Human-Centered AI: Reimagining Work, HR, and Trust

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Welcome to Insights at Work. I am delighted to have Matt Burns here today.
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And Matt, before we dive into the nitty-gritty of the conversation,
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I thought it might be a great idea to tell our listeners a little bit about
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yourself and your background and what you're doing now.
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Sounds great first off thanks for having me it's great to be with you and a big fan of the show,
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my name is matt burns i'm a former hr
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executive turned entrepreneur and during
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that entrepreneurial chapter of my career i've had many sub chapters i've launched
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a virtual reality conference and an ai company but the bulk of my time is spent
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helping small to medium businesses in canada think through innovation and transformation
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a topic that couldn't be more present than today,
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Absolutely. That sounds like an interesting career journey. And I'm sure throughout
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our discussion, we'll hear a few learnings and tidbits that have come from that
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journey that you've taken.
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So with that, why don't we dive right in?
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Today's discussion is really going to center around AI, workforce trends,
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how it all comes together, how that's impacting businesses across Canada,
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as well as leadership within those businesses.
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So let's jump right in and start with the human aspect of AI.
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So we've heard a lot about AI over the last several years at this point.
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And one of the things that we learned from our recent trends report,
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that in reality, most businesses, most people agree that having a human touch
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in partnership with AI is really essential.
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So when we think about that concept, what does human-centered AI really mean?
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Beyond the policies and compliance, how do you envision that takeaway?
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Well, I thought the trends report did a really good job of kind of outlining
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a few different key subtrends.
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And when I think about the human side of AI, I agree with you.
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I think it's instrumental in seeing the rate of change, but also the rate of
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progress that we're all hoping for against a backdrop of a lot of fear.
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And the fear is a very human instinct because it's the art of the unknown.
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And you and I are similar. We spent a lot of time in this profession.
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I remember going back, my first
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job in HR, I was filing paper into manila folders into filing cabinets.
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So we, you know, flash forward 20 years from now, it's a very different world.
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But the fears that existed then are the fears that exist today,
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which is, will I be relevant in a future state that includes artificial intelligence? Yeah.
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And the short answer is, if you want to be, you will. And when I think about
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the human side of AI in particular, I think about a few things.
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I think about the idea of creating opportunities for knowledge sharing and innovation.
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Far too many organizations that I connect with are still thinking about doing
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department-specific siloed pilots or that are innovating at the individual or
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team level. And which is amazing and a great starting point.
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But in its rush to empower individuals and small teams, you may be missing an
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opportunity to support the broader organization if the innovation reveals great
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opportunities for, for example.
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Productivity gains or for creativity gains.
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So that's one area that I think is a really important piece that we'll talk
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more about in our conversation.
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The one I'm really thinking about a lot lately, though, is this idea of shadow HR.
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The hard truth is that the majority of employees, and those include HR employees,
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have both an AI application that they use at work and then one that they use at home.
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And how they speak to those two different interfaces is often very different.
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There's often a higher level of trust with your personal artificial intelligence,
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whether it's a chat GPT or a Gemini or an Anthropic account.
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Than with if you have your company specific information
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because there's always this concern around privacy and
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confidentiality and in having
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that dynamic exist many work-based questions are being asked outside of the
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broader organizational you know if you will infrastructure which again is a
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silo so we've actually increased the number of silos in some cases which when
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we bring it all back together could have incredible benefits.
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But if we continue to push past the human side of this question, the trust, the respect,
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the, you know, just the employee experience of all of it, we miss an opportunity
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to not only contract silos, but actually go the opposite way.
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That makes tons of sense. And I think that's a really great point that you bring
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forward around the idea of shadow HR.
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The technology industry, as we all know, many years ago went through something
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similar when cloud-based tools started to come to light and employees saw the
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opportunity to increase their productivity,
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to perhaps execute on something quicker, get a second opinion on something.
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So I think that's a really great piece that you've brought forward in terms of framing it that way.
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And if we start just looking from the very beginning, so once we dive into the
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human elements of breaking down silos and making sure that everything is aligned
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and really taking advantage of the power that AI could offer,
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before we get to that.
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What would you say around, you know, five years out from the down the road,
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how organizations need to look at AI governance or how you think they will be
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looking at AI governance?
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Do you think that's going to transition, become a little bit more like the way
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that we look at financial government or governance, I should say,
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not government, with formal oversight, audit structures, accountability?
