Assembling the AI Workforce: Strategies for Building an Effective Team to Develop and Apply Emerging Technologies

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Abstract: This article examines strategies for building effective artificial intelligence (AI) workforces in organizations. Drawing from recent research, it outlines a comprehensive approach to developing internal AI capabilities through strategic hiring and talent development. The framework begins with establishing core technical foundations through targeted recruitment of AI specialists, followed by applying these capabilities to initial use cases while expanding teams to include diverse skill sets. The article emphasizes the importance of interdisciplinary collaboration, continuous learning, and creating an innovation-focused culture. Through practical examples and evidence-based recommendations, it provides organizational leaders with actionable insights for assembling AI teams that combine technical expertise with business acumen to drive long-term competitive advantage.

Artificial intelligence (AI) and related emerging technologies like machine learning, deep learning, computer vision, natural language processing, and robotics are advancing rapidly and beginning to transform industries. Many organizations understand they must develop internal AI capabilities to stay competitive, lead innovation, and gain strategic advantage. However, assembling an effective workforce with the right skills to develop and apply AI presents unique challenges that require careful planning and strategies different than traditional hiring approaches.

Today we will explore strategies that organizational leaders can employ to build an AI workforce poised for success.

Research Foundation

A wealth of research provides insights into the skills needed for AI roles and strategies to assemble effective teams. AI jobs require technical skills like programming, math, data analysis, and software engineering, especially deep knowledge of machine learning frameworks and algorithms (Guzman & Satar, 2021). Perhaps more importantly, human skills like communication, collaboration, creativity, and the ability to understand users and apply technology to business problems are also essential for developing impactful AI solutions (Ransbotham, Kiron, Gerbert, & Reeves, 2017). Interdisciplinary teams that bring together technical experts with domain experts and business leaders tend to produce the best results (Fjeld, Achten, Hilligoss, Nagy, & Srikumar, 2020). Given these factors, assembling a workforce with the right combinations of hard and soft skills is critical. Attracting talent also requires establishing an AI culture and providing opportunities for learning, experimentation, and growth (Ransbotham et al., 2017).

Building the Foundation: Developing Core Capabilities

The first step in building an AI workforce is to develop core internal AI capabilities by hiring a nucleus of technical talent focused on machine learning and related fields. Many organizations make the mistake of waiting until they have a clear use case or product in mind, but top AI companies understand the importance of developing foundational skills first (Daugherty & Wilson, 2018). To get started, leadership should identify several high-potential areas like computer vision, natural language processing, optimization, or reinforcement learning where initial expertise could be applied across domains later on.

Recruiting this core group requires targeting technical masters and PhD graduates directly from top AI programs. Advertising roles on job boards aimed at AI researchers like jobs.deepai.com and attending academic conferences helps raise awareness. Job descriptions should emphasize long-term career growth opportunities and a culture valuing experimentation over short-term deadlines. Compensation packages competitive with cutting-edge tech employers are also important to attract top talent. Partnerships with local universities help source candidates with domain expertise crucial for applied research.

Core Technical Skill Areas for Initial Hiring

• Deep learning frameworks like TensorFlow, PyTorch

• Machine learning algorithms like regression, clustering, neural networks

• Statistical modeling, data analysis, visualization

• Programming languages like Python, C/C++

Once the first hires come onboard, leadership must provide infrastructure, resources, and mentorship to foster their skills development. Building experimental labs with high-powered GPUs or cloud credits for research encourages creativity. Brown bag seminars and hackathons spark idea sharing and collaboration across departments. Sending new hires to top AI conferences and networking events expands their knowledge and exposure to innovations. Overall, treating AI as a long-term strategic priority yields dividends as capabilities mature over time.

Applying Core Capabilities: Developing Initial Use Cases

With foundations established, the next step involves applying core technical skills to real-world problems through initial AI use cases or prototype products. This transition period proves the value of AI investments, further develops talent, and identifies additional skill gaps to address through focused hiring. Engaging business stakeholders and domain experts upfront helps identify high-impact but manageable pilot projects.

For example, in manufacturing, early efforts could focus on using computer vision to automate quality inspections or optimize assembly line workflows. In healthcare, mining clinical notes for insights using natural language processing provides value while developing core NLP abilities. Across industries, chatbots or virtual assistants incorporating basic AI skills offer opportunities for experimentation and scaling capabilities over time. Early use cases should involve cross-functional teams to foster collaboration between technical staff and operational leaders from the start.

