It’s 2030. Every tool in your stack has AI running about. Your CRM cleans itself. Your accounting system reconciles daily, no questions asked. Billing? Fully automated. Chatbots handle 90% of customer support by pulling directly from your internal systems. Maybe, just maybe, there’s even a retro-style, all-in-one AI-driven ERP that actually works. Shrug. You still have to make the hard decisions.
But it’s not 2030. It’s today. And if you’ve been building for a few years, you’re already buried under a pile of clunky tools, shadow spreadsheets, and black-box data. Everyone’s talking about AI, but in reality? Your data team, RevOps team, and accounting team are still spending hours reconciling numbers across disconnected systems. You’re in legacy tech hell, and you start to dream of the clean-slate world where “if we just started now…” everything would be seamless.
The truth? You shouldn’t wait. Your competitors aren’t. You need to do two things:
Start using what AI is actually good at right now.
Future-proof your setup so when/if AI becomes fantastic, you can plug and play.
To do both of these, you need to structure your data. The standard advice? “Modernize core systems. Upskill staff. Partner across business units. Establish governance.” (Thanks, ChatGPT.) It’s fine advice. But it’s also slow. Meanwhile, decisions need to be made.
The shortcut? Build a top layer that ingests the data from all your tools. Run AI on the top layer. Not another siloed point tool, but an all-encompassing overview that ingests your data and makes it speak. Ideally, it comes with AI capabilities already embedded and a company focused on keeping it current.
This allows you to use AI now to reconcile data, build reports, and generate analysis. It will also allow you to future-proof your setup by cleaning up any data errors, or fixing them quickly as they occur. 2030 isn’t going to fix “Bad data in, bad results out.”
Sure, I’ll throw Abacum into the ring. But whether you buy something or build it yourself, here’s the simplest version of what you can start doing right now:
Get a tool that ingests your systems’ data. Or, if you’re scrappy, automate exports and push them into a central database. Set up APIs into the current models.
Use AI to flag anomalies, automate reporting, and reduce manual effort across the stack. When something breaks, give immediate feedback to the culprit to fix it immediately.
Keep iterating. Better data → better reporting → better systems.
Either way, there is urgency and it won’t “just happen.” You need to take the steps now to bring it into your organization so that you can make better decisions now and set yourself up for later.
