Anyways, my name is Liz Ryan. I am, VP of marketing at Bloom Filter. We are a b to b SaaS company here in Chicago.For my background, I actually have a bachelor of science, from Arizona State, but a master's, in computer science from DePaul University here in Chicago. I spent about ten years running my own email marketing and marketing automation agency and then spent a few years, in strategy technology consulting, before coming on over to Bloom Filter, a little over a year ago. So what I wanna talk about, my subject is, beginning, balancing leadership and technology in the enterprise, the AI era. So to me, why this conversation matters. So, with the rapid rise of AI, and its impact in our personal and professional lives, women in technology are uniquely positioned for this moment. And I say that, yesterday, I believe, or a couple days ago as I was, putting the finishing touches on my presentation.

Something came up in my feed, and it was, a, image of all the companies that are currently, laying off or optimizing, around AI agents in, instead of around people. So layoffs and and and who they're laying off and how many people and why, as they, optimize on AI. And, of course, it was noted that all the CEOs in this in this graphic were men, and that men are leading the charge in a lot of this just by the nature of being, in higher leadership positions often cases. But it gives us a position as women in tech, women in leadership, start to, address and to lead this conversation. So when I think about that, you know, this we we've been duped before. So, you know, if you think back into, the nineteen fifties, you know, housewives were they were promised, freedom from, from washing dishes and and and hand washing things.

And and that technology like washing machines and vacuum cleaners and dishwashers were just gonna give them more time and more freedom, which is what they ex what they expected. What they got was the time saved turned into more expectations. And I feel like we see that a lot in current day. And is is this something that's gonna follow the same pattern? Are we are we, as a society, but especially women, going to be overburdened and overloaded with additional, expectations as AI promises to give us the ability to be more efficient and and, quite frankly, free up some time. So, of course, we all know that AI is a new paradigm shift. So what AI can do, obviously, it can automate tasks. It can free up time. It can enhance creativity. It can do a lot of things, within technology, from a thinking, building, learning standpoint.

And and, you know, even putting together this presentation, you know, I use some chat g p t to help me, ideate and to help me optimize, and I'm not great at creative. So using the designer, the AI designer in in, PowerPoint to help make everything, presentable, it it makes the experience better. It doesn't just save up free time, but it makes the experience better for everyone based on skills I may or may not have. But then we think about who's actually shaping this AI. Right? So bias in, bias out is why we must show up. You don't need to code in order to shape it. LLMs and AI reflect the data that they are trained on. I think we all know that. Women bring critical lived experiences and perspectives to everything we do, including, AI, language models.

And if we are not in the room, someone else is, by example of, the, image I, described with all the the male CEOs. So I think about that in terms of bringing technical and strategic worlds together. So it's a leadership opportunity. Within all of our organizations, we have more technical, roles and, and people, and we have less technical. And we wanna make sure that we invite all of those people into the conversation and help to, train the train the the language models as well as to, create those experiences that benefit all of us, not just, the tech savvy. So I think it gives us a great opportunity as leadership, to translate AI capabilities into business impact, shift the conversation from how does it work to what it can do from us. So I think a lot of us in tech, especially, kinda get nerded out, start nerding out on, like, exactly why it works, exactly how it can work.

And to a lot of people within our organizations and within around the world, that's not necessarily that important. It's really how can we be strategic in using it, not necessarily, being super technical in how it's built out. And we must connect that technical potential to our organizational priorities. So I think about that in in terms of, you know, we don't need to be engineers to lead these initiatives and how we can start connecting those tech the technical potential to all those things like speed and efficiency and, ROI. So I think about that in terms of, you know, going from tech to value. So simplify how we use AI without, dubbing it down. So talk business, not tech. Break down complex complex, AI ideas into a value oriented narratives.

