Developing a Strategy for the GenAI Era - Part 1

# Developing a Strategy for the GenAI Era - Part 1 ## Assess the Terrain Do you remember Netscape? 1996 they controlled nearly 80% of the Web-Browser market. Today, users start forgetting what a browse is, simply because browser-engines are part of our daily life and deeply embedded in many of our devices we use. What started to be a novel and genesis idea in 1989 ended up to be a commodity today. When you read this article, you most likely look to a Chrome, WebKit, or Gecko engine. Browser engines, as a fundamental component of the World Wide Web, have reshaped nearly every business and the way we handle knowledge today. The most valuable enterprises today would not exist without them. This is evolution. ![[Evolution of Web Browser.png]] *Wardley Map of the Evolution of Web Browser. Nodes in red are historical.* GenAI will follow a similar path in its evolution. This demands the competition in our market. But even that we know that the impact of GenAI will be as huge as with the World Wide Web, we can't foresee how it will change our business and life for sure. We are now very much at the same stage in product evolution as we were with the Mosaic browser in the 1990. We are just starting to understand what is possible. But what we can do now is develop a strategy for this uncertainty and new upcoming opportunities to make your enterprise fit for the GenAI age. > The overarching Strategic Doctrine for an Enterprise should be: Focus on Evolution I recently (January 2025) came across one of many illustrations showcasing a vast array of tools and services related to GenAI. From big names like OpenAI and to fundamental services like Hugging Face. New stars like DeepSeek still missing. ![[GenAI Tools and Services 2024.png]] OK, it’s likely that in five years, many of these names will fade from memory, much like Netscape. Other significant players of the future are probably not yet visible in the current landscape. But assessing your terrain of business in the context of GenAI tools and practices seems like a good place to start. Unfortunately, illustrations that merely list the numerous GenAI tools in the market are entirely useless for making strategic decisions for your business. We need a map that sets your organization, product, and services in the context of evolution and the GenAI market. Thankfully, Simon Wardley created an excellent [[Wardley Mapping|mapping technique]] specifically for this purpose. The [[Evolution of Web Browser.png|map of the Evolution of Web Browser]] I showed before is such a Wardley Map. Besides the horizontal flow of evolution, the map shows the components like the application framework Electron relative to the value chain (y-axis). This is an essential second dimension. It shows us the dependencies from higher ordered components (e.g. services, tools, practices ...) to lower, fundamental one. Here is my current, possibly biased and limited perspective on the GenAI market and potential forces. This is illustrated on a [[Wardley Map|Wardley Map]]. ![[Generative AI Market.png]] It is now easy to see why Nvidia has become so valuable. Their chips are products they can sell per piece, and everyone depends on them. Even the hyperscalers like Google, Amazon, and Microsoft need to buy them yet. Patents, specialized knowledge, and the fact that the model architectures have been optimized for the unique chip designs are preventing competitors from keeping up with Nvidia. However, we will see how long this advantage lasts. Optimized chip design for GenAI is definitely a candidate to become a commodity soon, much like how jeans in the Gold Rush started to become commodities. But first, it made Levi's a giant in the supplier industry. It is also easy to see that GenAI models are the fundamental component and logic for the tech giants' push in the direction of commodity. That's why especially smaller companies and institutes releases their models as open weight models (which it not entirely open source, simply because the training data and the algorithms learn the models are closed). Still, GenAI models in general are far away from being commodities. As the uprising of DeepSeek has shown, new approaches and algorithms like reasoning are creating competition and opening potential for entirely new higher level services to customers. Now think about how everything would change if a highly optimized photonic chip, as described in [[Field-programmable photonic nonlinearity|this paper]], moved into the GenAI landscape. And there is a strategy behind why OpenAI has [hired (or better acquired)](https://openai.com/sam-and-jony/) Jony Ive, the former chief designer of Apple. ## The Question to everything ... My map of the GenAI market still poses several challenges: 1. It is probably missing positional practices, tools, models, and players you should be considering. 2. It completely lacks your customer, product, and service. 3. Entire Industries are not considered: E.g., Medicine development, agriculture, robotics... O.k. let's change that and create a map for everything. Look at the map. Then, continue reading. ![[Business model and Generative AI Market.png]] You see: It is not your map. Even if you are familiar with [[Wardley Mapping|Wardley Maps]], you probably don't understand my intention and thoughts behind it. To be honest, I don't fully grasp it either. I come up with new ideas and insights every time I look at the map and update it. Furthermore, the true value emerges when creating maps collaboratively with others. While maps are inherently incomplete, they abstract connections within a system, enabling effective discussions. If you want the answer to the question about the universe, life, and everything, you get: 42 So, for a better understanding of your terrain, you need to define the questions you would like to be answered. Fortunately, we can extract some more specific questions from the map. I have brought them into a map again. ![[Questions for an Enterprise about GenAI.png]] I structured the map into four sections along the value chain. In each section, GenAI will drive evolution forward. However, this structure is mainly to improve readability. There are no strict boundaries, and you may find a structure that fits your business better. 