AI in ERP projects is getting useful before go-live. It can flag data quality gaps, process mismatches and user training risks during implementation, reducing the expensive rework that usually appears after launch.\n\nYou need to GetAgentIQ!\n\nLearn more at getagentiq.io
Finance teams still lose hours chasing project status across ERP workstreams. AI can flag migration risks, test gaps and cutover blockers early, giving steering committees facts before delays hit the plan. You need to GetAgentIQ! Learn more at getagentiq.io
ERP implementations often fail at the last mile: user adoption. AI can coach finance teams inside the workflow, explain exceptions in plain English, and reduce training drag, so new systems deliver value faster after go-live.
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Over the last 20+ years in finance systems, I’ve seen plenty of ERP programmes promise transformation and then get buried under customisations, workarounds, and manual effort.
That is why I think AI in ERP should be approached very carefully.
The opportunity is real, but it is not about bolting a chatbot onto the side of your finance platform and calling it innovation.
Bain recently reported that more than 80% of ERP transformations still miss budget, timeline, or value goals. That should be a warning to every CFO and transformation lead. If the core design is poor, AI will not rescue it. It will just help you scale bad process faster.
Where I see genuine value is in using AI much earlier and much deeper in the implementation lifecycle:
• surfacing process exceptions before they become control issues
• identifying data quality risks ahead of migration
• accelerating test script creation and regression testing
• improving user support with role-based guidance inside the flow of work
• reducing dependency on tribal knowledge during cutover and hypercare
Done properly, AI can help finance teams move towards a more touchless operating model, where ERP becomes a decision-support engine rather than just a transaction system.
But the sequence matters.
First standardise.
Then simplify.
Then automate.
Then apply AI where it improves judgement, speed, or control.
Finance leaders do not need more tech theatre. They need cleaner data, stronger processes, better adoption, and measurable outcomes.
That is where experienced finance transformation thinking still matters.
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Find out how we can help you navigate your AI adoption journey at getagentiq.io