And on the topic of, discovery and ideation of, AI use cases. My name is Ingrid O'Seng. I'm a, cloud advisor and Azure specialist at Microsoft.Can you shift the screen, Jessica, to the next slide?

Oh, yeah. Yes.

And I spend my days, advising large organizations on digital transformation and innovation in the cloud. I have a background in management engineering and software development. And, with me today, I have my beloved colleague, Jessica Perez, who's the technical specialist at Microsoft. She has her key competence within product development, artificial intelligence, and cloud architectures. She has more than eighteen years of experience across geographies such as Latin America, Western Europe, and, The Nordics. And we have a fully packed agenda today. We will go through why this requires your attention right now, where to look for AI opportunities, how to think when looking for AI opportunities, how to generate those high impact use cases, what makes the use case worth pursuing, how to go from vision to value, and also what's in store for the near term future according to the work trend index.

We will also do a short demo for you to show some capabilities and, of course, summarize the key takeaways so that you can bring them directly back to your desk. So let's jump into it. First and foremost, why does this matter now? AI is no longer a future bet. It's a present imperative. CEOs and CXOs are under pressure to innovate, to optimize, to differentiate, and AI is the lever and your best friend. According to, the 2025 work trend index annual report, 82% of leaders say this is a pivotal year to rethink key aspects of strategy and operations, and 24% say that there are companies that already deployed AI organization wide. And technology is shifting rapidly. We have already seen a major shift in the opportunities AI presents when moving from traditional AI focused on task automation and predictive analytics to generative AI.

Suddenly, we have systems that can mimic human behavior, generate all sorts of text and images, and personalize our interactions. And in this very moment, we're moving even further ahead towards agentic AI where systems proactively identify opportunities, initiate actions, collaborate with humans, and we will see more of how, that will affect us in the near term future later in the presentation. But, major announcements in tech and AI definitely comes at a much, much higher, pace than ever before. And we do not know what the next big thing will be, But what we do know is that your data will never be ready for the next big thing, nor will your people if you haven't already started experimenting and implementing use cases with generative AI and even agentic AI. You can discover and ideate as much as you want and maybe even come up with the most disruptive use case there is. But even the best use case is useless if your people cannot build it, use it, or govern it. People and culture are core enablers for AI transformation.

AI literacy to AI fluency takes time and commitment, and it's far easier to change your technology than to change your culture. So if you haven't already jumped on the AI train, it's time to go into discovery and ideation mode. So one way to start the discovery process is to look for business problems. Signals of processes that could benefit from automation or optimization with AI is, for example, repetitive rule based tasks, usually high volume processes that follow predictable patterns and consumes valuable human time. Data rich inside poor areas where data is abundant but underutilized also signals opportunities for pattern recognition or summarization. Processes that rely heavily on expert judgment or manual communication, often slow or inconsistent and difficult to scale, are ideal for AI assisted decision support, drafting, and knowledge sharing.

Painful or inefficient moments in the customer journey where responsiveness, personalization, or clarity is lacking is also signals that indicates potential with AI and valuable information trapped in documents, emails, or expert heads might be right for Gen AI powered retrieval, summarization, or synthesis.

Lastly, there's likely an opportunity to elevate customer experience and engagement through dynamic content, conversational interfaces, or hyper personalization. Another way of doing the discovery process is by flipping the script completely and reimagining how you approach problem solving. Instead of starting with a business problem and asking, can AI help us with this? You instead ask, what can AI do now that wasn't possible before, and how might it transform our business, our product, our customer experience? With AI first thinking, you start with capabilities, not problems. Giving cutting edge AI capabilities, you ask, if this was a team member, what would we ask it to do? Secondly, you assume abundance, not scarcity. Traditional thinking is constrained by time and attention, but AI first thinking assumes infinite scale.

What would you do if you had a thousand tireless analysts, designers, researchers? And lastly, think in systems, not in tasks. Encourage designing AI augmented systems, not just automating individual tasks. So instead of automating invoice processing, imagine an AI that takes care of the entire vendor relationship life cycle. And when we shift from traditional problem solving to an AI first mindset, we do not only do a technical pivot, but a strategic one. When we start with what AI can do rather than what's broken, we unlock ideas that leapfrog the competition. It's not about incremental improvement, but about reimagining what's possible. By aligning, with where AI is going, not just where it is already, we build solutions that are resilient, scalable, and relevant tomorrow, not just today. AI first thinking also nudges organizations out of cautious experimentation and into bold transformation.

