Artificial Intelligence (AI) is currently one of the most important topics in the context of digital transformation. Successfully leveraging AI requires breaking through technical and organizational silos. With a holistic and interdisciplinary approach, innovative solutions become possible.
In this two-part series, “Leading with AI – thinking strategically, executing effectively”, we present two essential elements of our diselva AI Excellence Framework. The first part focuses on “Thinking Strategically,” while the second part is dedicated to “Executing Effectively.”
At diselva, we are highly passionate about technological innovation and have extensive experience in successfully implementing new solutions. At the same time, we are aware that when it comes to trends, the very first idea is rarely the one that leads to success.
To guide our clients successfully into the future with AI, we developed the diselva AI Excellence Framework. It helps address strategic top-down components and implementation-driven bottom-up initiatives in a holistic way.
The diselva AI Excellence Framework is based on proven models and naturally integrates the challenges of AI at the necessary points. The elements of the framework are briefly described below.
AI Exploration & Strategy (Discover)
A key element of strategic thinking is discovery. How do the organization’s business and business processes work in detail, and what new opportunities arise from them? On a technical level, existing data and applications play an important role in the context of AI. Enterprise architecture methods help to classify and assess them. Initial ideas for the use of AI begin to emerge at this stage.
AI Feasibility & Use Case Selection (Validate)
Collecting AI use cases is a motivating and creative process. What innovative application areas for AI exist within the organization? This is followed by the more labor-intensive phase of validating the use cases with respect to business fit, data fit, AI fit, and governance fit, during which risks, costs, and benefits are also analyzed. The result is a sound decision-making foundation for the concrete use of AI.
AI Prototyping & Experimentation (Prototype)
To implement AI solutions effectively, we work in an agile and scientific manner. The solution is developed as comprehensively as necessary, deployed experimentally in practice within a defined framework, and the resulting outcomes are evaluated. If the solution proves successful, it can be further developed and rolled out.
AI Deployment & Integration (Scale)
Operating and integrating AI solutions is complex and should not grow purely organically. They must be part of an overarching IT strategy. Defining a unified platform is essential for integrating data and applications via interfaces and for establishing a robust security architecture. AI models have high hardware requirements. Whether in the cloud or in an in-house data center, strong platform elasticity is an important success factor.
AI Governance & Continuous Optimization (Sustain)
The diselva AI Excellence Framework enables rapid and effective implementation, even while maintaining targeted strategic thinking. However, this high speed requires a sustainable core. Clear guidelines and controls for data protection, and especially for handling personally identifiable information (PII), must be continuously taken into account. The AI solutions in use are continually improved to deliver maximum value and to make the latest developments practically applicable.
You can learn more about “Leading with AI – thinking strategically, executing effectively” in the second part: “Leading with AI – executing effectively.”
For more information about diselva’s offerings, we look forward to hearing from you.