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Do you see that path happening or do you see something a little bit different? I see it happening.
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And I see it lent to your point, relying upon the expertise of our cross-functional partners.
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You mentioned finance. I would throw marketing into the mix.
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These are two organizations that are very accustomed to doing things like A-B
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testing concepts, running structured and measured pilots, and then having the
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accountability to report those results back to the organization to inform a business decision.
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Oftentimes in hr we can default to
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our work is very confidential it's very secretive
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and those things are true but in an anonymized data you know context we are
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at times able to infer things like sentiment and productivity and you know wellness
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in the organization so i do see the maturation of our ai governance within the
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hr profession moving more towards that structured ability.
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Because I think that we talked about silos a few different ways.
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Another silo we could talk about is just breaking down the traditional silos between functions.
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Because now we have access to information that is cross-functional information.
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And in particular, it's in HR where we often have broad access to information,
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including employee information, which can be used to develop any number of measures
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and metrics or identify opportunities within organizations.
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Wonderful. And I think the idea of breaking in those silos and building a broader
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sense of integration is really something that is already coming to light.
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I think there's a number of organizations that are starting to see the benefits
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of bringing together some of those functions and, to your point,
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having access to some of that sentiment-related data.
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And for years, HR has really operated a little bit independently from things like IT and operations.
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But AI is really having a strong influence on bringing these functions together.
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So when we take a deeper dive into that silo destruction, what does that integrated
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experience look like? Like, I mean, we can talk about HR, IT operations, but even beyond.
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What do you think that looks like if you're blue skying down the road for organizations
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to bring those functions together and integrate?
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Well, this is why the human side
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is so critically important because we're recycling the same questions.
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To your point, talk about cloud being one innovation point.
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AI is going to force a level of integration because organizations will rely upon the currency.
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That's the information within the entity. and when
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if i could blue sky it to me it's not that complicated
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like it's it's almost like when you lead any transformational
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project what's your north star where are we going as
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an organization and how do we measure when we get there and if we can just rally
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around that as a senior executive team by function and establish where is our
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north star and how we plug into it then it makes all the downstream integrations
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of information and technology and people and thinking and ideas a lot more.
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Readily apparent and a lot more valuable. Whereas if we are operating from the
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bottom up and we're trying to innovate at the individual and small team level,
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that's when we create more silos and more friction points because it assumes
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that a small pilot will cascade through the organization and it may not for any number of reasons.
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But I think when you have that top-down view, that North Star view,
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that's a great place to start.
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And a lot of organizations kind of skip that step of their own detriment.
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Absolutely. It really is bringing together those structural and cultural pieces
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like you would for any change management process, if you will.
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We all love change management.
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That's not going away. It's just a new topic of conversation, a new path forward.
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So if we look at that integration and we have other areas of the business being
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a little bit more centralized in influencing HR and also adding data points for HR decisions.
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When we start to look at AI having the potential to make recommendations,
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and I know in some cases this is already happening, but for the sake of argument,
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let's say no one's doing it yet.
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If AI starts making recommendations about, you know, hiring or performance or compensation,
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again, I think we jump back to that human role and what role should HR play
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in making sure that those decisions are aligning to whatever corporate values
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exist within an organization.
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So making sure they're ethical, making sure they're transparent.
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How can we be sure that they're unbiased?
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What does HR play in that type of transformation inside of an organization?
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For many, it's an exciting new role. I haven't been to an HR conference in 20 years.
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We didn't lament the abundance of administration and the lack of thinking and strategic time.
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AI unlocks all of that at the individual team and organizational level.
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So when we think about how HR can participate in this process,
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to me, we are the designers, or in this case, the redesigners of work,
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which means looking at the work at the task level and then determining where
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the task should best live, who needs to be informed, who makes the decision,
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where are the accountability lines, and then we build up an organizational structure from there.
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I think we've been living within this old paradigm that was not AI enabled,
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that was very predictable and siloed and in a lot of cases top down.
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And that got us to this point and got us a long way. But as AI has so much more
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empowerment at the individual level, the ability to bind all that together creates
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new opportunities, but it also creates new challenges.
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And this is where it becomes very human and very personal.
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If you are an individual that through the innovations of AI in your department,
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two-thirds of your job has suddenly gone away or been automated,
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you're going to take that a lot differently than somebody whose job is maybe
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only impacted at the margins.
