How to build an in-house AI team

By: Janice Mejías Avilés, Edited by: Gabriela Pérez Jordán

Published: March 19, 2025

Building an in-house artificial intelligence (AI) expert team can be a strategic investment for businesses seeking to develop AI tools to automate time-consuming, costly processes or improve return on investment (ROI).

This guide outlines the core AI team structure and roles for an in-house team, their responsibilities, and the value they may bring to your organization.

What is an AI team, and what do they do?

As your business scales its AI initiatives, specialized roles may be required based on operational needs, industry, and regulations.

An in-house AI team can provide businesses with direct access to specialized expertise, allowing them to respond quickly to industry shifts. They may also inform overarching organizational goals with AI-driven insights.

For non-tech organizations, a dedicated AI team may help mitigate the impact of market changes, support proactive adaptation, and align AI initiatives with long-term operational growth strategies.

Did you know?

According to the World Economic Forum's 2025 Future of Jobs Report, businesses are prioritizing AI-driven strategies to stay competitive. Nearly half (49%) are shifting their business goals to capitalize on AI opportunities, while 77% are focused on reskilling and upskilling their employees to work effectively with AI. Additionally, the report notes that 62% are actively hiring AI experts to strengthen their operations.

How to upskill your workforce for AI

Building an internal AI team starts with training your employees to become specialists in their roles and providing them with the necessary tools to achieve this.

Once you assess their knowledge and skills, you can create an upskilling strategy that supports continuous learning. Online AI courses and programs may help employees gain expertise while balancing their current workloads.

EdX delivers online AI courses and programs from top universities worldwide, covering all skill levels. For mid- to senior-level professionals, consider sponsoring or providing tuition reimbursement for specialized AI training, such as:

• Executive education programs: These intensive, cohort-based AI leadership development courses (6-8 weeks) can help foster strategic thinking and stakeholder communication.
• Professional certificate programs: These self-paced programs (2-10 months) are focused on building practical AI skills and award a verified certificate upon completion.

Benefits of an in-house AI team

Building an internal AI team allows businesses to drive AI initiatives that add value to their operations by:

• Improving or developing new products and services by creating enterprise AI solutions
• Automating repetitive tasks and streamlining processes and operations by integrating AI automation tools
• Building proprietary AI models tailored to the business's needs rather than relying solely on third-party enterprise AI solutions
• Optimizing senior-level decision-making and resource allocation through AI-driven data analysis and predictive modeling
• Maintaining data security and regulatory compliance through AI governance and risk management

Key roles you need for an in-house AI team

The structure of an internal AI team will depend on industry-specific and organizational needs, but there are several core positions most organizations should consider.

Chief AI officer (CAIO)

At the leadership level, the chief AI officer (CAIO) is responsible for defining and overseeing an organization's AI strategy and implementation roadmap. The CAIO manages specialized internal teams and identifies opportunities where AI can streamline or scale business operations.

Key skills

• Technical knowledge in AI, machine learning, data science, natural language processing (NLP), and software engineering
• Business acumen to align AI strategies with company objectives and drive measurable impact
• Strategic leadership skills to effectively manage in-house AI teams that lead innovation
• Strong communication skills to translate AI concepts and initiatives to stakeholders, senior executives, and clients

AI product manager

AI product managers oversee AI projects from ideation and development to market release or deployment. They work at the intersection of customer needs, technological development, and business strategy, supporting viable and scalable solutions that align with business objectives.

Key skills

• Strong communication skills to coordinate with engineers, data scientists, and senior leadership
• Product management experience to lead initiatives from concept to launch
• Market research expertise to identify AI opportunities and refine solutions
• Understanding of AI, machine learning, data science, and software development pipelines

Machine learning (ML) engineer

Machine learning (ML) engineers are essential for businesses integrating AI into their operations, products, or services. They design, train, and optimize ML models, transforming raw data into AI-driven solutions that add business value.

ML engineers lay the technical foundation for AI systems, overseeing deployment and monitoring performance

Key skills

• Expertise in AI, ML, and data processing, including experience in model training and optimization
• Proficiency in deep learning (DL) and natural language processing (NPL) for AI applications
• Strong programming skills in Python and R
• Experience deploying and scaling AI models on cloud platforms or on-premise infrastructure
• Strong mathematical and statistical knowledge in probability, linear algebra, and data analysis

AI data engineer

AI data engineers design and maintain scalable data pipelines and architectures, ensuring that AI models operate on high-quality, structured data, essential for accurate predictions and automation. They manage ETL processes (extract, transform, and load) to integrate data from multiple systems into a single platform.

They often oversee cloud-based data storage solutions and implement security measures to protect sensitive information.

Key skills

• Expertise in database management, including SQL
• Strong programming skills in Python
• Experience with cloud platforms and data warehousing solutions
• Solid understanding of AI, ML, NLP, and large language models (LLMs)

AI data scientist

AI data scientists build and refine AI algorithms and models to uncover meaningful insights from data. They work closely with AI engineers and AI data engineers to develop scalable solutions, often integrating ML models or working with NLP for AI-driven products.

AI data scientists also analyze data patterns, identify trends, and create visualizations to provide stakeholders with actionable insights or data-driven strategies.

Key skills

• Proficiency in data visualization tools, such as Tableau or Power BI, to present AI-driven insights
• Programming skills in Python and R for developing, testing, and optimizing models
• Understanding NLP techniques for working with unstructured text data in LLMs
• Expertise in Excel and SQL for data analysis

Is building an in-house AI team worth it?

Not every business requires an in-house AI team. For small and medium-sized businesses outside the tech industry, outsourcing AI roles or working with consultants may be the most practical and cost-effective approach.

However, companies at high risk of AI-driven disruption — such as accounting, healthcare, and media — or seeking to develop AI-powered products and services may need to invest in hiring. Starting with a small but scalable team can provide businesses greater control over AI implementation and allow for growth.

Before committing to building an AI team, businesses should weigh their goals and operational needs against the costs of upskilling employees, hiring new talent, and investing in the necessary infrastructure and tools.