Road to Project Zero

Most AI pilots work, and then they stall before they reach enterprise scale. One big reason is hidden knowledge: expertise spread across your company that nobody wrote down. Everyone holds a piece of it, nobody sees the whole picture, and if your people can’t see it, neither can AI.

In Episode 1 of The Road to Project Zero, Joe Pardavila from Forbes Books sits down with Chris Byler, Principal of Applied AI at geniant, an AI consulting, design and engineering partner for the enterprise. Chris is helping build Project Zero, geniant’s platform for taking AI from successful pilot to enterprise scale.

They cover where hidden knowledge comes from, including “CEO flybys,” job-security hoarding and the telephone effect. They also get into why many leaders still treat AI as a purchase rather than a new way of working, and how knowledge graphs keep AI aligned with your business. Chris explains how Project Zero takes in company knowledge through its “Sense” and “Extract” modules, why a ticketing system is the first view of the knowledge graph, and why the hardest part is keeping people from handing their judgment over to AI.

If your team has an AI pilot that worked but hasn’t scaled, that’s exactly the problem geniant solves. 

Learn more about Project Zero and start a conversation with their team at geniant.com.

Subscribe so you don’t miss the next episode, and share this one with a leader on your team who’s trying to make AI work at scale.

What You’ll Learn

• What “hidden knowledge” is, and why it quietly stops AI from scaling across an enterprise
• How company culture creates knowledge silos, including job-security hoarding, CEO flybys and the telephone effect
• How a knowledge platform can keep private information private while still sharing what other teams need, and how it gives people credit for their work
• Why most AI adoption stalls: companies see AI as a SaaS tool instead of a way to connect people, ideas and systems
• The ’80s computer comparison: why “AI writes my documents” misses the bigger picture
• Knowledge graphs in plain English (nodes, edges, and Wikipedia as an example)
• Why a ticketing system is the first user experience of a knowledge graph, with work locked to humans or handed off to agents
• How Project Zero’s Sense and Extract modules collect organizational knowledge without becoming “Big Brother”
• What becomes possible afterward: software factories, marketing building its own architecture-aligned products, and CEOs getting their dashboards without pulling engineers off other work
• The hard part: catching AI hallucinations (“finding the lie”) and keeping humans from surrendering their abilities to AI

Timestamps

00:00 – Why AI pilots stall: the hidden knowledge problem
00:57 – What makes Project Zero different (and where the name came from)
02:18 – What hidden knowledge looks like inside a company
04:05 – Culture, job security and “CEO flybys”
06:00 – Sharing knowledge without breaking security
07:50 – Getting credit for your work
08:58 – What’s slowing down AI adoption
11:45 – The “credit card AI” mindset of legacy companies
13:00 – The lesson from computers in the ’80s and ’90s
14:00 – AI as a “capable individual”: the house-building analogy
16:20 – Alignment: giving AI the minimum knowledge it needs
17:00 – Knowledge graphs in plain terms
19:40 – What a knowledge graph looks like (the Wikipedia example)
22:15 – User experience: the ticketing system as a knowledge graph
24:30 – Making a flood of AI output manageable: a visual graph of the work
27:00 – Generating reports, charts and tables on demand from your knowledge
28:30 – How hidden knowledge gets collected: Sense & Extract
30:20 – Not Big Brother: organizational knowledge, not employee monitoring
32:45 – Humans in the loop: experts signing off
33:30 – What becomes possible: software factories and beyond
34:45 – Marketing, CEOs and work across departments
37:30 – The hard part: “finding the lie”
39:00 – Don’t surrender your abilities to AI
40:10 – Keeping the genie in the bottle
41:00 – The one takeaway
43:00 – Coming up next: knowledge acquisition in practice
43:55 – Wrap-up