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Building an Effective AI Team: Strategy, Roles, Org Design, and Implementation

Updated: Jul 20, 2023

I am frequently asked about the need for dedicated AI teams, where such a team should report inside of organizational structures, what type of talent is needed, and how to get started. Here, I offer research-backed thoughts to address these questions.


What I learned from my doctoral research of 46 enterprise uses of AI.

Just this month, I published research that examined 46 enterprise AI use cases from late 2021 through 2022. There were 5 clear themes identified that are needed to drive success with AI:

  1. Value creation - you must pursue real business problem solutions - no "toy" AI

  2. Focus on customer needs aids decision-making and prioritization

  3. Collaborative teams with modern skills are successful with AI - silos are deadly

  4. Shifting culture to embrace failure and iterative learning is vital

  5. The critical role of data - there is no avoiding this enduring fact

Based upon this research-backed grounding, let's dig into whether you should create a permanent AI team, who should lead it, the roles needed for success, and what steps to take to get started.


Should you create a dedicated AI team?


Well, sorta. I recommend establishing an AI function that reports to the Chief Strategy Officer, or in organizations with forward-thinking technology leaders such as Chief Information Officers, this team can also thrive under their leadership. This function should be staffed with key roles using agile, business problem (value creation) specific teams. The organization would not have the same permanent team, although some members could see repeated tours depending on the value situation. Leadership, however, needs to be permanent and is often best aligned under the AI Strategy Analyst (definition below). 


Why ad hoc AI teams with permanent leadership are preferable.


When it comes to building an AI team, many organizations face a critical decision: should they create a dedicated AI team? I suggest considering a hybrid approach that includes ad hoc teams with permanent leadership. This approach offers several advantages including a strong focus on business value creation and accountability, more innovative solutions, greater flexibility, access to niche expertise, lower costs, continuous learning, and resource optimization. Below, I explore these benefits and why ad hoc AI teams with permanent leadership are a smart choice for many organizations.

  • Business value focus and clear accountability -  By reporting directly to the C-level executive responsible for strategy, the ad hoc AI teams will be focused on driving business value and results, rather than technical outputs.

  • Innovation - By forming teams tailored to each project, you allow for a diversity of perspectives, approaches and ideas. This can spur more creative and innovative solutions.

  • Flexibility - Ad hoc teams can be formed quickly for specific projects and disbanded once the work is complete. This allows for agility and flexibility in staffing based on need. In contrast, a permanent team has a fixed cost structure that may not match temporary project demands.  

  • Access to expertise - Ad hoc teams allow you to bring in the most appropriate internal and external experts for each specific project. A permanent team may lack niche expertise needed for certain AI initiatives.

  • Cost - Ad hoc teams are "on demand" and only staffed when needed. This allows you to avoid the fixed costs of a permanent team when there are no projects requiring AI.

  • Learning - Each new ad hoc team faces a unique problem and sets of data. This forces continual learning and the avoidance of "rote" approaches that can happen on permanent teams.

  • Resource optimization - Since ad hoc teams are temporary, enterprise resources like Data Scientists and Business SMEs can be assigned to multiple teams over time, exposing them to more use cases.


What roles should you staff on your AI team?


Staffing your AI team with the right mix of skills and expertise is critical for success. Key roles to fill include an AI strategy analyst, business SMEs, a customer advocate, data experts, an enterprise IT architect, a security and privacy expert, an ethical AI specialist, a project manager with an agile mentality, a business value owner, and leadership committed to collaboration and value creation. In addition to specific roles, team members should possess qualities such as curiosity and resilience. Each role is explored below.

  1. AI Strategy Analyst - Scan the market daily and stay current with tools, regulations/law, potential partners, synthesize intelligence into recommendations, lead with the mindset of an entrepreneur who envisions creative solutions within the boundaries of internal governance structures, and continually guard against technology-powered biases (this role can be filled by an external partner, but it's preferable to make this the permanent leader of the team)

  2. Business SME(s) who deeply understand the specific business problem to be solved (internal resource)

  3. Customer Advocate - Ruthless focus on customer needs to drive decision-making and prioritizing (internal resource)

  4. Data Experts - from both the technology side and the business side. Pick ONLY the data critical to solving the business problem and ensure it is in high quality condition (tech side can be external partner, business side should be internal resource)

  5. Enterprise IT Architect - use their expertise in designing and implementing large-scale IT systems and solutions to be responsible for ensuring that the AI solutions are integrated into the organization's existing IT architecture in a way that is scalable, secure, and aligned with overall IT strategy (internal resource).

