It's 8:55. The board meets at 9:00.
By Shane Heddy, Cofounder, CRO, Nth AI. Published 2026-11-10.
Three systems, three answers to the CFO's question, five minutes before the board meets. Shane Heddy on why AI answers aren't trusted yet, what data leaders have already tried, and how to get to one number everyone accepts.
You have three answers to the CFO's question, and you can't stand behind any of them.
Picture this. The message comes in from the CFO: what was revenue for this product line last quarter, compared with the quarter before?
Easy question. You open the AI assistant your company rolled out this year, and it answers in seconds. Then you check finance's report. Different number. The CRM has a third.
None of them is wrong, exactly. Each system defines revenue its own way. Finance counts it one way, sales another, supply chain a third. But the CFO doesn't need three right answers. The CFO needs one answer to make a decision on.
Same data. Different definitions. No decision.
So you do what everyone does. You give a range, add a caveat and promise a follow-up. The board moves on. And somewhere in that room, someone quietly decides AI answers can't be trusted yet.
What you actually want
Nobody wakes up wanting a context layer. You want outcomes:
- One number everyone accepts. Finance, sales and supply chain looking at the same answer and agreeing on what it means.
- The next question answered today. Right now, a new cut of the data means a new report, another analyst, and "we'll put it on the roadmap and get back to you in three weeks." The executive needs it now.
- A data team that isn't buried. Not a request queue where the business hears "we'll get to you in six or eight months."
- AI you'd let the whole company use. Fast answers are easy. Answers people actually act on are the point.
What you've already tried
Another report. Every new question becomes a new report. The backlog grows faster than the team can hire.
A consulting team. They land, build something good, then roll off. Your team either learns to run it or doesn't. Sources stop connecting, definitions change, and the answers slowly drift away from the business. Everyone asks the same question at the start: "what am I going to do when you guys leave?"
Your own assistant. It looks great in the demo. Then someone gets a wrong number, and trust is gone. Before long, it's shelfware.
None of these failed because the people weren't smart. They failed in the same slow place: organizing the data around how the business actually works, and keeping it that way. That's always been people and time against a problem.
Same morning, different five minutes
Now picture the same 8:55 message.
You ask the same question and get one answer. It tells you which definition of revenue it used and why, and it traces the number back to the systems it came from. If the data isn't there to back it, it says so instead of guessing.
You walk into the board meeting with one number and know exactly why it's right. When the CFO asks the follow-up, you answer it in the room.
How we get you there
That's what we built Nth AI Nexus™ to do. AI agents do the data build end to end: connecting your sources, working out the business logic with the people who know it, and building the layer your AI needs to give answers you can act on. It lands in your own environment, on the stack you already have.
It starts small on purpose:
- One team that already has a reason to move.
- Two or three data sources.
- Three questions that would be hugely valuable if answered.
- Success criteria we agree on before we start.
A forward deployed engineer works alongside your team at the start, then hands you the keys. You own it. As definitions change and new logic comes in, it stays current, so it doesn't drift the day we leave. Most teams see their first production value in about four weeks.
I spent 26 years in enterprise sales watching good teams lose that five minutes before the board meeting. It doesn't have to go that way anymore.
What's the question you'd want answered at 8:55?