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- Florin Rotar, Chief Technology Officer, Avanade
Could the potential of ChatGPT and generative AI capabilities compare with what mechanization did to spark the Industrial Revolution? If so, how can we best approach using AI as a springboard for evolution in today’s digital workplaces?
In this episode of Digital Workplace Impact, host Nancy Goebel welcomes back Florin Rotar, Chief Technology Officer at Avanade, a leading provider of innovative digital, cloud and advisory services. In previous years, these two have collaborated on a number of projects, presenting a shared focus and unique perspectives on how digital technology is changing work and people.
In a matter of a few short months, ChatGPT has become a catalyst for change, curiosity and creativity in workplaces everywhere. In this conversation, Florin and Nancy really get to the heart of the hype, the risks and the opportunities, and ask whether ChatGPT and emerging AI capabilities will democratize artificial intelligence at large?
So, for a human-generated view on AI, join us and listen to the conversation. Hear more about how the future might unfold, along with some great advice on how best to seize the opportunities this pivotal moment brings.
Show notes, links and transcript for this episode:
[00:00:00.490] – Florin Rotar
This generative AI is going to force a rethink between the sort of the relationship and the engagement between people and technology. Anything as, as fundamental as, as the one during the Industrial Revolution. It is going to impact everyone. It is going to blur the distinction between, you know, knowledge workers and there I use the term blue collar workers. And I think the successful organizations here will be the ones which deeply understand how to leverage this to support employees creativity, quality to share sort of the lessons learned and the experience around how this will strengthen people’s sense of workplace contribution.
[00:00:50.450] – Nancy Goebel
I was just delighted to welcome back Florin Rotar, chief technology officer at Avenade into the Digital Workplace Impact podcast studio. Over the years, Florin and I and our respective organizations have collaborated on a number of projects simply because of our shared focus and unique perspectives around how digital technology is changing work and people. You may recall that Florin joined us in the studio last year to talk about the metaverse at the height of its focus as a new set of capabilities for digital workplace professionals. Today’s conversation centered around generative AI and within that ChatGPT. Not only did we talk about what these capabilities are, we talked through the hype, the risks, the opportunities. We also talked at length about how ChatGPT in particular is very much on par with what mechanization did to spark the industrial revolution and what the web browser did in opening up the internet age. ChatGPT is clearly a force that will democratize artificial intelligence at large and in the workplace. In a matter of a few short months, we’ve seen it emerge as a catalyst for workplace change, curiosity, and creativity within our circles. Join me now in conversation with Florin.
[00:02:35.450] – Nancy Goebel
This is Nancy Goebel, your host for Digital Workplace Impact podcast, which, of course, is brought to you by Digital Workplace Group. Happy listening,
[00:02:47.110] – Nancy Goebel
Florin. I have to say, I’m so excited to have you back in the studio once again. Last year, we had a chance to catch up about the metaverse and how Avenade was utilizing it internally and of course, some of the wider industry trends that were at play at the time, not only within Avenade’s client base, but our wider industry circles. And today we have a chance to come together to talk about another big topic, topic of the moment within the digital headquarters, and that is generative AI and within that, ChatGPT. So thank you so much for dipping out of what we know is a busy schedule to come and talk about something that’s got quite a lot of hype attached to it at the moment.
[00:03:41.770] – Florin Rotar
Indeed, but also a lot, a lot of potential and a lot of value as well. So good to be here with you, Nancy. Thanks for having me again.
[00:03:51.200] – Nancy Goebel
Always a pleasure. And so I’m sure that quite a few of the practitioners in our circles have been actively exploring both generative AI and ChatGPT. But I do think it’s important to provide a little bit of grounding before we jump into an exploration of this space together. And so, for anyone who hasn’t heard these terms before, can you start with a little bit of a grounding there?
[00:04:23.290] – Florin Rotar
Yeah, I will certainly try. So what we’re talking about here is generative AI. There are many types of AI, but basically generative AI is a type of AI where we’re using algorithms that use existing content like text or audio files or video or images to create new content, which is plausible. But there are many different types of content that can be created. It could be text, it could be computer code, it could be images or video or audio or 3D models or any other type of content like a drug design or a chip design. So that’s basically what generative AI does. And it is based on the science and the art, I should say, which has been around for several decades actually, which is neural networks, which finally has sort of come of age and reach prime time.