  6. Security and Privacy Expert - addressing security and privacy concerns related to AI systems can be complex. This person must be both technically knowledgeable and working closely with the AI Strategy Analyst to ensure implementation of robust security measures, privacy safeguards, and compliance with constantly evolving data and AI protection regulations/laws and internal governance structures (internal resource).

  7. Ethical AI Specialist - ensures responsible and ethical practices within the AI team. This role develops and implements ethical frameworks and guidelines for AI development, promoting fairness, transparency, and accountability (in the beginning, most organizations need to bring in this expertise from the outside until internal team members are upskilled).

  8. Project Manager with agile mentality - small, iterative efforts - some efforts won’t work - learn and pivot (preferably well-respected internal resource)

  9. Business Value Owner - this person is responsible for defining measures of success and keeping the team laser-focused on driving towards this value realization (pros/cons for both external and internal resources)

  10. Leadership who are committed to value creation, applaud learning as ROI, create a culture that is (small) failure absorbent, abhor organizational silos, and champion/fund collaboration

Note: Team DNA - All team members must possess natural curiosity, an appetite for continual learning, resilience when small efforts don’t produce expected results, a collaboration-first work approach, and a desire to solve real business problems (versus tech for tech’s sake).


Steps to assembling your AI teams.


Assembling an effective AI team requires thoughtful planning and coordination. To ensure success, it's important to follow a series of steps, including clarifying the business problem or opportunity, identifying and securing key internal resources, mapping needed expertise to roles, prioritizing project management, seeking external partners selectively, establishing clarity of roles and responsibilities, gaining governance and ethical clarity, fostering psychological safety, establishing OKRs for value metrics, and iterating quickly. Below, I address each of these steps and provide guidance on how to assemble a high-performing AI team that can achieve your business goals.

  1. Clarify the business problem/opportunity - Make sure there is a clear understanding of the specific business issue the AI initiative aims to solve or opportunity it seeks to capture. This will guide the rest of the team assembly.

  2. Identify and secure key internal resources - The Business SMEs, Customer Advocate and Business Value Owner should be identified first. These internal perspectives are critical to ensure the AI initiative aligns with business needs.

  3. Map needed expertise to roles - Determine which of the defined roles require internal resources vs external partners based on availability of expertise in-house. Consider the pros and cons of each option for each role.

  4. Prioritize project management - Appoint an experienced Project Manager, preferably an internal resource, as early as possible. They will help coordinate and integrate the other team members.

  5. Seek external partners selectively - Bring in partners to fill skills/expertise gaps that cannot be met by internal resources. Focus on temporary partnerships, not long-term vendor relationships.

  6. Establish clarity of role/responsibilities - Make sure each team member's responsibilities are clear from the start. Develop a responsibilities matrix mapping roles to key tasks.

  7. Gain governance and ethical clarity - Identify existing applicable organizational governance and ethical frameworks. Then, assess the need for added AI safeguards to combat bias, privacy and security issues, malicious use, unintended consequences, etc. Within smart governance guardrails, innovation flourishes and risks are mitigated.

  8. Foster psychological safety - The team should spend time building trust, rapport and an environment where people feel safe contributing ideas and taking risks. This enables innovation and resilience.

  9. Establish OKRs for value metrics - Define the specific Objectives and Key Results that will guide the team's work and determine success. These should tie to the desired business value.

  10. Iterate quickly - Plan for an iterative process where the team assembles, does work, learns, and potentially makes changes to roles/members for the next iteration.

Conclusion.


Driving business value through creating an effective AI team involves a strategic combination of dedicated leadership, ad hoc teams, and a carefully curated mix of roles and expertise. By reporting to the CSO or technology executive, and focusing on value creation, these AI teams can drive real business impact through innovation. Embracing a flexible approach that leverages both internal and external resources, while fostering an environment conducive to learning and collaboration, is foundationally critical for success. Assembling a team that is adept at tackling the unique challenges of AI initiatives will ultimately result in successful, value-driven outcomes and keep your organization at the forefront of the rapidly evolving AI landscape.

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