The Pattern I Keep Seeing
I’ve been running a marketing agency since 2009. Over sixteen years, I’ve worked with hundreds of clients across dozens of industries. From healthcare, manufacturing, SaaS, retail, finance, and professional services.
And here’s what I’ve learned: the same workflows break in the same ways, regardless of industry.
A SaaS company struggling to scale content production has the same structural problem as a manufacturing firm trying to manage customer onboarding.
The symptoms look different. The root cause is identical.
Broken workflows wrapped in duct tape and good intentions.
For years, I helped clients optimize these workflows. Better project management. Clearer processes. More efficient handoffs. It worked, to a point.
But there was always a ceiling. A limit to how much you could improve a system that was fundamentally designed for a different era.
Then AI arrived.
And I watched every single client make the same mistake.
They bolted AI onto broken workflows and expected transformation.
They added ChatGPT to their content process without redesigning how content moved through the company’s organizational structure.
They automated email sequences without fixing the underlying customer data architecture. They deployed AI tools without changing how teams actually worked.
The result?
Chaos with better sentences.
That’s when I realized: the bottleneck wasn’t the AI. It was the system the AI was being plugged into.
So I started building ContentHub OS. not as another AI tool, but as a system designed from the ground up to model how work actually flows through a content business.
Not automation. Structure.
Because here’s what I learned after sixteen years and hundreds of clients:
You can’t automate chaos. You can’t scale broken processes. And you can’t bolt intelligence onto a system that wasn’t designed to support it.
Now, McKinsey just released data that confirms exactly what I’ve been seeing.
The Data Confirms It
McKinsey’s November 2025 State of AI report dropped last week, and the numbers are stark.
88% of companies say they’re using AI.
Only 39% report any bottom-line impact.
Let me say that again: nearly 9 out of 10 companies have adopted AI. But fewer than 4 out of 10 are seeing actual financial results.
That’s not a technology problem. That’s a structure problem.
Here’s what else the data shows:
- 67% are still stuck in pilot mode — experimenting but never scaling
- 64% say AI has improved innovation — but can’t point to revenue or cost reduction
- Only 6% qualify as “high performers” — companies seeing both significant EBIT impact (>5%) and meaningful value from AI
Translation: Everyone has AI. Almost no one has results.
The Intelligence Gap
This isn’t about access to technology. Every company can spin up ChatGPT, Claude, or Gemini. Every company can buy AI tools. Every company can hire consultants to build them an “AI strategy.”
But here’s what McKinsey found separates the 6% from everyone else:
High performers aren’t using better AI. They’re using AI better.
And the difference comes down to one thing: they redesign workflows instead of automating them.
The Real Problem (And Why Most Companies Get It Wrong)
Most organizations approach AI by asking:
“Where can we plug AI in?”
The 6% who are winning ask:
“How do we rebuild this process so AI actually works?”
That’s the difference between automation and transformation.
Automation vs. Transformation
Automation = Taking an existing workflow and making it faster Transformation = Redesigning the workflow so it works fundamentally differently
Here’s an example:
Automation approach:
- You have a content process: Idea → Draft → Edit → Approve → Publish
- You add AI to speed up drafting
- The workflow stays the same, just faster
Transformation approach:
- You realize the bottleneck isn’t drafting — it’s the approval loop
- You redesign the process: Idea → Draft → AI Review → Publish (approval happens via system permissions, not email chains)
- The workflow is structurally different
The automation approach makes a broken system 10% more efficient.
The transformation approach fixes the system.
Why Most Companies Choose Automation
Because transformation is harder.
It requires:
- Admitting the current system is broken
- Convincing leadership to invest in redesign, not just tools
- Getting teams to change how they work
- Building new processes from scratch
Automation feels safer. Faster. Less disruptive.
But here’s the problem: you can’t automate your way to intelligence.
What High Performers Do Differently
McKinsey didn’t just identify the gap. They identified what the 6% are doing that everyone else isn’t.
1. They Redesign Workflows
High performers are 2.8x more likely to fundamentally redesign their workflows — not just automate them.
What does this look like in practice?
Example: Customer support
Everyone else: Add AI chatbot to handle common questions, escalate complex ones to humans
High performers: Redesign the entire support system so AI handles triage, routing, knowledge retrieval, and ticket summarization — while humans focus entirely on complex problem-solving and relationship management
The difference? The first approach makes the old system slightly better. The second approach creates a new system where AI and humans each do what they’re best at.
