Latest / Wired to Build / Guest Intro - Author Jamie Lynn Cooke

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Guest Intro - Author Jamie Lynn Cooke · Wired to Build

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Hey, what's going on everyone? Welcome to Wire to Build, a
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data-driven, technology focused podcast digging into how
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building owners and operators use technology to manage the
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full life cycle of their projects.
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I'm Nick Caravella, your showrunner and curator, and I'm
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excited that you're joining us today to talk about the people
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that drive the change in our industry.
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You. Today we're joined by Jamie Lynn
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Cook, an author, consultant and educator who sent her career
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turning big ideas into operational reality.
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From bringing agile out of software world an into business
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operations to helping organizations think critically
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about their next big technology shifts, Jamie has been in the
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room where big change happens. Welcome to the show.
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Thanks for having me, Nick. Thanks for joining us.
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I'm really excited. So you know, one of the things
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that Jamie and I geeked out during our pre call was around
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AI and whether you're leaning in in the right way, whether it
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could be leaning in too fast, making in some mistakes or not
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leaning in enough and leaving opportunity on the table.
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Jamie's perspective here is going to give you a framework
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for thinking about how you're using it and how you could be
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getting more today. So in this conversation, we're
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going to get to know Jamie, her background, the turning points
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have shifted, how she works, and the lessons she's learned from
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past technology shifts. Next couple weeks we'll be then
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diving into deep conversation around AI governance,
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productivity, and how to make sure the promises of AI don't
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turn into costly surprises. So Jamie, before we jump into
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today's big ideas, I'd love to start with where your focus is
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on productivity and how and delivering value and how that
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really began. Was there a moment in your
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career where this started to become your compass?
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Nick, when you first asked me that question, I had to reach
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back a little bit because I couldn't necessarily pinpoint
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one point in time. I did go back to.
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So my degree is engineering design and psychology, so
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ergonomics. And like most college graduates,
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I got an entry level role that was being a software tester.
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And my job was to find functional errors in the
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software. That's why they hired me in that
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role. I ended up seeing errors that
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were not functional. They were design errors, they
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were usability issues. There were things that were
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stopping people from delivering the software or delivering the
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outcomes from the software well. And so I inserted myself into
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the software design process and progressively took on a business
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analyst role so that we can make sure that what was being
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delivered met the business needs and was usable after production.
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And when I took on the business analyst role, I started to
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notice that delivery timelines were not being met and we were
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over budget or quality was being sacrificed to meet deadlines.
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And so I inserted myself into being a product manager to make
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sure that things were delivered on time.
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And, and that's really been the path of my career is I'm in a
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role, I see something that's not being run efficiently, we're not
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delivering value in the organization.
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And I insert myself to, to get it fixed and then end up somehow
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taking on that role as well. And so I guess you can say
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that's sort of where the, the productivity focus started, but
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I don't think there was an official point at which, you
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know, I made a career shift to focus on productivity.
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It's sort of evolved naturally. I think that's how it started.
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Well, I think it really kind of lends to your background
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probably in ergonomics, which like for anybody who's new to
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the term, it's like how we feel and and touch the world around
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us essentially for lack of better words.
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And there's an aspect of where business processes can
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oftentimes be the same and the pain that's inherited by the
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people in the process often times hit those walls, get
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frustrated and, and deal with the, the lack of business
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ergonomics, I guess, if you will.
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And like, I feel like, you know, your story about moving into
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like the quality side of things and project management is pretty
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similar to when I moved into technology and kind of had this
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the same pain. So I think that there's that
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aspect of like how does the business operate and how do the
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people operate within the business that often times gets
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missed when you're trying to then ultimately either deliver a
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product that's separate of your business mechanisms.
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So I think, yeah, it's that that genuine curiosity maybe.
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And this was something that we I talked about on the episode
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that's coming out the month before yours, which we're
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actually going to talk about here.