[00:05:42.800] – Nancy Goebel
And so characterize ChatGPT within that frame of generative AI, Florin.
[00:05:51.430] – Florin Rotar
ChatGPT is an implementation, an instantiation or realization, I should rather say, of a large language model. So a large language model, you know, the, the way I think about it is a instantiation of a neural network which has been trained with large, large amounts of text. In the instance of ChatGPT, it’s been trained on large parts or parts of the content available on internet and it’s using statistical likelihoods on tokens. So without getting too technical here, think about tokens as characters or words. So every time you’re asking it something and it produces a response, it comes up with the most likely or the most appropriate next token. So if I would say the first token being Florin, then the next likely token could be is, has, was, whatever, let’s say it goes for is. So Florin Is, and then it’s trying to figure out what is the next most appropriate token after Florin is and so forth. So, yeah, ChatGPT is a type of generative AI. It uses large language models and it’s based on neural network sort of science and art, I should say, because there is a fair amount of lore and art into the whole training using leveraging neural networks.
[00:07:43.650] – Nancy Goebel
I think that’s a really helpful starting point for today’s conversation. And Florin we know that there has just been quite a lot about this space and ChatGPT in particular in the press a lot of late. Clearly it is a disruptive force and that can be one that carries both benefits and challenges to organizations. And I wonder if you’ve got a nice way of framing some examples of what those look like as a way of taking us to the next level in the conversation and maybe even put a little bit of a workplace bent to it, certainly.
[00:08:30.050] – Florin Rotar
So we do believe this is really fundamental. And I would go as far as saying that generative AI is probably the most important technology trend we’ve seen in the last 2030 years. And I don’t think I’m exaggerating too much when I say that. So to take a couple of steps back, the reason we think this is so fundamental it is because generative AI acts as a democratization engine for artificial intelligence. So the way we explain it, or the way I would use an analogy for this, I tend to think about generative AI as the equivalent of web browsers in the mid 90s. So for those of you who were around at that point in time, it was quite a while ago when you remember internet. It was sort of something which was done or used by a very small minority of people and it was used by technology enthusiasts and you had to use technology sort of means like FTP or find your way around bulletin board systems and so forth to use it. And then what happened with the advent of web browsers is that everybody who could point and click was able to use internet, which completely changed the nature and the usage of internet.
[00:10:20.140] – Florin Rotar
It went from being sort of something that was used by a small minority to something which was available to the vast majority of people. So we believe that generative AI and large language models are basically a bit of an equivalent, an imperfect equivalent, but an equivalent of the web browser, in as much as it truly democratizes AI from something that was only used by a small minority of very technology savvy companies and cloud hyperscalers who could afford to invest hundreds of millions, if not billions of dollars to make it work. So what we’re seeing right now is a number of ways we see if I can take sort of a digital workplace bend to it. We see this right now fundamentally changing the space of knowledge management. So that is something that a lot of the customers that we work with are looking into and basically fundamentally changing the approach to knowledge management as an example. There are many more examples.
[00:11:36.100] – Nancy Goebel
For sure and we’ve been talking about it a lot in terms of how it’s impacting roles that are content and coordination centric. And so not only in terms of the knowledge management side of things, but when knowledge workers are coming together and need to drive programs and projects, that space is being impacted quite greatly early on as well.
[00:12:04.470] – Florin Rotar
Yeah, absolutely. There’s so much the change is so fundamental around the knowledge management. To maybe give you an example. So let’s think about an asset heavy industry which is having to manage very complicated and interconnected set of assets and maybe it’s in, you know, let’s, let’s say in oil and gas, maybe it’s, it’s pipelines and the structures and the infrastructure on refineries. So if something goes wrong, let’s say there is a valve stuck on a specific pipeline. Historically, until not too recently, what people had to do is basically gather a team of experts, many with institutional knowledge, around that specific environment. They would have to look at pipe drawings, historical sort of records loads, and maybe dozens of applications and systems to try to figure out what’s happening here. How have we solved this before, what are the interdependencies, what are the risks? And the risks can be very real. I mean, if you do something wrong on the refinery, it could trigger a pretty serious health and safety incident. So using GPT models and fine-tuning bills and training bills on an autology of sort of the pipeline language and training and fine-tuning those with information from all of those systems, you could literally take somebody could simply ask.
