MANAGING AI PRODUCTS AND SERVICES
STAKEHOLDER
SATISFACTION
VALUE
CENTRICITY
SCALABLE
INFRASTRUCTURE
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Translation between domain experts, IT and data specialists
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Effective time and budget management
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Continuous progress tracking
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Clear proof-of-value
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End-user acceptance testing
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Benefit quantification
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Potential substitute analysis
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Complexity reduction
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Flexible and efficient IT architecture
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Clear oversight of unit economics
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Sufficient maintenance capacity
Success depends on the ability to incorporate tech, talent and continuous learning into the business
KEY PRINCIPLES
Organization
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Ensure the problem-solution-fit and measurable progress
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Translate between business, technology and domain experts
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Drive internal and external marketing activities to ensure long-term stakeholder support
Value
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The value of AI is its supportive function to make better decisions or automate tasks entirely
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Actual value generation depends solely on the acceptance of the (internal or external) beneficiary
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Requirements and functionalities need to be closely aligned with domain experts
Data
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Algorithms are just tools and depend ultimately on the data input quality
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The engineering competence is more important than the data volume to achieve good results
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Connecting different data sources contextually meaningful unleashes the real potential of AI
Infrastructure
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Suitable infrastructure built-up is first priority before starting application development
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Reducing complexity as much as possible is the key to larger scalability
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All AI initiatives shall be thought of as continuously improving products with no expiry date
Contact
You are about to kick-off a new AI initiative and looking for guidance how to bring internal or external services successfully to life?
Let's discuss the most effective way
to build and scale your AI-powered application.
Tobias Bohnhoff
AI Projects Lead