• Programming Languages and Algorithms: Proficiency in programming languages such as Python, Java, or R is essential. These languages enable team members to write efficient code, manipulate and analyze data, and implement machine learning algorithms. Understanding algorithms, from classic ones like linear regression to cutting-edge deep learning models, empowers AI professionals to leverage mathematical principles and statistical techniques to train and optimize AI systems.
• Data Analysis and Management: AI relies on data, making data analysis and management skills crucial. AI team members need to be adept at collecting, cleaning, and preprocessing data, ensuring its quality and relevance. They must possess the knowledge of statistical methods, data visualization techniques, and data engineering practices to extract insights from complex datasets and prepare them for AI model training.
• Machine Learning and Neural Networks: Technical skills in machine learning are at the heart of AI systems. Understanding machine learning algorithms, such as decision trees, support vector machines, or convolutional neural networks, enables AI team members to create models that can learn from data and make intelligent predictions or decisions. Expertise in neural networks and deep learning architectures equips professionals with the ability to develop complex models capable of handling tasks like image recognition, natural language processing, and reinforcement learning.
• AI Frameworks and Tools: Proficiency in AI frameworks and tools (such as TensorFlow, Keras, PyTorch, Caffe, Scikit-learn, and others) are essential for building AI systems efficiently. These frameworks provide pre-built modules, libraries, and APIs that simplify the implementation of AI algorithms and models. Technical skills in utilizing these tools effectively enable AI team members to accelerate the development process, streamline model training, and optimize system performance.
• Domain Knowledge and Application-Specific Skills: Technical skills also encompass domain knowledge and application-specific expertise. Understanding the nuances of the industry or problem domain in which AI is being applied allows professionals to tailor AI solutions to meet specific requirements. For example, healthcare-focused AI projects may require knowledge of medical terminologies and regulatory considerations, while AI solutions for financial services may demand expertise in risk assessment and fraud detection.

• Effective Communication and Technical Expertise: Communication bridges the gap between AI professionals and others who may not possess technical backgrounds. AI experts with strong communication skills can articulate complex technical concepts in plain English, ensuring that everyone involved understands the goals, challenges, and progress of the project. By effectively conveying ideas, AI professionals foster collaboration, gather valuable insights, and create a shared vision for AI initiatives.
• Collaboration and Problem-Solving: Collaboration is at the core of AI development, and soft skills such as teamwork, empathy, and active listening facilitate effective collaboration among diverse team members. AI teams with strong collaboration skills can effectively pool their technical expertise, brainstorm ideas, and solve complex problems together. By leveraging their collective intelligence and diverse perspectives, AI teams can overcome challenges, refine AI models, and optimize the performance of AI solutions.
• Critical Thinking and Technical Innovation: Critical thinking, coupled with technical expertise, leads to innovative AI solutions. AI professionals with strong critical thinking skills can evaluate different approaches, challenge assumptions, and identify potential shortcomings or biases in AI models. They can think creatively to address issues such as data biases or fairness concerns, ensuring that AI systems are developed responsibly and ethically.
• Leadership and Team Empowerment: Effective leadership in AI projects involves establishing and maintaining a collaborative and inclusive environment, empowering team members, and harnessing their full potential. AI leaders with exceptional interpersonal abilities can inspire and motivate their team, foster a culture of continuous learning, and provide guidance in navigating complex technical challenges. They encourage interdisciplinary collaboration, respect diverse perspectives, and drive the team towards achieving AI objectives.

• Training Programs and Workshops: Implement specialized training programs and workshops focused on enhancing soft skills. Offer courses in effective communication, leadership (including how to navigate the phases of team development), problem-solving, critical thinking, and collaboration. These programs can provide AI professionals with the necessary tools and techniques to effectively apply soft skills in their work.
• Interdisciplinary Collaboration and Knowledge Sharing: Encourage interdisciplinary collaboration by creating opportunities for AI team members to work alongside experts from diverse fields such as psychology, design, ethics, and business. This collaboration allows for cross-pollination of ideas, encourages different perspectives, and broadens the skill set of AI teams. Foster a culture of knowledge sharing, where professionals can learn from each other and leverage their collective experiences and expertise.
• Real-World Project Engagement: Provide AI team members with opportunities to work on (other) real-world projects, allowing them to apply their soft skills in practical scenarios. Engaging in projects that involve interaction with clients, end-users, and stakeholders helps AI team members develop effective communication, problem-solving, and teamwork skills.
• Continuous Learning and Professional Development: Encourage AI team members to engage in continuous learning and professional development activities. This can include attending conferences, participating in webinars, reading industry publications, and pursuing certifications in relevant areas. Promote a growth mindset among AI team members, emphasizing the importance of lifelong learning and staying current with the latest developments in both technical and soft skills domains.
• Mentorship and Coaching: Establish mentorship and coaching programs where experienced AI professionals guide and support individuals with less experience. Mentors can provide valuable insights, share their experiences, and offer guidance on developing skills. Regular feedback and coaching sessions help AI professionals identify areas for improvement and provide targeted development opportunities.

