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Earlier this year, we surveyed 17 YPO-level CEOs in the Commercial Real Estate industry about their use of AI. The findings should be treated as directional, but, anecdotally, they’re consistent with what we see across industries.
The executives were clearly convinced that AI mattered.
Fourteen of 17 rated AI an eight-to-ten priority.
Thirteen described leadership as strategic or actively pushing adoption. In 15 firms, the initiative was being driven by the founder, CEO, or president.
But only four firms, less than 25%, reported multiple live, non-chatbot AI workflows.
Note: non-chatbot = buying a Claude or ChatGPT subscription. Not your own custom chatbot with your proprietary information in it.
That is a pretty massive gap between conviction and execution.
It also illustrates that executive enthusiasm can create urgency, permission, and budget, but it cannot create a production workflow by itself.
Leadership can purchase ChatGPT or Claude for the company. It can encourage experimentation, attend AI conferences, create a steering committee, and ask every department to submit ideas.
None of that answers the operating questions that determine whether an initiative gets beyond the pilot:
- Which process should actually be automated first?
- How does that process work today?
- Which systems and data does it depend on?
- Who owns its performance every day?
- Where must humans review assumptions, exceptions, or outputs?
- How will the company know whether the solution is creating value?
Without answers to those questions, an AI strategy often becomes a collection of pilots.
Every department has ideas. Every executive has a favorite use case. A few prototypes look impressive. But nobody has established which opportunity is most valuable, most feasible, or most likely to survive contact with the real operating environment.
This is why sponsorship (green-lights from leadership) and ownership (those leading the implementation) are different.
The executive sponsor creates permission for the initiative to happen.
The operating owner makes sure it works repeatedly.
Production requires someone who understands the workflow, owns its results, handles exceptions, gathers feedback, and ensures the solution continues delivering value after the launch team moves on.
That is precisely the gap Automation Mining closes.
We begin by aligning leadership around strategic objectives and the process-level problems worth solving. We then validate those problems with the people closest to the work.
Through focused process workshops, we map the current workflow, supporting systems, data sources, bottlenecks, stakeholders, and operational nuances. Instead of evaluating ideas based on excitement, we score them against a consistent set of business criteria.
We then narrow the field to the one-to-four initiatives with the greatest enterprise impact and deliver:
- A prioritized AI and automation roadmap
- Documented current-state and future-state workflows
- A change-management plan
- High-level implementation requirements
- Statements of work that allow execution to begin
This is how leadership enthusiasm becomes an operating plan.
The CRE survey suggests that the market does not have a shortage of AI believers. When you go to conferences, happy hours, or Forum meetings, everyone is talking about AI.
The shortage is in companies that know exactly what to automate, why it matters, who owns it, and what must happen to move it into production.
Enthusiasm gets AI onto the agenda.
Automation Mining turns it into something the business can actually execute.