[00:13:52.590] – Florin Rotar
I have a valve 7538 on pipeline one, two, three. ABC on location. X stuck due to corrosion and the pressure is 780 PSI. What do I do about it? And literally you can get a quite likely response back in a matter of minutes and probably have a very plausible way of solving that problem in a matter of hours versus weeks. I mean, that’s just an example, but there are so many more out there. It’s amazing the impact this is having to the customers that we’re working with.
[00:14:33.830] – Nancy Goebel
And this is an example that’s more technical in nature. Certainly we’ve seen early examples of how GPT is changing marketing and advertising from producing campaigns, whether advertising or social media copywriting, so producing articles and blog series and the like. And so these are some of the near reach examples that have been put in front of us. But certainly we’ll start to see a level of innovation happening in and around knowledge heavy functions as we move forward and there are some opportunities to improve productivity, among other things. Any specific thoughts there you’d like to highlight? Vis a vis productivity.
[00:15:34.010] – Florin Rotar
What we are seeing here is going to heavily, heavily impact every knowledge worker category on the planet, but also every, what I would say frontline worker or non-knowledge worker category on the planet. I think it’s going to blend that distinction which we’ve had around since I don’t know, the industrial revolution. It requires serious thinking around change, enablement and the roles and responsibilities of people. Because the way we see this happening is that it’s not a replacement for jobs, it’s not a replacement for people. It is about having a co-pilot, if I can use that word, where you truly tag teaming with a machine to achieve something which is better than you could have achieved by yourself. But that doesn’t happen automatically. There is really a lot of work that needs to be done on the human side to sort of empower, if I can use the word human flourishing, to figure out how people’s creativity work quality can be used to strengthen their sense of workplace contribution and value and not to see generative AI as a replacement. So I think there is a lot of change enablement and change management, frankly, on a planetary scale, which will happen over the next two, three, four years.
[00:17:19.570] – Nancy Goebel
And it’ll be interesting to see if one of the outcomes of just that is to see whether we become even more naturally curious in doing what we do. Because even in the early stages of experimenting with ChatGPT, the way in which you frame questions on the user side of things is different from how you would approach things in, say, an enterprise search context.
[00:17:51.390] – Florin Rotar
Yeah, absolutely. I do think that as the cost of generation is approaching zero, I think we will see a lot more of the creativity and the curiosity flourishing, because it is simply possible to do so within the confines of time and money and other resources that people have been bounded by previously. So, if I can use an example, so we’re working with a luxury car manufacturer and we’re using a generative model to create inspiration for new car models. So, again, this is a company which is extraordinarily proud of its heritage, of its design language, of its style. And coming up with new designs is something that, again, historically would have required quite a long period of time for people to be able to channel and experiment with sketches, with ideas. And there’s only so much you can do within the workday. So literally having the ability to say, well, give me a, you know, an SUV model in the style of xenomorph science fiction. With the cab crew set up, set into Los Angeles of 2030, serving a family, it’s not something you would have sort of that design idea is not something people would have normally had maybe the chance and the opportunity to do.
[00:19:42.270] – Florin Rotar
And now you can get a really high quality and to my eyes, at least like really inspirational and high quality design idea, again in a matter of minutes and hours rather than days and weeks. And we’ve seen this if you’re cynical, you would think that designers would reject this because it threatens their livelihood, their job, their creativity, their style and human touch. But we’ve seen actually, the complete opposite, that they’re using this to stimulate them to investigate new avenues, to test new ideas, to let their hair out, so to say, and be a lot more creative than they were historically. So it’s really interesting to see how this changes. It changes everything. To be honest.
[00:20:34.750] – Nancy Goebel
I think it’s so refreshing to hear this point of view because in so much of what we see in the media at the moment, it’s a topic that’s raising questions about jobs going away or issues with organizations like Samsung that leaked important IP by just trying to do something positive and troubleshoot code. And so along the way, we need to be listening and having conversation from all angles so that we are stepwise in approach but harness the power of all the positive sides of what these new capabilities can bring to us. And so I think conversations like this take on added importance in helping to round out the picture and even in a short period of time we’ve talked about how generative AI and ChatGPT in particular can assist with troubleshooting issues, helping to inform new ideas and a variety of other ways of enhancing productivity and creativity. And that’s an important part of becoming more naturally curious in the day-to-day work that we do and leveraging these capabilities as you described as part of the collaborative process as opposed to thinking of it as a competitive factor, if you will.
