Building High-Performance AI Teams: A Strategic Approach to Success

Table of Contents

1. Key Highlights:
2. Introduction
3. The Need for a Strategic Framework in AI Implementation
4. Essential Roles in AI Teams
5. Clarifying Team Structures: Platform vs. Product Teams
6. Addressing the Skills Gap: The Training vs. Hiring Dilemma
7. The Outsourcing Option: Leveraging Third-Party Expertise
8. Building a Culture of Collaboration and Learning
9. Solid Leadership as the Cornerstone for Success

Key Highlights:

• The demand for AI solutions in business has skyrocketed, prompting organizations to take a strategic approach in building high-performing AI teams.
• Effective AI teams require collaboration between domain experts, IT specialists, and user engagement to ensure successful project delivery and adoption.
• Organizations must focus on training existing talent, while also considering external partnerships and hybrid approaches to address the skills gap in AI-related roles.

Introduction

The rise of artificial intelligence (AI) has transformed the business landscape, creating immense opportunities for efficiency and innovation. As organizations strive to harness AI's potential, the complexity of building and managing effective AI teams has become apparent. Executive sponsorship, once viewed as a hurdle, now acts as a vital component in driving AI initiatives forward. Today, Chief Information Officers (CIOs) face the challenge of meeting an unprecedented volume of requests for AI solutions, and they are rising to the occasion by adopting strategic methodologies that emphasize collaboration, user engagement, and skills development.

Firms are realizing that the success of AI projects hinges not just on advanced technology but also on the human talent behind it. As illustrated by the experiences of industry leaders like Katrina Redmond, CIO of Eaton, and Kathy Kay, EVP and CIO at Principal Financial Group, a well-structured AI team consists of diverse roles that bridge the gap between business and technology. This article delves into the key components of building high-performance AI teams, exploring essential roles, strategic collaboration, skills development, and the impact of leadership on driving AI initiatives.

The Need for a Strategic Framework in AI Implementation

The demand for AI solutions is propelling organizations to construct dedicated teams tailored to their business needs. Katrina Redmond’s innovative “AI factory” at Eaton exemplifies how organizations can align their AI strategies with operational requirements. By creating a collaborative structure where subject matter experts work alongside AI teams without strictly reporting to IT, Eaton fosters a dynamic decision-making process focused on value generation.

The idea is to build cross-functional teams that leverage diverse expertise, including AI leads, product owners, ML engineers, data engineers, and cloud engineers, working alongside business domain experts. Establishing a vertically integrated model not only expedites project development but also enhances communication and understanding of business priorities.

Bridging the Gap Between Technical and Business Teams

Kathy Kay of Principal Financial Group emphasizes the importance of defining business problems in ways that technical teams can grasp. Within effective AI teams, it is crucial to appoint product managers who possess domain expertise to perform the vital function of translating business needs into technical requirements. This symbiotic relationship ensures that the AI solutions developed are both relevant and practical.

Implementing a collaborative approach between technical and business teams streamlines the project lifecycle. By prioritizing user engagement from the outset, organizations can increase adoption rates while minimizing resistance to new technology.

Essential Roles in AI Teams

Creating high-performing AI teams requires an understanding of the diverse skill sets necessary for success. Experts advocate for a comprehensive team that encompasses various roles across different functions. Hugh Burgin, AI automation and analytics leader at EY Americas, identifies key categories of expertise essential for delivering impactful AI solutions.

1. Executive Sponsors: Each AI initiative should have executive sponsorship, providing guidance and resources.
2. Domain Experts: Having experts involved ensures that projects are framed correctly, reflecting real business needs.
3. End-User Engagement: Including potential end-users early in the development process ensures the AI solution meets their expectations and needs.
4. Transformation Engineers: These individuals are responsible for integrating AI solutions with existing business processes.
5. Project Managers and Change Management Professionals: Offering leadership in product delivery, these roles are critical for effective change adoption and operational success.

Additionally, AI teams benefit from having dedicated professionals for testing, validation, and ethical considerations, creating a robust framework supportive of sustainable AI development.

The Importance of AI Ethics

As industries increasingly leverage AI solutions, ethical oversight becomes paramount. Arun Chandrasekaran, distinguished VP at Gartner, highlights the role of AI ethicists who are responsible for upholding AI application standards, ensuring fairness, transparency, and compliance with regulations. Their inclusion is especially critical in highly regulated sectors, such as finance and healthcare, where bias could result in significant repercussions.