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We talked about curiosity and a detail on like why that's so
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important to like innovation and business process and all of
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those things. So I think it's a natural
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organic path to just be curious of like, why am I not getting
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the outcomes that I'm looking for or why are we not delivering
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them? And so I think you, you say
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pretty clearly like this is not a nice to have kind of mindset.
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We in capital projects and business operations, it's
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everything because it ties to people, it ties to efficiency.
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Is it just enjoying what we do? Is that something that you know
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was coming from seeing promises not delivered or where was the
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things that got you to just really start to dig into the
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whole, I guess business process optimization part of the
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company's problem? So I first I want to agree with
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you. The productivity is everything
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and, and people sometimes mistake hard work as being put
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up, you know, productivity. I've worked 60 hours a week,
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therefore I was productive. And, and really at the end of
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the day, the, the productivity is measured by how much business
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value you deliver. Whether you do that in two hours
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or 20 hours or 200 hours, that's the metric is what you're
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delivering. Does it meet the needs of your
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stakeholders? Does it empower the team to be
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more effective? Does it achieve the strategic
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goals of the organization? Is it maintainable?
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Is it extensible? Is it adaptable as internal or
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external requirements change? That's what makes something
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valuable. And and the most exciting thing
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is when I can help people or empower people to deliver high
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value but equally high quality. So I mentioned in the project
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management piece of it that inevitably when things get
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rushed and you're pressed for a fixed deadline, it's quality to
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get sacrificed. Everybody cuts corners.
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You've got to push to meet the deadline.
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And so yes, you are being productive, you're delivering an
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output, but is that output going to cause more pain when it's
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live or expose your organization or you make the team have to do
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rework or impact your customers or your reputation in the market
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for the sake of having met a deadline.
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And that's really the differentiator is not trying to
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deliver something, it's trying to deliver the right thing and
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deliver it in a way that really empowers and enables the teams
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to do their jobs. I think it's really like, we
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haven't even said the word agile yet, but like everything you
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said, like speaks to delivering value quickly to the customer
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and ensuring everybody is rowing in the same direction.
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Now I, I realize you've, you've written a little bit on this
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topic and it's like part of the reason why I think you're out of
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this show for sure. And with that focus on
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delivering real value, Agile really felt like, or at least
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feels to me like the natural fit.
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Can you explain to me what learning about agile was like
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and how the you had aha moments of how we should be applying
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this into our work? Absolutely.
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So I worked in Australia for a bunch of years and I worked for
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a consulting technology group and in the early 2000s I was
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introduced to the agile method concept in the software
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development context like most people were.
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So Agile has its history in Henry Ford and manufacturing and
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leaning development. And there's there's other
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sectors and other industries that have used agile, but it's
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become more prominent in software development.
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And I got to give full credit to a colleague of mine named Rowan
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Bunning, who took it upon himself to learn everything
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about Agile and then bring it back to the team and and empower
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all of us. But the thing that that struck
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me reasonably quickly after I learned about agile for software
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development is that agile concepts, agile methods can be
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applied and deliver value for anything in the organization.
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Any deliverable you have, whether it's a marketing
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campaign, whether it's an event you're planning, whether it's a
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report you're creating, can benefit from the same approach,
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which is engage stakeholders in the process.
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Have incremental time box deliverables so that people are
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constantly working towards a near term deadline, which is an
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incentive to keep producing. Make sure that what the people,
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the stakeholders are getting are tangible things that they can
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respond to. It's not conceptual 2
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dimensional specifications, it's something they can actually work
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with. Try give you feedback and then
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be prepared to incorporate that feedback and adjust the process
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as you go. So one of the the challenges, if
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you will, with agile is that people didn't quite understand
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the entire process. So they would adopt small
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practices and call it agile. So for instance, we would start
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with a doorstop specification for software that signed off and
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everybody's approved it and we would deliver it incrementally.
