There is a very rapid pace of advancement in AI. Understanding the latest capabilities and trends of AI is the prerequisite for an effective discussion on the opportunities for your business. Here is how we can help: Join our AI Bootcamp
Explore current AI capabilities through practical examples and use cases, discovering how similar businesses are enhancing their products with AI technology.
Learn about common business processes where AI has shown early success, understanding the basic concepts of agentic process automation and enhancement.
Get an overview of typical AI infrastructure components and data requirements, understanding what makes a good foundation for future AI initiatives.
Build awareness of AI as a critical future capability across leadership and teams. Create initial excitement and understanding of AI's strategic importance for long-term competitiveness and growth.
Key Opportunities arise at the intersection where AI capabilities (better) solve real business challenges. Using formats like AI Design Sprints (TM) we help to discover the AI 'Opportunity Space' relevant for your business. Dependent on your focus, we look at best-AI-practices to improve products, business processes and organisational workflows. Here is how we can help: Book our AI Opportunity Mapping
Map current product portfolio to identify customer pain points and unmet needs. Match these opportunities with AI capabilities to enhance user experience, personalization, and product intelligence.
Analyze existing workflows to identify bottlenecks, manual tasks, and efficiency gaps. Match these areas with AI capabilities to improve speed, accuracy, and resource utilization.
Assess current data landscape and infrastructure readiness for AI. Map available data assets, identify quality gaps, and outline basic requirements for initial AI use cases.
Map organizational opportunities for AI adoption and identify potential capability gaps. Understand which teams could benefit most from AI and what skills might be needed for future initiatives.
We advocate the Playing to Win strategy framework and believe it is a suitable toolset also in the context of AI. There are many strategic choices to make. We help to identify critical questions and support you in making the right decisions. See more here: AI Strategy
Define vision and ambition for AI-enhanced products and services. Identify critical requirements including essential data assets, key AI competencies, and investment needs to transform your product portfolio.
Identify core business processes and workflows for AI transformation. Evaluate opportunities for efficiency improvements, quality enhancements, and the balance between augmentation and automation.
Design future platform architecture aligned with requirements. Make critical make-or-buy decisions, select platform providers, and establish MLOps practices for sustainable AI operations.
Plan AI talent acquisition and development strategy. Define organizational setup and accountabilities to embrace AI, while establishing robust governance frameworks for compliance and ethics.
Implement initial AI solutions and gather valuable insights. This phase is crucial for three key reasons: • Risk Mitigation: Early prototyping reveals technical challenges and integration issues before full-scale deployment, significantly reducing implementation risks and costs. • User Validation: Real-world testing provides invaluable feedback on user acceptance and actual business value, ensuring AI solutions truly meet stakeholder needs. • Organizational Learning: Hands-on experience with AI implementation builds internal capabilities and creates a practical foundation for broader AI adoption.
Develop and test initial AI features with a selected user group. Focus on core functionality and user experience, gathering feedback to validate business value and technical feasibility.
Implement pilot program for selected AI-enhanced processes. Monitor efficiency gains and gather user feedback to validate improvements in speed, quality, and resource utilization.
Deploy initial AI models in a controlled environment. Test integration with existing systems, establish performance benchmarks, and validate technical architecture decisions.
Execute first phase of AI training program. Build practical experience through hands-on implementation, and establish initial governance practices.
Moving from successful prototypes to production is a critical phase that determines the real business impact of AI initiatives. Effective operationalization ensures reliable, scalable, and maintainable AI solutions that deliver consistent value across the organization. This phase transforms promising AI experiments into robust, enterprise-grade solutions that drive tangible business outcomes.
Launch AI-enhanced products to market. Establish monitoring systems for user adoption and satisfaction, while preparing support structures for commercial deployment.
Scale successful AI process improvements across the organization. Implement change management practices and establish operational procedures for AI-enhanced workflows.
Establish production-grade AI infrastructure with automated deployment pipelines. Implement monitoring, logging, and maintenance procedures for reliable operations.
Implement AI Center of Excellence with dedicated team. Establish governance framework and support structure for organization-wide AI adoption.
Continuous improvement is fundamental to maintaining competitive advantage in the rapidly evolving AI landscape. Regular iteration based on real-world performance data ensures AI solutions stay effective and aligned with changing business needs. This ongoing optimization process helps organizations stay ahead of the curve while maximizing the return on AI investments.
Continuously enhance AI features based on user analytics and feedback. Implement regular improvement cycles to optimize performance and user experience.
Optimize AI-driven processes through continuous monitoring and feedback loops. Expand automation capabilities and refine workflows based on operational insights.
Regularly upgrade AI models and infrastructure components. Optimize performance, reduce operational costs, and incorporate emerging technologies and best practices.
Evolve AI capabilities through structured knowledge sharing and innovation programs. Continuously enhance governance practices and team competencies.