The AI Adoption Episode: Freedom & Frameworks That Actually Work

What if the best employee AI adoption strategy is one most companies refuse to try?

While business leaders rush to mandate AI from the top down, Softchoice discovered something counterintuitive: the most successful transformations happen when you let people experiment on their own terms. (Safely!)

This episode takes you inside Softchoice's organic approach to AI adoption—from a VP who started experimenting with AI nine months before ChatGPT launched, to an engineering team that went from skepticism to shipping code in a single prompt, to a solutions architect who hit 300% of plan by letting AI handle his meeting notes.

You'll learn:

• Why psychological safety beats corporate mandates for technology adoption
• How to balance experimentation with responsible guardrails
• The "crawl, walk, run" framework that scales innovation
• Why one person with AI can match an entire marketing department's output

Featuring:

• Craig McQueen, VP of AI Solutions, Softchoice
• Andrew Campbell, Engineering Manager – App Development & AI Solutions, Softchoice
• Geoffrey Whalley, Solutions Architect, Softchoice
• Danielle Ryterband, Associate General Counsel & Privacy Officer, Softchoice

This isn't just an AI story. It's a framework for how change actually happens in organizations—and how IT leaders can apply it to any transformative technology.

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This episode is brought to you by Arctic Wolf Incident Response.

When a cyber attack hits, Arctic Wolf's insurance-approved team gets you back to business fast with 24/7 containment, digital forensics, and full business restoration.

Learn more at softchoice.com/arcticwolf

#ArtificialIntelligence #ITLeadership #DigitalTransformation #ChangeManagement #Softchoice #MidMarketIT #AIAdoption

The Catalyst by Softchoice is the podcast dedicated to exploring the intersection of humans and technology.