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We would deliver something every, you know, two or three
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weeks and we call that agile. And what that basically is
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incremental waterfall, right? The fact that you have a
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specification fully bedded at the beginning and it's never
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changing completely defeats the entire purpose of agile and
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responsiveness. I have found in my career, even
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though the software development world is how is introduced, I've
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found that that method applies to everything that I'm doing.
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You are constantly communicating stakeholders, you're giving them
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ongoing feedback. And in doing that, it's, it's
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quite an amazing outcome because what happens is there are far
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fewer surprises. You're not having one meeting at
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the beginning, going off in a room for months and building out
10:16
whatever you think they want, coming back with a ready to go
10:20
live version of something and being surprised when either the
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requirements have changed or, you know, the the needs that
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they thought they had that they expressed on paper are actually
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not their needs when they start to work with it.
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And so you get pushed back and they won't sign off on the
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deliverable or they, you know, they, they raise issues or they
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delay payments and you're sitting and going, wait a
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second. That's what you asked me to do.
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I don't understand why there's a disconnect when you have these
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incremental deliverables, these tangible results, these time box
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so that you're not sitting off indefinitely.
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You're communicating regularly, you're engaging the people who
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are receiving the deliverable. It's an incredible, incredible
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outcome because the people not only are familiar with what
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you're delivering, they've already adopted and, and
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embraced it. Some have already started
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training on it and crazy concept, they come back to you
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for more work because they've had such an amazing experience.
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So this, this really was an incredible way to to move
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forward in everything, not just software.
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Well, and it's, that's fascinating, you say, because
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construction as a whole is like endemic of the waterfall
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delivery method. It's like, I have scope, I have
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to deliver it to this person and they got to coordinate it across
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these three people. And it just if anything, we get
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to the the incremental waterfall.
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The waterfall would just that happens to have sprints but
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isn't really agile. Our team, we just recently
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published a white paper on this of taking a more model centric
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delivery to process. And for everybody who's
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listening, when I say model centric, I do not mean the BIM
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model. I mean like information model
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before the pitchforks come out for the model gurus out there.
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But it was really about trying to start to take the things that
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work from design and build an integrated project delivery and
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figure out how do we take the people who are going to be
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installing work and get them to the table of the design
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conversations so that we could be a little bit more agile
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about, well, what are we actually installing?
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Is these are the kinds of thinking that like, I guess when
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you're talking to an organization who wants to start
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on their agile journey, I think I find it pretty common that we
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make the stunt, the mistake of chunking it.
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How do you help organizations take that step back to like look
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at the overall outcome and deliverable so that you can
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actually sequence agile into the way you deliver?
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What happened with it, The agile exposure that I got at the
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beginning and the fact that I saw potential for it to be
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bigger, It prompted me to write my first book, which was
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released in 2010. I want to say it was called
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Agile Productivity Unleashed and the the premise was what you're
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talking about. How do you take any business
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activity and create an agile path to get there to the largest
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extent you can? Not everything is suited for
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this. There are some things that
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actually have to be, you know, framed upfront and, and you
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can't really go and pull it all out.
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And construction tends to fall into that category, some aspects
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of it, but the, the, the concept behind it, right?
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The idea that you should be engaging and communicating, you
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should be getting feedback at times when it makes the most
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sense to there's, there's an incredible visual if you've ever
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seen it, sort of like a wedge that talks about the cost of
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change and the cost of change in initial discussions as it's
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narrow into the wedge and it's relatively small.
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If you have clarification and it causes you to change the
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specification or the the model, whatever you're working on at
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that point, the change is minimal, right?
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You're changing some words in a document or in your notes as the
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actual specifications or whatever, using to document
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start to get more and more into the process, the cost of change
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becomes bigger. And when you start to build out
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something and then realize the change is needed before it's
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live in operation, that's a bigger cost.
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After it's live in operation and you have to pull it back out
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again, that's an even bigger cost.
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So the question for the organization is, is what is the
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quickest, you know, the the last responsible moment.
