Data Readiness Assessment
Data powers AI. No technology or AI algorithm can overcome a lack of data. Companies may be data rich, but if data hygiene is poor or the data is siloed, the value that data provides is significantly reduced.
We help identify third-party, contextual data sources to augment your existing data and reveal deeper insights. In collaboration, we develop your data acquisition strategy to ensure you create and collect the right data for optimal AI readiness. The Data Readiness Assessment involves an assessment of your data and technology necessary to execute business relevant applications for artificial intelligence.
Data Readiness Assessment Phases
Interviews
Uncover data and technology dependencies in context with corporate strategy.
Data Census
Audit existing data sources and their suitability for use in machine learning applications.
Data Analysis
Review existing structured and unstructured data sources for utilization as input features in machine learning applications.
Readiness Report
Present an AI difficulty rating and data augmentation opportunities. Recommend top practical machine learning use cases that drive the most value in the shortest time frame.
AI Executive Education
The foundation of a good strategy is good intelligence. KUNGFU.AI provides education and use case exploration around artificial intelligence. Our half-day and full-day workshops expose strategy leaders to how AI is transforming business today, the importance of data acquisition, and gets teams started with strategy development exercises.
Solve a big problem and begin building your AI solution in five days.
Practical AI Workshop
A sustainable strategy requires 80% of your time understanding the problem space. The remaining 20% is experimenting with models. The Practical AI Workshop quickly builds your AI roadmap, de-risks development, and accelerates adoption. The Practical AI Workshop is a four-day process for identifying, curating and prioritizing the most practical and relevant use cases for artificial intelligence. Following that, our team builds minimal viable products and test hypotheses. Ours is a lean innovation approach that incorporates leading-edge design thinking and uses proven methodologies from entrepreneurial product development.
Practical AI Workshops takes your company from start to deployment in four stages:
Business goal alignment and define challenges. Connect corporate strategy to AI program strategy and define success. Interview business leaders.
Define data opportunities. Inventory available data and needs. Generate plan for data acquisition.
Generate AI use cases predicated on data identification and business objectives. Begin to filter and prioritize use cases.
Define AI Roadmap. Define prioritize use cases and project scope.
Practical AI Workshop Tools
The AI Canvas
Corporate strategy and business objectives need to be deconstructed in order to see where artificial intelligence can and should be inserted. This allows us to quantify the benefit of the enhanced capability versus the overall costs. Once value is understood, we rank-order the opportunities for AI from highest to lowest value to create a roadmap. The AI Canvas provides real business value by providing a starting point and definition for implementation of AI applications.
“Quit wasting time on ideas that get stuck.”
Strategy/Objectives
How do you plan to grow the business? How will you measure the success of an AI program?
Data Wealth
Where is there wealth in your data?
Data Constraints
What are the gaps in your data?
Tech
What are the infrastructure requirements to achieve artificial intelligence?
Prediction
What are the top practical use cases for AI prediction?
Automation
What are the top practical use cases for AI automation?
Stakeholders
What are the personnel and cultural implications for adoption?
Benefits
What are the outcomes for the organization?
AI Organizational Heatmap
We will create your full artificial intelligence roadmap that includes a future vision for multiple use cases across the organization, prioritized by feasibility, time, data dependencies, and value. Organizations are left with a clear view on which parts of the business are most ripe for AI, and where an impact can be made over time.