[00:22:15.530] – Florin Rotar
Yeah, obviously we are at least going into this very open-eyed and with quite strong sense of responsibility of helping our customers around how to approach this in an ethical way because there are really substantial benefits but there are also risks which needs to be managed and mitigated. So you mentioned topics like information security and privacy and being very cognizant around how one doesn’t overshare sensitive personal or corporate data, how you manage the risk of model hallucinations where you may have unreliable or biased or inappropriate or frankly incorrect response, how you really think about the social benefits of this. Like anything, this could be used to create misinformation and instead focusing on how could generative AI contribute to social goods like education, financial opportunity, better access to health care rather than just a crazy increase in digital noise which I think is the last thing we would want. And then obviously the intellectual property is something that every single one of our customers is focused on and none of our customers are using the how should I put it, the publicly available ChatGPT. We’re using the Azure OpenAI version where you can manage the security, you can manage the privacy, you can manage the intellectual property and make sure that data isn’t leaked and you have the right governance in place.
[00:24:22.240] – Florin Rotar
So there are ways of managing all of this, all of the risks, all of the potential challenges, but it doesn’t happen by itself. It needs to be a conscious effort and a conscious approach.
[00:24:36.470] – Nancy Goebel
It’s been interesting to pick up on this topic of risk in the early days to see that not only are professional services and technology firms leaning into this is a powerful set of capabilities, but early days we’ve also seen quite a bit of movement within the consumer goods industry and that’s in sharp contrast with a lot of the financial services as an example of regulated industries where in some cases there have been some pretty strong and public declarations that a number of the banks will not touch this early days for use across employee base at large and have gone so far as to block access to the publicly available versions of ChatGPT. With that in mind. So there’s a sense of some organizations have a foot on the gas pedal and others have a foot on the brakes. But we’re seeing quite a lot of conversation about the importance of leveraging in those instances where experimentation is actively underway. Not only the Azure OpenAI instances that you can safeguard as you’ve described. But of course, there’s the Microsoft Copilot group of organizations that are early adopters that are looking at embedded versions of ChatGPT into the different parts of the Microsoft productivity suite.
[00:26:20.580] – Nancy Goebel
And I’m sure there’ll be other variations along the way as the wider set of technology providers enter into the space as well. Early days. You have some successes like the high-end automotive example you gave earlier, but certainly there’s a lot of trial and error involved, whether it’s Avenade for Avenade or Avenade out to clients. And can you share any sort of real-life examples of troubleshooting, some of the work that you and your team have encountered, and even an early approach to resolving these kinds of things? Because, again, it is early days for us, even though AI has been around for some time.
[00:27:14.970] – Florin Rotar
It’s a very good question, Nancy. I would say though, in the very early days of this maybe November-December time frame, we did see some of our financial services customers take a bit of a knee-jerk reaction or other health sort of regulated industry and basically ban ChatGPT. But I think most of them realize that this is a bit like the equivalent of banning internet for your users in the mid-90s or restricting their access to internet. You know, they realize that’s probably not an avenue which is going to be particularly viable in the long term. So we are actually seeing healthcare organizations which are really leaning into this. We’re seeing loads of insurance companies, banks leaning this. I think again, the media loves the hyperbole and the extreme examples. I understand organizations which are being worried about sort of the public consumer version of the models and those being used for business purposes. That’s probably not a good thing. But at least from my vantage point, we see pretty much uniform interest around most industries, even regulated industries. But to go back to your point around, we’re practicing this ourselves because we do like, as I think I’ve said before, to be client zero.
[00:29:02.300] – Florin Rotar
So to do to ourselves everything that we suggest and we work with clients on. An example which is kind of top of mind for me right now is around account planning. So our fiscal year is starting to sort of approach a point in time when hundreds and thousands of our account executives are starting to think about their account plans for next year. And there is historically loads of effort being involved in creating PowerPoints of account plans and those PowerPoints being reviewed by managers and then having calls and meetings to discuss it, to improve it, to figure out how can we have the most sort of the best possible account plans where we truly understand our customers’ challenges and opportunities and we figure out what the best way that we want to help our customers in the next fiscal year and the implications of that. So I’ve just been flabbergasted, frankly, and I don’t use that word easily, but I am flabbergasted about the quality. So we’ve created a pilot where we have GPT review account plans and I’m amazed by how good those are. But also, and again, I’m amazed a little bit about the need for change enablement, for people to understand that this will change the way they work.