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It's a concept in lean development, but the concept
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still applies here. What is the last responsible
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moment the decision needs to be made and how can we make that
14:39
decision as early on in the process so that you don't get
14:43
caught in the invest and pull back cycle within reason.
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There are points of no return in any process.
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So you know, once you've sent the broadcast out to the media
14:53
and you've put the product out there, it's harder to unring the
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bell. But the the basic concept is the
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last rounds, 1 moment, the latest point.
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You can do it as early in the process to minimize the impact.
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And a key part of that is engaging all the key
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stakeholders. So you really want to make sure
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that you have all the people impacted by what you're
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delivering and their input, right?
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And, and you engage them in these conversations.
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If you have a meeting and they're sitting there silently,
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you have to elicit feedback from them.
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And often the greatest way to elicit feedback from anybody is
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to show them a straw man of something that is either
15:33
deliberately or unintentionally wrong, and they will immediately
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jump on what's wrong about it. And you've gotten them into that
15:40
conversation, even if it's, it's literally started with an idea
15:44
that was so far fetched that there's no way that would be the
15:47
final solution. You've inspired the discussion,
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right? So that's and that could apply
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to anything. That's funny because it's like
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sometimes the best marketing starts with a typo.
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It's just like having the thing that's wrong gets the gets the
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people who were closed off and didn't want to engage in a
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conversation in the first place. Like be like, I don't trust you
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because I didn't have all this expertise.
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So this is what you need to hear.
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And then you just you kind of broke the code.
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Now they're talking and now we have a discussion, which is
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really powerful. You know, we started off this
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conversation around agile and that was purposeful because to
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me, I see a lot of similarity around the hype cycle of AI.
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You know, years ago, everybody's jazzed about agile.
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People are hiring scrum managers, but I think they ran a
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lot, a lot of folks ran into the challenges that we talked about.
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And so maybe you went back to the waterfall way of things and
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now we're seeing folks jump into the boat of AI.
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Personally, I feel there's similarities.
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I'm curious, what are the similarities that you might be
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seeing? Or if you disagree, share with
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me why. Why is AI now taking a focus for
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you? So, so there are definitely
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similarities in the new technology hype and the
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excitement about, you know, either one of those things
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coming into play. It's a word that immediately
17:02
elicits people thinking, you know, this is going to be a
17:05
great problem solver. This is going to be the miracle
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cure to a productivity issues or a customer dissatisfaction or
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whatever we're trying to resolve.
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There are some very big differences in my mind.
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So agile struggle with the fact that it was predominantly
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focused on tech audiences. The messaging was in tech
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circles, it was from tech people.
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Because of that impetus to get agile into organizations tended
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to be a groundswell up the tech teams, the IT teams were trying
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to convince senior management that we need this and that was a
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disconnect that was a problem. The decision makers were not
17:42
bought in and there's only that much that a groundswell effort
17:45
can do without leadership spy in right.
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And I end up writing another book which was really a focus on
17:51
agile value for executives because there was this
17:55
disconnect and the messaging wasn't out there.
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So, so that was one thing that I found different with AI, it is
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ubiquitous. Everybody knows about it.
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Anybody with a device can access it, you know, so, so the
18:05
awareness of it and the endorsement of it is less of a
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groundswell up. It really is a everybody knows,
18:12
including leadership. So that hurdle doesn't need to
18:15
be overcome. But with Agile, there was
18:17
another issue which I mentioned before, the fact that in order
18:21
for it to be effective, it needed to follow some
18:24
reasonably, I don't want to say strict, but it had methods and
18:28
it had protocols that needed to be followed in order for it to
18:31
really do its job. AI seems to be the opposite.
18:34
This is the wild, Wild West. Everybody is given the direction
18:38
of do whatever you can to get value from AI, right?
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And it becomes a free for all. And that's sort of what it is
18:46
right now. Everybody who can create an AI
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agent, go and do that. And if you find something
18:51
successful, you know, show it to the rest of the teams that that
18:55
seems to be the directive. And I, I should use the word
18:58
directive in air quotes. On the one hand, everybody knows
19:02
about it. But because there's so much that
19:05
can be done, which is a powerful, wonderful thing.