2. They Set Growth Goals, Not Just Efficiency Goals
80% of companies focus AI on cost reduction.
High performers use AI for innovation and revenue growth in addition to efficiency.
Why does this matter?
Because when you only optimize for cost, you’re asking: “How do we do the same thing cheaper?”
When you optimize for growth, you’re asking: “What can we do now that we couldn’t do before?”
That’s the difference between incremental improvement and actual transformation.
3. They Invest Like They Mean It
High performers spend >20% of their digital budget on AI.
Everyone else spends ≤10%.
You can’t half-ass transformation. You either commit the resources to redesign the system, or you end up with expensive pilots that never scale.
4. They Have Leadership Buy-In That Actually Shows Up
High performers are 3x more likely to have senior leaders who actively champion AI adoption — including role modeling its use.
This isn’t about executives saying “AI is important” in an all-hands meeting.
This is about executives using AI tools themselves, asking how workflows are being redesigned, and holding teams accountable for transformation, not just adoption.
5. They Define When Humans Override AI
High performers are more likely to have clear processes for when model outputs need human validation.
They’re not blindly trusting AI. They’re designing the handoffs.
This is critical. Because AI will make mistakes. The question isn’t if it will fail — it’s what happens when it does.
High performers have systems in place to catch errors, validate outputs, and ensure humans are in the loop at the right moments.
The Framework I Use (And Why It Works)
After working with hundreds of clients and building ContentHub OS, I’ve developed a framework for how to think about this.
I call it Organizational Intelligence.
What is Organizational Intelligence?
Organizational Intelligence is when your systems are smart enough to remember their own logic, predict their own needs, and improve their own workflows over time.
It’s not about having AI tools. It’s about having a system that thinks.
And to get there, you need three roles working together:
The Three Roles of Organizational Intelligence
1. The Architect — Designs the logic and structure of the system
This person (or team) maps out how work should flow, where decisions get made, what rules govern the process, and where AI should intervene vs. where humans should override.
2. The Operator — Manages outcomes and oversees performance
This person monitors the system, tracks results, identifies bottlenecks, and makes adjustments. They’re not managing people — they’re managing the intelligence layer of the organization.
3. The Automator — Connects the tools that carry out the logic
This person implements the technical infrastructure — the workflows, integrations, AI models, and systems that execute what the Architect designed and the Operator monitors.
Why This Framework Works
Because it separates thinking from doing.
Most companies conflate the two. They hire someone to “implement AI” and expect them to also figure out how the system should work.
But you can’t automate what you haven’t architected.
And you can’t architect if you don’t understand what the system is supposed to achieve.
The Architect designs the system. The Operator monitors the system. The Automator builds the system.
When all three work together, you get Organizational Intelligence — a system that doesn’t just execute tasks, but understands why it’s doing them and how to improve over time.
What This Means for You (The Audit)
If you’re using AI but not seeing results, the fix isn’t a better model.
It’s a better system.
Here are five questions to ask before you add AI to anything:
1. What problem are we actually solving?
Most AI implementations fail because they’re solutions looking for problems.
Before you add AI, get clear on the actual bottleneck. Is it speed? Quality? Scale? Decision-making? Handoffs?
If you can’t name the specific problem, AI won’t solve it.
2. Are we automating or transforming?
If your plan is “take this existing process and add AI to make it faster,” you’re automating.
If your plan is “redesign this process so AI and humans each do what they’re best at,” you’re transforming.
Automation is fine for incremental gains. Transformation is what gets you to the 6%.
3. Do we have the Architect / Operator / Automator roles in place?
If you don’t have someone thinking strategically about how the system should work, someone monitoring whether it’s working, and someone building the technical infrastructure — your AI implementation will fail.
You don’t need three separate people. But you need all three functions covered.
4. Have we defined when humans override AI?
AI will make mistakes. The question is: what happens when it does?
If you don’t have a clear process for validation, escalation, and human override, you’re setting yourself up for chaos.
5. Are we optimizing for cost, or for capability?
If your only goal is “do this cheaper,” you’re not thinking big enough.
The companies winning with AI are asking: “What can we do now that we couldn’t do before?”
That’s the difference between incremental improvement and actual transformation.
What Redesigning Actually Looks Like (A Real Example)
Let me show you what transformation looks like in practice.
