1. Building high-performing AI teams
We have seen how leaders foster an AI culture, which then gets infused into the roots.
In this video, we will learn the characteristics of high-performing AI teams and how to build them.
2. Characteristics of a high-performing team
Most AI projects stem from initially vague business problems and involve many uncertainties. A dynamic high-performing team can navigate such fuzziness to provide high business value.
But what does such a team look like? Let's explore their characteristics.
3. Technical knowledge
AI teams possess a holistic mix of diverse yet interrelated skills. It is important to start with core fundamentals like linear algebra, probability, and statistics that help build foundations to generate meaningful insights from the data.
Besides a firm grasp of theoretical concepts, proficiency in software programming language enables the team to build prototypes quickly.
It also involves understanding various algorithms and their limitations and when to use them.
4. Quick thinker and problem solver
The team also needs to swiftly adjust its approach to changing project scopes or when faced with unforeseen challenges and obstacles.
In today’s rapidly evolving technological landscape, securing the right AI talent goes beyond academic qualifications and technical prowess. It focuses more on problem-solving capabilities and the ability to quickly ramp up to newer initiatives.
5. Business acumen and communication
Besides technical acumen, data scientists focus on understanding business problems before advocating specific technology as a potential solution.
This brings us to effective communication. The team can articulate complex technical concepts to non-technical stakeholders and gather requisite details.
6. AI-centric culture
The attrition in AI teams is generally very high, highlighting their intent to work on challenging projects.
To harness this drive, the organizations are taking several measures to promote AI-centric culture, such as conducting hackathons to bring newer ideas to life, fostering the AI community to exchange ideas, and providing opportunities to present at conferences.
7. In-house or outsource?
Companies face a tough choice: cultivate in-house AI expertise or outsource it, especially with growing security concerns.
While nurturing in-house talent can be expensive, such investment in upskilling can be a long-term strategy to ensure consistent innovation and growth. On the other hand, external experts bring specialized skills without the long-term cost commitment.
8. Org design
We have learned how to build AI teams and are fostering the right AI culture, but are they working most effectively? Primarily, teams are structured in one of two ways - centralized and decentralized.
Centralized teams foster an AI community, ensure data governance,
9. Org design
establish best practices and frameworks,
10. Org design
and manage talent strategy.
Decentralized teams work on specific requirements of the individual business units.
11. Hybrid team structure
In recent years, hybrid structures have gained popularity. It brings together cross-functional teams consisting of product and business managers and data and AI experts to accelerate the delivery of the assigned project.
Such a structure offers benefits like enhanced visibility that saves redundant efforts, faster collaboration by reducing the silos, and
greater autonomy to make decisions.
It's worth noting that structuring data science teams remains a challenge with no one-size-fits-all approach.
12. Career growth with business growth
Organizations need to look beyond business deliverables and continue investing in the professional growth of their teams.
It can be achieved through mentorship, providing a platform for experiments, and exposure to new technology and algorithms.
Additionally, organizations must update their job titles and descriptions to match industry standards. Appropriate and well-recognized job titles can significantly boost morale and clarify an individual's career trajectory.
13. To summarize
High-performing teams emerge when effective organizational design converges with a nurturing culture that prioritizes their future growth.
14. Let's practice!
We have all the ingredients to build a high-performing data science team.
Let's see how it strengthens the strategic execution of AI projects.