Season 1 / Episode 3

How to Build a Smart Organization

Mike Reeves
Mike DeVenney
"I personally think AI is the electricity of our generation. It is going to change the way we live and how we work in very positive ways. So, I am definitely a supporter and a proponent of AI, but I think we need to have a thought first about how we're actually going to use it. Lack of strategy creates a lot of uncertainty for employees, which then puts up this wall of resistance, which doesn't have to be there."
Mike DeVenney,
Founder at WorkInsights

About the Episode

Emerging technologies, like artificial intelligence, are bringing forward a lot of fears around job displacement to the surface. However, they present a significant opportunity to augment and support roles across every level of an organization through co-intelligence. 


Mike Reeves welcomes Mike DeVenney to discuss how leaders can build smart organizations by bringing out the best in both employees and technology, and putting it together. A data-driven technology and change-management professional, DeVenney gives us insight into the surprising keys to successfully implementing technologies like AI to employee satisfaction and retention all while increasing productivity.


Mike DeVenney: I love this idea of building smart organizations that's based on co-intelligence. And the co-intelligence to me is on two levels. So co-intelligence originally means bringing out the best capabilities of both people and technology. I think the second level is bringing out the best from both leaders and employees in the whole workforce. So, co-intelligence to me would be those two pieces.

Only 10 percent of organizations include their employees int heir planning for AI, which is mind boggling. So, that would, that would be my background is that I don't think we're asking the right questions and we're definitely not listening.

The idea is that, You want to make the best decisions you can,I think that's what every leader wants. You want to build a smart organization where people and technology work together. That can only happen if you get the data on what people are thinking, how they feel and where they want to take action.

Mike Reeves: This isSolving for Change, the podcast where you'll hear stories from business leaders and technology industry experts about how they executed bold business transformation in response to shifts in the market or advances in technology.

In every episode, we'll explore real-world strategies and technologies that fuel successful evolution. I'm your host this month, MikeReeves.

In today's session, I'm very excited about our guest and, I'll say a long time friend now that we've done a lot of work with in MOBIA, and today's session we've titled it, how to build a smart organization. And, Mike DeVenney is our guest today. We've done a lot of work with Mike over the years and, he's done an amazing amount of consulting around culture and business transformation and change. Today we're going to talk about that and we're going to get very specific into a few areas around how you build a smart organization. I think it'll be clear what we're exactly trying to define and discuss as we get into the topic today.

What I'd like to do right now is just to give Mike a chance to welcome him to the show and do a quick introduction on his background and, lots of depth to talk through today. I'm super excited about having you on the show so, thanks for joining.

Mike DeVenney: Well, thanks Mike.

And happy to do it. And I don't, I never think there's a quick introduction for me. I've got my own company, WorkInsights, which is really focused on data around understanding how people work and how they do the things that they do or don't do. And, that comes from background as an investment analyst.

So I'm an addict with numbers and love to understand them and I had a consulting company for years looking at, leadership and organizational development. So, really fascinated again by how people work together and how leadership impacts that. Then moved into kind of putting them all together that making decisions, a lot of times, is based more on anecdote than actual facts.

So, I looked at if we could measure and provide data, in terms of how people feel, think, and act, then that would really help leaders make the best decisions they can. And now with technology so prevalent, particularly AI, this has all come together in terms of like really understanding how people will respond. And knowing that before you make decisions.

Mike Reeves: Thanks for that and we'll definitely dig into those types of themes here today in the session. Excuse me. The, one thing I'd like you to do, as we tee it up and discuss this session and, going to be a theme, everyone's been talking about certainly around technology and how you introduce it into an organization.

And right now, everyone's talking about AI, so we're going to talk about AI today, and large language models in particular, and generativeAI. It's a pretty frothy topic these days, and so what I'd like to do is maybe start with, we've called this session today "How to Build a SmartOrganization." Maybe take a minute, and if you don't mind, give us a definition of what is a smart organization.

Mike DeVenney: So a smart organization for me is one that's based on co-intelligence, which means we're bringing out the best of people and technology and there's a lot of challenges in terms of the perception that AI will be a job displacement rather than a role augmentation. I think the whole idea of a smart organization is engaging employees in the process of implementing new technologies so that you bring out the best in both.

