Artificial Intelligence
Ensuring future viability through AI
Artificial intelligence (AI) is the greatest technological and innovative leap of the digital age. Its impact on the economy, society, and politics is considered to be as disruptive as the introduction of the printing press, the steam engine, or electricity—or perhaps even as disruptive as all of these inventions combined. However, the transformational challenge posed by AI is likely to go beyond these groundbreaking innovations of the past, as it encounters the global complexity, interconnectedness, and speed of the 21st century.
For companies and organizations, AI will change both internal processes and interactions with customers, suppliers, and service providers. AI will have a massive impact on markets and business models, on success factors and regulations. The successful use of AI will determine the competitiveness and thus the existence of companies in all industries and all regions of the world.
eStrategy Consulting empowers companies to recognize and tap into their AI-driven opportunities and potential – as well as to identify the risks of not taking advantage of these opportunities.
We provide support across the three major levels of AI-driven transformation:
- AI in operations, i.e., improving the quality and efficiency of activities in an organization’s day-to-day operations, for example, through the targeted use of existing SaaS-based generative AI tools such as ChatGPT, Gemini, or Copilot, AI-augmented workflows, prompt engineering, digital assistants, or no-/low-code-driven task automation.
- AI in automation, i.e., the automation of entire workflows or process chains, e.g., based on standardized LLM-supported services and agent-based AI approaches, via predictive automation, intelligent process automation (IPA), cognitive capture, semantic search, or ML-Ops/AI integration layers.
- AI in business model innovation, i.e., the analysis of the impact of AI on existing business models and their successful adaptation, as well as the development of new business models, always with a clear view of market and customer requirements and the opportunities offered by AI.
We support our customers with these issues, seamlessly from strategy development to implementation management:
- How can you identify and evaluate the right potential applications and disruption requirements for AI in your own organization? Quickly, collaboratively, and in a standardized way?
- Which internal processes offer the greatest potential for AI-supported automation?
- How can we achieve scalable automation and decision support through the use of standardized LLM-based services and agent-based AI approaches (e.g., from the AWS ecosystem)?
- How do you control the implementation of AI initiatives and how do you manage the associated transformation processes?
- What best practices in prompting and in the use of increasingly integrated GenAI tool suites are crucial for leveraging the full potential of these technologies in a corporate context?
- How can AI be used to create attractive ways of working and working environments in order to remain relevant in the labor market?
- How can AI be used for internal knowledge transfer and training?
- What foundations, such as master data governance, process standards and integrations, and sufficient data flows, need to be created in order to support AI in the best possible way?
- Where is AI changing competitors’ products and value chains, and thus also the behavior of users and customers? Which previous success factors are losing value as a result and need to be rethought?
- How can AI be used to create a competitive UX for customers?
- How can existing business models be redesigned, personalized, and automated through the targeted use of AI along the entire customer and buying journey—for example, in e-commerce—in order to tap into new revenue potential?
Cases
AI-based predictive maintenance to reduce unplanned downtime of printing machines
The printing industry suffers significantly from unplanned downtime of printing machines. In response, an AI-based predictive maintenance solution has been introduced that evaluates performance data in real time to detect potential production failures early and provide solution-oriented reports for customers.
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