Innovation with data and AI requires more than just safeguarding privacy risks. Especially when things become complex—where ethics, human rights, and compliance intersect—it is essential that the right people come together and engage in meaningful dialogue. A DPIAMA combines a DPIA and an IAMA, bringing business, development teams, and compliance together at one table.
In our High on AI-podcast, we talk through the real world stories and use cases of business and organizations successfully introducing AI into their everyday work lives, to do all the things AI promises to do, can do and more.
In the first part of this blog series I indicated that there can be several forms of development in an organization, but always with a common goal: realizing value. With portfolio management you find each other by continuously managing this in a coherent manner. In the follow-up blog I provided a set of concrete tools to continuously manage the highest value in a coherent manner. In this third and concluding part, I provide tips for portfolio organization, the portfolio process and continuous improvement thereof.
Organizations with multiple development forms share a common goal: realizing value. Portfolio management enables the highest value by continuously managing this in a coherent manner. This follow-up provides a set of handles.
In organizations with many IT initiatives (development of front-end, ERP system, data platform, infrastructure, etc.) the following sometimes occurs: There is a lack of coherence between all these initiatives, people find it difficult to make choices and are mainly driven by costs, not by value. Decision-making starts slowly and it takes a long time before initiatives are realized. All this does not benefit the working atmosphere, which may make it difficult to retain employees
On February 3, 2023, we organized a round table on data and analytics for civil society organizations. Large and small municipalities, provinces, ministries and the House of Representatives joined the virtual table. The central question at the table: How do we get from pilot to upscaling? Many organizations have set up innovation teams or data labs. After initial successes, it often proves to be a challenge to ensure that data applications are well embedded in the organization, are scalable and are developed further in order to actually realize the added value of the new data application for the business. In this round table, we presented four dilemmas and discussed success factors for technical and organizational upscaling. With some additions from our side, a view of the afternoon is given in this insight.