19:09
It's everybody trying to do everything at once without any
19:12
real structure. And that's dangerous in a
19:15
different way. So that's that's my answer to
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your question. I love the distinction you make.
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So I want to like almost walk back all the similarities I see.
19:23
You'll be like, wow, it's actually completely different
19:25
because you're you're right. Like I don't see people walking
19:27
around the office asking for agile as much as I would say I'd
19:30
see them walking around the office asking for AI.
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And I love that you use the the phrase like directive in quotes,
19:37
because I think it does capture the spirit of like use it, but
19:41
we don't know what for kind of mindset that the industry may
19:45
have as as opposed to And then we're going to get into
19:48
governance and and talk about it in depth of the next episode.
19:52
But like why having a plan becomes so important And you use
19:56
this term on our pre call about cartoon dollar signs and to
20:00
describe how some leaders approach new technology.
20:04
Is that what's happening? Is that why it's the the
20:06
directive use at all costs? Can you expand on what cartoon
20:10
dollar signs means and why there's so much energy to like,
20:13
use it without thought almost? So, so the promise of AI is an
20:17
incredibly powerful promise. You know, you're going to be
20:20
able to replace a lot of the work that your teams do.
20:24
You're going to be able to deliver faster and, and at a
20:27
higher volume and you're going to be able to reduce your
20:31
overhead costs. And, and from a conceptual
20:35
standpoint, that sounds incredibly appealing, especially
20:39
from a dollar standpoint, right? So immediately people are
20:41
seeing, especially, you know, public companies and, and people
20:46
have commitments to their shareholders.
20:47
They're seeing this opportunity to have major cost savings and,
20:53
and in addition to cost savings, we can deliver faster and, and
20:58
higher volume and reach bigger markets.
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And you know, this is the most amazing thing in the world and,
21:04
and we should be using it as fast as we can.
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And sadly, and I'll go into this in the next session, you know,
21:10
cutting the team as much as we can so that we can take
21:13
advantage of this at a while we're overhead.
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All of that, all of that is based on the the idea and vision
21:21
of AI and not the reality. And I always don't even want to
21:25
hold it off for the next episode because you said so many things
21:27
that I think are so important when thinking about AI.
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And maybe this is what really sets the stage about governance
21:34
and and strategy being important is you said a couple of things.
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You know, the, the cartoon dollar signs come up from the
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concept of being able to replace the teams that I have, be able
21:45
to do more with less, be able to essentially do things and, and
21:49
layoff folks. And, you know, I think those
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cartoon dollar signs have to be measured against topics like,
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well, we're eliminating entry level jobs.
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So now, Howard, we, we complain about young people and the
22:04
profession already. Now, when we eliminate their
22:07
opportunity to learn what's going to happen and expose them
22:10
to things that, yeah, may be pedantic to someone with 30
22:13
years of experience, but was how they learned 30 years ago to
22:18
also the impacts of teen culture.
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And, you know, how do we help put, and this is I think gets it
22:24
to responsible AI, us AI into the hands of the right people to
22:28
leverage the efficiencies as opposed to eliminating the
22:32
people who have the context to actually can make the AI get the
22:36
best results possible. Now, that was a big kind of
22:40
idea. Where does that start to really
22:43
start to pay like paint the picture of how organizations
22:47
need to be thinking about AI and leaning into it as opposed to
22:53
kind of, I don't know what the what the appropriate word may
22:57
be, but just just saying like how you use AI at all costs,
23:01
find your business value outcomes, and you know, we don't
23:05
care who may be impacted by these efficiencies.
23:08
So then let me jump in ironically, that that's a great
23:10
lead into the book that I'm writing that hopefully will be
23:14
released by the time this airs. It's called AI firing freeze.
