Main Themes:

Rapid Evolution of AI Market: The AI market is experiencing explosive growth, driven by advancements like Generative AI and the democratization of AI skills. This translates to an abundance of off-the-shelf AI tools and solutions, making the "build vs. buy" dilemma more relevant than ever.

Complex Decision-Making: Choosing between building and buying AI systems isn't straightforward. Organizations need to carefully consider several factors, including total cost, data and capability requirements, regulatory risks, existing operating models, data privacy challenges, and potential vendor lock-in.

Importance of Strategic Alignment: The ultimate decision should align with the organization's specific needs, resources, and strategic goals. While buying offers accessibility and cost-effectiveness, building in-house might provide greater customization and competitive advantage.

Key Ideas & Facts:

Cost Comparison: "Understanding the cost comparison requires forecasting and business case modelling which provides insights to what the costs would be for both building or buying the model and operationally running it." Accurately comparing total costs, including implementation and operation, is crucial for determining the most cost-effective solution.

Data, Capability & Time: "We may be able to deliver an AI model internally at a comparable cost to what we can get in the market. However, if we do not have access to capabilities and datasets to create competitive models, we may lose out to competition." Building competitive AI models demands adequate data, expertise, and time, which may not be feasible for all organizations.

Regulatory Compliance: "New regulations are continuously being proposed for AI across the EU, the most recent and prominent one being the agreement of the AI Act." Emerging regulations like the EU AI Act introduce compliance requirements and potential penalties, which organizations need to consider when choosing between building and buying.

Operational Model Fit: "AI models and tools require continuous maintenance regardless of whether they are built or bought." Organizations need to assess if their existing operational models (e.g., MLOps) can support either built or bought AI systems, including deployment, monitoring, and maintenance aspects.

Data Privacy: "AI use may sometimes require sensitive data either related to the business or your customers." Handling sensitive data requires careful consideration of data privacy risks, legal agreements (e.g., DPAs under GDPR), and compliance with emerging regulations.

Vendor Lock-In: "Organizations have no control over the performance of products provided by vendors or the future state of that vendors business." Relying solely on external vendors for AI systems can create dependency and potential risks related to performance, cost, or vendor stability.