[00:30:57.000] – Florin Rotar
So our need to sort of stop organizing those big calls, which are taking the time of a lot of people and being tracked in Excel spreadsheets and the sort of knee-jerk reaction around reviewing those PowerPoints and so forth and trusting self-empowering people to do 80% of the work themselves with the help of an AI sort of sales copilot, which is helping them do that. So, even though I’ve been in the knowledge in the digital workplace space for 25 years and I should know about the importance of change enablement and change management, I’m still surprised about the non-trivial effort required for people to understand and embrace and get this into the natural way of working. So yeah, that’s probably the bit which surprised me, not the technical bit. That was surprisingly easy. It’s the human side, which is, again, humans don’t change quite as fast as computers.
[00:32:12.970] – Nancy Goebel
Yeah, I have long said that you need a change to be able to move people through change. And there’s almost a coattail effect that you need to create based on something wider that’s happening in an organization to allow technological changes to flourish in the ways that you’ve described. And I know that for years, as we’ve done our digital workplace maturity benchmarking, organizational readiness is already something that was lagging on the maturity scale. So as digital workplace practitioners and leaders, we are going to be called upon in a whole new set of ways to really help mobilize the change that’s needed not only at a stakeholder level, a leadership level, but a wider workforce level as well.
[00:33:14.750] – Florin Rotar
I sort of alluded to this before, but I do think this generative AI is going to force a rethink between the relationship and the engagement between people and technology. Anything as fundamental as the one during the industrial revolution. It is going to impact everyone. It is going to blur the distinction between knowledge workers and dare I use the term blue collar workers. And I think the successful organizations here will be the ones which deeply understand how to leverage this to support employees creativity, quality, to share sort of the lessons learned and the experience around how this will strengthen people’s sense of workplace contribution. We need to be really careful that it doesn’t create new distinctions between augmented and non-augmented workers. And frankly, I would go as far as say we will need new team structures and new organizational models as a result of this. I know I’m sounding maybe a little bit, I don’t know, hype-ish and I really do try to avoid, how should I put it, oversell or over position this, but I generally think this is a pivoting moment, certainly as big as anything I’ve seen in the last 25, 30 years or maybe even deeper than that.
[00:34:56.990] – Nancy Goebel
I personally don’t feel like you’re coming from a place where you’re exaggerating the potential and the power of what we have in front of us. As you say, the biggest challenge will be enabling people to embrace it in new and different ways. And I think about the point in time earlier in my career where people would talk about knowledge as power and that was a reason to hold on to knowledge. Whereas we’re almost turning that paradigm on its head in saying the more we share the knowledge that we have, the more we can harness it for all of these different purposes anchored in creativity and connection and create a different kind of knowledge revolution, if you will.
[00:35:54.260] – Florin Rotar
That’s so well said, Nancy and I couldn’t agree more with the notion of sharing. And I think we’re seeing a different type of sharing emerging as well across organizational boundaries even where sort of teams of companies, and sometimes those teams of companies are across industry, are practicing shared cognition. So for example, a manufacturer which creates a new material for tires, they’re doing that because they use the shared data available used to train a generative AI across the manufacturer, across government, across a chemicals company, maybe across the investment arm of a financial services company. So we’re seeing this sort of catalyst of multiparty systems and sort of the data footprint and the capability and the people teaming across organizations rather than being sort of restricted to one organization or even to one department within organizations. So I think I truly, truly buy in and believe in the shared knowledge power of this. It’s going to be a catalyst for that and as you said, shared knowledge across people, across departments in an organization. But we’re starting to see early signs of this being a catalyst across organizations as well for sharing across organizations.
[00:37:35.630] – Nancy Goebel
And so with that in mind, bringing it back to who our audience, which is digital workplace leaders and their teams, what’s your best advice for them during these formative stages of bringing generative AI and ChatGPT into the workplace?