23:19
And, and the concept behind it is basically, look,
23:22
historically, technology when it was introduced, always bought
23:26
the fear of jobs being replaced, right?
23:29
The industrial revolution and you had the, the Luddite
23:33
movement and you had, you know, people breaking machines because
23:36
they were going to replace our jobs.
23:38
And, you know, and then of course when computers came in
23:41
and and the big mechanical brain was going to replace what we do
23:45
and it's going to get rid of jobs and electricity brought the
23:47
same thing. And I mean, over the over
23:50
technology history, the introduction of any technology
23:54
has always instilled this concept that we can replace
23:56
people. And the reality is the net
23:59
result is it actually increased jobs because there is a concept
24:04
that basically says when you introduce technology, products
24:07
are cheaper products that are cheaper, increase the demand for
24:10
those products, therefore you spread and expand your markets
24:13
and your geographies and it creates more business and the
24:16
cycle repeats itself. That basic concept, right, is
24:20
what we're dealing with with AI right now.
24:22
So you're getting Microsoft and Amazon and UPS and others who
24:27
are broadcasting the amount of layoffs they're doing and
24:31
planning. And because AI is going to
24:33
replace so much and you have various, you know, key people in
24:37
the AI world saying the same thing.
24:39
And the truth of it is the World Economic Forum has, has done
24:43
some huge analysis on this. And there will, they are
24:47
predicting there will be by 2000 and 3092 million jobs lost due
24:52
to AI and 170 million jobs created.
24:56
So the net is 78,000,000, more jobs will be created.
25:02
It's going to shift the workforce like computers did
25:05
when they came in, like the industrial revolution did when
25:08
it came. It's going to change what people
25:10
do from not focusing on repetitive tasks that can easily
25:15
be replaced by AI and really focusing on the things that they
25:20
have expertise in. So yes, a chat box could replace
25:24
your customer service representative to answer some
25:27
questions, sometimes accurately, sometimes not, but we'll get
25:30
into that. But that doesn't mean you cut
25:33
your customer support team or service team by the same amount.
25:37
It means that you can repurpose them to do, you know, everything
25:42
from building better trusted relationship to your customers
25:45
to hitting your, you know, support backlog on things you
25:49
never had time to get to, to building more proactive tools in
25:53
your products and services so that people don't have as many
25:56
questions or things are more clear.
25:58
I mean, like the list could go on.
26:00
It's it's basically the concept of instead of assuming that AI
26:04
is an unusual reaction to layoff, people don't use as an
26:09
opportunity to downsize, uses an opportunity to grow your
26:11
organization. The net result might be that you
26:14
retain the same amount of staff. The going back to your question
26:17
about the executive dollar sign is right.
26:20
The net result might be that you have the same number of people,
26:24
but the amount that you can deliver with that same number of
26:27
people. And by the way, entry level,
26:30
this concept of, well, if we have AI, we don't need to hand
26:32
higher entry level, we'll skip that wrong on the ladder and
26:35
everybody can jump to the next one is, for lack of a better way
26:39
to say it, perfectly insane. Because the entry level people
26:43
you're talking about are the people who've grown up with AI
26:47
personally and in school. They have used it probably more
26:50
than anybody in your current organization.
26:52
And they come to the table with these inherent proven AI skills
26:57
that are exactly the foundation of what you need to repurpose in
26:59
your organization. They are the feature skills you
27:02
need. And if you're not bringing them
27:05
in and leveraging those skills, you're making a big mistake.
27:08
Oh. Man, I, I love all of that just
27:11
because it is, it's the, it's the native way of thinking.
27:14
And I think this is where AI can be scary is the confirmation
27:18
bias. And I feel like, you know,
27:20
sometimes the hesitation is like we're afraid of the state of it
27:24
is today. But if we think back, you know,
27:27
8 months is kind of how long we've been really talking about
27:30
AI on this podcast. And it hasn't even really been
27:33
that long, but it has rapidly changed in the eight months.
