Decision intelligence

And now we're seeing the pushback from... industry because precisely what you would think happened, happened, right? There was a high rate of failure in data science projects.

Hello and welcome to another episode of the Decision Intelligence Lab podcast, along with my co-host Michael Watson.

I'm thrilled to welcome Jeffrey Kam as our guest today.

Now, Jeff has had a strong and long career in the world of data science and decision sciences, including... 20 years as department chair at the University of Cincinnati, and most recently as chair and past program director of the Master's in Analytics program at Wake Forest University.

And so, you know, the overall, the overarching...

I guess, results of that paper that we had in Harvard Data Science Review was data science is obviously data-centric and decision science is decision-centric.

And the question is, you know, is there a mismatch between what's going on in industry and what's going on in academia? And I would say, especially now with the push coming from companies coming from industry on decision intelligence that we're seeing, right? Industry's out in front and realizing, well, it's just not about the data.

And so academia is following now.

So we add our functionality all across that.

But that functionality, yeah, does all the main processes and what the new AI infrastructure layer that the decision intelligence layer will do is just apply more technology in the right places in the right way as a collective of agents and not these individual ones that often do little things, which are all great.

But what I've seen over the last year is that people are a little bit jaded by the talk of agents.

It's just a reinvention of a bot in some cases.

Chapter five, decision intelligence, turning insight into behavior and behavior into advantage.

Introduction.

Most organizations do not fail at intelligence because they lack data, models, or analytical talent.

If decisions remain unchanged, the system has not yet succeeded, regardless of model accuracy or analytical sophistication.

The last mile is more of an organizational gap than a technical gap.

Decision intelligence is where decision aperture either succeeds or fails.

It is one of the core conditions that determines whether intelligence translates into action rather than remaining informational.

The Discipline Between Insight and Action Closing the last mile requires redesigning decision-making.

Welcome to the Leaders Insights Podcast.

Today's episode, Decision Intelligence and Embedded Analytics, Making the Decision in the Unit of Design.

Every company I talk to has dashboards coming out of their ears.

So why are they all still making terrible decisions?

The museum stays empty.

The museum stays empty.

Sources for today's episode.

Cade Nuggets, The New Stack, O'Reilly Radar, Towards Data Science, MIT Sloan Management Review.

Yeah, I really, sorry, sorry I jumped in because I really love these questions because this is kind of the fundamental shift between the current user experience and what we are about to shift to.

So think about it like dashboard tells you what happened, okay? And decision intelligence or proactive AI tells you what to do next, okay? Or actually shows you what's going to be going on next if it's really autonomous.

So for years we were building, like hospitality business was building around reporting.

occupancy, ADR, rev dev, ticket, owner statement, response time.