Everyone is “doing AI” right now.

Copilot licenses are being rolled out.
ChatGPT pilots are running.
Forecasting models are being tested.
Dashboards are being redesigned.

But when I speak with mid-market CEOs and CFOs, the story is the same:

“We’ve experimented with AI… but we’re not seeing real impact.”

Here’s the hard truth:

The issue isn’t AI.

It’s the operating system underneath it.

What I See Repeatedly in Mid-Market Companies

Across manufacturing, healthcare, logistics, and services, the pattern is consistent:

• ERP data doesn’t match finance reports
• Teams reconcile numbers manually in Excel
• CRM, operations, and accounting don’t align
• Every department has its “own version” of the truth
• Reporting cycles lag behind real decisions

Then AI gets layered on top.

When the model produces questionable outputs, trust erodes.

AI doesn’t fix messy data.

It amplifies it.

AI Is Being Treated Like a Feature — Not Infrastructure

In many mid-sized companies, AI shows up as:

• A predictive dashboard
• A chatbot
• A sales forecasting model
• A proof-of-concept pilot

But the workflows don’t change.
Decision ownership doesn’t change.
KPIs don’t change.

So AI becomes a demo.

Not leverage.

Scaled AI requires:

• Clean, automated data pipelines
• A unified reporting model
• Governance and security
• Defined decision frameworks
• Process redesign

That’s not a data science problem.

That’s a data engineering and operating model problem.

The Talent Reality No One Talks About

Large enterprises have AI centers of excellence.

Most mid-market firms have:

• One IT generalist
• An infrastructure-focused MSP
• A Power BI “power user.”

That’s not enough to industrialize AI across finance, operations, and sales.

And hiring a full AI department internally is rarely realistic.

So pilots stall.

Momentum fades.

Budgets shrink.

What Actually Works

In our experience at Fuzzitech, scaled AI in the mid-market follows a very clear path.

Fix the Data Backbone First

Before talking about models:

• Integrate ERP, CRM, MES, and finance
• Eliminate reconciliation at the source
• Build automated pipelines
• Standardize definitions
• Create a trusted reporting layer

When trust improves, adoption improves.

Without trust, AI dies quietly.

Design Around Decisions — Not Algorithms

Instead of asking:
“What model should we build?”

Ask:
• Which decisions move the margin daily?
• Where is working capital trapped?
• Where does scheduling break down?
• Where do delays create risk?

Then embed AI into:

• Production scheduling
• Inventory optimization
• Sales forecasting
• Quality alerts
• Cash-flow visibility
• Patient throughput (in healthcare)

AI should live inside workflows.

Not in a side dashboard, no one checks.

Move From Pilot to Platform

AI that scales isn’t a one-time project.

It’s:

• Reusable architecture
• Secure cloud foundation
• Standardized ingestion
• Modular AI services
• Ongoing optimization

That’s how companies move from experimentation to operational leverage.

The CFO Perspective (This Is Where It Gets Real)

CFOs don’t care about LLMs.

They care about:

• Margin expansion
• Reduced downtime
• Faster close cycles
• Lower inventory carrying costs
• Better forecasting accuracy
• Reduced labor inefficiency

If AI doesn’t move one of those metrics, it won’t survive the next budget review.

The Bottom Line

Mid-size companies aren’t struggling because AI doesn’t work.

They’re struggling because their data foundation and operating model weren’t built for scale.

AI is not a feature.

It’s an operating layer.

And building that layer requires:

• Practical data engineering
• Clean architecture
• Decision-driven design
• A scalable delivery model

That’s where real, measurable value gets created.

 

If you’re leading a mid-market organization and wondering why AI hasn’t delivered ROI yet, I’d love to hear what you’re seeing internally.

Where is AI getting stuck in your organization — data, trust, adoption, or ownership?

Let’s make AI operational — not experimental.

Rizwan Khan
Founder & Managing Partner
Fuzzitech
Helping Mid-Market Companies Turn Messy Data into AI-Ready Operating Systems