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And that's where we have to be sensitive as an HR group to understand what are
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the implications of the decisions we're making as an organization and what are
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the human implications of those decisions.
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And we've been through this. We've seen great transformations and we've seen
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transformations that for all the best of intentions didn't go that well.
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It's really hard to get trust back in an organization when you break it.
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It's really hard to build momentum if you continually have missteps that are
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not properly communicated or properly socialized.
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And that's where I think HR plays a critical role is we're all in this kind
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of like phase of everyone wants to move super fast and everybody wants everyone
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feels like they're behind.
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And if you convince yourself through reading LinkedIn posts and watching things
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on YouTube, you might believe that you're really behind the curve.
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But the reality as most of us are still very much experimenting and very slowly
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moving into this new AI world,
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HR gives us an opportunity to frame that transformation in a really positive,
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human-centered way and create good news stories, as opposed to it being a Band-Aid
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being pulled off slowly.
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Absolutely. And just diving a little bit deeper in on that idea around empowering
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employees to make decisions right closer to the work.
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When we think about things like AI agents who are going to help accelerate that,
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do you foresee things moving more towards a model of decentralization or do
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you think it will actually have the opposite impact and things will centralize
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even further with a point of decision control, if you will?
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It's a really interesting question and the companies that I'm working with are
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going in a couple different ways.
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So I'm personally invested in understanding which
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of those ways ultimately yields the best outcomes i believe it's organization
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specific so it's very much one organization is a very much individual empowerment
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focused lots of sandboxing lots of you know how do you make your day-to-day
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a better experience for you while increasing your productivity.
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And this organization traditionally has an employment brand that is very employee
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centric and employee centered and already creates large amounts of time in a
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work week for these employees to ideate and brainstorm and strategize.
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So in that case, they're continuing that model with a new tool,
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in this case, artificial intelligence, which is used to further supplement their
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research, their accelerate their path to productivity with administration and writing and editing.
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But largely, they view it as how do we make our superhuman employees even more
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satisfied, impactful, you know, and productive in the future.
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Another organization I'm working with has a very top-down approach.
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I'm not saying one is right or wrong, but their approach to employee empowerment
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is very different. It's a more regulated environment.
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There are a lot more rules within the organization and the industry that govern
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employees' activities and actions. And therefore, there's not as much room for
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sandboxing and whiteboarding and brainstorming.
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It's very much a matter of how do we reduce risk?
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How do we increase productivity and how do we remove siloed information,
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which in itself can introduce a new kind of risk?
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So what I'm envisioning for organizations really is looking at their current
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operating model, looking at their organizational culture, and in some ways,
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wrapping it in artificial intelligence, again, based on the outcomes that they're trying to achieve.
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So if you're trying to achieve a singular vision around artificial intelligence,
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and we're all feeding to that singular vision, it's going to look very different
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than if we're trying to improve or enhance employee productivity and creativity and autonomy.
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Me. So I think that's a really interesting piece for me.
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And where I get even more interested is when we start talking about agents communicating with other agents.
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So yes, employees working with agents is going to be one thing.
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You'll have people that are going to support you with your administration or
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your writing or your editing or your researching or your image creation or any
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number of other activities which would have gone and been farmed out to a third-party
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resource or a contractor or even to somebody down the hall in a different cubicle.
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That's going to absolutely happen. Where to me it gets really exciting is when
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you look at the agents collectively and how can they communicate to enhance
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each other's work. So we talked about silos before.
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Wouldn't it be wonderful if in the creation of your employee survey,
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you had an agent that was able to communicate with agents within other divisions
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of the company to understand and bubble up any of the current trends happening
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in finance or marketing or IT?
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And use that context to provide a deeper level of analysis once we get the survey results back.
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For example, that today presently needs to be one human being sending an email
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or popping by somebody's desk to another to get the information.
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In the future, that can just be, if well thought through, part of an ecosystem
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of information where agents are collecting and supporting and moving work forward.
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But some of that work can then occur autonomously within the agents to enhance
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all of the outputs coming from that model.
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Absolutely. That brings to mind kind of the promise of, I don't want to date
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myself, but many, many years ago, the promise of big data.
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And I feel like we're actually at a stage now where we can activate that data
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and turn it into something meaningful.
