The Maslow’s hierarchy post resonated with a lot of you, so let me break down the actual framework. Five layers that build on each other, and if you skip one, the whole thing falls apart. But here’s what’s different now, LLMs can contextualize in ways traditional automation never could, which means we can be a bit more forgiving about perfect standardization.

Simplify is still the foundation. Before you automate anything, before you even think about AI agents, you need to eliminate as much of the unnecessary complexity in your processes. This isn’t glamorous work. It’s saying no to exceptions, consolidating workflows, and removing steps that exist because “that’s how we’ve always done it.” The difference now is that LLMs can handle some variability that would have broken traditional automation, but you still can’t build on chaos.

Standardize comes next. Once you’ve simplified, you need consistency. Same inputs, same outputs, every time. This is where you document what actually happens versus what you wish happened. Here’s where the technology helps, LLMs can work with ambiguity better than rule-based systems ever could, so your standardization doesn’t have to be perfect to start seeing value.

Digitize is where your standardized processes move into systems. Not spreadsheets, email, or analog paper processes. Real systems of record that capture your business logic and make it accessible. The context window of modern LLMs means they can pull from messy data sources and still make sense of it, but you still need that data captured somewhere.

Automate is the layer most people jump to first, and that’s the mistake. Automation only works when the three layers below are solid. This is your RPA, your integration layer, your APIs and data warehouses, your MCP connections. You’re orchestrating the digital processes you built. LLMs make this more resilient because they can handle edge cases and inconsistencies that would crash traditional workflows.

Agentic sits at the top. This is where your AI agents actually deliver value because they have clean, standardized, digitized, automated processes to work with. The ability to contextualize means your agents can navigate imperfect data and still execute, but without the foundation, you’re just building expensive demos.

I’m going to break down each layer in detail over the next few posts. Starting with Simplify, because that’s where most AI strategies actually fail, even with all the flexibility LLMs provide. More coming soon.

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