Building an AI Team

A strong AI team forms the basis of a successful business. Any organization who wants to implement AI must first gauge their strength and weakness.

Also, with the speedy acceptance of AI applications, organization are not only hiring AI experts, but their entire business strategies are revolving around building an experienced AI team.

There are multiple ways to organize AI teams. The teams are structured as 

Building an AI team

Centralized	Distributed	Hybrid
Characterized by an individual or a team responsible for the control, implementation, and maintenance of the AI strategy.	Defined by individual users or business units (BU) maintaining and controlling their own AI plan.	Combines a centralized enterprise strategy with a decentralized execution and implementation. Also, allow for a holistic view of AI implementation with central control on policies, framework, reporting, plus best practices for the business units to follow.
The individuals or team are responsible for making decisions and providing direction for the implementation of AI program across the organization.	Ensures that the strategy for adoption of AI is created by the local users or BU.	Provides the BU with enough autonomy to manage business unit-specific data and offers channels of influence to gather input for datasets impacting enterprise data.
Individuals may be a CEO, chief AI officer (CAIO), AI product Manager, and so on	Unlike centralized, there is no single owner responsible to implement, control, and maintain but each individual user or BU has a degree of autonomy	Example: Centralized CAIO office with a decentralized (virtual) user.

Skill Sets of the AI Team

AI teams need various skills to be successful from communicating to business leaders to integrating code into a software system. When looking for potential AI team candidates, you will find their technical and quantitative skills vary. Members of an AI team will ideally have some of the skills and behavioral characteristics as mentioned below:

Quantitative and Technical Aptitude

Expertise in mathematics or statistics and proficiency in quantitative skill.

Namely, software engineering, machine learning, data engineering, natural language processing (NLP) programming skills.

Skeptical
Mindset and critical thinking

It is important that an AI engineer can examine their work critically rather than in a one-sided way.

Curiosity and creativity

Being passionate about data and finding creative ways to solve problems and portray information

Communication and collaboration

The team member must be able to articulate the business value in a clear way and collaboratively work with other groups, including project sponsors and key stakeholders.

Business acumen

Possess understanding of market, customer, and how the core business runs.

Roles and Responsibilities of AI Team

Roles have evolved that fit on different places on the AI spectrum. Well-defined roles and responsibilities are the pillar for effective AI adoption.

The roles described below ensure that data collection, software development, and AI Implementation are mapped back to the organizations overall strategy. Let us discuss the responsibilities of some of the key roles in detail.

Software Engineer

Optimizes code and deploy applications

Data Scientist and Data Engineer

Provides technical expertise for analytical techniques and data modeling

Build data ingestion, storage, and infrastructure

AI/ML Engineer

Pave through traditional software models and Machine Learning models

AI Product Manager

Outlines WHY we are developing this product

Chief AI Officer

Overlooks operational needs

AI Strategist

Bridges gap between IT and Business