Mike Reeves: Maybe we can also just overlay on that, a little bit of background. I know you do a lot of time researching the Canadian market, the U. S. market, and globally in terms of some of the themes around AI. How technology is getting introduced into organizations and how you communicate that and handle the change management piece, which I think are things you just hit on. If you look acrossCanada right now, we know investment has been a challenge. We know innovation is a challenge. If you look at the scorecards in terms of how we're doing globally, we certainly wouldn't be at the top of the list in terms of performance. I wonder if you want to pause there and maybe talk through a couple anecdotes from some of the research and the insight that you have.

Mike DeVenney: The biggest thing we're facing right now is this productivity gap and the focus, I think, for a lot of leaders around AI has been, this is the answer to the productivity gap. We have been gradually and steadily declining in our productivity, not only against the U. S., but against other advanced economies.The question often becomes, are we simply not working hard enough, which is not it at all.

What it comes down to are two big issues: is that we don't innovate as much as we could. We have one of the lowest rates of applied innovation of any advanced economy. It doesn't mean we don't have a lot of really smart, and innovative and imaginative people. It's just that we don't capitalize on it. Which is the second part is, we don't invest in training and development like other countries do. So those two together are a bigger part of why we're not keeping up with the other nations in that advanced economy group. So, for me that's a big piece and an anecdote very quickly is that I heard a company this week that they want to pilot an AI project as quickly as possible.

So it's very much what I've seen at other ones; AI is basically a solution looking for a problem. So the CEO wants to move out quickly and the whole premise of how this is going to work is based on anecdote. So people have said this is the best place for them to apply it, so that's what they're gonna do. And I think, they're gonna hit a massive brick wall, within the next three to four weeks as they roll it out.

And this is where, for me, a smart organization again is, how do we include the employees who know best, I would say, how work is actually done where there's opportunities to add value for clients or customers. And that's something we don't do very well, like only 10 percent of organizations include their employees in their planning for AI, which is mind boggling. So yeah, that would be my background is that I don't think we're asking the right questions and we're definitely not listening.

Mike Reeves: So let's, let's dig into that a little more.

I'd like to talk a little bit more about some of the challenges, or the issues, that companies are having before we get into the solutioning and your path or recommendation of, how you work through some of the challenges, but, just a couple of things you hit on there. So, everybody comes in with this notion of we want to take AI and they want to just start torun in the organization and look for a business problem to solve. And I think, sometimes you have success with that. Oftentimes, to your point, you don't. The majority of the times you don't.

And so there's a set of, it seems to me, a set of standard issues that are evolving or materialize and that you can see that are repeating themselves over time as companies try to look at not just AI, there's lots of other technology. Or new fancy widgets that have come out over the years that everyone's trying to take into a company to try and, you know, create some sort of benefit or value. And so, if you look at what some of the heavier issues are today, it's certainly like the planning and the strategy, and then you get to the communication piece. So, I wonder if we can just talk about those two pillars for a couple of minutes?

Mike DeVenney: Yeah, so there's a number of big, I think, hurdles that organizations, I wouldn't say face, but have put themselves into. So the first one is that. Only 8 percent of organizations... I mean, I'm a data nut, so there's going to be a lot of stats.So, only 8 percent of organizations actually have a strategy for AI. So, that does not bode well for what's going to happen.

I personally think AI is the electricity of our generation. It is going to change the way we live and how we work. And I think, in very positive ways. So, I am definitely a supporter and a proponent of AI. I just think we need to have a thought first about how we're actually going to use it.So that lack of strategy creates a lot of uncertainty for employees, which then puts up this wall of resistance, which doesn't have to be there.

But the fact is, there's a second hurdle that, as I mentioned before, only 10 percent of companies will actually involve and include employees in the development of AI solutions. So, one, there's no strategy. And two, we haven't actually included employees in looking at where AI could help them. So you're really stiffening the resolve against trying things with people.

And I think that's probably the two biggest things which leads to the third issue, which is we never get better at change. It seems like JohnCotter was in the 1990's speaking about how we can really improve the way we move change forward. Supposedly we listened, but we haven't listened well, because right now the stat is only 12 percent of, particularly digital and AI transformations, only 12 percent will actually create successful results.