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I know we've, over time, gotten better and better at that, but the launch of
00:17:17.862 --> 00:17:20.242
generative AI, AI agents,
00:17:20.422 --> 00:17:24.962
really brings that to the forefront and brings it to light and is something
00:17:24.962 --> 00:17:29.362
that's really exciting, I think, for not just HR, but every function inside
00:17:29.362 --> 00:17:32.802
of an organization, which is really quite amazing.
00:17:32.802 --> 00:17:39.442
But with that being said, as organizations start to discover these opportunities,
00:17:39.442 --> 00:17:43.422
and perhaps, as we know, some will be quicker than others to get there,
00:17:43.722 --> 00:17:49.082
but that may adjust the skill set that they're looking at inside of their organizations.
00:17:49.082 --> 00:17:51.322
Are there any particular skills
00:17:51.322 --> 00:17:56.862
that if, even if you're a young HR professional coming into a new role,
00:17:57.262 --> 00:18:02.582
what types of things do you think that these future HR leaders really need to
00:18:02.582 --> 00:18:04.242
start developing and looking at?
00:18:04.282 --> 00:18:06.982
What can they bring to the table that's going to be something more?
00:18:07.377 --> 00:18:10.517
Very different, perhaps, from the more established professional.
00:18:10.517 --> 00:18:11.977
Where can they start that journey?
00:18:12.437 --> 00:18:17.637
I think they can start it at home. So for those of us who use AI on a daily
00:18:17.637 --> 00:18:20.557
basis, ask yourself how you're using it today.
00:18:20.957 --> 00:18:25.377
If it's a better version of Google for you, then you're at phase one.
00:18:25.617 --> 00:18:29.477
You're at the information stage where you can pull from more sources of information
00:18:29.477 --> 00:18:34.597
and, in most cases, get a very good outcome when you ask very specific questions.
00:18:35.077 --> 00:18:38.457
But what you'll quickly learn in that process is the quality of your prompt
00:18:38.457 --> 00:18:40.677
determines the quality of your output.
00:18:41.177 --> 00:18:47.337
And it's that critical thinking element that I think is even more important in an AI-enabled world.
00:18:47.677 --> 00:18:50.777
So you're right. When it comes to big data, we've been talking about it for
00:18:50.777 --> 00:18:52.117
now going on a couple of decades.
00:18:52.117 --> 00:18:59.117
And I've given countless keynotes around the world on how to take action with information.
00:18:59.117 --> 00:19:01.997
Beyond building a business case it's the number
00:19:01.997 --> 00:19:04.977
one request that i get because most hr teams struggle
00:19:04.977 --> 00:19:08.557
to make sense of the information either they
00:19:08.557 --> 00:19:13.777
lack the information itself or they lack the computing power to be able to analyze
00:19:13.777 --> 00:19:18.177
and put information against it but now with artificial intelligence democratize
00:19:18.177 --> 00:19:25.297
you now have given that power to the individual you could sit down with your employee survey data,
00:19:25.497 --> 00:19:30.657
your compensation data, and do deep levels of analysis that were not available
00:19:30.657 --> 00:19:35.437
to you unless you had resources or a large budget or technology or all three
00:19:35.437 --> 00:19:36.497
of those things to make it happen.
00:19:37.085 --> 00:19:40.805
So the thing that concerns me is that now with this powerful tool that will
00:19:40.805 --> 00:19:46.265
accelerate to the quickest outcome and get very surface level with missing the
00:19:46.265 --> 00:19:51.605
opportunity to go a level deeper, which is really the power of artificial intelligence.
00:19:52.225 --> 00:19:56.145
Artificial intelligence at this stage of the game is math.
00:19:56.605 --> 00:19:59.825
And math is informed by context.
00:19:59.825 --> 00:20:02.845
The more context that you provide to
00:20:02.845 --> 00:20:05.985
your ai agent whether it's internal or external by way
00:20:05.985 --> 00:20:09.465
of your prompt or introducing additional information the
00:20:09.465 --> 00:20:14.745
more the the more crystallized outcome you're going to get if you know what
00:20:14.745 --> 00:20:18.705
good looks like and that's where again the critical element part comes into
00:20:18.705 --> 00:20:22.585
this which is if you don't know what you're looking for and you're just driving
00:20:22.585 --> 00:20:27.685
towards an outcome you will get productivity but you may not get the depth and
00:20:27.685 --> 00:20:29.305
quality that you're looking for.
