40% of automation teams aren’t ready for AI — Here’s what they’re missing

AI’s not new. It’s just urgent now. Every roadmap, every board update — it’s in there. Leadership wants results, yesterday. So teams scramble, launching an AI assistant here, a predictive model there. Tack on a chatbot. Maybe a workflow or two. But the minute someone asks, “Is this actually working?” it all gets quiet.

That’s because most systems and teams just aren’t built for AI yet. The hype is running ahead of the architecture. According to Redwood Software’s “Enterprise automation index 2025,” nearly 40% of organizations admit they’re not ready to adopt or implement AI-driven automation. 

This isn’t about getting access to a model. It’s not even about building one. Readiness is the boring stuff under the hood: data quality, process consistency, orchestration, exception handling. That’s what makes AI useful (or useless).

Redwood’s data shows that fewer than 6% of companies have reached autonomous automation in any major business process. Most are still crawling, even in high-stakes areas like quote-to-cash. So when an AI tool misfires — maybe it reorders the wrong part, flags a non-issue or misroutes a service ticket — the real problem isn’t the AI. It’s everything feeding into it.

You invested in automation, but did you stabilize it?

More than 73% of companies say they increased automation spending last year. Only 36.6% feel ready to apply AI. That should tell you something. A lot of teams bought the tools, but the groundwork isn’t there.

If your workflows are still rule-based and brittle, your exception handling is half-human, half-spreadsheet and your APIs are duct-taped together from three systems ago, using AI won’t solve that. It’ll just scale the mess instead. This is what stalls pilots: not bad models — broken environments.

About your workflows…

You’ve got platforms everywhere. CRM, ERP, maybe a ticketing system for customer support or field service. They work (sort of), but they’re not exactly talking to each other.

You’ve got automation, but it’s scattered across tools. And it’s hard to know what’s actually happening when something goes wrong. Now imagine dropping AI into that mix and asking it to make decisions, in real time and with real impact. 

We’ve seen this movie. The pilot works, and the chatbot answers a few questions. The predictive model makes a recommendation. But there’s no feedback loop, no clean handoff, no way to measure if the output actually helped. So the pilot ends, and nobody wants to own it anymore.

Here’s what you’re skipping — and shouldn’t

Most AI efforts start with the use case. 

• Let’s automate onboarding
• Let’s forecast demand
• Let’s personalize support

Fine goals. But what about:

• Who owns the outcome when AI makes a choice?
• Can you see — and explain — how it got to that choice?
• What happens when something breaks at 3 AM?
• Can the system ask for help? Does it even know it needs to?

Governance. Data orchestration. Exception flows. Alignment with actual business outcomes.

These are boring, but when you skip them, you end up with a demo, not a system.

What the mature teams are doing

They’re not chasing shiny tools; they’re tuning the engine. Here’s what we see from the AI-ready crowd:

• End-to-end workflow automation that connects systems, not just apps
• Clean, version-controlled datasets that support real-time decision-making
• Clear governance rules that define what AI can (and can’t) do
• Exception handling that doesn’t depend on someone checking their inbox

Redwood customers who follow this approach are:

• 2x as likely to cut manual workloads by 50%
• 1.6x as likely to improve operational efficiency
• More likely to reduce costs by over 50%

These teams aren’t more enthusiastic about AI-powered solutions. They’re just more prepared.

Are you actually ready for AI?

It’s a fair question. Before you deploy another chatbot or predictive model, take a hard look at what’s underneath:

1. Are your business processes clearly mapped and automated?
2. Can your systems handle complex tasks — or just repetitive ones?
3. Are your AI models getting data they can actually trust?
4. Do your outputs link back to measurable outcomes?

AI won’t magically fix disorganized operations. But it will accelerate what’s already there — good or bad. If you haven’t mapped your exceptions, validated your inputs or built real-time visibility across systems, you’re not ready yet. And that’s okay. That’s fixable.

But don’t plug in AI and expect it to clean things up. Clean first, then scale.
Want to know how your automation foundation stacks up? Download the full report and benchmark your AI readiness.