What is AI Readiness?
AI-readiness refers to the state of preparedness of an organization to adopt and implement artificial intelligence (AI) solutions to meet strategic business goals.
It involves evaluating the organization’s capabilities in relation to potential AI use cases that augment strategy. The goal of assessing AI-readiness is to identify the organization’s strengths and weaknesses and provide recommendations on how to improve its readiness for AI adoption.
From the outset, our aim is to help clients adopt a Holistic AI methodology.
How is AI Readiness Assessed?
Typically, our experts conduct an audit using interviews. It might involve any or all of the following steps:
Understanding the business objectives
For the first step, we conduct interviews to understand key business objectives and goals, including any strategic or innovation related initiatives. We identify the areas where AI is suitable for improving business processes, reducing costs and enhancing customer experience.
Nominally, we tend to explore opportunities by paying attention to the different innovation types (per Geoffrey Moore’s model)
Identifying data sources:
Next, we identify data sources available within the organization and begin to explore overall data quality and the suitability of data systems in supporting AI models:
- Internal data sources
- External data sources and partnership opportunities
- Data integration capabilities
- Data storage and tooling (e.g. discovery, catalogs, etc.)
- Data engineering
Assessing data management practices:
We assess data management practices. These are are critical for the success of any AI initiative. The audit service should assess the data management practices followed by the organization, including data cataloging, curation, discovery and related data services.
Assessing available skills and expertise:
We evaluate the skills and expertise of the team responsible for implementing AI solutions. This includes assessing the knowledge of AI technologies, data science, and machine learning. We will also survey the wider readiness for more “Direct AI” operating models and assess accordingly.
Identifying potential AI use cases:
Although not strictly necessary for an initial AI-readiness audit, we often seek to identify potential AI use cases. We base these upon the business objectives of the organization, the data available and skills of the team. The process prioritizes use cases based upon their potential impact. We use matrix ranking according to levels of readiness and:
- Business value
- Feasibility (against the readiness audit)
- Complexity (i.e. resources)
- Time to value
- Risk
AI safety and trust:
We include an assessment of related AI-safety issues:
- AI transparency: How transparent are the AI systems being used, and can their decisions be explained?
- Fairness and bias: How can biases be identified and addressed in AI systems to ensure that they are fair and unbiased?
- Security: How can AI systems be secured against cyber threats and other malicious activities?
- Privacy: How can the privacy of individuals be protected when using AI systems, and are the data protection laws being followed.
What happens next?
Based upon the assessment, we provide recommendations as to how the organization can improve its readiness for AI adoption. This includes recommendations about data management, IT infrastructure, team skills, and potential AI use cases. Our recommendations can include any of the following:
- Data quality suggestions
- Enhanced data management including product-centric methods
- IT tooling
- Training and education (including re-skilling)
- Pilot program for potential AI use cases
- A roadmap for moving up the AI maturity curve
- Any strategic initiatives (e.g. M&A acceleration, partnerships etc.)