1. Consumer & Customer Needs - **Genesis:** What could excite customers, but they can’t yet articulate it? What objections might they have? (persona, jobs, pain points) - **Custom:** What do customers expect now? (better price, faster service, convenience). What do they reject? (privacy concerns, data-protection issues, algorithmic bias) - **Product (+Rental):** What can (new) competitors offer now? - **Commodity (+Utility):** What will customers simply expect as a standard offering? 2. Process Knowledge - **Genesis:** What completely new product or service designs can be made possible with GenAI? - **Custom:** How can we become more efficient with GenAI agents? - **Product (+Rental):** How can we protect our proprietary process and business knowledge? Which services can we offer based on our process know-how and data? Suppliers can now offer new services—what should we outsource? 3. Data - **Genesis:** What unique and yet protected data do we have? - **Custom:** How can we make our data ready for GenAI? - **Product (+Rental):** What data do we already own, and what should we consider outsourcing? - **Commodity (+Utility):** What should we publish as open data to foster ecosystem growth? 4. Skills - **Genesis:** What domain-specific skills do we still need internally? - **Custom:** What do our existing employees need to learn? - **Product (+Rental):** What skills can we hire or contract from the market? I hope you find inspiration from them. Choose one of the questions your business might need answers to first. Start with that. Let's check some examples how that can look like. ## Example 1: Automotive Supplier In this example, a supplier in the automotive industry tried to understand how they could make the evaluation step in the order process more efficient and meaningful. ![[Wardley Map Automative Supplier.png]] Looking at the map, it becomes pretty clear that everything depends heavily on the data they have. Some data sources are external and not yet structured. So, a first strategic goal could be to experiment and gather some data sources into datasets that an AI Agent can use. Increasing efficiency and the quality of services is what most businesses focus on first when they bring GenAI into focus. The reason might be that this is quite obvious, as the example shows. Even though this is not a bad optimization goal, it could be a short-sighted decision because you might miss some important aspects of the market. ## Example 2: Market Transformation for Scrum-Training and Certification driven by GenAI DasScrumTeam AG is a training and consulting boutique specialized in helping organizations utilize Scrum to improve their product development. The uniqueness of DasScrumTeam is that it combines long experience in the field of Agile with the expertise to conduct in-person training classes. The question now is, how will GenAI impact the market for DasScrumTeam? Since the company is small and is not intending to scale massively, the conclusion is that most of the business will soon, or is already being, taken over by highly scalable training institutes like the [International University](https://www.iu.de). They would have had that anyway. Now just be much faster due to GenAI. For example, [Duolingo](https://www.heise.de/news/Duolingo-verdoppelt-Angebot-an-Sprachkursen-10367788.html) just doubled the amount of online classes with GenAI in one year. Before that, it took them 10 years for the same amount. ![[Market Transformation for DasScrumTeam Driven by GenAI.png]] You might be thinking now, if it was clear that this was happening anyway, why didn't the Product Owner see it coming five years ago?He did, but he was not taking action. Because he did not believe it was happening so quickly, and the company was very successful with what they were doing. I know that because I was the Product Owner. Simon Wardley calls this pattern [[Climatic Patterns by Simon Wardley|inertia]]. However, there are plenty of opportunities on the left side of the map. Since the company's goal is to remain boutique, here are the strategic investments. ## The Answer to Life and Everything ... At first, it may seem that evolution is a far-fetched concept to set as the fundamental strategic doctrine for an enterprise. But what is an enterprise? It is a belief system created by humans, who are the product of billions of years of evolution. In our examples, the future development of enterprises depends on lower-ordered systems. Since GenAI is a new technology bridging process knowledge and data — which most enterprises highly depend on — it will accelerate evolution by bringing more order into these areas. In other words, it reduces local entropy. This requires energy, which is very close to what [[Life, Energy, and Entropy|we understand life to be]]. So, [[Generative Artificial Intelligence]] (GenAI) is not [[Artificial Life]]. But GenAI will become part of our lives. You are probably thinking: *"Nice philosophical thought, but how do we put these insights into action?"* This is where the next fundamental Strategic Doctrine comes in: [[1. Empirical Control|Empirical Control]]. The [[Anticipate Advance Assess|Anticipate, Advance, Assess]] Loops in [[AME3]] are embedding this doctrine into the organization. ![[AAA-Loop Strategy and Tactic.svg]] The [[Anticipate Advance Assess|Anticipate, Advance, Assess]] Loops in [[AME3]]. ## Next Step! As the examples have shown, assessing your terrain by mapping the evolving GenAI landscape is only the first step. By asking the right questions and visualizing your position, you create a strong foundation for strategic decision-making. But this can only be the first step. You need to anticipate, advance, and assess again to iteratively adapt your strategy. Read more about this in the [[Developing a Strategy for the GenAI Era - Part 2|second part]].