Hello, everyone. Good morning, good afternoon, good evening from wherever you are joining for my presentation and around the world. I know we have some people from Boston and Berlin.Feel free to use the chat to let people know where you're from. I'm really glad you're joining this morning to hear my talk about how I think we can move software development teams from fear and skepticism to confidence and ownership with AI. My name is Renee Fisher. I'm a vice president of engineering at Coconut Software, and I've been in the software development industry for over two decades. I started out as a junior dev. I've had many roles over the time. But as a leader, one of the things that I know I'm known for is leading with empathy, knowing that if I'm supporting my team in the right way that they need it, they will be successful, which makes me successful, and the company successful.

Over the last few years, but, really, let's just focus on, like, the last six to nine months, We've all seen an explosion of AI tools and capabilities entering our organizations, especially with agentic coding. And most of the conversation we've been hearing is focused around the productivity gains, faster delivery, more output, and the ROI the company is going to achieve. But what I've experienced in leading my teams through this AI transition is the hardest part is not technical or even about a rollout plan. It is about how it impacts our human nature. While leaders all talk about big gains the company is gonna get, the developers are experiencing something very different. Fear, uncertainty, loss of identity, tooling fatigue, and they're really questioning their future. So today, I'm gonna talk about some of the mistakes that we're making or maybe I'll say that I've made in our departments in trying to do this, and instead present a framework that I think will help us adopt AI in a way that builds trust instead of fear and empowerment instead of disengagement.

So welcome to my talk about the human side of AI adoption. So first off, I wanna talk about the fact that the barrier to success is not actually the technology. Most software organizations are not lagging in integrating AI because developers can't learn the tools. Developers are incredibly capable learners. That's a key part of their job since technology is always changing. And I'll say I started out by telling my team that AI is just another tool that they're gonna use as part of their job like any other tool. I was doing that so that I could get their buy in. And throughout this presentation, you will hear me call it a tool.

But for developers, that is incorrect framing and a major simplification of what AI is capable of and what it's actually going to do. This isn't a new code repo or a front end framework that they're working with. AI, and specifically agents that can do parts of their job, changes their workday and how they show up every day. It's completely different. And for many developers, because I've been one, coding is not just a task that they do. It's their identity. It's their craft. It's their pride. So when leaders are framing AI primarily around tool efficiencies or maybe cost reductions, what the developers are hearing is very different, and what they think is, what's gonna happen to me? Our developers are exhausted. They've lived through years of new languages, frameworks, and transformations.

So when we add AI on top of that, which I know we'll all agree is the most disruptive tech, change in tech we've had in a long time. Any resistance that they have to that is not about anti innovation. It's an accumulation of them having change fatigue combined with complete uncertainty of their future. So if they're complaining about how AI hallucinates or fails, that's not them thinking that they're better than the tech or that even the tech is bad. If you talk to the developers, they are worried about what is gonna happen to our production systems, how might it impact our customers, and what's it gonna do with the data we store. This change is cultural, and it's very personal to them. So let's talk more about what developers are thinking. Leaders will often resume resistance to this change is maybe irrational or ego driven. In reality, most resistance is deeply rational.

Developers are asking very human questions because if the AI is writing the code, what happens to my expertise? What happens to those junior devs we just hired? How are people actually gonna learn the skills to ensure that this AI is building the secure, maintainable, performant, and reliable software systems we need? How are we gonna maintain quality? And what skills are still gonna matter for me? I personally think one of the worst things a leader can say is you won't be replaced by a AI, but you will be replaced by someone who knows how to use it. And I can say that sentence because I've said it multiple times to my team. Because as a leader, it makes complete sense to me. We think it motivates them and shares the obvious reality of the situation in their future because it is a true statement about what's gonna happen.

But as I've learned, it increases their fear because we just confirmed that replacement is now part of the conversation. Developers are very logical. They need transparency, context, and reasoning. But as logical as they are, this change really does impact them emotionally. So let's move on to other things that don't work so well. One of the most dangerous patterns is forcing AI adoption before their trust in AI exists. So when an organization is mandating AI usage too aggressively, the teams will hide their concerns. Their usage may become performative, or they're just trying to game the system to make it look like they're using AI, or people will quietly use it without even discussing the risks of what they're doing with it. One of the big obstacles I see is a lack of sharing with what they're doing with AI.

If they share criticisms, they're worried that, you know, me as a leader, I'm not gonna think they're a team player or maybe they're not on board with this technology change. And if they just share their positive experiences, you know what they're worried about? That their peers are gonna go, oh my god. They're just trying to please leadership. So perhaps the biggest mistake is over indexing on talking about the productivity gains. If the AI conversation is about doing more with less, they will think of it as a workforce reduction strategy. So instead, as leaders, we need to be connecting AI to their learning, their sustainability, preserving their competitiveness in this industry, and helping them focus on higher value thinking. Alright. Let's talk about more shifts that I think we need to make. We need to change the narrative from AI as a threat to a collaborator.

I wouldn't position it as replacing their work, position it as removing low value friction from their day so that they can spend their time on meaningful problems. This distinction matters enormously because our developers do not fear hard work. We all know ones that are gonna work through the night when are needed and will jump in and help each other out to solve problems under critical time pressures. What they actually fear is becoming irrelevant. If you can frame AI around reducing repetitive tasks, removing the work they don't like to do, accelerating their learning, helping them experiment, and reducing their cognitive load, they'll become a lot more open to us. And any of you out there that have been in software development long enough knows that in your product, there is a feature that someone built about ten years ago that was never documented, and no one currently knows how it works.

