ML & AI Strategy Development

In our ML Incubator, participants will learn that every successful ML & AI initiative starts with the analysis of the corporate strategy. A transparent goal needs to be established in accordance with the elaboration of the desired core competences within the organization. These core competences should be embedded in the corporate vision by means of a strategic ML & AI goal and form the basis of the technological roadmap.

WE Reply with:

• Introduction of basic functionalities of ML & AI
• Respective client’s industry and benchmarking use-case analysis
• Use Case Development: from ideation to AI roadmap
• Best practices on how to leverage Big Data, Cloud environments and existing data platforms
• Establishment of organizational alignment with ML & AI practices: Process vs. data-driven organization
• Selection of the right skill sets for the client’s AI journey
• Support in ecosystem development
• Assistance on choice of tools — self-made vs. bought-in — BI tools, Data Science Suite or Analytics Sandbox
• Evaluation of challenges and sharing of best practices for a tight collaboration between Business and Data Science units

In the first step — ML & AI strategy development — we focus on an enhancement of the client’s respective operational setup and organizational processes, establishment of evaluation and success metrics, quality-gates, and the use of cross-project synergies as well as measures to reduce the complexity.

In order to define the core elements of ML & AI use case industrialization it is important to define the challenges of determining the status quo. We start with the consolidation of different target images and approaches of ML & AI existing within the client’s organization. The target images are broken down from the group IT-Strategy level to department level in order to define concrete and appropriate measures. Together with the client we build a framework that takes into account defined objectives with different corresponding time horizons (short, medium and long term according to the customer’s wishes). Such framework ensures alignment among all involved stakeholders regaring objectives of this project and the associated organizational and technical challenges.

On the technical side, we recommend the usage of a “Technical Architecture Maturity Test” to evaluate the existing technical architecture. By assessing various aspects, the team generates a list of prioritized technical improvement proposals that will help increase the efficiency and quality as well as secure effortless maintainability of ML projects. This lists servers as a basis to create a catalogue of concrete measures to be implemented. Furthermore, we test the overall infrastructure, the tools currently in use and the process used to implement new use cases. Based on these test we advise the client on what should be continued to be used and where are changes are recommended.

Following the AS-IS analysis, the target architecture and (if necessary) the revised process model is defined while taking into account all stakeholders’ requirements.

At the end of the first step of ML & AI use case industrialization there is a clear vision of direction in which the client’s overall ML & AI strategy develops.