We're failing less. So, that's what we took from Cotter and other change leaders is that we have fewer initiatives failing, but the bulk of them just sit there and wallow. So we've got better at not failing, but we've got worse at actually succeeding. So that's kind of a different way of looking at it.

So I think the biggest challenge we face is that the answers that we're looking for are right in front of us, and we don't ask for them.Which is employees hold the most... To me, employees hold the most, opportunity for organizations to move forward, but we don't take advantage of it. We get excited when we hear about these co-labs with universities and organizations and the brilliance of students and how great this is. I am not knocking that for a minute because that's where a lot of innovation happens, but we also could do co-labs with our own employees.

They're the ones that are inside of what you are trying to do as an organization. They have the most understanding of clients and customers of anybody within the organization, particularly at the executive level. And they see the work day to day, but yet why can't we do a co-lab, if we want to call it that, with employees and bring them into the equation?

I think leaders get concerned about, "Well, this will just slow us down." Actually, it's going to speed you up. It doesn't take long to ask and hear the answers. But by not asking, you're going to get mired down in problems within two to three months of starting whatever initiative you're looking at. And that's why change initiatives don't achieve the results we're hoping for. So, that would be a couple of big pieces for me.

Another one that came out from Conference Board of Canada are reports showing that 63 percent of employees do not trust the executive innovation initiatives. So, that's not good!

So when you're trying to move quickly and move something likeAI, which, everyone's had that moment in the last year where Chat, or DALL-E, or whatever has just WOWed you and this is going to change our lives. So we want to use it, but we're not going forward in a way that's going to make areal lasting difference. And that to me, I think we've added to the productivity gap or perception gap.

Mike Reeves: And do you have maybe a couple of notions in terms of, a lot of companies would – I'm sure this resonates in terms of what you just described – but if you look at the strategy piece, you build the strategy piece, you look at the communication piece and you look at the change piece, and all this wraps around culture in some capacity. But, are there a lot of commonalities that you see when you start to link those three things together in terms of common sets of issues?And then what I'd like to move into maybe after – you talked through a fair bit of that – but I wondering if you could... Is there a linear approach that you could take to linking those three things together? Then maybe we can work our way into how you can prescribe some options for folks to try and build something in terms of a plan to move forward with.

Mike DeVenney: I think the biggest thing is this disconnection and the way we do things now creates and solidifies this disconnection. In work I've done over the last year around quality of work in organizations, what really blew my mind was that 45percent of employees don't trust the decisions being made by executives, and don't feel that their voices are being heard. This is not a new problem. So this has been going on for a long time, but I think our leadership approach has kind of evolved to sell and tell.

So, we've got something we want to do, we've developed a strategy at the executive level, we've had a number of off sites. We put this all together, it's been based on what a consultant might tell you. No offense to consultants, since I am one. This strategy is then rolled out and then it just hits a brick wall. And again, it's because the decisions made within that strategy have not included the voices of employees. Therefore, it doesn't resonate, and that's the big problem. And another piece that comes out of listening to employees is – again, this rush to move AI forward – we're looking at two issues that I think are going to really boggle people up, which is, we're all talking about skill development. Well, we actually should ask people what skills they have and what they need to develop.

What is phenomenal to me is that, it's a, it's around 56percent of employers say that they're going to reskill and upskill employees internally for AI, but yet they don't actually know what skills they're going to be trying to develop. That would be good to know.

So, things like this create a lot of uncertainty for employees.With the leftover hangover from COVID, and the uncertainty we have in the world, work is something we look to for not just purpose and meaning, but also for some consistency in our life. So, when that doesn't have, or doesn't make sense to us anymore, that's what's really fueling the big turnover issues that we're having. It's not, I know everyone wants to point to pay, but it's not.It's pointing to this lack of certainty as to what my future is in the company."No one's listening to me. Decisions don't make sense. So maybe I should go somewhere where it'll make more sense."

So I might have meandered there a bit, but, I do.

Mike Reeves: No, no, that was great and, I think, a lot in there. We were talking about communication, culture, comfort, and making sure people understand how they fit into an organization. And not just today, but the longer term. Especially, when you're looking at what people would view as disruptive change potentially, and so much more about the disruption, rather than the change and the positive side of what potentially can be the result. The communication is, and the planning around that communication, and how often you communicate is certainly very key.

