The following are the outputs of the captioning taken during an IGF intervention. Although it is largely accurate, in some cases it may be incomplete or inaccurate due to inaudible passages or transcription errors. It is posted as an aid, but should not be treated as an authoritative record.
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>> and without the challenge, it became one of the top GenAI. It is literacy they come to resolve that sort of challenge as we would see in the next section.
So as I said in the beginning, well are many organizations and without that, there are many points related to their practices when it comes to AI adoption. We have high maturity organizations which came out to be around 9, 10%. Out of 650, we only have 65 organizations that are mature when it comes to AI adoption in the world. And we learned, you know, (?) when it comes to AI adoption and scaling AI adoption in the older. I want to highlight what action needs when it comes to our analysis to the data points that we connected. We need to understand that the common for the AI mature organizations applied or several businesses and processes. It is not a solution or model within production. We need to focus on customer activity, application and we need to focus on predictability and software for scenarios. This is related to the different business processes. You need to understand the maturity as well. There are many use cases. Most organizations are still in the beginning of the journey. They are still with one use case. The mature organizations build that and deployed already five AI use cases in production. And they're not in the stages of that deployment. These AI adoption models and use cases have stayed in production for at least two years on average, which which is have generated a lot of revenue for the organization.
So, (?) we understood that AI focused on the bottom as you see on that paragraph of that diagram. So basically the bottom has the foundation of AI mature organizations relating the operated model that is being used to scale AI within the organization related to AI engineering practices and pipelines that should be put in place and definitely upscale because we learned in the previous surveys talent is a major issue. So upscaling is one way to resolve that gap in the organization as well as change management and definitely the government's aspect which is on trust and risk management and trust, risk and security. So the one on the top whether it is related to the AI use cases, AI trends, the next big thing and the AI models that are open influx when it comes to announcement, you know, this is something you should not be focusing on if you want to really mature your journey when it comes to AI. You need to focus on the foundational element as we will go through in a moment.
So as I said, you know, AI, you know, in the previous diagram, the ones on the top are really shy. You need to shy away from shiny objects. Delays the focus when it comes to foundational and fundamental components and helps you. Hopefully reach 2 years production when it comes to AI adoption. But if we talk about the scalable AI operated model and I am conscious of the type, so I will try to speed up. You know, when it comes to the operating model, scaling AI requires different AI operating model. Previously, central teams may have succeeded to maybe pilot AI in the organizations, but in order to really scale it, you need to think about hyper model where very central AI capabilities need to take place. They work in tandem and in collaboration with other business units within the organizations in a very specific sort of manner when you need to knowledge about that mature AI organizations. Distribute the AI budget across different sort of business project instead of being concentrated on one or two projects.
And this is one example of, you know, hybrid operating model. There are things related to AI strategy, AI architecture and, you know, some of the sent matter expert when it comes to AI domain, but you also have the edges, the business units and business processes where many of the innovation are being adopted and in order to adopt that, you need to upscale the team as we will see in a moment.
But there is no one size fits all. Every hyper model will benefit from one organization to the other. But at the same time, you need to centralize everything in one domain and think about, you know, what makes sense when it comes to hybrid in your organization. If you come from software and software engineer background, you will definitely relate to what I'm saying what I'm going to say now. In software engineering, you have the pipelines that helps you to, you know, develop and design specific components in scale to production. Set AI. You need to have a mechanism to help you manage AI design and deployment end to end with a very automated fashion if your organization in your organization and components in your organizations, you will not be able to scale every AI model that you adopt within the organization. So the main focus for AI engineering in general dedicate the AI team and should double down in AI engineering capabilities and practice in the organizations. Right? And you need to understand that these AI engineering practices and capabilities will help you even, you know, ready your data when it comes to AI adoptions. Traditional AI using different AI techniques that we talked about earlier.
And again, matire organizations double down mature organizations double down and deploying AI solutions. If you focus on the solutions, you will relate to the composable and reusable counts that you need to lay down if you want to become serious when it comes to AI adoptions in your organizations.
Now it is even more difficult to get it done. Okay. So basically AI design patterns very similar to software engineering design patterns. If you don't come from that field, they're nothing but Lego blocks you connect them like this or like that to come up with a specific shape that you have in mind, but basically AI design helps you to bridge the different use cases with the right AI solution architecture that you have that you want to build for your organization. And they have one example in the next slide.
