Everyone is talking about how AI helps organizations move faster. Almost nobody is talking about what happens six months after that.
A business user can now ask an AI assistant to build a workflow, generate code, design an integration, or automate a process and get something that actually works. We’ve seen it ourselves. The results are often surprisingly good.
That’s exactly what makes this worth talking about.
The risk isn’t that AI produces bad work. The risk is that AI produces work that’s good enough to deploy. And that’s usually where the technical debt starts.
A few years ago, building a custom process in Salesforce meant business analysis, solution design, development, testing, and a deployment plan. Today, someone types “how do I automate this process?” into an AI assistant and gets a working answer in seconds.
That’s a genuine breakthrough. It’s also a new kind of risk. The barrier to building something has collapsed, allowing organizations to create complexity faster than ever before.
AI is exceptionally good at solving the problem directly in front of it. Need a workflow, a formula, a validation rule, or a piece of code? It’ll get you there.
What it won’t tell you is everything that doesn’t fit in the prompt: your growth plans, your acquisition strategy, next year’s reporting requirements, the dependencies between departments, compliance considerations, the politics between two teams that don’t quite trust each other, or where this decision sits in your broader platform architecture.
None of that shows up in a prompt. All of it shows up later, when it’s the difference between a solution that scales and one that becomes someone’s problem to fix.
Technical debt rarely arrives all at once. It accumulates quietly.
A workflow gets deployed. A field gets added. An automation layer is added on top of last quarter’s automation. Each individual decision looks reasonable in isolation.
Then a new requirement arises, and the original design no longer fits. Teams start stacking solutions on top of solutions. Documentation falls behind. Ownership gets fuzzy. Eventually, the organization is spending more time managing the system than benefiting from it — and the irony is that almost every step along the way was made in the name of efficiency.
This is the opportunity we keep coming back to with clients: governance.
Most organizations are adopting AI faster than they’re building any process to review or validate what it produces. That’s understandable; the technology is moving quickly, and nobody wants to slow down to write a policy. But the need for oversight hasn’t gone anywhere. If anything, it’s grown.
AI can accelerate execution. It can’t replace accountability. Someone still has to ask the questions that don’t show up in the output itself: Is this the right solution? Will it scale? What are the downstream impacts? What assumptions is it quietly making? Does this align with where we’re actually headed as an organization?
Those questions don’t go away just because an answer arrived in five seconds instead of five weeks.
At Appluent, we think AI is one of the most important shifts we’ve seen in this industry, and we tell every client to embrace it. We use it every day ourselves.
But effectively adopting AI takes more than just generating answers quickly. It takes governance, architecture, business context, and the kind of experience that comes from having watched a shortcut turn into a six-month rebuild.
The organizations that win won’t be the ones moving the fastest. They’ll be the ones who pair speed with judgment.
Because the goal was never to build something quickly. It’s to build something you’re still glad you built a year from now.