Transcript

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This episode of The Catalyst is brought to you by Arctic Wolf. Incident Response. When a cyber attack hits Arctic Wolf's insurance approved team gets you back to business fast with 24 7 containment, digital forensics, and full business restoration. visit.com/arctic Wolf to download their incident response data sheet and take the first proactive cyber defense. Geoff Whalley has a problem that most people would envy. He's really, really good at his job. He's a solutions architect at Softchoice, a worldwide technology company, which means when mid-size companies need to figure out their IT challenges. They called Geoff and last year they called him a lot. Think I participated in over 1200 customer facing meetings last year. It's about six a day, about six customer facing, over 200 days of work, six meetings a day, every day for a year, each one requiring his full attention, detailed notes. And thoughtful follow-up. The kind of work that made him indispensable to his customers and completely exhausted, and then something changed. Yeah, I've completely stopped taking notes. I'll be brutally honest right now. This is the catalyst brought to you by Softchoice, a worldwide technology company. I'm Heather Haskin. This season we're exploring how small teams achieve big dreams, how mid-market organizations tackle enterprise level challenges without enterprise budgets. We're telling these stories differently this time as audio documentaries that take you inside the moment. When everything changes, and today we're going to hear how AI adoption actually works when you get it right, not the top down mandate approach that most companies try. The kind that creates resistance and ultimately fails. Not the chaotic free for all. That leaves people confused and frustrated, but something in between. An organic approach that might just change how you think about introducing any new technology to your organization, because here's what we've learned. When people discover AI tools on their own terms, when they're given the freedom to experiment freely, when they can see immediate value in their daily work, that's when real transformation happens. To understand what happened to Geoff. To dozens of other people at Softchoice, you have to go back a few years back before people had heard of chat GPT, back when AI felt like science fiction. This was February, 2022. Chat, GPT wouldn't launch until November. Most people had never heard of OpenAI. The company existed, but it was obscure. A research lab that sold API access to a handful of specialized companies, but Craig McQueen was paying attention. I've always been a bit of a technology nerd, so always keeping an eye on what's going on in the industry. And uh, one company had caught my eye six or seven years ago, which was Open ai. Craig is now Soft Choices VP of AI Solutions, but in February, 2022, that title didn't even exist. AI and the workplace really wasn't on anyone's roadmap. It wasn't in the strategic plan. Craig was just a guy who noticed things early. There were a couple companies such as Jasper and Ryder that started building these products on top of open ai even before chat GPT existed, and I started playing with a couple of these and was blown away. So blown away in fact that Craig started experimenting not with business applications at first, but with something more personal. I remember, I think it was in the fall of 2021 or something like that where it would complete a story for me. I started a Halloween story and asked it to finish it, and I was just amazed that on the context of what I started with, it actually was able to complete it. While everyone else was debating the future of work after COVID, Craig was asking AI to help him write children's stories, and it worked. So Craig did what any curious person would do. He wrote about it, a LinkedIn article published in February, 2022, about the potential of generative ai. I, I used AI to construct about half of it, which was sort of the point. And I think what's interesting is people didn't notice a difference. Remember, this was nine months before chat, GPT launched before the world when AI crazy, before every tech conference had panels about prompt engineering. Craig published an article that was half written by ai. And nobody could tell. The LinkedIn article actually didn't get much attention. I think what's interesting is people didn't notice a difference and, and that's why it might not have got as much response because nobody said, aha. Yeah, I can see right here. This is terrible, the way this sentence was constructed. Here's the first lesson for it. Leaders the future often arrives quietly. Craig was experimenting with something that would actually matter, not because he was trying to be ahead of the curve, but because he was genuinely curious about what this technology could do. Now, let's fast forward a few months to November, 2022. Chat, GPT launches. Suddenly, everyone's an AI expert, but Craig had been living with this technology for almost a year. You know, what I remember very specifically is, is within a month or two, it was the fastest adopted application ever seen, and that just shows that people were finding value or interest in it and, and therefore it spread virally. And that's when Craig realized something important. This wasn't just a cool demo, this was going to change how people work. And if Softchoice was going to help their customers navigate this transformation, we needed to understand it ourselves. But here's what Craig didn't do. He didn't write a memo mandating AI adoption. He didn't create a task force or hire consultants. Instead, he did something much more interesting. He started talking to people who were already experimenting because it turns out Craig wasn't the only person at Softchoice who was curious about AI across different departments. People were quietly trying things out, not because they were told to, but because they had problems to solve. Take Andrew Campbell, an engineering manager on Softchoice app development team. For coding specifically, it was around the same time that, uh, I believe chat GPT was released publicly and obviously got a lot of media attention around the same time, GitHub co-pilot was being released into the public as well. Andrew's team builds custom applications for Softchoice customers, complex stuff. The kind of work we're getting it wrong is expensive and getting it right takes time. So when AI coding tools started appearing, Andrew's reaction was measured. Um, it wasn't really a surprise what chat GPT and GitHub copilot could do initially in those very first releases was very rudimentary. It was very, very basic. This is the second lesson. Skepticism isn't resistance, it's professionalism. Andrew's team wasn't dismissing ai. They were evaluating it properly. Could it handle the complexity of real world development work? The answer came gradually as the tools got better, as his team learned how to work with them. Something shifted. I'll admit, I don't remember specifically who brought it up. We have team meetings and conversations continuously. So while things like chat, GPT or GitHub copilot were very popular media wise, it was just kind of a thing that we were naturally talking about. That phrase is revealing naturally, talking about not mandated. Not assigned natural, because when people see value in something. They talk about it, they share discoveries, they help each other get better at it. And then Andrew had his breakthrough moment. His team was working on migrating an application from one database system to another. The kind of tedious error prone work that usually takes days or weeks, I think it was called code, basically gave it that current app uses this ORM. We wanna use a different one, rewire everything to make this work. I was completely surprised it was. Worked a hundred percent on the first prompt, let that sink in a complex technical migration that would normally take his team days or weeks done perfectly in a single prompt Andrew's reaction. I was honestly, honestly very surprised that it worked correctly on the first try. This is where skepticism turns into belief, not because someone told Andrew's team that AI was important. Because they experienced its power firsthand in their own work, solving their own problems. Meanwhile, across the company, Geoff Whalley was having his own revelation. Remember Geoff, the guy with 1200 customer meetings. He wasn't trying to be an AI pioneer. He was trying to survive his workload. The first real use case for AI for me was I was working with a very large team of account executives covering the mid uh, United States. I found myself in back-to-back meetings every single day from instant onboard. But then Geoff discovered Microsoft Co-Pilots meeting transcription and summarization features. I thought I was a good note taker and, and what I quickly realized was I was really missing key details as I was, you know, processing information, trying to write that into a OneNote. Here's the third lesson. AI doesn't just make us faster. It