00:20:29.465 --> 00:20:31.625
So I think critical thinking is really, really important.
00:20:32.125 --> 00:20:38.285
And then being able to build really compelling stories with the information.
00:20:38.285 --> 00:20:41.005
And you can leverage artificial intelligence to help you do this,
00:20:41.125 --> 00:20:46.225
but the ability to bring a number to life is critically important when you're
00:20:46.225 --> 00:20:50.025
trying to make a case, whether it's for change or resources or actions.
00:20:50.645 --> 00:20:55.645
If you just simply put a number in front of most people, that's not enough to really move the needle.
00:20:56.305 --> 00:20:59.365
When you tell the story, the impact to the employees,
00:20:59.685 --> 00:21:02.805
how it introduces risk to our reputation or to our organization,
00:21:03.105 --> 00:21:08.885
those things, that context is what brings it to life at every level of the organization,
00:21:08.885 --> 00:21:11.985
whether you're talking to your C-suite or your frontline employees.
00:21:12.165 --> 00:21:17.245
So I think the ability to take that information and apply some sort of storytelling
00:21:17.245 --> 00:21:19.145
element to it is critically important.
00:21:19.385 --> 00:21:23.605
And then the last thing is just having the discipline around measurement.
00:21:24.205 --> 00:21:27.645
It's really easy to just again accelerate
00:21:27.645 --> 00:21:30.485
to the end result because that's the quickest path
00:21:30.485 --> 00:21:33.405
to productivity you check something off your to-do list you have enough
00:21:33.405 --> 00:21:37.865
things on my list today one thing comes off of it but if you miss the opportunity
00:21:37.865 --> 00:21:43.325
to measure and you're missing an opportunity to provide again further information
00:21:43.325 --> 00:21:47.265
that can be used to build a business case i'll give you an example i'm working
00:21:47.265 --> 00:21:51.425
with a client right now that is revamping their entire sales process.
00:21:51.665 --> 00:21:58.785
They feel like the speed from proposal through to the client signing is taking
00:21:58.785 --> 00:22:03.745
too long, largely because of their own internal encumbrances and checks and balances.
00:22:04.405 --> 00:22:09.325
Now, they've had small pilots within the organization that have revealed incremental
00:22:09.325 --> 00:22:12.625
changes, but the challenge has been that those pilots have happened in silos.
00:22:12.845 --> 00:22:17.185
And those pilots really have taken the organization in five different directions
00:22:17.185 --> 00:22:19.045
as opposed to one singular direction.
00:22:19.205 --> 00:22:24.205
The result being that the pilots took longer, and the benefits that were felt
00:22:24.205 --> 00:22:28.345
within those individual pilots did not properly cascade through the remainder of the organization.
00:22:28.785 --> 00:22:32.245
It was only when they provided some governance and some measurement and some
00:22:32.245 --> 00:22:35.705
structure around the pilots that were occurring that they were able to bubble
00:22:35.705 --> 00:22:40.885
up, these activities work, these activities don't, and then most importantly,
00:22:41.125 --> 00:22:45.085
share that information through the broad organization so we all can benefit from it.
00:22:46.259 --> 00:22:51.299
That makes tons of sense. And again, almost rolling back into the idea of building
00:22:51.299 --> 00:22:54.599
that strong case of managing change, right?
00:22:54.799 --> 00:23:00.819
Folks tend to gravitate towards new things when the new things are easier than the old thing.
00:23:00.939 --> 00:23:04.459
And that sounds very simplistic in its truest form.
00:23:04.599 --> 00:23:07.319
But the reality is, as humans, that's how we operate.
00:23:07.519 --> 00:23:11.779
And being able to leverage that data and to make sure that we're pushing that
00:23:11.779 --> 00:23:15.659
envelope forward, I think, is highly effective. And I love how you spoke about
00:23:15.659 --> 00:23:18.379
the idea of building that prompt skill set.
00:23:18.579 --> 00:23:23.019
I often have conversations with folks and use the idea of, you know,
00:23:23.079 --> 00:23:28.959
if you ask any AI tool to build you a picture of a house, it'll build you a picture of a house.
00:23:29.099 --> 00:23:32.239
But it may be not anything you're looking for.
00:23:32.659 --> 00:23:36.239
The more in-depth that you can get with that analogy around,
00:23:36.479 --> 00:23:41.839
you know, hey, it's going to be in a mountain town and I only have, I want logs.