• Accenture (2023, March 30). Accenture Technology Vision 2023: Generative AI to Usher in a Bold New Future for Business, Merging Physical and Digital Worlds. Available at: https://newsroom.accenture.com/news/accenture-technology-vision-2023-generative-ai-to-usher-in-a-bold-new-future-for-business-merging-physical-and-digital-worlds.htm.
• Alsever, Jennifer (2020, January 20). Medicine by Machine: Is A.I. the Cure for the World’s Ailing Drug Industry? Fortune. Available at: https://fortune.com/longform/ai-artificial-intelligence-medicine-healthcare-pharmaceutical-industry.
• Anthropic (2023, March 14). Introducing Claude. Available at: https://www.anthropic.com/news/introducing-claude.
• Berlin School of Business and Innovation (2022, September 22). How Do Technical Abilities Combined with Leadership Skills Fuel Career Growth? Available at: https://www.berlinsbi.com/blog/career-advice/how-do-technical-abilities-combined-with-leadership-skills-fuel-career-growth.
• Bhatti, Shawaiz, Demir, Mustafa, Cooke, Nancy J., & Johnson, Craig J. (2021). Assessing Communication and Trust in an AI Teammate in a Dynamic Task Environment. In: 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS): 1–6. DOI: 10.1109/ICHMS53169.2021.9582626.
• Brooks, David (2023, February 2). In the Age of A.I., Major in Being Human. The New York Times. Available at: https://www.nytimes.com/2023/02/02/opinion/ai-human-education.html.
• C3.ai (n.d.). Best Practices in Prototyping: Cross-Functional Teams. Available at: https://c3.ai/introduction-what-is-machine-learning/cross-functional-teams/.
• Crampton, Natasha (2021, January 19). The Building Blocks of Microsoft’s Responsible AI Program. Microsoft. Available at: https://blogs.microsoft.com/on-the-issues/2021/01/19/microsoft-responsible-ai-program.
• Dolev, Niva, & Itzkovich, Yariv (2020). In the AI Era, Soft Skills are the New Hard Skills. In: Artificial Intelligence and Its Impact on Business, pp. 55-77. Charlotte, NC: Information Age Publishing.
• Dzombak, Rachel, & Palat, Jay (2021, August 30). 5 Ways to Start Growing an AI-Ready Workforce. Pittsburgh, PA: Software Engineering Institute, Carnegie Mellon University.
• Elliott, Joshua (n.d.). Artificial Social Intelligence for Successful Teams (ASIST). Arlington, VA: Defense Advanced Research Projects Agency (DARPA).
• Exceptional Agility (2023, April 10). What's the Future of Agile? Available at: https://exceptionalagility.com/blog/files/the-future.html.
• Fountaine, Tim, McCarthy, Brian, & Saleh, Tamim (2019). Building the AI-Powered Organization. Harvard Business Review, 97 (4): 62–73.
• Gartner (2020, December 15). How to Staff Your AI Team. Available at: https://www.gartner.com/smarterwithgartner/how-to-staff-your-ai-team.
• Gong, Michelle; and Zhernov, Anton (2019, November 18). Advanced Machine Learning Helps Play Store Users Discover Personalised Apps. Google DeepMind. Available at: https://deepmind.google/discover/blog/advanced-machine-learning-helps-play-store-users-discover-personalised-apps/.
• Gorman, Austin (2023, April 27). How to Strengthen Your Soft Skills for AI and the Future of Work. Fast Company. Available at: https://www.fastcompany.com/90887540/how-to-strengthen-your-soft-skills-for-ai-and-the-future-of-work.
• GovExec Events (2020, July 1). Human Machine Team: The Intersection of Diverse Skill Sets [Video]. Defense One. Available at: https://www.youtube.com/watch?v=93wbNLKpbxg&t=1s.
• Graffius, Scott M. (2023, May 1). Fueling the Development of Innovative and Life-Changing AI Solutions [Presentation]. Talk delivered to an audience of Technology professionals (including Data Scientists, Machine Learning Engineers, Data Engineers, AI Researchers, Project Managers, Business Analysts, UX Designers, Software Developers, Cloud Architects, Data Privacy and Security Specialists, and others involved or interested in AI) at a private event in Mountain View, California, United States. Digital Object Identifier (DOI): 10.13140/RG.2.2.27956.73601. DOI link: https://dx.doi.org/10.13140/RG.2.2.27956.73601.