So this example relates to what we call retrieval research model or RAG retrieval augmented generation with the general GenAI large language model. Basically the retrieval augmented generation happens to serve multiple use cases within the organization whether related to customer or operational excellence or other sort of scenario like employee productivity or others. But basically this component here could be reused across many other AI adoption techniques and scenarios within the organizations and you can crop it with the right model that you can employ for the specific use cases that we you want to really scale in your organization. But basically, you know, built that as a Lego component or as a useable sort of component will help you to adopt other use cases in your organizations.
And the third sort of teaching from the survey that we have done is the focus on upscaling and change management. So upscaling alone, you know, focusing on the AI associate should not be your only concern. You need to think about how could, you know, adopt AI literacy programs within the overall organizations that help, you know, every associates within the organizations understand the capability of AI and how they can use it in their concontinuous. This is where GenAI literacy programs can help. But definitely change management and we will see in a moment how change management techniques can help you maximize the value related to your organizations across different spectrums and domains.
So as we see here from that case study, basically this, you know, case study or the lesson that I want to highlight in this case study is that you need to be systemic when it comes to upscaling your associates within the organization. I mean, you should not focus only on the pros who use and reuse and create AI models or become really strong prompt engineers, but also focus on the bigger sort of group that may not really need advanced capabilities when it comes to AI, but they need moderate sort of capability, but the general, associate having online courses distributed across will help you to really each every single associates within the organization. Basically layers sort of systemic approach when it comes to AI literacy or adopt AI in your organization is very important to reach out more people.
And again, change management is very important and change management, you know, techniques will help you to maximize the business outcomes when it comes to cost saving, customer experience or even activity for the employees. The applications you will not be able to reach very high, you know, sort of impact when it comes to different values when it comes to business outcomes.
And the last thing they want to focus is AI governance in general. Trust, risk and security management is one of the frameworks that we often highlight and it is basically focusing on the facts that governance is being applied by diverse role. AI associate or savvy people will not help you to reach high AI governance. You need to think about different dimensions and different perspectives that needs to be put in place by the diversity or fraud. And the budget authority especially when it comes to AI privacy and security is very important. They need to be own bide central units that help you to adopt the governance make an itch related and business impact when it comes to breaching or enabling AI privacy is very important.
So again, the AI framework will help you prism and apply the governance mechanism utilizing different components in the tourism technologies and connecting with AI systems and the organizational governance practices in the that you have in your organizations.
And the last thing I just want to highlight is that AI adoption faces maturity. We focus on what is above the surface rather than build strong roots that will help you mature with time and reuse the components as you go.
And this is we have done everything that we have side and the time is (?) when it comes to AI adoption and you need to provide models for using the right formula described earlier and for lessons gone through for creating hybrid in that case and utilizing AI engineering and upscaling literacy program as well as investing in AI ee as well as investing in AI trust and security in general. This is really, really important to push the needle forward and become AI mature organization. Thank you very much. We'll stay around if you have any questions. Yeah. Go ahead.
>> (off microphone)
>> That's fine because the people on the web they need to hear you, but I can hear you now.
>> You talk about 10% (?) (off microphone)
>> On the next big thing. They focus on the shiny object instead of the fundamentals. The different foundational components in your organization without this, right, you maying successful in one AI use case. You may be successful in one AI specific business process or specific unit. But you will not be able to scale different models crease the organization. It relates to AI maturity. If you want to become AI enabled organization different regional and different business units, you need the foundation. So they do not focus on the foundation. That's fine. That's fine. We can hear.
>> We now see we have some sales. (?) (low voice)
>> Very great question. Upscaling is very important as I mentioned before. Your question is very great. How we can (?). I am (?) this question coming. Usually when you look to what is happening, even me I am looking at what is next. Keep continuing what you're doing. Yeah. I was reading that it says what do we expect. Really the future is faster. So we have to upscale ourselves in the area that is interesting. So we cannot be (?) (low voice) thank you for the question.
>> (low voice) (off microphone)
>> The survey for the workshop. Thank you very much. (low voice)