Clarifying Team Structures: Platform vs. Product Teams

Organizations such as Black & Veatch serve as models for differentiating between platform teams and product teams in their AI strategy. Mike Adams, EVP and Chief Digital Technology Officer, outlines how platform teams focus on optimizing investments made in strategic AI platforms, while digital product teams concentrate on developing tailored AI capabilities that yield long-term value.

By viewing AI through these lenses, organizations can leverage both the broad capabilities offered by enterprise platforms and the nuanced solutions created by specialized product teams. A focus on adaptability and communication among these teams can lead to transformative outcomes across the organization.

Addressing the Skills Gap: The Training vs. Hiring Dilemma

With the competition for AI talent intensifying, organizations are rethinking their hiring strategies. As Kathy Kay suggests, internal talent development becomes crucial, as cultivating existing employees can fill the growing need for AI-skilled professionals. This process often involves pairing less experienced staff with mentors and providing opportunities for practical experience through "stretch assignments" that allow employees to explore new responsibilities.

Black & Veatch has embraced cross-training initiatives as part of their strategy, enabling technology professionals to adapt to the evolving demands of AI. Similarly, Eaton has transitioned existing innovation teams to focus on AI, extracting valuable lessons from pilot projects in the process.

The Role of Courting External Talent

Despite the push for internal talent development, external hiring may still be necessary for specialized roles. In instances where rapid scaling of capabilities is required, organizations can benefit from partnerships with external consultants who provide expertise and training. These collaborations can help bridge skills gaps while simultaneously offering internal staff exposure to advanced AI practices.

The Outsourcing Option: Leveraging Third-Party Expertise

While prioritizing internal talent development is advantageous, organizations may find that certain complex skills, such as those possessed by data scientists and AI engineers, are elusive in the job market. Hugh Burgin informs that external partnerships can temporarily fill this void by partnering with organizations that offer specialized competencies. This approach not only supports skill development but can also catalyze organizational learning.

As more companies seek to outsource certain AI functionalities, they benefit from the insights shared by consulting firms, as well as organizations like Hugging Face and OpenAI. These firms provide a blend of product configuration and engineering support tailored to specific business needs.

Building a Culture of Collaboration and Learning

An organization's culture plays a pivotal role in determining the effectiveness of its AI initiatives. Collaboration across departments fosters a sense of cohesion, allowing teams to share insights and lessons gleaned from various projects. It's essential to emphasize mutual understanding and define clear objectives across all levels of the organization.

Leadership must also prioritize creating an environment conducive to learning and development. This means allowing teams to undergo trial-and-error processes, as understanding data management is critical to successful outcomes. For instance, the lessons learned from Principal Financial Group’s unexpected findings in their early AI project illustrate the necessity of strong data comprehension to avoid irrelevant outputs.

Solid Leadership as the Cornerstone for Success

The effectiveness of an AI initiative is inextricably linked to the clarity of its strategic vision, as well as the strength of its leadership. In emphasizing collaboration with third parties for initial projects, organizations can build a foundation of skills that can be scaled over time. Delivering AI effectively requires flexibility, foresight, and the willingness to adapt to change.

As leaders define areas for AI transformation, they must engage with stakeholders productively to generate buy-in and create integrated, cross-functional teams capable of achieving shared business goals.

FAQ

What roles are critical in an AI team? An effective AI team must include executive sponsors, domain experts, product managers, data scientists, AI engineers, change management professionals, and ethicists to ensure comprehensive development and ethical compliance.

How can organizations address the AI skills gap? Organizations can cultivate internal talent through training programs, mentorship, and stretch assignments while also partnering with external talent and consulting firms to fill immediate needs.

Why is user engagement crucial in AI projects? Involving end-users early in the development process ensures that the solutions created align with their actual needs, improving the likelihood of successful adoption and minimizing resistance.

What is the difference between platform teams and product teams? Platform teams optimize existing AI capabilities from strategic providers, while product teams focus on developing business-specific AI solutions that deliver unique value.

How important is ethical oversight in AI initiatives? Ethical oversight is critical for ensuring fairness, transparency, and compliance, especially in heavily regulated industries where biased AI outputs can cause significant harm.