• Graffius, Scott M. (2023, April 26). The Science of High-Performance Teams [Presentation]. Talk delivered at the DevOps Institute’s SKILup Day 2023 Conference. Digital Object Identifier (DOI): 10.13140/RG.2.2.15888.28169. DOI link: https://dx.doi.org/10.13140/RG.2.2.15888.28169.

• Graffius, Scott M. (2023, January 9). Use the Phases of Team Development (Based on Bruce W. Tuckman's Model of Forming, Storming, Norming, Performing, and Adjourning) to Help Teams Grow and Advance: 2023 Update. Available at: https://scottgraffius.com. Digital Object Identifier (DOI): 10.13140/RG.2.2.10720.35846. DOI link: https://dx.doi.org/10.13140/RG.2.2.10720.35846.

• Graffius, Scott M. (2022, May 13). Want Happier and More Productive DevOps Teams? [Presentation]. Talk delivered at DevOpsDays Geneva, Switzerland 2022 Conference. Digital Object Identifier (DOI): 10.13140/RG.2.2.22252.85127. DOI link: https://dx.doi.org/10.13140/RG.2.2.22252.85127.

• Graffius, Scott M. (2021, October 5). Navigate the Phases of Team Development with Speed and Agility for Happier and More Productive Teams [Presentation]. Talk delivered at the Institute of Electrical and Electronics Engineers IEEE Day 2021 Conference. Digital Object Identifier (DOI): 10.13140/RG.2.2.20055.19365. DOI link: https://dx.doi.org/10.13140/RG.2.2.20055.19365.