makes us better. Geoff wasn't just saving time, he was capturing information that he was missing. He was being more present in conversations. He was serving his customers better and the results spoke for themselves. Being able to be present, engaged in, you know, over a thousand meetings allowed me to hit. 300% of plan. 300% of plan. That's not just productivity improvement, that's transformation. But wait, three different departments, three different approaches to ai. Was this organized, coordinated part of some master plan? Not exactly, but here's the interesting part. This wasn't chaos. While people were experimenting freely, they weren't experimenting recklessly, and that's where Danielle Writer Band comes in. Danielle is soft choice's, associate General Counsel and privacy officer. When teams across the company started using AI tools, Danielle's job was to figure out how to do it safely. So in terms of my initial reaction, when teams started experimenting with AI tools, honestly, I would say it was a mix of curiosity, excitement, and a healthy dose of caution. Healthy dose of caution, not panic, not prohibition. Caution.'cause Danielle understood something crucial about innovation. You don't enable it by saying no. You enable it By creating guardrails. We developed a responsible use of AI policy, which defines the standards and criteria for using AI tools for business purposes. So the overriding goal is to ensure that our personnel are using AI responsibly and ethically, while also safeguarding the interests of the company and our customers. This is the fourth lesson. Successful AI adoption requires what Danielle calls being a yes and partner to innovation. Not the department of no, but the department of Yes. And here's how we do it safely. We really try to be the team that says, yes, we can explore this, but let's do it with the proper guardrails in place. So you have organic experimentation happening across departments supported by thoughtful policy frameworks, individual curiosity backed by institutional support, freedom. With guardrails and it's working, AI has changed the way my team works. By giving everyone the tools and abilities to be a faster and more confident problem solver, being able to, you know, be present and engaged in conversations was a massive win for me. But here's what I keep thinking about. This isn't just a story about ai. It's a story about how change actually happens in organizations, how innovation spreads, how you get people to adopt new tools and new ways of working. Because when you step back and look at what happened at Softchoice, you start to see a pattern, a framework that any IT leader could apply, not just to ai, but to any transformative technology. When was the last time you thought about what would actually happen if your organization got hit with a serious cyber attack? Would you know exactly what to do? Would you have the forensics expertise in-house, the negotiation skills, the restoration playbook to get critical systems back online? The reality is most mid-market IT teams are under prepared, not because they're not capable. But because incident response requires specialized expertise that most organizations simply don't have on staff, and that's where Arctic Wolf incident response comes in. They provide end-to-end incident response from the moment you call their twenty four seventeen jumps in to contain the threat conduct digital forensic. And fully restore your business operations. Everything from root cause analysis to negotiating with threat actors is handled by their specialists, not your already stretched IT staff. And here's why that matters. Arctic Wolf has resolved over 1000 incidents in the past year alone. Getting organizations back online in an average of 22 days, that's 15% faster than the industry standard. They've also reduced ransom demands by an average of 92% and their insurance approved on over 30 panels worldwide. If you don't have a dedicated IR team ready to go, and most mid-market organizations, don't download the Arctic Wolf incident response data sheet and take the first step towards proactive cyber defense. What's been particularly interesting observing the Softchoice evolution is seeing this balance of organic initiative versus corporate strategy that balance organic initiative versus corporate strategy. It's not either or, it's both. Craig calls this the crawl, walk, run approach, but it's really about creating the conditions for successful adoption. Here's how it actually works. First, you identify the early believers, not necessarily the most senior people, but the most curious ones. The people who are already experimenting, already trying to solve problems. So at Softchoice, because we've had people adopt it within their department or try some things, we've learned from what they've done, and that helps incorporate it into our strategy. Second, you give them psychological safety to experiment. You don't mandate tools, you don't create elaborate rollout plans. You let people discover value on their own terms. The one thing that's really nice about working with Craig's team is we, we have a lot of freedom to. Explore new areas with things like ai. Third, you provide support without control. This is where soft choices center of excellence comes in. Not to mandate adoption, but to help people who want to adopt. The first organizational obstacle is to make sure that you have a business case, allocated budget, and then treat it as a prioritized initiative, just like a project where you've got certain activities and things to measure. Fourth, you measure outcomes, not activity. Geoff's 300% plan achievement. Andrew's team shipping code faster. The legal team drafting contracts more efficiently. These aren't vanity metrics, they're business results. Finally, you let success stories spread naturally. When people see their colleagues getting real value from AI tools, they want to try them too, not because they're told to, but because they can see the benefits. And here's what Craig has observed about this approach. Each individual within an organization does have certain expectations of what they do for the business. There are certain metrics to hit and those using AI are gonna be in a much better position. To be able to hit their business metrics, uh, over time. This is what Craig calls the great equalizer effect. AI doesn't just help individuals. It helps smaller teams compete with larger ones. It helps mid-market companies act like enterprises. You could have one person using generative ai. Create amazing copy, create the marketing plan, and be able to execute with a very similar level of proficiency and quality of a whole marketing department. Think about what this means for the mid-market companies that most of our listeners work for. You don't need massive budgets or huge teams to compete anymore. You need the right tools and the right approach to adopting them. I started this story with Geoff Whalley's problem being so good at his job that it was killing him. Six meetings a day, every day for a year. The kind of workload that makes you question whether success is worth it. But Geoff's story isn't just about finding better tools. It's about finding better ways to work. About being more present with customers, about achieving results that seemed impossible before being able to be present, engaged in, you know, over a thousand meetings allowed me to hit 300% of plan. I had three reps in my district who actually went to President's Club, which is unheard of. And that's what happens when you get technology adoption, right? When you create the conditions for people to discover value on their own terms. When you support experimentation without mandating it, when you measure outcomes instead of activity, it's not magic, it's not luck. It's a framework, and it's a framework that works not just for ai, but for any transformative technology. The future is coming, whether we're ready or not. The question isn't whether change will happen. The question is whether we'll lead it or let it happen to us. The teams that figure out how to experiment safely, learn quickly, and scale successfully, those are the teams that will shape what comes next. While those will be the ones that still exist at an organization versus those that don't adopt, I'm Heather Haskin. This is the Catalyst by Softchoice, a worldwide technology company. Thanks for listening. If you are an IT leader who wants to create this kind of transformation in your own organization, Softchoice can help learn more about our generative AI adoption solutions@softchoice.com. The catalyst is produced by Pilgrim Content executive producer Tobi Dalrimple, editing by Ryan Clark with support from Philippe Demas. Joseph Byer and the marketing team at Softchoice. For more episodes of the Catalyst, visit softchoice.com/podcast. Thanks again to Arctic Wolf incident response for sponsoring today's episode. If you want to be ready when a cyber attack hits and recover faster. When it does, visit softchoice.com/arctic Wolf to learn more and take the first step towards proactive cyber defense.