Companies rushing to adopt AI often face a deceptively simple question: when is a project really ‘done’?
The traditional mindset of deploy, hit ROI targets, and move on doesn’t always work here.
As Erik Delorey, Director of Innovation at Miratech, puts it when describing the problems of legacy approaches to tech investments:
“You invest this money, you get this ROI, and then you move on to the next project.
“And what happens in AI systems that aren’t maintained, optimized, or reinvested in? They’re going to drift.”
One problem is that AI isn’t just a piece of software; it’s an ongoing partnership between machine learning and human learning.
The technology continues to evolve, but the people using it often don’t.
Budgets get approved for deployment, yet follow-up training rarely happens.
As a result, systems continue to improve while the humans using them fall behind, creating a slow, almost invisible decay from that initial ‘wow’ moment when ROI looked promising.
Continuous Attention, Not One-Off Projects
For Delorey, it doesn’t matter whether you call it ongoing development, iterative deployment, or regular optimization; without “constant attention and investment,” your AI deployment will stall.
For CX leaders, the takeaway is simple: AI projects aren’t checkboxes; they’re living systems that need technical upkeep and human support to thrive.
And one of the keys to maintaining a healthy AI lifecycle is transparency.
“Getting reasoning visibility right is crucial,” Delorey explains.
“If your platform doesn’t offer it out of the box, push the vendor or build a custom solution. You need to see how key decisions are made.”
This isn’t just a technical nicety; it’s how organizations can spot gaps, adjust outputs, and make sure AI evolves with customer needs.
Reviewing reasoning data every two to three months, monitoring performance metrics, and retraining staff keeps the human-machine partnership alive.
Delorey suggests thinking of AI like a car: skip the oil changes, and you’ll pay later. Skip data updates, agent training, or attribute reevaluation, and the same principle applies.
Yet budgeting for ongoing AI investment is often the most challenging part. Initial ROI is easy to justify, but continuous improvement often gets overlooked, as Delorey explains:
“Companies get into subscription models to avoid annual maintenance fees, but where’s the 15% that should go back into continuous improvement?”
AI ROI as a Journey