• Graffius, Scott M. (2021, February 20). But First, the Team! [Presentation]. Talk delivered at the Brno, Czech Republic DevConf.CZ 2021 Conference. Digital Object Identifier (DOI): 10.13140/RG.2.2.29016.72964. DOI link: https://dx.doi.org/10.13140/RG.2.2.29016.72964.
• Gupta, Arun (2022). Non-Technical Skills Eat Technical Skills for Breakfast [Video]. Recording of Arun Gupta, VP and GM of the Open Ecosystem at Intel, speaking at the All Things Open Conference. Available at: https://www.youtube.com/watch?v=WNPxRBhYNU4.
• Humpton, Barbara (n.d.). The Future of Jobs. Siemens. Available at: https://www.siemens.com/us/en/company/press/siemens-stories/ceo-perspective/the-future-of-jobs.html.
• IBM (2019, September 26). Assemble the Team to Support a Data-Driven Project. Available at: https://www.ibm.com/garage/method/practices/culture/assemble-team-for-data-driven-project.
• IBM (2019). Everyday Ethics for Artificial Intelligence. Available at: https://www.ibm.com/watson/assets/duo/pdf/everydayethics.pdf.
• Institute of Electrical and Electronics Engineers (IEEE) (2021, November 8). The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. Available at: https://standards.ieee.org/industry-connections/ec/autonomous-systems.html.
• Kenan Institute (2023, February 20). The Must-Have Skills in the Era of Artificial Intelligence: How AI’s Democratization Will Impact Workers. Available at: https://kenaninstitute.unc.edu/commentary/the-must-have-skills-in-the-era-of-artificial-intelligence-how-ais-democratization-will-impact-workers.
• Lindzon, Jared (2017, February 28). How AI is Changing the Way Companies are Organized. Fast Company. Available at: https://www.fastcompany.com/3068492/how-ai-is-changing-the-way-companies-are-organized.
• Marr, Bernard (2023, March 20). Beyond The Hype: What You Really Need to Know About AI In 2023. Forbes. Available at: https://www.forbes.com/sites/bernardmarr/2023/03/20/beyond-the-hype-what-you-really-need-to-know-about-ai-in-2023.
• Massachusetts Institute of Technology (MIT) Lincoln Laboratory (2023, January 17). Human-AI Performance Incubator. Available at: https://www.ll.mit.edu/r-d/projects/human-ai-performance-incubator.
• Massachusetts Institute of Technology (MIT) (2021, January). Cognitive Science as a New People Science for the Future of Work. Available at: https://workofthefuture.mit.edu/wp-content/uploads/2021/01/2021-Research-Brief-Polli-Kassir-Dolphin-Baker-Gabrieli.pdf.
• Massachusetts Institute of Technology (MIT) Sloan (2019, October 15). Winning With AI. MIT Sloan Management Review and Boston Consulting Group. Available at: https://sloanreview.mit.edu/projects/winning-with-ai.
• McNeese, Nathan J., Demir, Mustafa, Chiou, Erin K., & Cooke, Nancy J. (2021). Trust and Team Performance in Human–Autonomy Teaming. International Journal of Electronic Commerce, 25 (1): 51–72. DOI: 10.1080/10864415.2021.1846854.
• Mearian, Lucas (2023, February 24). Skills-Based Hiring Continues to Rise as Degree Requirements Fade. Computerworld. Available at: https://www.computerworld.com/article/3689170/skills-based-hiring-continues-to-rise-as-degree-requirements-fade.html.
• Meta (2017, October 11). Inventing the Future. Available at: https://www.meta.com/blog/quest/inventing-the-future/.
• Microsoft (2019, June 15). Is the Latest Technology the Key to Your Team’s Success, or is There Something Else? Available at: https://devblogs.microsoft.com/premier-developer/is-the-latest-technology-the-key-to-your-teams-success-or-is-there-something-else.
• New South Wales Technical and Further Education Commission (TAFE NSW) (2019). Soft vs. Hard Skills: Why Successful Australian Businesses Need Both. Ultimo, New South Wales: TAFE NSW.
• Nvidia (n.d.). Partner Innovation. Available at: https://www.nvidia.com/en-ph/self-driving-cars/partners/.
• O’Dea, Blathnaid (2023, February 17). These are the Top Skills AI and Analytics Professionals Need. Silicon Republic. Available at: https://www.siliconrepublic.com/advice/top-skills-ai-data-analytics-professionals-need.
• Olmstead, Brett (2023, February 15). Driving AI Success by Engaging a Cross-Functional Team. Available at: https://www.datarobot.com/blog/driving-ai-success-by-engaging-a-cross-functional-team/.
• Organization for Economic Cooperation and Development (OECD) (n.d.). OECD Programme on AI in Work, Innovation, Productivity and Skills. Available at: https://oecd.ai/en/work-innovation-productivity-skills/key-themes/skills.
• Oswald, Andrew. J., Proto, Eugenio, & Sgroi, Daniel (2015). Happiness and Productivity. Journal of Labor Economics, 33 (4): 789-822.
• Project Management Institute (2020, March 10). Tomorrow's Teams Today: The Future of Teaming: Creative, Collaborative and Agile. Pulse of the Profession. Available at: https://www.pmi.org/learning/library/pulse-indepth-tomorrows-teams-today-11941.
• PwC (2017). Sizing the Prize: What’s the Real Value of AI for Your Business and How Can You Capitalise? PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution. Available at: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.
• Rayside, Derek (Ed.) (2023, March 29). Software Engineering Capstone Design Project Handbook (2023 Edition). Waterloo, Ontario, Canada: University of Waterloo.
• Romsey, Joseph (2020, November 30). 5 Tips to Help Workers Upskill and Adapt to Artificial Intelligence. Society for Human Resource Management (SHRM). Available at: https://www.shrm.org/resourcesandtools/hr-topics/technology/pages/how-hr-can-help-workers-upskill-and-adapt-to-artificial-intelligence-5-tips.aspx.
• Samsung (2017, May 4). Artificial Intelligence and the Role of Workers. Available at: https://insights.samsung.com/2017/05/04/artificial-intelligence-and-the-role-of-workers/.
• Stanford University (2019, September). Gathering Strength, Gathering Storms. Stanford, CA: Stanford University.
• Tredinnick, Luke (2017). Artificial Intelligence and Professional Roles. Business Information Review, 34 (1): 37–41.
• Ungerleider, Neal (2023, January 1). AI and Jobs: The Human Angle. Available at: https://www.sap.com/denmark/insights/viewpoints/ai-jobs-human-angle.html.
• United Nations Educational, Scientific and Cultural Organization (UNESCO) (2021, July 2). AI Ethics: Another Step Closer to the Adoption of UNESCO’s Recommendation. Available at: https://en.unesco.org/news/ai-ethics-another-step-closer-adoption-unescos-recommendation-0.
• United States Artificial Intelligence Institute (USAII) (2022). Factsheet: Fast-Track Your Artificial Intelligence Career in 2023. Stamford, CT: USAII.
• United States Congress Committee on Science, Space, and Technology (2019, September 24). Artificial Intelligence and the Future of Work. Report for the One Hundred Sixteenth Congress. Available at: https://www.congress.gov/116/chrg/CHRG-116hhrg37740/CHRG-116hhrg37740.pdf.
• University of Leeds (2023, March 2). The Top 5 AI Skills You Need to Land a Job in Artificial Intelligence. Available at: https://pg-online.leeds.ac.uk/blogs/5-skills-needed-for-ai.
• World Manufacturing Foundation (2020). 2020 World Manufacturing Report: Manufacturing in the Age of Artificial Intelligence. Available at: https://worldmanufacturing.org/wp-content/uploads/WorldManufacturingForum2020_Report.pdf.

