Barry O'Reilly

I’m excited to share that my new book, Artificial Organizations, is coming out in mid-March!

I’ve been working on this over the past number of months, driven by a simple question I keep hearing from executives:

How do we pair human intuition with machine insight to actually get better outcomes, not just more activity?

This book is for leaders navigating that shift in how we work in high-paced environments.

Not AI as a tool. Not AI as automation.

But AI as a thinking partner that helps you make better decisions, faster without losing judgment, context, or humanity.

Sign up at artificialorganizations.com to be notified of the official book release!

What your teams measure tells them what you expect them to improve.

If the scorecard rewards AI usage, people have a reason to use more AI. The connection to better business performance still needs to be established.

John Marcante and I developed the AI Outcome Scorecard around five dimensions: decision quality, decision velocity, customer outcomes, organizational learning, and leadership capacity.

These give leaders a starting point for choosing measures relevant to their work. For a customer service team, that might mean shorter resolution times alongside sustained customer satisfaction.

For an executive team, it could mean fewer decisions reopened because important context was missed.

The leadership work is making those expectations specific enough to guide decisions and assess progress.

Read more: barryoreilly.com/explore/blog/s…

Start your week with a question for your team: where is AI actually making our work better?

You might be producing drafts faster, but spending longer reviewing them. Or getting more done individually while the team’s workflow still feels slow.

That’s what we’re exploring through the Artificial Organizations Measure AI Outcomes Program: how improvements in individual work translate into better team performance and business results.

I’d love to hear your experience. Set aside about 10 minutes to reflect on how you and your team use AI and contribute to our emerging benchmark.

It’s free to take part, and the benchmark closes November 30.

Where do you think AI value is getting stuck in your organization?

geni.us/measuring-ai-o…

Throughout every transformation we’ve led, organizations behave the way they’re designed to behave.

That includes how they approach AI. When success means launching a pilot, teams can reach that milestone without establishing whether the work improved.

Before introducing a change, leaders need to make the expected improvement clear and understand how the work performs today.

That gives teams a basis for assessing results and deciding what to change next.

The cycle illustrated here puts AI within the change being tested, with results assessed against a baseline and learning used to guide the next change.

John Marcante and I explore this in our third article in The Adaptive Enterprise series.

👉 Read more: barryoreilly.com/explore/blog/s…

A faster first draft is useful. What happens after that draft tells us more about whether AI is improving the work.

If review takes just as long, decisions remain unclear, or the customer sees no difference, we need to understand where the benefit stops.

That’s what I want leaders to examine when they talk about AI outcomes. Following the work through to the result gives us a clearer view of where value is appearing and where it gets stuck.

Where are you seeing the clearest improvement: in the work, the decisions, or the results?

Baseline your AI capabilities against the market in 10 minutes and get early access to global insights only participants see:
artificialorganizations.com/measuring-ai-o…