, creates massive governance and compliance headaches down the line. 7. No clear role for the AI The check: do you know exactly where AI should step in to automate or assist, and where human judgment is still required? The risk: you build a shiny new AI feature that has absolutely no clear role in your team's day-to-day work. If several of these questions made you wince or were tough to answer, your project probably isn't ready for AI just yet. Don't panic! The best first step right now might just be doing some basic data cleanup, mapping out your workflows, integrating your current systems, or trying out simpler automation. Remember: AI readiness starts with business readiness. Always check the foundation before you approve the build.