00:23:41.839 --> 00:23:46.179
And I would love to have a fireplace. You're going to get a much better picture
00:23:46.179 --> 00:23:47.979
of that house that you're trying to build.
00:23:48.099 --> 00:23:52.579
And I think the same goes for things inside of any business and that type of
00:23:52.579 --> 00:23:53.839
analysis that's happening.
00:23:53.999 --> 00:23:57.259
So I really like that you zeroed in on that idea.
00:23:57.839 --> 00:24:04.059
When you look at the intersection of AI and employee experience and organizational design,
00:24:04.059 --> 00:24:09.679
so all of those pieces that HR stretches across, what do you think will be the
00:24:09.679 --> 00:24:13.279
biggest leadership blind spot from all of this data,
00:24:13.439 --> 00:24:17.519
from all of this information, as we kind of move forward over the next five
00:24:17.519 --> 00:24:19.799
years? Where do you think leaders are going to struggle a little bit?
00:24:20.539 --> 00:24:25.279
I think leaders are going to struggle with information that's put in front of
00:24:25.279 --> 00:24:27.699
them that may contradict what they believe to be true.
00:24:28.739 --> 00:24:32.439
When you look at the employee experience in a non-AI capacity,
00:24:32.599 --> 00:24:34.219
let's just say it's very organic.
00:24:34.819 --> 00:24:38.479
Each person's experience is more or less unique to them.
00:24:38.999 --> 00:24:42.539
Maybe they have a consistent onboarding team resource.
00:24:42.799 --> 00:24:47.439
Maybe they receive a consistent manual or two, but their experience is largely self-directed.
00:24:48.131 --> 00:24:52.611
When you start to put in place structured onboarding and structured measurement
00:24:52.611 --> 00:24:59.491
and then layer AI in through that process, you get so much more richness of data and inputs.
00:24:59.811 --> 00:25:04.151
You get to find out how engaged people are in terms of reading materials,
00:25:04.151 --> 00:25:08.131
how much time they spend researching the organization, how much they interact
00:25:08.131 --> 00:25:09.611
with their colleagues or their manager.
00:25:09.811 --> 00:25:13.451
You get to find all these great inputs that would not have been present if you
00:25:13.451 --> 00:25:16.571
hadn't put them against a digital layer and started measuring them.
00:25:17.151 --> 00:25:20.131
The challenge is that many of us have gotten to the places of leadership that
00:25:20.131 --> 00:25:24.371
we are in today in a non-AI environment, when we didn't have the benefit of
00:25:24.371 --> 00:25:28.111
measurement and we didn't have the ability to see the impact of our actions
00:25:28.111 --> 00:25:29.491
oftentimes in the short term.
00:25:29.891 --> 00:25:34.571
So when we start to see like within broad, let's just say for the organization's
00:25:34.571 --> 00:25:39.611
sake, we've illuminated through analysis over our onboarding plan that managers
00:25:39.611 --> 00:25:42.191
don't spend enough time with one-on-ones with their employees.
00:25:42.351 --> 00:25:44.471
That's one of the findings of our analysis.
00:25:44.971 --> 00:25:47.571
Well, if I'm a manager that doesn't believe in having one-on-ones,
00:25:47.711 --> 00:25:51.891
that immediately comes into friction with me and the way I run my department,
00:25:52.151 --> 00:25:55.551
the way I manage my leadership style, the way I interact in the organization.
00:25:56.131 --> 00:26:00.311
I instead have a preference for going for lunches with my employees once a month.
00:26:00.431 --> 00:26:03.231
And rather than doing 15-minute quick little huddles once a week,
00:26:03.411 --> 00:26:06.551
I like to spend an hour, an hour and a half of somebody outside the workplace
00:26:06.551 --> 00:26:10.031
to really get to know them as an individual and to build a deeper relationship.
00:26:10.511 --> 00:26:13.851
Again, I'm not saying one is right or wrong, but that individual approach to
00:26:13.851 --> 00:26:18.271
leadership would come in conflict with a more systematic approach to onboarding.
00:26:18.451 --> 00:26:21.711
And I think we're going to have that tension show up in a few different areas
00:26:21.711 --> 00:26:25.231
where whether it's compensation, whether it's org design, whether it's the employee
00:26:25.231 --> 00:26:28.671
experience, where what got us here won't get us there.
