Buy the tool before you understand the business, and that’s what gets automated

PLUS: BCG just confirmed what our own research said about who wins with AI

From the aibl team

It feels like we’re at a watershed moment in how we approach AI. Everything I’m hearing, reading, and learning from our own research points to the same theme. Most companies fell into a trap of starting with the AI and working backwards. That hasn’t worked.

What works is very human. A deep re-examination of how the business operates, and how people contribute to or block processes. That’s what builds a well-defined strategy.

You’ll see it in this week’s research article, which plucks out what I learned from BCG’s most recent AI at Work survey of nearly 12,000 people. Their findings line up almost exactly with our own.

You’ll also see it in our lead piece on aiblLIVE. We built the day around the same idea: adoption is a sequence of hard, human decisions, not a purchase.

For me the bottom line is this. The kind of real change that moves revenue and how we fundamentally work is never as simple as buying technology, however remarkable. It means doing the hard work and asking the hard questions.

You can’t buy your way past the hard part

There’s a pattern in every piece of research we run. The thing that predicts whether AI works is never the tool. It’s the human work around it: a clear question, data people agree on, a standard someone owns.

The same is true of how leaders learn to do this. You don’t move from stuck to confident by sitting through talks. You get there in a sequence.

First you need to believe it works for a company like yours. Then you need clarity on what it actually looks like in practice. Then you need the capability to do it yourself. Skip a step and it doesn’t hold.

Most events ignore this. They stack keynotes and panels, and everyone leaves with notes they never open. The sequence is missing, so the learning doesn’t stick. It’s the same mistake companies make when they buy the tool before mapping the work. The order is wrong, so the outcome is thin.

This is why we built aiblLIVE the way we did. The day is sequenced, not stacked. Operator case studies to show it works. Live demos to show what it looks like. Hands-on workshops to build the thing yourself. A closing debate to decide what you’d change on Monday.

No keynotes. No fireside chats. No vendor pitches dressed as insight.

You can apply the same test to any event before you give it a day of your time. Does it just fill your head, or does it move you through the sequence to action?

aibl Research: BCG’s AI at Work Study

I told you so. It’s petty, I know, but it feels good when a monster like BCG reinforces a message from your own research.

Back in March, we talked about how the predictors of ROI were the “hard work” variables: strategic clarity, governance maturity, and cross-functional alignment. This matches what we learned from BCG’s recent AI at Work Study.

Before we get to that though, it’s worth sharing a topline finding about the headline metric of AI: time saved. Over 40% of frontline workers save a day or more per week, which should be, at the least, a real boost to productivity and potentially to innovation.

Yet people won’t turn that time into gold without guidance. Two-thirds say they get limited or no direction on how to redirect their time, and over half don’t direct it to what they consider “strategic” work.

BCG breaks companies into two groups based on their approach. The tool-first respondent orgs are the “deploy” group, contrasted with a “reshape/invent” group that has rebuilt workflows end-to-end.

In measure after measure — value captured, proving impact, worker confidence — the latter group outperforms by roughly 20 percentage points.

Product spotlight of the week

This week the aibl team has been tracking Gong, a revenue AI platform built around a simple gap: nobody can listen to every call, so coaching stays patchy and deal risk gets spotted late.

Gong records, transcribes, and analyses sales conversations. Managers get coaching prompts, objection patterns, and early warnings on deals that look shaky. Its 2026 updates add AI agents that update CRM fields and draft follow-ups from what was said.

It suits mid-market teams already running pipeline reviews, rather than teams after simple call notes. Pricing is quote-based with no public tier, and onboarding typically takes weeks.

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