So when a random issue in that area of the code pops up, if you can utilize AI to help understand that code to solve the problem, that will greatly reduce stress from the developers. This allows them to really think of AI as a collaborate collaborator or enhancement to how they do their job. So let's talk about the people focused adoption. Through my experience leading all kinds of changes and transformation through companies, I found these five pillars matter the most. You gotta have psychological safety on your team, gotta provide time to learn, build grassroots ownership, integrate it into existing workflows, and replace the human oh, sorry. Not replace. Sorry. Bad word. Reinforce the human value in this change. This doesn't, let's be fair, increase AI adoption metrics immediately, but it does increase the trust, and that trust is what's going to build sustainable AI transformation for your company.

So let's walk through each one. First off, psychological safety and time to learn pillars. Alright. Psychological safety a team has, whether it's AI or not, is really the key to their success in anything. But when you are driving major transfer transformation, it is extremely important. We must make it safe for our developers to say, I'm skeptical. I don't trust the output. I'm worried about the quality, and I'm worried about my future. These statements should not be treated as resistance to innovation. They are part of a healthy transformation conversation in order to get everyone on board. The next item is giving your team time to learn. This isn't like learning a new software languages where the basic fundamentals are really all the same.

We can't assume everyone adopts at the same pace, and we know everyone learns differently. Some people are gonna jump right in. They're excited. They're gonna try everything, and others need to see quality results before they fully trust it. And since using AI properly and correctly is a skill that must be learned, the teams need training, experimentation time, examples, coaching, peer learning. Without a protected space to learn, AI adoption becomes one more stressful expectation layered onto their existing workload. Alright. Let's talk next about ownership and integrating it in your framework. Individuals will support and advocate for change that they help create. One of the strongest ways to reduce fear is involving developers directly in evaluating the tools, brainstorming the best ways to use it, and creating prototypes, defining the guardrails, and sharing their lessons learned.

When adoption comes from the grassroots instead of being dictated top down, ownership of it increases dramatically, and ownership creates accountability, and accountability creates success. Next time oh, next, make sure you start small where you use it in your workflows. If you've got, like, fifteen year old software that's a monolith that your devs are working with, I would not start by suggesting that AI is going to write the next production feature. That will scare the developers who are maintaining that software. Begin with low risk workflows and tasks where the devs' trust can grow. Some examples include documentation generation, summarizing PRs, creating tickets or planning, and creating unit tests. Include it where developers will still validate the output to build their confidence. Now it is possible to do an immediate transformation and change of how you use AI in your software delivery life cycle, but that increases risks and creates a volatile development environment.

Instead, to create sustainable change and confidence, really do think about integrating it in where it works best. As I've been sharing since the beginning of the talk, centering the AI transformation around human nature and value will make it successful. And this is what I really think is the most important leadership responsibility in this entire transformation. Developers need to understand that AI probably gonna automate parts of their job, but it does not eliminate the need for human judgment. Humans are still gonna provide the ethics, context, accountability, creativity, architecture, and prioritization decisions. AI is going to be used to help you with figuring out how to make those decisions, but a human still makes the final judgment call or decision.

And here's some framing that I think might help resonate with your team if they can't see how their job's going to evolve. The developer is like the pilot in a cockpit of the plane. A plane does a whole lot while pilot sits there, but the pilot creates the plan to set it up on its path. It they oversee what it's doing. They check that things are going to according to plan. The pilot takes over when needed. The pilot is the one solving the most complex problems they endure and ultimately provides the judgment and is accountable for what that plane does. So in our industry, AI is going to accelerate, assist, and generate, but the human remains accountable.

And that can really help our developers from thinking and being replaced to, okay. I see my role is evolving. I think we can all agree. We're currently living in uncertain times, which is probably the understatement of the day. So one difficult reality we have as leaders is we cannot promise certainty or stability right now. The technology landscape is changing too quickly. But as leaders, we can provide transparency, learning opportunities, growth, and honest communication. Teams do not expect their leaders to predict the future or guarantee what's gonna happen, but they do expect their leaders to guide them through this uncertain time with some honesty and empathy. And organizations that can do this well will not only adopt AI into its software development life cycle successfully, they will retain stronger cultures while doing it. Alright. Final takeaways.

If there's one thing I hope you leave with today is AI transformation is not simply another tooling rollout. It's transforming our team members' whole identity of who they are and what they do. And that successful change requires empathy, trust, communication, and patience. And I personally don't think the organizations that are using the most AI tools or integrating integrates them in first will be the most successful with it. I think the organizations that create the environments where the devs feel safe enough to learn, adapt, experiment, and evolve will be the most successful over time. Because ultimately, this transition is not about the technology and how the code is created. It really is about the people using it. So thank you for attending my talk. I hope you enjoyed it and maybe got something from it. It is just based on my experience. So my contact information is here. Please reach out.

I'd love to continue this conversation and learn how other leaders are making AI transformation successful at your companies. Please enjoy the rest of the conference, and I can't wait to meet