00:26:28.851 --> 00:26:32.931
And we can't justify doing the same thing because the information in front of us,
00:26:33.512 --> 00:26:40.012
points to the best outcomes on average when we do A, B, C, not X, Y, and Z.
00:26:40.132 --> 00:26:43.612
I think that friction point is challenging because, and we've seen this with
00:26:43.612 --> 00:26:48.732
big data as well, if the information conflicts with what we feel is the right
00:26:48.732 --> 00:26:52.652
thing to do in some organizations, we resist the change altogether.
00:26:52.972 --> 00:26:57.492
And that's where it becomes dangerous, which is we can choose to evolve our
00:26:57.492 --> 00:27:00.252
approach to leadership based on information within the organization,
00:27:00.252 --> 00:27:05.852
or we can stubbornly say that I've gotten to this point based on this and this
00:27:05.852 --> 00:27:06.672
is how I'm going to continue.
00:27:07.312 --> 00:27:12.572
That in and of itself is going to inhibit the progress both for you as the individual,
00:27:12.572 --> 00:27:14.312
but more broadly the organization.
00:27:14.312 --> 00:27:18.392
So I see that being the tension point, which is, again, I'm doing all these
00:27:18.392 --> 00:27:21.432
things that I haven't been able to measure, but I think I know that they're
00:27:21.432 --> 00:27:25.832
right when the measurement reveals that I may have some opportunities in my leadership style.
00:27:25.832 --> 00:27:33.232
I love that, really doubling down on that idea of building a flexible mindset, really,
00:27:33.392 --> 00:27:39.292
not just growth one, but having the ability to maybe take pause and recognize
00:27:39.292 --> 00:27:44.532
that just because we didn't have the data before doesn't mean that we necessarily
00:27:44.532 --> 00:27:46.092
had all the information we needed.
00:27:46.092 --> 00:27:49.212
And to be able to shift when we do have that information. That's definitely
00:27:49.212 --> 00:27:51.972
something that takes a little bit of skill in getting used to,
00:27:52.072 --> 00:27:56.932
I think, in a lot of scenarios, especially when you've been doing something for a long time.
00:27:57.612 --> 00:28:01.472
I'm really enjoying this conversation, and we've already been chatting for close
00:28:01.472 --> 00:28:04.552
to half an hour, so I'm going to land the plane, as they say.
00:28:04.712 --> 00:28:09.372
But before we kind of close off the discussion, I have a bit of a two-part question.
00:28:09.912 --> 00:28:15.272
So one, when we look at organizations in the future, whether it be a small business
00:28:15.272 --> 00:28:19.792
or a large enterprise, when they get this right, when they have that responsible
00:28:19.792 --> 00:28:21.552
use of AI, they're using the data,
00:28:21.792 --> 00:28:25.432
they have integrated teams, they're creating a great employee experience,
00:28:25.612 --> 00:28:26.832
they're empowering employees.
00:28:27.432 --> 00:28:33.532
What does that workplace look like in 2030 and how will it actually feel for
00:28:33.532 --> 00:28:36.852
a worker who is inside of one of these organizations?
00:28:37.986 --> 00:28:41.126
It's a really interesting question that I'm giving a lot of thought to,
00:28:41.246 --> 00:28:43.566
because I think for most of us, we're kind of in it right now.
00:28:43.686 --> 00:28:48.886
And it could be hard to look beyond that kind of six to 12 month horizon with
00:28:48.886 --> 00:28:51.866
all the things happening in the world to make any real concrete plans.
00:28:52.366 --> 00:28:55.886
But let's just assume that we can wave a magic wand and to your point,
00:28:56.046 --> 00:29:00.326
five, 10 years down the road, we've got to this utopian AI governance,
00:29:00.966 --> 00:29:03.926
human employee experience integration. It's all working well.
00:29:04.426 --> 00:29:10.126
I think it looks a lot more talent centric. I think it looks a lot more about
00:29:10.126 --> 00:29:15.106
meeting the employees where they're at and wherever they're at is great because
00:29:15.106 --> 00:29:20.546
through a combination of culture and technology and tooling and support within
00:29:20.546 --> 00:29:22.626
the organization from your peers and your leaders,
00:29:22.986 --> 00:29:27.426
you're going to grow those skills in a way that just frankly wasn't possible before.
