The Gap Between Buying AI and Operationalizing AI
Watch this on-demand session on AI adoption in business. Two former enterprise IT leaders break down the five tests a workflow has to pass, with real examples.
- ✓ The five tests to apply to any workflow, from trusted data through to the named person who stays accountable
- ✓ Why a chat window everyone uses individually leaves your operating model untouched, however high the license count climbs
- ✓ How a national door service company put decades of repair knowledge in front of a thousand field technicians, with every answer linked to its source
- ✓ What changed when those same technicians could price a job on site, using customer history and margin data at the moment of the assessment
- ✓ Why catching a problem where it happens changes the process itself, instead of automating one human step inside it
- ✓ The adoption problem nobody plans for, which is a working tool your team does not know exists
Where AI adoption in business gets stuck
In this webinar, Frank and Dan trade the patterns they have watched play out across manufacturing, field service, and construction. They start with the uncomfortable one, which is why so much AI spending turns into individual speed and never into capacity for the business.
Frank walks through the five questions he asks of any workflow before he will call it operational. The one about accountability catches most teams off guard. Dan takes the same idea into construction, where a queue of subcontractor bids used to wait on a human review. He explains what changed once that feedback started arriving in minutes instead.
They also get into the part nobody writes into a business case, which is what happens when the tool works perfectly and your team does not know it exists. If you are deciding where your next AI dollar goes, this is the conversation worth having first.
Make it work for the way you work.
