Inspiration for the road ahead

Somewhere in your business, someone's fixing a spreadsheet. This is what dirty data looks like - friction, slowing everything down. And it's a massive problem.

IDC research shows 88% of AI proofs never make it live. I'm in the thick of this development with clients, and here's four mistakes I see them making.

When we walk into a any lead-to-cash implementation, one of the first areas we evaluate is the product catalog. This comes before the quoting tool or approval workflows. Yes, the catalog itself. Because your product catalog is where complexity begins - and it cascades through quoting, approvals, billing, and reporting. It's the hidden-in-plain-sight source of truth for a lot of what goes wrong in revenue lifecycles. The SKU Explosion Problem A common pattern looks like this:

In Lead To Cash, there's a simple test you can do to identify friction - I call it the Swivel Chair test.

I'm talking with a lot of executives saying yes, they're investing in AI ... or being forced to by their Boards. But voices trail off when I ask about revenue impact.

AI agents behave far more like employees than applications, and organizations that fail to recognize this are the same ones wondering why their AI investments are stalled, shelved, or quietly creating risk.