00:29:28.226 --> 00:29:32.106
Corporate learning has been very much like traditional education,
00:29:32.306 --> 00:29:35.606
and that's peanut butter approach and kind of giving everybody the same thing
00:29:35.606 --> 00:29:37.326
or defaulting to compliance.
00:29:38.126 --> 00:29:41.766
Artificial intelligence is going to create and unlock doors around coaching
00:29:41.766 --> 00:29:45.086
and mentoring and developing and skills building that just simply hasn't been
00:29:45.086 --> 00:29:49.446
available to most organizations, in particular, small to medium businesses.
00:29:49.706 --> 00:29:52.906
So that's really, really exciting. And I think because of that,
00:29:53.126 --> 00:29:58.166
it frees up the organization to build an aspirational culture where they no
00:29:58.166 --> 00:30:02.766
longer have to trade off between productivity and employee satisfaction.
00:30:03.733 --> 00:30:07.993
That's always been a tension point where it's like doing the right thing oftentimes
00:30:07.993 --> 00:30:12.333
takes longer and is more expensive. It's kind of a rule that you'll see in some organizations.
00:30:12.533 --> 00:30:17.133
That isn't actually true now. I think with thoughtfulness and thoughtful design
00:30:17.133 --> 00:30:22.273
and great stakeholder engagement and the ability to test and measure and iterate
00:30:22.273 --> 00:30:24.573
with that kind of North Star in mind,
00:30:24.573 --> 00:30:30.333
you can create better outcomes that save the company time and money and that
00:30:30.333 --> 00:30:31.893
are better for all the stakeholders involved.
00:30:31.893 --> 00:30:35.593
That's what gets me really excited is this is a complete seed change.
00:30:35.733 --> 00:30:39.893
You get to wipe the slate clean and go, what should this look like?
00:30:40.213 --> 00:30:45.373
Now, to be fair, the transition from today to that state will come with bumps,
00:30:45.373 --> 00:30:48.693
and it will come in some cases with contraction of workforce,
00:30:48.853 --> 00:30:50.453
but in others, expansions of workforces.
00:30:50.453 --> 00:30:56.513
So I think it's a really interesting question because it opens up so many possibilities
00:30:56.513 --> 00:30:59.273
for empowerment and growth.
00:30:59.873 --> 00:31:04.373
And then it's incumbent upon the organization to create the structure and the
00:31:04.373 --> 00:31:09.333
programs and the incentives to ensure those things take place.
00:31:09.333 --> 00:31:14.653
Because if we don't do that, my fear is that this can become a very draconian
00:31:14.653 --> 00:31:19.373
instrument that can be used to try and squeeze every bit of productivity from
00:31:19.373 --> 00:31:24.813
the employee or from the organization and miss the opportunity to augment that
00:31:24.813 --> 00:31:28.333
pursuit of productivity with things that we know are good for us.
00:31:28.333 --> 00:31:33.353
There's lots of science that proves to happy, engage, motivated employees,
00:31:33.553 --> 00:31:37.713
deliver better outcomes from the organization in both the short and the long term.
00:31:37.953 --> 00:31:41.653
And AI gave us all a giant tool to accelerate that conversation.
00:31:43.028 --> 00:31:46.348
Matt, wonderful. I'm on board with your vision. I'm there for it.
00:31:46.448 --> 00:31:49.928
It sounds good to me. I know it will be a few bumps along the way before we
00:31:49.928 --> 00:31:55.588
potentially make our way there, but I think it's promising and it's interesting
00:31:55.588 --> 00:32:00.888
and exciting, I think, for every organization in some capacity.
00:32:00.888 --> 00:32:04.148
So I'm really looking forward to what the future brings.
00:32:04.168 --> 00:32:06.408
And I can't thank you enough for joining us today.
00:32:06.668 --> 00:32:09.928
I really enjoyed the discussion. I think I could talk to you for another two
00:32:09.928 --> 00:32:13.688
hours and we could have a great discussion. So I really appreciate you taking
00:32:13.688 --> 00:32:15.568
the time to join us today.
00:32:15.788 --> 00:32:18.948
And maybe we'll make a habit of it and we'll talk to you again.
00:32:19.808 --> 00:32:21.788
Thank you for your time today. I'm looking forward to keeping in touch.
00:32:22.548 --> 00:32:23.728
Wonderful. Thank you. Thank you.