Strategy 2: Experiment with own GAI Use Cases for Micro-Optimizations

Many companies have already started to experiment with generative AI use cases across various departments. This experimentation enables them to learn and identify the feasibility, risks, and potential of specific use cases. However, it's crucial to first assess your needs and business requirements before implementing any tools in an ad-hoc manner. To this end, it makes sense to develop an overarching use case canvas and create a value-effort matrix for each case, including ROI and feasibility data.

If you don't have this data yet, conduct thorough research, involve AI expert companies, and run agile enablement sprints of two weeks to test- let’s say a tool or question you have. Based on your use cases and the north star vision you aim to achieve, you can formulate an AI vision. Since the AI strategy needs to align with the company’s overarching vision and OKRs, it is crucial to work collaboratively to align the AI vision with your company goals.

Once you have your vision in place, the next step is translating these learnings and requirements into actionable AI directives for data scientists and the Ai teams. Adopting this approach helps you to have a guidance.

For example, Microsoft Copilot is effective at addressing a wide range of use cases and can therefore be beneficial. Most companies implement Copilot as part of their AI Strategy to enable workforce tasks to be done in a more efficient way. We experienced Copilot to be sometimes very generic and thus not solving specific client needs. Additionally, you may not want to expose all your data to Microsoft, which happens as soon as you activate Microsoft Copilot Pro for your company. This is especially critical for insurance companies and banks where CID and personally identifiable information must be masked before being shared with third parties, potentially leading to risks. Microsoft Copilot is certainly a valuable AI workspace tool that enhances productivity in daily tasks across your company, however again, do not start with the tool in mind but with the problem you want to solve. Microsoft Copilot will not solve all issues. You need to know what issues you want to solve first.

Strategy 3: Innovate with Generative AI

The more mature companies are with the use and knowledge about AI, the more they start exploring innovative new ways of building new business models and products. We have seen exciting new ideas coming up through our Generative Ai workshops and consulting of companies that have potential to disrupt industries. Use new onboarded people, interns and interested business person from your departments to come up with great new ideas in hackathons or Gen. AI events. You will receive interesting new ideas and use cases.

However, it is crucial to first consider the specific pain points you need to address for your industry and clients before implementing any AI tools.