What I'd like to kind of dig into now is, and this goes, as I mentioned at the front end of our podcast, you've done a lot of wonderful things with us. I know you've got this set of assessments and diagnostics that you can come in and really help a company gain some rich insights across a number of dimensions of an organization. Lately it's, as we've been talking about and everyone's talking about, it's around AI and generative AI and what that approach is. I know you've been doing a lot of research and you've got a set of diagnostics now that you've brought to market to be able to help companies with the topics that we're talking about here.

So, let's maybe move into shifting into talking about, OK, what can we do? What is the approach? So, you can come in, you can help with a diagnostic or an assessment and maybe talk about that a little bit in terms of what you'll come in and do. And then, how do we start to overlay that into, or transfer that into, a plan in terms of a playbook that you can potentially start to build, and organizations can start to implement to help them address some of the issues that we're touching on here.

Mike DeVenney: I think there's a few big questions that leaders need to look at. You know, one is, how do we involve people in the development of AI solutions? What are the fears and concerns that employees actually have? And then, are we doing the right things to support, this idea of a smart organization where people and technology work together?

The way to get there in my mind is start with the problem. So understanding what the problem is that you're looking to resolve. That could be operational efficiency. It could be creating new revenues. It could be enhancing customer experience. It could be all three of those. And the second piece then would be going with an analytic to the employee base.

So, I customize and develop analytics that really look to resolve the specific problem that you're facing, or maybe it's an opportunity you want to capture, and getting the perspectives of employees on that. I useAI in what I do, so it's looking at both quantitative, and more importantly, sentiment.

The sentiment analysis can be absolutely stunning because it tells you very clearly how people feel and what they think and what are the types of communications that will resonate most with them. I've heard so many leaders say, "Well, we have forums, we have open discussions." And my response is, "That's really good, but that's for giving out information, not for getting."

People typically will not say what they think in that type of situation. And I had one CEO who said,"I've spent the last month talking to pretty much every employee in the company." And I said, "That was really good, but my guess is you didn't really get what you were looking for." He had all kinds of little file cards with what each person said and didn't know what to do with them. AI could have helped with that. But anyway, you really have to ask employees directly and give them an opportunity to respond anonymously. To me, the ability to say what you think without being identified, without being have some reaction to, is critical.

So we get that information, and as you've seen it, it's extensive. And then the whole point is we take that based on the original problem or opportunity. We look at the key insights. There are always surprises, which is what I love about what I do, because I'll go into it thinking, "Hmm, this will probably come out." And then, "WOW, didn't see that."

I just finished a large project for a sector that wants to implement AI. What came out was completely not expected: it was a concern for discrimination against people in underrepresented groups. That AI would be reserved and skills would be provided only for those in other groups. So, that's something the organization took very seriously and is now developing a whole initiative around the fairness of opportunity, which I thought was just a fantastic outcome from it. But, they wouldn't have known that if they hadn't have asked. Everything I do is based around how do you make the best decisions possible.

You do that with data that gives you a really objective look at what's happening. And from both, like I said, the quantitative: how people score certain things. To the qualitative in terms of the sentiment analysis.But also, giving people a chance to say where they think opportunities exist and what things cause them the most friction in terms of getting what they want done. That's where you've got some real interesting use cases that people will support. From there it's helping people implement.

That might be more long winded, as always, but the idea is that you want to make the best decisions you can. I think that's what every leader wants. You want to build a smart organization where people and technology work together.

That can only happen if you get the data on what people are thinking, how they feel, and where they want to take action.

Mike Reeves: I can say we've experienced that first-hand with working with you and this wasn't meant to be about your consulting and your service, but I can certainly speak and to, first-hand, of the richness that we've experienced as a result. BecauseI find when you're trying to build strategies, oftentimes leadership teams will build strategies in a vacuum.

To your point, you really need that collaboration and that bottom up build in terms of what folks in the organization are feeling and whether or not you've included and they feel inclusive in that process around trying to build a plan for a year. For a fiscal year as an example.

I think it's really super important, what you just described that more leaders take the time and pause and really think about that grassroots or ground-up build of a plan in terms of anything that you're trying to do. But specifically these days, again, back to the AI and gen AI initiatives that companies are trying to bring into their organization. There is so much value of richness there that can be established and gained, if the approach is done right. Unfortunately, what happens is a lot of companies don't do that right, so they set themselves back.And/or they say, "Oh, we tried this and we're not going to pursue that anymore this year or into the foreseeable future." So, lots to work on there for every, for sure.