The Old Content Workflow (Automation Thinking)
- Idea submitted via Google Form
- Draft written (by human or AI)
- Draft sent to editor via email
- Editor reviews, sends feedback via email
- Writer revises
- Draft sent to approver via email
- Approver approves or sends back for more revisions
- Final version sent to publisher via email
- Publisher schedules in CMS
Bottlenecks:
- No visibility into where content is stuck
- Approval loops happen via email (slow, untrackable)
- No connection between ideation and performance
- No system memory (every piece starts from scratch)
AI “solution” most companies try: Add AI to step 2 (drafting) to speed up writing.
Result: Content gets written 30% faster, but still takes 2 weeks to get approved because the bottleneck wasn’t drafting — it was the approval loop.
The Redesigned Workflow (Transformation Thinking)
- Idea logged in system with metadata (topic, audience, goal, format)
- AI generates draft and performance prediction based on past content
- Draft auto-routed to editor based on topic/expertise (no email)
- Editor reviews inline, with AI-suggested improvements based on brand voice analysis
- System tracks revision velocity and flags if something is stuck >24 hours
- Approval happens via permissions (not email) — approvers see dashboard of pending content
- AI suggests optimal publish time based on audience engagement patterns
- Content published, performance tracked, insights fed back into system
What changed:
- Structure: The system knows what stage each piece is in
- Visibility: Everyone can see where bottlenecks are
- Intelligence: The system learns from past performance and suggests improvements
- Efficiency: Approval happens in a dashboard, not an email thread
- Memory: The system remembers what worked and applies it to future content
AI’s role:
AI isn’t just “writing faster.” It’s:
- Predicting performance
- Suggesting improvements
- Routing work intelligently
- Flagging bottlenecks
- Learning from outcomes
Result:
Content goes from idea to publish in 3 days instead of 14, and performs better because the system is learning from every piece.
That’s the difference between automation and transformation.
The first version makes one step faster.
The second version redesigns the entire system so it’s smarter, faster, and continuously improving.
Where This Is Going
Here’s what I believe:
The next decade of business will be defined by Organizational Intelligence, not AI tools.
The companies that figure out how to build systems that think — not just execute — will dominate their industries.
The companies that don’t will be stuck in pilot purgatory, endlessly experimenting with AI tools that never scale.
Why Structure Is the New Competitive Advantage
For the last 20 years, the competitive advantage was speed.
Move fast. Ship fast. Iterate fast.
But now, everyone has access to the same AI tools. Everyone can move fast.
The new competitive advantage is structure.
The companies that can design systems where AI and humans each do what they’re best at and where workflows are intelligent, not just efficient, those are the companies that will pull ahead.
What This Means for You
If you’re a founder, operator, or business leader, here’s what you need to do:
- Stop thinking about “AI strategy” and start thinking about system redesign.
AI isn’t a strategy. It’s a tool. The strategy is how you rebuild your organization to take advantage of it.
- Invest in the Architect role.
You need someone thinking strategically about how work should flow, where AI should intervene, and where humans should override. If you don’t have this, your AI implementations will fail.
- Measure transformation, not adoption.
Don’t track how many people are using AI tools. Track whether workflows are actually improving. Are things moving faster? Are decisions getting better? Are outcomes improving?
- Build systems that remember.
The difference between a tool and intelligence is memory. If your workflows don’t have memory and every project starts from scratch , then you’re not building intelligence. You’re just automating.
- Be willing to break things.
Transformation requires breaking the old system and building a new one. That’s uncomfortable. But it’s the only way to get to the 6%.
Final Thought
McKinsey’s data is clear:
88% of companies are using AI. Only 39% are seeing results.
The gap isn’t about technology. It’s about structure.
The companies that figure this out, that redesign their workflows, invest in Organizational Intelligence, and build systems that think — will be the ones that dominate the next decade.
The companies that don’t will keep chasing tools, burning budgets on consultants, and wondering why their pilots never scale.
You can’t automate chaos.
You can’t scale broken processes.
And you can’t bolt intelligence onto a system that wasn’t designed to support it.
But if you’re willing to do the hard work and to redesign, not just automate, then you can build something that actually works.
That’s what I’m building with ContentHub OS.
And that’s the shift I’m writing about here.
Because the future isn’t about having better AI.
It’s about building better systems.
✌️ Audra