For 2025, Merriam-Webster’s HUMAN editors chose SLOP as the Word of the Year.
They defined slop as “digital content of low quality that is produced usually in quantity by means of artificial intelligence.”
While we may think of AI Slop as the endless digital content intended to amplify doomscrolling on social media platforms, our focus at HandsFree is on how AI Slop is creating the next challenge for successful ERP projects.
The Problem
For almost ten years, we have been highlighting the abject failure rates of ERP projects. That topic is now in vogue with an endless parade of newcomers pointing out the obvious. We have moved on to providing practical tools, processes, and insights for ERP project success.
And now the AI push, the latest shiny object to distract from the simple fact that ERP projects are largely poorly executed.
Let’s be clear: AI can enhance the productivity of ERP and CRM platforms. However, it can’t fix fundamentally poorly implemented systems, and it's not a magic bullet.
AI agents layered onto a broken ERP don’t solve problems. They industrialize them.
Instead of fixing root causes like poor process design, bad data, or misconfigured workflows, AI agents automate the chaos, amplify errors at scale, and make failure faster, harder to trace, and more expensive to unwind.
This is where AI Slop is creating significant problems in the ERP and CRM space: expectation setting.
The difference between AI hype and reality created significant issues for AI vendors in 2025. Every good salesperson innately understands the value of under-promising and over-delivering. AI for ERP and CRM widely abused that rule in 2025. Continuing to set unrealistic expectations in 2026 will only further erode trust and slow the adoption of valuable AI capabilities.
AI Slop of the Year Award – ERP Category
Agentic ERP. A concept so laughable that it will require a separate blog.
The Troublemakers
The valuation of AI companies and their related infrastructure must eventually align with the reality of business adoption that drives meaningful revenue.
Without that, the AI circular financing scheme will eventually affect share prices and significantly impact AI publishers and the economy overall. Because companies that provide AI tools for businesses clearly understand this issue, the shit has to continue to roll downhill:
Software Publishers are under increasing pressure to demonstrate success, and AI revenue will have no choice but to aggressively hype what AI can do.
The Software Publisher Glazing Community makes money by perpetuating the myth that everything is excellent and getting better. I have sat in on the demos, attended the conferences, and read the endless articles (no doubt AI-generated for the most part). I know no one is highlighting the practical limitations of AI in the ERP and CRM space.
The Consultants are suddenly all AI experts with twenty years of experience. However, as we know from bitter customization experience in the ERP and CRM space, developers develop, and now AI consultants do AI stuff. Who asks the critical questions: Is this necessary? Is this addressing a symptom rather than the root cause?
A Practical AI Thought for ERP Leaders
Let's start with some fundamental assumptions:
- AI can be an incredibly productive tool in your ERP and CRM ecosystem.
- It is not a magic bullet. For executives from my generation, a helpful analogy for how AI can add value to an ERP and CRM platform is to think of it as “Macros on Steroids.
- AI will not fix a bad ERP or CRM implementation. There are AI tools available to help with successful ERP and CRM implementations, but no AI tool has yet been invented to undo a fundamentally flawed implementation.
And now the guidance.
Ignore both the hype of what AI can do and the stories of its failures.
Remember, the one fundamental truth of AI is that it will only provide results as good as the underlying data that feeds it.
Do not hesitate to aggressively pursue high-impact AI projects to increase the productivity of your ERP and CRM systems.
BUT…when evaluating an AI project, ask for the data that:
- Indicates this will be a success for your company.
- The AI solution will have to reference an optimal result.
AI is data, and bad data is bad AI.