• Twitter,
• Facebook, and
• LinkedIn.

• Implement and use the most popular agile framework―Scrum;
• Deliver products in short cycles with rapid adaptation to change, fast time-to-market, and continuous improvement; and
• Support innovation and drive competitive advantage.

• 🇦🇺 Australia
• 🇦🇹 Austria
• 🇧🇪 Belgium

• 🇧🇷 Brazil
• 🇨🇦 Canada
• 🇨🇿 Czech Republic
• 🇩🇰 Denmark
• 🇫🇮 Finland
• 🇫🇷 France
• 🇩🇪 Germany
• 🇬🇷 Greece
• 🇭🇺 Hungary
• 🇮🇳 India
• 🇮🇪 Ireland
• 🇮🇱 Israel
• 🇮🇹 Italy
• 🇯🇵 Japan
• 🇱🇺 Luxembourg
• 🇲🇽 Mexico
• 🇳🇱 Netherlands
• 🇳🇿 New Zealand
• 🇳🇴 Norway
• 🇪🇸 Spain
• 🇸🇪 Sweden
• 🇨🇭 Switzerland
• 🇦🇪 UAE
• 🇬🇧 United Kingdom
• 🇺🇸 United States

• 🌏 More countries

• 🇦🇺 Australia
• 🇦🇹 Austria
• 🇧🇷 Brazil
• 🇨🇦 Canada
• 🇨🇿 Czech Republic
• 🇩🇰 Denmark
• 🇫🇮 Finland
• 🇫🇷 France
• 🇩🇪 Germany
• 🇬🇷 Greece
• 🇮🇳 India
• 🇮🇪 Ireland
• 🇯🇵 Japan
• 🇱🇺 Luxembourg
• 🇲🇽 Mexico
• 🇳🇱 Netherlands
• 🇳🇿 New Zealand
• 🇪🇸 Spain
• 🇸🇪 Sweden
• 🇨🇭 Switzerland
• 🇦🇪 United Arab Emirates
• 🇬🇧 United Kingdom
• 🇺🇸 United States

