How can you build the right team to manage AI algorithm portfolios?

1 Define the roles and skills

The first step is to define the roles and skills that your AI algorithm portfolio team needs. Depending on the size and complexity of your portfolio, you may need different types of AI professionals, such as data scientists, data engineers, machine learning engineers, AI architects, AI ethicists, and AI project managers. Each role has its own responsibilities and skill sets, such as data analysis, data processing, model building, model testing, model deployment, model monitoring, model governance, and model ethics. You should also consider the domain knowledge and business acumen that your team members need to understand the problems and solutions that your portfolio addresses.

3 Foster collaboration and communication

The third step is to foster collaboration and communication among your AI algorithm portfolio team members. AI projects are often interdisciplinary and complex, requiring input and feedback from different stakeholders and experts. You should create a culture of collaboration and communication that encourages your team members to share their ideas, insights, challenges, and solutions. You should also use the appropriate tools and platforms that facilitate teamwork, such as project management software, code repositories, data platforms, model platforms, or communication channels.

4 Evaluate and improve performance

The fourth step is to evaluate and improve the performance of your AI algorithm portfolio team. You should establish clear and measurable goals and metrics that reflect the value and impact of your portfolio. You should also monitor and track the progress and outcomes of your AI projects, as well as the performance and satisfaction of your team members. You should provide regular feedback and recognition to your team members, as well as identify and address any issues or gaps that may arise. You should also encourage your team members to learn from their successes and failures, and to seek new opportunities for growth and innovation.

5 Align with ethics and values

The fifth step is to align your AI algorithm portfolio team with the ethics and values of your organization and society. AI solutions can have positive or negative effects on people and the environment, depending on how they are designed, developed, deployed, and used. You should ensure that your team members are aware of and adhere to the ethical principles and standards that guide your AI practices, such as fairness, accountability, transparency, privacy, or security. You should also involve your team members in the ethical decision-making and governance processes that affect your portfolio, such as risk assessment, impact assessment, or audit.

6 Adapt to changes and challenges

The sixth step is to adapt your AI algorithm portfolio team to the changes and challenges that may occur in the AI landscape. AI is a fast-evolving and dynamic field, with new technologies, applications, and regulations emerging constantly. You should keep your team members updated and informed about the latest trends and developments that may affect your portfolio. You should also empower your team members to experiment and explore new possibilities and solutions that may enhance your portfolio. You should also prepare your team members for the potential risks and uncertainties that may arise from the use and misuse of AI, and how to mitigate them.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?