Continuing to Hire for Interdisciplinary Teams

As initial AI projects take shape, organizational needs and skills requirements expand. At this stage, leadership must carefully plan further hiring to assemble truly interdisciplinary teams. Expanding the stack of technical skills through targeted recruitment helps unlock more complex use cases. Hiring data engineers enables integrating diverse data sources for model training, while UI/UX designers ensure human-centered solutions. Bringing in software engineers focuses efforts on developing production-grade products.

Additional Technical Skills for Medium-Term Hiring

• Data engineering for data preparation, ETL, pipelines

• Software engineering for full-stack development

• UI/UX design for human-centered solutions

• Domain-specific technical experts (e.g. computational biology)

Equally important, recruiting those with “T-shaped skills” who can synthesize technical and non-technical perspectives fosters collaboration between functions. Individuals fluent in both computer science and business strategy, for example, help translate ideas into realities. Experts in fields where AI can have impacts, like healthcare clinicians or industrial engineers, add user understanding. A mix of technical masters and broader business-minded generalists balances depth and breadth of perspectives. Including those from diverse backgrounds also sparks new ideas through different life experiences.

Talent Activation: Ongoing Learning and Growth

Once assembled, ongoing talent activation through continuous learning prevents skills from becoming outdated in the fast-moving AI landscape. Core technical staff require regular exposure to the latest research at top conferences, through publishing papers, and visiting other labs conducting cutting-edge work. Bringing outside subject matter experts in for workshops and seminars expands horizons further. Sending newer hires back to elite universities for specialized certificate programs helps close competency gaps.

Regular internal training also keeps the workforce agile through "AI academies" focused on emerging techniques. Project rotation, hackathons, and "20% time" for exploration are hallmarks of innovative AI cultures that cultivate new ideas. Evaluating skills and interests allows optimizing team assignments over time. External partnerships open doors for consulting gigs applying abilities to other domains. An environment where learning is valued as much as short-term output leads to long-term retention of top talent. Overall, fostering a spirit of constant learning keeps capabilities and solutions ahead of the curve.

Practical Application and Examples

A global agricultural technology company understood that incorporating AI into farming equipment and services offered massive opportunities for growth. They began by hiring PhDs focused on computer vision and deep learning to tackle automation tasks like plant disease detection and yield forecasting. Researchers developed foundational models analyzing satellite imagery.

Simultaneously, the company launched pilot projects with farmer cooperatives. One used computer vision models on tractors to identify weeds in real-time, automatically adjusting herbicide application. Another incorporated weather and soil data into yield predictions, improving harvest planning. These initial efforts proved AI’s value while refining technical staff skills.

Continued hiring brought in data engineers to integrate diverse operational data sources, software engineers to build iOS/Android apps, as well as agronomists familiar with farmer needs. Cross-functional teams applied expanded capabilities to new projects involving predictive equipment maintenance, customized field operations based on property boundaries, and AI-driven market analysis.

Regular conferences, internal "AI Day" events, and external partnerships kept the workforce learning. Staff consult other agribusinesses on AI strategy. Rotation exposes technical experts to business contexts. Focusing first on strong foundations enabled evolving exponentially over several years from standalone projects into strategic AI-powered platforms central to business operations.

Conclusion

Assembling an effective AI workforce requires careful long-term strategy rather than quick fixes. Leaders must prioritize establishing strong technical foundations before rushing into specific products or use cases. Hiring should bring together diverse combinations of skills to build interdisciplinary, collaborative teams. Continuous talent activation through ongoing learning keeps capabilities cutting-edge in a fast-evolving field. With this comprehensive approach, organizations can develop internal AI capabilities able to drive meaningful strategic and operational impacts across industries for years to come. Viewing AI workforce planning as an incremental, long-haul investment yields outsized returns as emerging technologies increasingly transform business landscapes.

References

1. Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Harvard Business Review, 96(4), 60–67.

2. Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. Berkman Klein Center Research Publication, (2020-1).

3. Guzman, A., & Satar, M. (2021, September 21). The skills required for AI and machine learning jobs in 2022. Towards Data Science.

4. Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017, September). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review.

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Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.

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Suggested Citation: Westover, J. H. (2025). Assembling the AI Workforce: Strategies for Building an Effective Team to Develop and Apply Emerging Technologies. Human Capital Leadership Review, 20(1). doi.org/10.70175/hclreview.2020.20.1.7