I'd like to spend maybe a couple more minutes now, we talked through the problems we've talked a bit about kind of approach and how you can try and look at, the transformation initiatives and bringing AI as an example into an organization and be successful with it.

How would you measure success? Let's talk a little bit about that. So, you've got a project, you got people, you did all the goodness that we just talked about, and then now you're looking at outcome. It's easy to establish, I'll say, some hard line operational KPIs but, there's a lot of other things you want to consider as well. And now it's back to the people piece. In terms of what I would consider like the super important piece, like you got to make it sticky there.

So, get people excited, get people comfortable, make sure you get over all those, those front-end encumbrances around job security and/or some wage degradation in some cases, some people seem to think that AI is going to take a portion of their income away from them once it takes over their job, per sé.

So maybe talk a little bit about what success looks like.

Mike DeVenney: Success without a doubt is about changing behaviour. You've succeeded if people actually change their behaviours. So on one side, they change their behaviour in terms of using and embracing AI, rather than being fearful of it. So that's where you can put in some rather bottom line metrics. And as you know, I'm a chartered financial analyst, came from the investment business, so return on investment is really essential for me.

There's also the aspect of, you know, how do we measure if we've done a good job in terms of bringing people in. So, the great thing about data is that when we do the first diagnostic, you've now got a baseline.

If we choose, say, three areas that are really important, and you mentioned communication. So, we could set up a pulse survey that would be just a few questions around that. So, you wouldn't have to wait a year to retest and see where people are. You could assess each month, maybe a list often questions about how we're doing here. And if you originally scored, let's say, which is the typical score around 62 or 63 on voices being heard. If that goes up to 70, you've succeeded. So you're going to see shifts in the way people perceive their working experience. And everything in my mind is measurable. So you just decide what it is you want to see, what behaviours that you want to see shifted and then we simply measure it. And the only way to measure it is to keep asking people.

People don't get fatigued by, I hear a lot of times about survey fatigue. That's when you don't do anything with the results. The whole point is that if you actually do something and people see it, they will not mind doing another survey. In fact, they'll get more engaged in it.

And I want to mention again, or mention just from an earlier comment, a lot of times I'll hear leaders say, "But this is gonna take us time. We're in a rush, we've got to get this out." And I've heard that so many times. I'm thinking, you're going to spend a lot more time, fixing what happens by not asking. And as you know, this doesn't take a lot of time. My thought is always, it takes me a few weeks and it's going to save you a few years of trying to deal with the fallout.

So it is being really clear up front on the behaviours that you want to see shift, so there can be the bottom line impact of the usage of AI and how that helps productivity, how it helps revenue generation, profitability, all those things. So, we can measure that. But there's also the behaviours you want to see that people feel like they're being heard. People feel like they have greater confidence in decisions and people feel like they believe the innovations are working. Again, we can measure that and track it and you can see it like over a real time that these changes are happening. And you'll feel it like that's another thing you start feeling it in the organization. There's a different energy, because people are seeing that you've heard them and that's just kind of make a landslide difference.

Mike Reeves: It's interesting, some things that you just touched on there that I think are super important is, you can quantify and you can actually create data around a lot of these soft issues or soft areas that previously you would just try and guess at or try to some sort of sentiment analysis, but not with any concrete information. What I really like about what you just talked about is that you can actually go in and quantify this stuff and you can measure a lot of these soft things that are so important to understanding how you can be successful and if you're being successful and things you can keep doing or things you need to continue to augment or change as you're going through this iterative cycle of improvement.

I don't know if you have any more anecdotes you want to share about that, but I really like those features because it really, back to the richness and the depth in terms of the employee engagement and feeling like they're being heard and that they are affecting change and they are being super important to the company and its purpose and its direction.

You really got a way to be able to measure those things, which is really cool.