27:39
I guess the, the, the curve of advancement, if you will, is way
27:43
different with AI than other technologies.
27:47
It's new models are coming out quarterly, new insights are
27:50
being able to be drawn. And like just this past weekend,
27:53
Sam Altman was going is like thinking about like, think of AI
27:57
as like having a librarian who knows everything that you can go
28:00
to. And this is having like this
28:02
deep set of intelligence that now we can query against has a
28:07
double edged sword. And this is another term and
28:10
it's not in the outline. So I apologize for throwing you
28:12
a curveball here. But the term I heard yesterday
28:16
was an expert generalist. And I thought there was
28:19
something really powerful about that because it kind of seagates
28:23
into the avocados ethos of being construction technology experts,
28:27
where we have a general overview of technology within the
28:32
expertise of construction, You know, where AI starts to give
28:36
folks access to broad bases of information.
28:40
Now having a generalist who understands a industry nuance
28:45
becomes a really powerful asset. And then also for the
28:49
specialists out there, and we'll talk about this on the next
28:52
episode too, is like AI is really just a basis, like an
28:54
average. And so you still need the
28:57
engineers, the scientists that will have you to elevate with
29:00
the context of of your organization, what the AI is
29:05
telling you to do. And so to me, it is like this,
29:07
this tooling relationship that allows us to all reach further,
29:11
see more, do more with the same amount of tools.
29:15
Agree or disagree? So, so I definitely agree.
29:18
And I think the analogy, the analogy I like to give people
29:22
is, is sort of the, the expert generalist kind of concept.
29:26
It's really an eager intern, right, Who happens to know
29:29
everything, by the way. But yeah, it's an eager intern
29:32
who has has all the motivation, knows what their goal is or
29:37
gives given direction and follows the direction, but needs
29:40
guidance from people who are truly in the industry, have the
29:45
experience, have the the knowledge and empathy that
29:48
humans do with, you know, the the things that right now.
29:52
So keeping in mind that we're talking about generative AI and
29:55
narrow AI, we're not talking about, you know, artificial
29:59
general intelligence is super intelligence or the machines
30:01
that go and make decisions on their own.
30:03
We're talking about things that follow protocol and procedure
30:07
and use data you've trained it on and you know, 99 times out of
30:11
100 are likely to get it right. But that concept of pairing the
30:17
the AI technology with the appropriate subject matter
30:20
expertise and the appropriate humans so that you jointly come
30:24
up with the best solution is, is to me critical right now in in
30:31
everything I'm researching and everything I'm doing for the
30:33
book. It's becoming more and more
30:35
apparent that this idea of AI running on its own, unless it's
30:40
incredibly low risk, what it's working on is going to get your
30:46
company, your organization into trouble.
30:49
And so that coupling of the expert generalists with the
30:52
expertise and gut instinct and intuition and, and empathy and
31:00
everything that the human brings to the process, that's the ideal
31:03
next to get the most out of AI and still keep your organization
31:07
on track for value and and safety, right?
31:11
Risk management. And this is, it's funny because
31:14
it's like we I went off the beaten path here and I feel like
31:17
I just went right to where exactly we wanted to kind of
31:19
close today's episode with. Because what this seems like is
31:22
the difference between success and failure comes down to one
31:26
thing that you've emphasized throughout your books and, and
31:29
in this conversation is governance.
31:31
So when you say governance, what does that really mean to you in
31:35
context? And how is it different from
31:37
what people are probably thinking of just writing the
31:40
rules? So, so governance and it needs
31:43
to be sort of really emphasized. There tends to sometimes be a
31:47
mental picture that governance is like bureaucracy Logan habit
31:51
governance for and they're going to stop me from being reductive
31:53
and I'm going to have to fill out all the paperwork and right
31:57
governance, if it's done right, is a framework.
32:00
It sets the guard rails. It's it's a clear vision.
32:02
It sets a method of management and measurement that's clear to
32:06
everybody involved in the process.