It encourages teams to test, learn, and iterate, fostering a culture where innovation isn't a site project. It's actually the default. And one of the most important aspects, when ideating around AI use cases is understanding the relationship between human and AI. It's not just about what that AI can do, but how it fits into our workflows, our decisions, and even our values. We can think of this relationship as Spectrum. On one end, we have human led AI where the human is fully in control and uses AI as a tool. For example, a doctor using AI to support a diagnosis. And then we move into human AI collaboration where both work together as partners. For example, a driver using a navigation system that is adjusting to traffic in real time.

Further along, we see AI led humans where AI takes the lead and guides human actions, maybe setting, priorities or assigning tasks. And lastly, we reach independent AI, where the system operates autonomously without human intervention at all, like smart infrastructure managing energy or track big flows in a city. Now here's the key message. The most successful AI use cases aren't just about technology. They're about augmenting human capability. So let's talk about a powerful metaphor from Microsoft Research that helps us think about how AI impacts human capability over time. It's called the steroids, sneakers, and coach model, and it's a great way to evaluate the long term value of an AI solution. Steroids. These give you a quick performance boost, but at a cost. Think of an AI tool that automates responses in customer service.

It might speed things up in the short term, but if the team becomes overly reliant on it, they may lose the ability to handle complex or nuanced cases. Over time, skills erode. It's efficient, but it's not sustainable. Then we move over to sneakers. A good set of running shoes help you go faster, but they don't fundamentally change you. They support your existing abilities without making you better or worse. For example, using AI to translate survey responses or summarizing meeting notes. It saves time, but it doesn't necessarily build any new skills. Lastly, we have the coach, and this is kind of the gold standard. A coach doesn't just help you perform better today, they help you grow. Imagine an AI that not only reviews your code but explains why it suggested the changes.

Over time, you become a better developer, and this is where AI becomes a long term enabler of human potential. So as we design AI use cases, the question isn't just what can this do for us now. It's what kind of relationship are we building with AI, and are we creating dependency, or are we building capability? And now that we have explored how to generate AI use cases, the next step is figuring out which ones are actually worth pursuing. That's where the AI use case radar comes in. This tool helps you prioritize by mapping each idea across two dimensions, business value and ease of implementation. On the business value side, ask yourself, does this use case meet a real customer need? Could it differentiate us in the market?

Will it generate new revenue or reduce costs? And most importantly, does it align with our strategic goals? Then on the implementation side, consider, do we actually have the data that we need, and is it high quality? Are there any privacy, security, or legal hurdles that we need to take into consideration? How complex is the integration, and do we have the right people and skills to make it happen. The goal here is not to overanalyze, but to get a clear picture of where your quick wins are and which ideas might need more time, resources, or risk management. And it helps you move from a long list of possibilities, to a focused, actionable road map.

Once you've identified a promising AI use case, the next step is, of course, to articulate it clearly, and this is where a business use case template like this, becomes incredibly useful. This template helps you move from idea to action by structuring your thinking around a few key questions, such as how AI can help solve a problem, required stakeholders, and business value. And it truly lets you visualize if you have a strong AI use case or if you need to go back to the drawing board. This is also a helpful tool to have when aligning teams, securing buy in, and ensuring that your AI initiatives are both impactful and feasible.

Thank you, Ingrid. And as we move from vision to value, it's important to understand what are those phases stages, from strategic ideation to rapid prototyping, business app creation, risk and ethics review, and launch and scalable, the product. So as you can see, these five stages are resembling the server development life cycle because it's really tight. So once that we have been discover and ideate over, one specific, use case that we want to implement, we really need to adapt to what it needs to be done to implement the AI in this, specific product or business application. In this case, I will say the most critical that we have seen is, as Ingrid mentioned, the, AI literacy. So people really need to have the skills in order to make realize the value that you have, visualized during the ideation and the discovery stages to make real value on this.