So instead of saying things like, we train the LLM or we automated responses based on customer language, tie strategy, align use cases, to OKRs, KPIs, things like cost saving, time to market, risk reduction, and things like that. So the other piece of that, right, is empowering diverse teams. So how AI requires it it it does require collaboration across disciplines. So inclusive innovation starts with cross functional participation. Technical teams, engineers, data science, of course, are generally involved in these initiatives, but then also bringing in nontechnical teams like product, marketing, ops, legal, to understand how we can use these these tools across all of the the different teams and understand the perspectives and how we can implement them from a technical standpoint as well as an operational standpoint.

At leaders, just making sure that we share goals. So, normalize different forms of contribution as well as recognize the diversity of experience. And so when we think about sharing goals, I think there's been a lot of talk, and, influence around, reducing headcount or, being able to go faster, faster, faster. But I I think that when you frame the goals as a leader around, how it's better for the company, for work life balance, for the world, for the types of, work that you can do, It can help to, to get everybody kind of on the same page and not think of it just as a technical, initiative.

So thinking about it from an ethical standpoint, you know, the ethical bar is pretty high, and the stakes are real. So some of the key questions for leaders, as we build some of these models and, as we use the different models that exist or who might be harmed by this model. Is the decision of using these models open and transparent? Do we sorry. I just lost my page again. Are we reinforcing bias or breaking it? And then to allow space for some of that descent review. So as you're working with people across these, diverse teams, allow them to tithe to to bring some of that feedback back into the organization, and then tie some of your ethics to your brand trust. Right? So if you start using, a lot of AI tools within, your organization, make sure they're trust they're trustworthy tools.

Make sure that you do have, guardrails around, how you're using them and, and the types of work that you're doing. So some of the things that, you know, we also think about is, how we use our team how our teams become not just users, but shape shapers in all of those ways. So whether it's training the data curation, helping with prompt design. So oftentimes, technical expertise and nontechnical expertise can collaborate on prompt design. Prompt design isn't necessarily a super doesn't always have to be a super technical art, I I say. But understanding how the LLMs work, that perspective, and those people can come in and help to optimize some some of the prompts and, especially when it comes to building agents and things like that, as well as use case design. Same thing. Having a nontechnical resource, understanding the real world application along with a technical resource that can help build it out, can be really valuable.

And then always having, you know, who's who's available and is the right role for some of the ethical review of the policy. So being able to, help with understanding the biases that might come out or, how how data is used. Is that making sure that, we have some of those rail guardrails in place as far as sharing, nonpublic data or feeding the LLMs with, copyrighted information on all those things. So, you know, when we think about that from a leadership standpoint, it really does become, less technical and more directional as a leader, and that's important. So, you know, when I think about that from, bridging the technical and strategic worlds as a leadership opportunity, I think about how we can translate AI capabilities into a business pack pack, into a business impact, and then also, you know, that shift of how it works and then how do we connect it to technical potential within organizations.

So my slide that would help to to exemplify this is, you know, we think about in my company and in my work, something like technical the technical input could be something like, what we're actually doing is an anomaly detection, or we're doing sentiment analysis or code rework analysis.

And when we frame it as a business, which is less technical and and great for the business stakeholders, we think about it. It's anomaly detection as technical aspect, but as a business aspect, it's predicting late projects early. And as a strategic outcome, it gives more reliable release planning. And that goes back to, you know, what our company does specifically, but, really, there's always that technical requirement and framing it in a way that understands what the business what the business outcomes are and the strategic outcomes are could help to frame it throughout the entire organization.

So, you know, the other thing we think a lot about is the innovation versus risk. Right? So I talked about that a little bit earlier as far as, like, what are we putting into the LLMs? What data are we, providing, the risk of whether it's client data or company data, and how we can, from a business operational standpoint, with some of that. So thinking about it in terms of, having clear guidelines, use use case frameworks and tool vetting and approved platforms. So, you know, I think when AI start started becoming really ubiquitous at companies, a lot of companies were thinking of it in terms of, okay. We gotta we don't want anybody to use it.