Mike DeVenney: A great example is like this one I've just been working on where, if I had gone in as a consultant the way I used to work , talking to people and then I came back to the leadership and said, "You know, there's a perceived discrimination against opportunity here." It's really easy for leaders to say, "Well, that's what you say, I don't see that." But when we do the analytic and come back with 35. 9 percent of employees feel discriminated against for opportunity. OK. OK, now we have to pay attention to that. Plus, we can correlate that to the actual things that are happening. Even more, we can correlate it to it's responsible for this much turnover.

So now, we're back into a constructive conversation, not based on anecdote. And like you said, I can then also add the sentiment to it that they have a 22 percent greater likelihood of having a negative opinion of the company than others. OK, so not only do they feel discriminated against, but they're probably talking to others negatively about the organization. So we've gone from a challenge we might be having to now we've got real objective data on what's happening and why. Most importantly, how we can resolve it.

So this organization, the CEO is excellent and she is on it!.So this is something they're working on and they're going to measure and change. So there's something where the behavioural change will be people feel there's a fair opportunity for learning and developing their new skills. That's something, again, we can measure. And my suspicion is that we'll see a lower turnover, risk.

So in those things, again... Sorry, I love data, but the whole idea of data is that whatever it is you're trying to change, and particularly with A. I. because it's still kind of a mystery for everybody. Well it's a mystery for everyone, I guess. The idea is that the more we can gain objective data from people who are actually going to use it, the greater the likelihood that we'll be able to successfully build that smarter organization

Mike Reeves: Really good insights there, I appreciate sharing all that I know you've got a lot of experience and richness around this. Data is super important, but you also bring the other pieces to bear which is, how do you communicate that and massage that back into the organization and not just do you know a data dump.It's super important that you overlay those two things together because they have to be happening together to be successful once you're in and you're trying to actually affect these changes and make these changes.

From here, what I'd like to shift into is maybe talk about, you know, again, we've talked from the start to the problems, the issues, and then through to some solutioning and some scenarios. Maybe, let's spend two minutes talking about what's next.

So a company, they go through this process and if you've come in and helped them, they've had some baseline of success to build from. Where do you go from there? So maybe it's a use case or two, maybe it's a department and some people change and stuff like that in terms of getting more comfortable. How do you continue to put scale around this thing and continue to keep the momentum moving?

Mike DeVenney: So first thing you'd have back is, you've gone through the data, you understand the landscape of where people are. You've had some real use cases that have been developed from the input of this co-lab with employees, if you want to call it that. There's going to be communication and it's always, as you mentioned, it's always around communication, so we're communicating. The big thing I do is help prioritize, what are the initiatives or what are the areas that will have the biggest impact for you? So, if we're talking about AI, so what AI use case can we move forward that will have the biggest impact?

There was a great case of a large bakery in the U. S. that, they were intent upon implementing AI. So, they created this AI system for, it was several hundred thousand dollars, that would assess, it would regularly check and order materials. Then they found out later that it only takes about an hour a week for the bakery staff to check on the level of materials to make the order. So what they did is they spent let's say, $280, 000 to solve a $14, 000 problem.

If they had asked, that would have been something that would have been kind of put to the side. Yeah, that's not worth it. So, biggest thing is, how do you prioritize based on impact, cost, and, and value created, and then help to move it through.

So, steps are really: understanding the challenges that you want to dive into. Let's say, it's finding a way to be more productive. And then we customize the analytic to your organization, we get the data, we bring back, show the insights around your specific issue. Plus, bring out things that came up in the data. Then we look at the insights in terms of how do you want to apply that. Then we look at how do you communicate, because the great thing about the data is that people tell you exactly how they want to be communicated to, which is great. So, that part's amazing. And then we help you roll that out. And, like you said, how to measure success.

Once you get that first use case out and going, people engaged, fully using it, big part of what I'm trying to do as well is help with user interface. It's a little off, but what I've found is that that seems to be one of the biggest sticking points with moving AI and other technologies out. If you make it difficult to use, people won't use it. So asking some questions around that specific use case will really help you in terms of ease of use for people. But once you get that first one out, then you can move it forward.

So, whether you've got a large healthcare company, construction company... There is no industry that's not going to be impacted by AI. The first thing would be putting together that analytic. Understand the context of the way people see their work, and from there developing where you can move AI use cases out. Then measuring the impact and building on it from there.

Mike Reeves: That kind of takes us to, we'll just spend a couple of minutes here, maybe talking about... We'll wind down our session today and it's been awesome, so I appreciate your time as always.

