Most organizations today are preparing for a world of pervasive AI. This evolution requires business and technical leaders to equip their organizations with a solid understanding of new technological capabilities, technical skills to leverage new technologies, and vision focused on new approaches to traditional IT workflows. But a lack of AI skills is among the top barriers for AI adoption. Though expert data scientists and AI practitioners are graduating from universities in record numbers, organizations still experience significant difficulties finding and attracting good talent, which makes AI upskill programs a priority.

What does it mean for an organization to be upskilled for the world of AI?

Every business will eventually become an AI business. And every business knows it needs to upskill its employees for AI. However, organizations struggle to determine what upskilling for AI means, and the specific actions they must take to develop those skills. What does it mean for an organization to be upskilled for the world of AI? Watch this presentation for comprehensive guidance.

AI is not monolithic. It is not defined by one set of skills, nor by a single role in the organization.

Some skills are relatively simple and foundational and must be developed extensively across the organization. Others are more complex, centered among smaller groups of higher-skilled professionals. It is critical to learn how multiple roles with multiple skill sets align and orchestrate their work in a unified framework focused on end results.

Build AI literacy, contextual AI knowledge and AI solution building capabilities

Organizations developing programs to upskill their employees should focus on skills progression starting with foundational elements for all and going deeper into levels of more complex specialization for specific roles. We see this progression of skills structured in three main levels: AI literacy, contextual AI knowledge, and AI solution building capabilities.

AI Literacy

These are the skills that should be developed extensively throughout the organization, focusing on the conceptual understanding of data, the ability to interact with tools that enable or are driven by AI, and the ability to identify opportunities for AI in the organization.

These should target both technical and non-technical professionals who should be able to:

• Read, understand, create and communicate data as information; think critically to find relevant insight in data; understand insight from charts while minimizing the risk of being misled by data and reaching harmful incorrect conclusions.
• Identify how AI technologies and processes can impact business goals; discern which technologies are appropriate; identify which data is needed; and understand how AI supports business. This skill set demands expertise in recognizing how to leverage technologies focused on prediction, natural language processing, visual and speech recognition, among others, and how to look at data as a strategic asset.
• Understand and master the methodologies used to orchestrate the work of different roles; AI requires a culture of iteration and experimentation, and deep rethinking of business and technical workflows.

Contextual AI knowledge

The next level of skills requires embracing AI technology capabilities and infusing them into other domains. The focus is to develop domain strategies using AI technologies, managing inputs and using outputs of prebuilt AI models. At this stage some skills should be developed across technical and nontechnical teams, with others in development, data engineering and data scientist.

Organizations need practitioners who can:

• Develop processes to identify business opportunities, data strategies, and how AI models can drive new value in alignment with business goals in specific domains.
• Identify when AI is the right approach to achieve specific business goals; prioritize and select use cases based on impact and ease of implementation; and define KPIs to drive AI implementations based on business priorities and in alignment with business goals.
• Determine the types of AI capabilities that can solve a particular problem and identify the different types of data that can be used to train AI models. This skill provides a great opportunity to explore potentially new datasets (structured and unstructured, external and internal, on premise or on the cloud), and to define data pipelines and data access processes.
• Leverage technologies such as prebuilt AI models (NLP, Visual Recognition), as well as frameworks (Tensorflow, Keras) that help accelerate solution building.
• Understand how to make decisions in a probabilistic environment and manage risk appropriately.

Building AI solutions

The next level of skills focuses on building AI solutions and developing the skills needed to manage an end-to-end AI production process. The data science role is the heart of the AI production cycle, with other business and technical stakeholders playing significant roles at different stages. Data scientists and their allied stakeholders typically:

• Develop frameworks/methodologies grounded in ethics and privacy principles designed to be implemented across the organization for building end-to-end AI solutions.
• Build machine learning models (Supervised/Unsupervised/Deep/Reinforcement), as well as deep learning models.
• Possess solid mathematical expertise (Probability, Inferential Statistics, Linear Algebra) which ensures machine learning models are built correctly.
• Deploy AI models into production and operationalize AI with trust and transparency.
• Ensure that AI recommendations are fully traceable, and that audit of lineage of the models and the associated training data can be performed.

The enormous opportunities and benefits artificial intelligence can bring to an organization require skills development programs designed to ensure consistency and intentional outcomes. A prescriptive approach to AI skills development is critical for success.

To learn more, check out our AI skills development programs.