We wanna make sure we understand the use and how to use it before we roll this out, to an enterprise sized company. And, really, that could backfire. Right? So I think of, some clients I've had in the past, where, you know, when ChattoptyPT came out, the company said, well, we're not using AI. Well, they were using it on their phone or they were using you know, going around the system. So it's it's a lot better, to have sanction tools with clear guidelines and guardrails as opposed to saying no, altogether. It can, absolutely backfire, and I've seen that in practice. So, again, we think about, policy versus prohibition, and then, you know, frameworks for the responsibility the responsible AI use of being very transparent about what the policies are, what the data security is, making sure that when, when and if, they're using it for things like, you know, teams teams are using it for things like content creation that we're being very clear on our attribution and disclosure of where the content came from.

But certainly encouraging experimentation. Protect the organization, but, encourage experimentation as, as, we we could use it within the company and help the company grow and for the benefit of the company. So, you know, we think about it from leading with empathy and strategy. So I think about it, you know, empowering your teams to test and learn again in a safe way, create, psychological safety. So I have seen a lot of, examples where somebody might, provide some content or provide some code, that's very clearly out of character or very clearly AI created. And instead of saying you know? And then they're them trying to be like, well, yeah, I used AI. Like, it's okay.

You can use AI, but let's let's think about it. You need to have a person that reviews. You need to have, when it comes to content, you need to have, guardrails in place when it comes to software development, or release, release schedules and and all those things. So it's really important to, again, create that psychological safety, so that people can experiment, you know, psychological safety as well as, safety for your organization. The other thing that I've come across and I've had a lot of conversations, with peers about are, employees who are anti AI, which, sure, there's a lot of reasons that maybe people are, and I understand that. But making sure that we explain, again, when you have clear policies around transparency, what the goals, the business objectives, all of those things, helps to really give people the, the a little bit more confidence that, you know, we're using AI for good, not necessarily for evil.

And then also explaining, like, these are these are you know, it's not going anywhere. These are tools that are gonna make your life, career, and everything better, in the long run. So, again, kinda leading with that empathy and strategy, but also, pulling it into some of the distractors or, you know, the people who, are are a little less confident in the, in the the vision of what a AI can, provide. So, you know, I I think about this kind of back, a little bit from where we started. It's really re reclaiming the promise of AI. So, I do believe that AI can make work work more meaningful. I think that we need to, go into projects and go into, hiring decisions and go into the growth of our companies With that in mind, understanding that we can reclaim time, that we can, you know, live our values if that's the value to the company, to have great work life balance, to have, you know, the the take the time off for to to rest and, be better at work, to make the work meaningful.

We all have busy work in our lives and really reclaim that promise of what AI can do and make life better. But we only can do that if we're really intentional as well as inclusive to how it's built and deployed across the companies. So when I think back as far as us women in tech and how, we could shape this, it's really important that we don't sit this one out, that we step up to the leadership, that we, join the steering committees, that we that we, experiment, may whether it's on a pet project or, on a team that we wouldn't normally participate in.

You know, mentor others as as our expertise come out, whether it's technical expert whether it's technical resources or whether it's, creative or, you know, operational or whoever else in your company that might not get that exposure, to AI and how it's really helping the business or just kinda hears about it over here.

Ask them to join, mentor them, have, you know, projects, pull them into the conversations, as well as asking the hard questions. So when you're in a position where you might see, you know, it being used unethically or kinda can see the vision of where the company might be going that doesn't, align with strategy, with strategic or business goals, or doesn't align with, the, the the, the ethics of the company, ask those hard questions.

Raise your hand and make sure that we are keeping our, you know, are keeping people honest, in what they say they're doing. And, you know, we've seen what happens when women aren't at the table. So I really encourage everybody to, to be diligent, to participate, and, to keep, you know, talking and having these conversations with others. So that was a speed version of my talk, with the late start.