If we talk about some of the key themes for today it's, you need to sit down and figure out what are your objectives. What are you trying to do or what do you want to do? Then you want to try and build a strategy around that. The other important elements are communication, change management, but also the inclusion of folks across the organization.

I think that's been certainly underscored by you today and it's important and a lot of the power comes from that grassroots or ground-up approach in terms of what you want to try and accomplish or do. I don't think you can over- communicate on any of that enough.

There's a lot of richness in here, there's more to the discussion today. I think we just kind of framed it up and hit a lot of the high-level, key elements of it. Is there anything else you'd kind of like to add into, into what we're describing here?

Mike DeVenney: I'll go back to the fact, that I use AI constantly. I absolutely love it. It has changed the way I do work positively and dramatically, so I see the opportunity. But, I do have this fear of how we are going to move it out and it's going to fall flat.

So, I love this idea of building smart organizations that's based on co-intelligence. And the co-intelligence to me is on two levels. So co-intelligence originally means bringing out the best capabilities of both people and technology. I think the second level is bringing out the best from both leaders and employees in the whole workforce. Co-intelligence to me would be those two pieces.

If an organization can move forward that way and not fall to the pressure of trying to get something out quickly. Doing what I do only takes about a month, so it's not going to stall you too long. In fact, it's going to help you speed things up quite quickly. So I think that first action for any leader to take is: find out how people actually do feel and think about AI or about their overall quality of work and where they see the challenges and opportunities in how work is done. And that is going to set the company up to really be successful.

Mike Reeves: That's great, I appreciate that. We'll get ready to close off here but, in terms of people that are going to watch this podcast, let's tell them how to get in touch with you. I can tell you that they can certainly come to MOBIA and get in touch, we can help with that. But, I know you have your own stuff that you hangout there, so I'll give you a minute to maybe share that.

Mike DeVenney: The easiest place to find me is to send me an email, that's the easiest way. And it's michael@workinsights. io. We can provide that somewhere, probably, on the podcast.

Questions are easy, send me a question about what you're doing and as you can tell I'm always happy to have a chat. If there's a question from the podcast that people want to investigate, just drop me an email, we can have a chat about it. There's no charge for having a conversation and then see where it can go from there.

But, I think the biggest thing is to know that there's ways to make these decisions far more objective for you. I think a lot of leaders are –there's just so much going on at once right now – and we've got definite cost issues coming in play because the economy is pretty uncertain right now and will be for a little while. So, any way that I can help on that side make things more certain, make things more objective, that's what I like doing.

Mike Reeves: Great.Well, thanks for that. As I said before, you've become a friend of mine over the last number of years and, it's been through the work and the things that you've done with us that I've started to get to know you and it's just been a pleasure. You've certainly provided a lot of richness for us at MOBIA, so I do very much appreciate that.

I was very excited to get you on the podcast today, so I do appreciate you taking the time. I know you got to run to another appointment, but thanks so much for coming on the podcast today. It's been a wonderful discussion and I'm sure we'll have you back on again.

Mike DeVenney: I appreciate it. And I have to say too that MOBIA is, without a doubt, my favourite client to work with. So, the feeling is returned.

Mike Reeves: Thank you for listening to Solving for Change. If you enjoyed this episode, leave us a rating and review on your favourite podcast service.

Join us for our next episode, it'll be coming out in the next few weeks. Thanks very much.

About our guest

Mike DeVenney

Michael is the founder of WorkInsights, a Nova Scotia based consultancy that provides comprehensive diagnostic tools and analytics that calculate the employee experience for organizations. A technologist and data expert with a background as an investment analyst, he's passionate about analyzing the hidden strengths and threats to a business in real-time and facilitating evidence-based decision making that eliminates costly guess work.

Michael is always open to discussing the potential impact of technology in business and can be contact at

About our hosts

Mike Reeves

Mike Reeves is President at MOBIA Technology Innovations where he leads the evolution of the company’s core services and go-to-market strategy. Building on 20 years of experience working with early-stage technology companies to develop their strategies, raise capital, and be acquired successfully, Mike is passionate about helping enterprises execute complex business transformations that support growth. His dedication to supporting leaders in leveraging technology to create competitive advantage inspired the vision for this podcast.

Keep Listening