32:08
But then most importantly, it empowers the teams to go do what
32:13
they do right? So the idea of governance being
32:16
a centralized high level function that requires
32:19
everything to be approved by see management and requires you
32:23
really need to set up clear expectations.
32:27
You need to understand what your exposures are, your security
32:30
risks and actively mitigate those.
32:32
You need to have a change management process and
32:35
escalation process. To be fair, you need to have a
32:38
decommissioning process as well. One of the things I love about
32:41
government is it tends to, and no disrespect to anyone here who
32:45
works for government, but it tends to have this compounding
32:49
governance concept or have administration where everything
32:52
is created and continues to exist, whether or not it still
32:55
has values. So AI governance at the
32:58
beginning is going to have to be a bit of a heavy lift.
33:01
There is arguably nothing in place in most organizations and
33:03
you're going to have to establish that framework and
33:06
establish and communicate it to the teams.
33:08
But once you've done that, they need to figure out what
33:12
maintenance mode is and how much hands on you need to be and how
33:16
much you can devolve to the different teams.
33:19
So they're empowered to do what they need to do and do it in the
33:22
way that they do best, right? So the teams know how they work
33:26
best. And by the way, one of the agile
33:28
concepts is when you give teams ownership of outcomes, they tend
33:33
to deliver better because it's their outcomes as opposed to
33:37
something that it is top down mandated.
33:40
So the more you devolve this out to the teams, that's fantastic.
33:44
The more you make sure that what they're working on aligns with
33:47
your strategic objectives, ensures ROI for the
33:50
organization, ensures risk management and due diligence.
33:54
And as long as you've created those guardrails and
33:56
communicated them, the governing shouldn't be an obstacle, it
34:00
should be an enabler. So that's really where you want
34:02
to go with this to the largest extent you can.
34:05
And obviously some organizations don't have the freedom, in some
34:09
cases they have compliance obligations or other things
34:11
where they have to put more of a a structure in place.
34:14
But to the largest that you can, don't make it heavy-handed
34:17
approach. Make a division and guidance and
34:19
then empower the teams to deliver.
34:22
I love that. And it's like, it's almost like
34:24
agile philosophy is what will empower good AI because it is.
34:29
I, when I learned about Agile, it was really powerful to
34:32
realize that like my role as a project manager wasn't to tell
34:36
people what to do. It was to empower them to get
34:39
their work done and ask them what they need.
34:43
And that whole shift really changed me from being the cat
34:49
herder to being somebody who could kind of shepherd the way
34:54
toward execution. And I think that that's a really
34:59
powerful thing we'll we'll dig into.
35:02
I'm really excited to kind of dig into a story that you shared
35:04
with me on our next call about Air Canada and how, you know, AI
35:08
gave a bad customer experience that they actually ended up
35:11
being on the hook for. And for us to start to explore
35:16
the, the risks that you might not see coming and how
35:19
governance can push the right guard rails to protect the
35:22
productivity gains that you're going to get from it.
35:26
So I'm really excited. This has been an amazing first
35:30
episode, as I mentioned on a freak, like right before this,
35:33
the time's going to fly. And you know, we'll be, we'll be
35:36
wrapping up. So that's where we are right
35:38
now. Excited to get back on with you
35:41
in two weeks. We're going to talk about those
35:43
guardrails. We're going to talk about how to
35:45
use it, how to enable flexibility.
35:48
So thank you so much, Jamie, for coming in.
35:50
I'm excited to have that conversation with you.
35:53
You know, if you're using AI right now or thinking about it,
35:56
this is the perfect time to pause and ask, are you building
35:59
guardrails or are you sprinting ahead without them?
36:02
And next episode, we're going to be back to dig into the
36:05
realities of AI governance and the places it can be used to
36:08
boost productivity, the situations where it can cost you
36:11
dearly and how to set your business up to succeed within
36:14
that next big shift. O make sure you subscribe so you
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who's on that AI journey so that they don't miss on these
36:26
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