AI Can Save You Millions—If You Don’t Screw It Up

Everyone wants AI. Few actually know what to do with it.

Boards talk about ChatGPT, execs want dashboards that update themselves, and consultants promise “intelligent automation” at scale. But 70% of digital transformations still fail. The reason? People rush to buy tools before fixing the problems.

I’ve worked with companies who got it right—because they started with the process, not the pitch deck. Here’s how we drove successful AI and automation adoption—saving time, cutting waste, and keeping quality high.

1. Stop Looking for Magic—Start Solving Real Problems

AI isn’t a silver bullet. It’s just another tool. But when used right? It’s powerful.

Before deploying anything:

• We mapped the real pain points
• Ran workshops with users
• Collected actual data
• Identified repeatable, rules-based tasks that sucked time

Only then did we look at tools like RPA, Camunda, or AI platforms.

Example: With Orbital Witness (AI for legal document analysis), we started by understanding where lawyers wasted time—then restructured the workflow, built templates, and only after that deployed the tech to help.

“AI isn’t the answer. The problem is.”

2. Build the Right Foundation Before You Deploy

This step gets skipped a lot. That’s why most pilots flop.

Before automation goes live:

• Your processes must be mapped (BPMN 2.0, Lucidchart, Visio)
• Your data must be clean
• Your rules must be written down
• Your people must know the ‘why’

In our MMC automation projects, we built Process Definition Documents (PDDs) and Solution Design Documents (SDDs). We didn’t guess. We captured every step, tested it, then automated it.

The bots we launched covered:

• Payroll accuracy checks
• Finance form clean-up
• HR inbox triage
• Legal intake sorting
  And they actually worked—because they were built on solid ground.

3. Start Small, Win Fast, Scale Smart

The biggest mistake? Going big too fast.

We started with six Camunda automation pilots:

• Scoped tightly
• Approved through structured intake
• Delivered clear ROI
• Tracked results

Then—and only then—did we scale to more processes with a project governance layer on top.

At a well-known Telecommunications company, our automation programme included a repeatable intake pipeline, cross-functional project stage gates, and a review board to keep things focused and clean.

“Don’t scale what you haven’t proven.”

4. Put People First—Because AI Is Useless Without Them

Here’s the part most leaders miss: people make automation work.

We didn’t just launch tools—we trained teams:

• Lean Six Sigma coaching so they could spot waste
• Hands-on walkthroughs post-go-live
• Dashboards so they could see the impact
• Local champions embedded into every rollout

In one case, teams went from “this tool is scary” to “this saves me hours” in weeks—because they had ownership.

“AI doesn’t replace people. It removes the boring stuff so they can do the smart stuff.”

5. Measure What Matters—and Share It Loudly

If you don’t measure it, you’ll lose momentum.

We tracked:

• Hours saved
• FTE impact
• Accuracy improvement
• Complaint volume
• Satisfaction score changes

Examples:

• Payroll accuracy jumped to 99.8%
• Manual effort dropped by 5 FTEs
• 10,000+ hours saved across projects
• Employee satisfaction rose after automation was embedded correctly

These weren’t side effects. They were the point.

Final Thought: Don’t Automate for Tech’s Sake—Automate for Impact

“If it doesn’t solve a real problem, don’t automate it.”

AI and automation aren’t about hype. They’re about clarity, structure, and execution. Get those right, and you won’t just deploy AI—you’ll build an operation that’s faster, smarter, and built to scale.

Want to drive real automation results without burning out your team or budget?
I help businesses cut through the buzzwords and build automation that sticks.
Let’s talk.
👉 improvewithrobert.com | Schedule a meeting below.