Benefits & Drawbacks of Hiring an Internal AI Team

Businesses face a crucial decision: whether to build an internal AI team or rely on external consultants. Here we’ll assess the benefits and drawbacks of hiring an internal AI team, including rough estimates for company size versus team size, the tasks they should undertake, cost considerations, return on investment (ROI), and performance measurement.

Benefits of Hiring an Internal AI Team

An internal AI team offers strategic alignment, deep integration with business functions, and control over proprietary technologies. Having an in-house team ensures that AI projects are tightly aligned with the company’s goals, allows for quick iterations and adaptations, and retains intellectual property within the organization.

Strategic Alignment

Benefit: An internal AI team can be more closely aligned with the company’s strategic goals. Internal teams have a deeper understanding of the company’s vision, culture, and long-term objectives, which can lead to more cohesive and strategically aligned AI projects.

Customization and Control

Benefit: Greater control over projects and processes. With an in-house team, companies can customize AI solutions to better fit their unique needs and maintain direct oversight, ensuring projects remain on track and meet specific business requirements.

Intellectual Property (IP) Retention

Benefit: Retention of intellectual property within the company. Internal teams ensure that proprietary technologies and data remain within the organization, reducing the risk of intellectual property leakage.

Quick Iterations and Adaptations

Benefit: Faster iteration cycles and adaptability. In-house teams can quickly iterate and adapt AI models based on immediate feedback and changing business needs, leading to more agile development processes.

Deep Integration with Business Functions

Benefit: Enhanced integration with existing business functions. Internal teams can work closely with different departments to seamlessly integrate AI solutions into existing workflows, improving overall efficiency and effectiveness.

 

Drawbacks of Hiring an Internal AI Team

While having an internal AI team has its advantages, it also comes with significant challenges such as high initial costs, difficulty in finding and retaining qualified talent, and the continuous need for training and development. 

High Initial Costs

Drawback: Significant upfront investment. Building an internal AI team involves high initial costs, including recruitment, salaries, training, and infrastructure setup.

Talent Acquisition Challenges

Drawback: Difficulty in finding and retaining qualified talent. The demand for AI talent exceeds supply, making it challenging and costly to attract and retain skilled professionals.

Long Ramp-Up Time

Drawback: Longer time to achieve productivity. Internal teams may take longer to ramp up and start delivering results, especially if they are new to the organization or the technology.

Continuous Training and Development

Drawback: Ongoing need for training and skill development. AI technologies evolve rapidly, requiring continuous investment in training and development to keep the team’s skills up to date.

 

Company Size vs. Internal AI Team Size

The size of a company significantly influences the ideal size of its internal AI team. Here are some rough estimates for team sizes based on the company size, ranging from small businesses to large enterprises, and we’ll discusses the focus areas and considerations for each size bracket.

Small Companies (1-50 employees)

Team Size Estimate: 1-2 AI professionals. 

Focus Areas: Initial AI projects, automation, and data analysis.

Consideration: Small companies should start with a small team focused on high-impact, low-cost AI projects to build foundational capabilities.

Medium-Sized Companies (51-500 employees)

Team Size Estimate: 3-7 AI professionals. 

Focus Areas: AI-enabled products, operational optimizations, and customer insights. 

Consideration: Medium-sized companies can scale their AI initiatives to include product enhancements and process improvements, balancing cost with the potential for significant returns.

Large Companies (501+ employees)

Team Size Estimate: 8-20+ AI professionals. 

Focus Areas: Advanced AI research, AI-driven business transformations, RPA, and enterprise-wide AI integration. 

Consideration: Large companies can afford larger, specialized teams that work on diverse, complex projects with long-term strategic value.

 

Tasks for an Internal AI Team

AI-Enabled Products

Task: Develop and enhance products using AI. 

Create intelligent features and capabilities that add value to existing products or develop new AI-driven products that meet market demands.

AI Optimizations

Task: Optimize business processes with AI. 

Use AI to streamline operations, reduce costs, and improve efficiency through process automation and intelligent data analysis.

Robotic Process Automation (RPA)

Task: Implement RPA solutions. 

Automate repetitive, rule-based tasks to free up human resources for more strategic activities, increasing overall productivity.

Data Analysis and Insights

Task: Analyze large datasets to generate actionable insights. 

Utilise machine learning and data analytics to identify trends, predict outcomes, and inform strategic decisions.

 

Cost Considerations

Building and maintaining an internal AI team involves significant costs, including recruitment, salaries, infrastructure, and training. Here are some rough cost estimates and the financial implications of hiring an internal AI team, helping you to plan their budgets effectively.

Recruitment and Salaries

Estimate:

 

• • Entry-Level AI Engineer: $70,000 – $90,000 per year.

• • Mid-Level AI Engineer: $100,000 – $130,000 per year.

• • Senior AI Engineer/Data Scientist: $150,000 – $200,000+ per year.

Infrastructure Costs

Estimate:

 

• • Computing Resources: $20,000 – $100,000+ annually, depending on scale.

• • Software Licenses: $10,000 – $50,000+ annually.

Training and Development

Estimate: $5,000 – $10,000 per employee annually for ongoing training.

 

Return on Investment (ROI) of an Internal AI Team

Measuring the ROI of an internal AI team is crucial for understanding its value. See below for how to calculate ROI, the expected returns from various AI projects, and the metrics that can be used to measure the performance and impact of an internal AI team.

Measuring ROI

Metrics:

 

• • Cost Savings: Reduction in operational costs through AI optimizations.

• • Revenue Growth: Increase in sales from AI-enhanced products.

• • Productivity Gains: Efficiency improvements from RPA and automated processes.

• • Time to Market: Speed of AI project deployment and impact on business outcomes.

Expected ROI

Estimate: ROI varies significantly based on the scope and scale of AI projects but can range from 20% to 200%+ over a 2-5 year period.

 

Measuring Performance

Key Performance Indicators (KPIs)

 

• • Project Completion Rate: Percentage of AI projects completed on time and within budget.

• • Model Accuracy: Accuracy and performance of AI models in production.

• • User Adoption: Rate at which AI solutions are adopted and used by employees.

• • Cost Efficiency: Cost savings achieved through AI implementations.

• Impact on Business Goals: Alignment of AI projects with strategic business objectives.

Expectations for an Internal AI Team

Short-Term Goals (0-6 months)

 

• • Pilot Projects: Initiate and complete pilot AI projects to demonstrate value.

• • Skill Development: Focus on training and skill-building within the team.

• • Infrastructure Setup: Establish necessary AI infrastructure and tools.

Medium-Term Goals (6-18 months)

 

• • Project Expansion: Scale successful pilot projects and expand AI initiatives.

• • Process Integration: Integrate AI solutions into key business processes.

• • Performance Tracking: Implement robust performance tracking and reporting mechanisms.

Long-Term Goals (18+ months)

 

• • Strategic Alignment: Ensure AI projects are fully aligned with long-term business strategies.

• • Continuous Improvement: Foster a culture of continuous improvement and innovation.

• Enterprise-Wide Adoption: Achieve broad adoption of AI solutions across the organization.

Conclusion

Hiring an internal AI team offers numerous benefits, including strategic alignment, customization, control, and deep integration with business functions. However, it also presents challenges such as high initial costs, talent acquisition difficulties, and the need for continuous training. Companies must weigh these factors against their specific needs and resources to determine the optimal approach.

By carefully planning team size based on company size, focusing on high-impact projects, and measuring performance through clear KPIs, businesses can maximize the ROI of their internal AI teams. While the initial investment is significant, the potential for long-term gains in efficiency, innovation, and competitive advantage makes it a worthwhile consideration for many organizations.