Some of the tools that you can actually use in, show the value of this new business application or enhancing some of the capabilities of your already applications that the applications that already exist. So you can use some, AI tools such as Lovable. That is a tool where you can design a user interface in matter of minutes. And this is built on AI, but this is just an example of how you can create value or rapid prototyping, creating a really fast forward user or final user feedback loop feedback together with your product team to realize, the value and assess that this is what it actually, the final user wants.

Right? So and this is just one example of how you can use AI into this cycle. But in reality, it should be embed in all of these stages because that's what it will create a structure and efficient approach to this innovation in within your, company. But now that we have seen, like, okay. Now I have my, use cases. I have already prioritized them. I have really an strategic ideation on that, and now I am realizing the value. Or I'm in this process. Like, what is the path forward to this? So for every leader and every employee, now it's the time to take decisive action and how to get started. So we can divide this in three phases, but the first phase will be like human with assistant.

So every employee has an AI assistant that helps them to work faster and better. So what does that mean? So you can actually, hire your digital or first digital employee to be ready to take actions and tasks that are traditionally done by humans, that could be from answering support tickets to drafting reports. So starting by this clear role or clear task, you can actually automate this task, and it can actually add value to the job that you are doing every day. Right? So and what it will be really, like, the this new phase for you is, like, not just about efficiency, but about building, like, the workforce that blends your human creativity with AI unique strengths. So in the phase number two, so it will be, like, define an a an AM team or human AI radio where you can actually have more than one assistant.

And then you can actually have, like, digital colleagues that are taking on specific task at human direction. So this can be, like, small business processes that needs to be automated. And one of these, and each of these specific agents can help you and can help the team and the organization to be more efficient and reduce time. Right? Like, it can actually, help you with some everyday work press practices, and also, provide their skills into the ongoing learning as also as, helping the human teams to, reshape reshape, their task and be even more efficient. And in the third phase, humans set the direction and human agent, and agents execute business processes and workflows, checking as needed. So this will be, like, the third phase when, really, humans can are leading these agents. And remember, this is the path forward.

This is what, the 2025 word trend index and month report is predicting as that that is really happening. So that's why I opened this slide. Like, this is now is the time to take an decisive action if you want to, go forward advance in that, as as as an individual and also as a company. So this is the things that we are seeing as as, near future. And then, okay, I have this information. I know I need to to create or to add AI. How I will scaling AI as a business capability? So, again, as Ingrid mentioned earlier, this is more about people. This is more about the culture that we want to build into the as a business capability, and it will enhance, or it will create a strategic differentiator for each of the company, and even as a personal level, how you, are adopting.

So first, or when it's the awareness phase, we are seeing, like, position AI as an strategic enabler. It's not just a technical tool. So it's, usually, is AI seen as a tech initiative, but it's more than that. So it needs to be in both cross functional teams, for example, HR, finance, and the product development teams. So it needs to be encouraging the experimentation for like the pilots and tolerate the failure that, is expected, and then iterate fast from that. So that will be, like, the adoption part of this, and then equip all the employees with tools and training to use AI. We have seen by experience that there is nothing more powerful to have really this training and access to AI.

So it that is when, like, individuals, contributors, use their own creativity to solve the challenges that they are facing daily basis. Right? So this is when you are actually seeing the the fostering of this, creativity or innovation together with the AI as enabled. So that will be, like, the next one, the integration phase when you actually AI is in the workflows, and you can actually build governance, ethics, and transparency into each of every single project that is, adding embedding AI and also making sure that this is aligned with your values as a as an organization.

And then, like, the final step will be, like, taking into the leadership, right, in your like, let's say, your same industry because let's not forget that every company is all the time doing a a, competing with some other competitors. Right? So this is what we believe AI will enable you as, having as a strategic differentiation when you already have it in, in place. So and what is the next step? So what it will be to to make this happen? So we are not only talking here about what is, like, the future because there is some examples of companies that they are already at that level. Right? And this is some examples of forward thinking companies and how they are using, AI already. So for example, they still have their companies that built an agent to identify and consolidate consumer insights.

