The real cost of treating AI like a tool rollout instead of a business redesign.
Most companies are completely missing the mark on AI.
They buy a batch of licenses, tell the team to go “experiment,” and then wonder why the bottom line
hasn’t shifted.
That’s treating AI like a tool rollout. It’s not. It’s a business transformation that happens to involve
technology.
If you want real business benefit, you have to stop looking at the tools and start redesigning how work
actually gets done.
When advising middle-market businesses on how to move past the playing phase, this is the framework we use to shift their execution:
1. Break the Silos
Departmental AI initiatives are a trap. Marketing is playing with content, sales is playing with email automation, and finance is analyzing data but nobody is swapping stories. The real value is cross- functional. It’s what happens when data and processes intersect between departments.
2. Capture the “Unknown Knowns”
Before touching a single tool, get the team in a room with a whiteboard and map the existing workflows.
Look for the tribal knowledge trapped in people’s heads, lost in Slack threads, or buried in a messy CRM.
Most businesses know far more than they realize; it’s just trapped. You can’t leverage AI until that
context is surfaced and structured.
3. Run the Work through a Harsh Filter
Analyze every step of those mapped workflows and ask four questions:
Accelerate: What works but takes too long? (e.g., budget reforecasting or custom proposals).
Optimize: Where are the cross-functional bottlenecks? (e.g., the handoff from marketing to sales).
Fix: Where is human quality too inconsistent? (e.g., standardizing client onboarding).
Remove: What manual work shouldn’t even exist? (e.g., manual CRM data entry).
4. Deploy in Disciplined Sprints
Do not try to fix the biggest, messiest problem in the business first. You’ll just set terrible expectations and stall out.
Now (30 Days): Form a small, agile SWAT team. Pick 2 or 3 low-risk, internal pain points and pilot them.
Next (60–90 Days): Establish clear human-in-the-loop guardrails based on those pilots, clean up the data context, and measure the wins.
Future (90+ Days): Scale what works. Only move to advanced, agentic automation once your baseline data and processes are tight.
The goal isn’t just to have AI in the organization. The goal is to build a better business one that helps the right people do the right work faster, smarter, and more consistently.
Stop rolling out tools. Start redesigning the work. Feel free to reach out to us if you have any questions.
Written by Warwick Absolon, Managing Director at D-Cyfr Consulting.