[00:37:58.710] – Florin Rotar
Yeah, I mean our my advice would be maybe I would advise three things. Three is the magic number. So first of all, to set the right foundation and to start simple in areas where they already may have deployed capability and they have a good set of quality diverse data. I think truly embracing the fact that AI is a productive assistant or copilot to help people, not replace them. So really, really focusing on the change enablement to help the users work iteratively from generated concepts that need to be tweaked, refined and enriched and approved. So for some people, this sort of iterative working approach is natural, for some it is not. But basically, point number two is realizing that people are still critical and will always be critical. And I would dare to say that this AI first mindset is people first mindset. And then we’ve seen the third point would be to establish a governance model and maybe look at the notion of AI, responsible AI ambassadors. So to have a centralized function, to think big, investigate validate, refine, but having the balance between ambition and risk, and to have this there, I use the word radical transparency on communication with the customers, the partners and the employees around the risks, the limitation and uncertainties.
[00:40:02.210] – Florin Rotar
Those three would be my guidance. Set the right foundation, start small, where you have data truly putting people first, not technology first, however cool the technology may be. And then two, point number three is to establish the governance, the governance model for responsible AI.
[00:40:23.110] – Nancy Goebel
That’s some very sound advice and is very much in line with why I look to bring someone like you into conversation in the studio like this, as we have these moments where the technology profile is changing within the digital workplace. So we’re fast approaching our final moments together. Florin, what have we missed?
[00:40:51.570] – Florin Rotar
So I think there are three topics which I would encourage your members to think about. Three topics which I don’t necessarily hear being mentioned often enough when people discuss generative AI. So the first topic I would mention is transparency. We’re starting to have AI, which is so good that it could pass for a human. And then I think it’s so incredibly important that we make it clear to the stakeholders, the users, that they’re dealing with a machine when they would reasonably assume or expect that they’re viewing human created content or interacting directly with a human, for example, in a customer service. I think this level of transparency and facing up to the risks, the limitations, the purpose is absolutely critical because if you don’t have transparency, you don’t have trust. And if you don’t have trust, everything falls apart. I think the second point I would add is that around sustainability and responsible sourcing so we’re still living in a world where the computing, the supercomputers, which are required to develop and train and run these algorithms, can easily have an outside oversized carbon footprint and really to think hard about the strategies to understand and manage and govern the energy use.
[00:42:48.180] – Florin Rotar
So I think the sustainability aspect is really important. And then I think also the responsible sourcing because at the end of the day, there are still people, thousands, maybe tens of thousands, maybe hundreds of. Thousands of people behind the scenes which are being used to train and fine tune this model. So truly, talking to your suppliers about the human labor involved in training the systems and your company’s values and principles, I think that is really important. And then I’ll come back to the point on human flourishing because I think it’s so important that we focus on this not as a piece of cool technology which is being used to generate the next blog post or the next silly TikTok video or whatever and just increase digital noise. But that rather we’re using this technology to contribute to social goods, to contribute to education, financial opportunity, health care, and to really think about this as an opportunity to fundamentally change business models and do that in a way where humans flourish. And I do see a bit of a knee jerk reaction right now to think too small around this being just a fancy way of automating a few things like document generation or making customer service agents a little bit smarter, when the potential and the opportunity is so much more fundamental.
[00:44:42.320] – Florin Rotar
So those would be the three things transparency because of trust, sustainability and responsible sourcing. And last but not least, the societal impact, the fundamental business business model impact and doing that in a way which empowers human flourishing.
[00:45:01.210] – Nancy Goebel
And those are some really powerful closing thoughts to bring our time to a conclusion. And certainly that doesn’t mean the topic ends here. I know that not only through DWG circles and Avenade’s, this is a topic that is going to really start to take hold as we collectively experiment with all aspects of generative AI, including ChatGPT, in reimagining the art of the possible within the digital headquarters of our organizations, individually and collectively. Florin thank you so much for joining me for this conversation. Truly insightful and fascinating.
[00:45:50.310] – Florin Rotar
Thanks Nancy. And again, I would love to hear from the audience of this webcast because I think the more we learn from each other, the stronger we get. And I don’t claim that we know everything or we have the truth, the whole truth and nothing but the truth. So I would love to hear about the experiences, the lessons learned, the opinions of the people of the webcast, and to learn from you as well. And I hope what I shared so far in this 45 minutes has been useful. And again, Nancy, a pleasure to talk to you and thank you for inviting me.
[00:46:34.810] – Nancy Goebel
Digital Workplace Impact is brought to you by the Digital Workplace Group. DWG is a strategic partner covering all aspects of the evolving digital workplace industry and boutique consulting services. For more information, visit digitalworkplacegroup.com.
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