So instead of shifting through scattered reports and ender enders back ups, teams can now pull up actionally actionable intelligence instantly or almost instantly. So Bayer also is using like, their r and d team is saving up to six hour a week when they are accelerating the development of products to drive innovation in agriculture. Also, Wells Fargo, who actually, deployed an assistant, to, in financial services that it has, like, 3,000 bankers across 4,000 branch. So now 75 of the searches happen through an agent, and this is including times of from ten minutes to thirty seconds. And I will show you an example of how you can actually today, like, hire your first digital assistant and start in this journey. So I will just start, stop the presentation, and I will show you can you let me know if you see, the right browser in red, please? No. Not yet.

Okay. Let me just then switch the presentation and then share again, window here. Can you see? Yes. Okay. So this is just my Microsoft, three sixty five Copilot, and I have created a briefing assistant. So I create this assistant with with having in mind that I want to hide this assistant because they really need help to prepare briefings for my meetings. So let me just, find the prompt that I created for this example. So in this example, I just use a very simple, prompt. I will show you afterwards how I actually build this and how it's working in behind scenes. But now let's use it. So I create this, prompt where I'm asking, like, create a brief for my upcoming meeting. The meeting will include the following companies and CEO representatives.

So I use, like, just a a list from a Apple company with Tim Cook, Alphabet with Sondar Pichai, Microsoft with Satya Nadella, and it goes on in that list. So I'm assuming these people will attend my meeting. So I will just send it, and then it's instantly start to create a, an overview of, each of these representatives. What is the the recent achievements? What is the market position of each of these companies and people who is attending the meeting? And then it also creates some key discussion points. So what are the things that I possibly focus on doing that meeting? Right? And generative AI, obviously, and then collaboration opportunities. So in this fairy tale meeting, right, where I will attend, so it will provide me an insight of what it will be interesting for me to discuss during that meeting.

But as you can see, my prompt is not really, like, really strong. It's not asking too much. It's not providing too much a context. But this is why it why I actually built this agent because I don't want to prepare the context every time. I don't want to create a really big or really, detailed prompt every single time. And for that, I use and, like, in order to create this assistant or this digital assistant, I use Copilot Studio low code, which it means I create everything as you see using the user interface without any code, assuming that I have no attacker profile, but still I can create or hide my first AI assistant.

So here, I actually create a description. Like, this is an agent that helps to prepare briefings for CEOs and CXOs focusing on recent progress and technology trends in generative AI analyst guidance and summaries of companies attending the briefing. So then I provide specific instructions, for example, like asking like, provide briefing, anchoring recent progress and analyst guidance, for example, from. Include a short summary, like, ensure the briefing is concise, informative, etcetera, etcetera. So I possibly could use knowledge bases from my own company, but I didn't use it in this specific example even though there is some, already, I have access, but I I didn't use it. And then I as you can see here in this start up prompts, so I can configure those prompts here based on each of the things that I want this assistant to help me.

Like, I can come here and say, like, what are the latest trends in generative AI that I should be aware of? And as you might see until this point, like, this is changing every single week. So this for me in, it's critical. So but this is just an example of how you can actually start today to hire this, like, your first digital assistant. So how you can, really, start to experiment and be conscious about what you, can actually automate in your daily basis work. Right? Like, just as simple as it is. So this is was just a very simple example. And let me just come back again to my presentation that, for some reason, I need to stop and go back to share. So okay. I'm assuming I yes.

So with this, what are the takeaways of this presentation? Start small and think big. So pilot with a high impact and low risk use case. Don't just stay updated on AI trend. Be bold and implementing them. Like, it's easy. Just just you just need to start. Invest in AI fluency, not just the tools, but the mindsets. So if you are attending decision, it's obvious that you are interested in this topic. Start today. Read a research research paper. Do an example research. I mentioned one tool that is lovable. What is lovable? If you don't know that, just go on. Like, there is some smart pieces of information that you can it doesn't require too much time. Just start slowly slowly. And finally, thanks a lot for listening to our talk, and please let us know your feedback in the chat.

Ingrid and I, we have been working with enterprise customer on their digital transformation journey for years. And if this resonates with you, we will love to continue the conversation after the session. So connect with us on LinkedIn and stay in touch.