• 🌏 More countries

Tags: #Accenture • #BSBI • #FastCompany • #MIT • #Gartner • #IBM • AI team • #AITeam • AI team skills • #AITeamSkills • Developing and Advancing Soft Skills in AI Teams • The Power of Synergy: Soft Skills and Technical Skills in AI Teams • The Role of Technical Skills in AI Teams • Understanding Soft Skills in AI Teams • United States Artificial Intelligence Institute • Stanford University • Software Engineering Institute • Microsoft • MIT • IEEE • IBM • DARPA • Graffius' work on AI projects • AI teams • AI • Artificial Intelligence • #AIForGood • #MachineIntelligence • #techinnovation • #AISkills • #AISoftSkills • #AIJobs • #ArtificialIntelligenceSkills • #ArtificialIntelligenceTeamSkills • #ArtificialIntelligenceSoftSkills • #AIResearch • #MI • #AIIsATeamSport • #BigData • #Collaboration • #Data • #Development • #DeepLearning • #AgileBlog • #Keras • #TensorFlow • #Caffe • #PyTorch • #ScikitLearn • #BusinessBlog • #TechnologyBlog • #ProjectManagementBlog • #Leadership • #Tech • #Teamwork • #AIStory • #Technology • #Blog • #TechnicalSkills • #TeamLeadership • #TeamDevelopment • #TeamCollaboration • #TeamBuilding • #SoftSkills • #Team • #ProjectManager • #ProjectManagement • #Management • #ML • #MachineLearning • #Innovation • #Future • #Entrepreneurship • #Engineering • #ChangeManagement • #ArtificialIntelligence • #AIteams • #AI • #Agile • #PwC • #Startups • #Science • #NeuralNetworks • #Programming • #AITradecraft • #autonomousSystems • #USAII • #FutureOfWork • #DataScience • #CognitiveComputing • #emergingtech • #IEEE • #Microsoft • #CoreSkills • #Interpersonalskills • #PeopleSkills • #transferableskills • #transversalskills • #PowerSkills • AI Skills • AI teams • AI Soft Skills • AI Hard Skills • AI team skills • AI technical skills • AI interpersonal skills

1. AI Research Scientist: AI research scientists are responsible for conducting research and developing cutting-edge AI algorithms and models.
2. AI Software Engineer: AI software engineers are responsible for integrating AI components into software applications. They bridge the gap between AI research and practical implementation, making AI systems usable by end-users.
3. Data Engineer: Data engineers are responsible for building and managing the data pipelines that feed AI models. They ensure that data is collected, cleaned, and prepared in a format suitable for analysis and model training.
4. Data Scientist: Data scientists are experts in data analysis and interpretation. They preprocess, analyze, and extract valuable insights from data, which is vital for training and improving AI models.
5. Machine Learning Engineer: Machine learning engineers focus on implementing and deploying machine learning models into practical applications. They work to ensure that AI models are integrated into software systems effectively.

1. AI DevOps Engineer: Combines development and operations skills to streamline AI model deployment. They automate tasks.
2. AI Ethicist: Ensures that AI systems are developed and used responsibly and ethically.
3. AI Infrastructure Engineer: Builds and maintains the infrastructure required for AI model deployment and scalability.
4. AI Legal and Compliance Specialist: Ensures AI projects comply with legal and regulatory requirements.
5. AI Marketing Specialist: Promotes and markets AI products and solutions to customers and stakeholders.
6. AI Operations (AIOps) Specialist: Focuses on the operational aspects of AI, including monitoring, maintenance, and troubleshooting.
7. AI Product Manager: Oversees the development and deployment of AI products.
8. AI Project Manager: Leverages general and specialized hard skills and soft skills, and applies their understanding of many aspects of artificial intelligence (such as machine learning, deep learning, automation, robotics, and natural language processing) to manage the planning, execution, and delivery of AI projects. Note: Titles vary by organization; the position may be called an AI Project Manager, AI Program Manager, Agile Project Manager, Technical Project Manager, Scrum Master, or another related designation.
9. AI QA Engineer: Ensures the quality and reliability of AI models through testing and validation.
10. AI Research Scientist: Conducts research to develop new AI algorithms and models.
11. AI Software Engineer: Builds and maintains software systems that incorporate AI components.
12. AI Trainer/Annotator: Prepares and labels data for training AI models, especially for supervised learning.
13. AI UX/UI Designer: Designs user interfaces and experiences for AI-powered applications.
14. Computer Vision Engineer: Specializes in developing AI models for image and video analysis.
15. Data Analyst: Examines data to uncover trends and insights that can guide AI projects.
16. Data Engineer: Manages data pipelines and prepares data for analysis and model training.
17. Data Scientist: Analyzes and interprets data to extract insights and inform AI models.
18. Deep Learning Engineer: Focuses on deep neural networks and architectures.
19. Machine Learning Engineer: Focuses on implementing and deploying machine learning models.
20. Natural Language Processing (NLP) Engineer: Specializes in working with text and language-related AI tasks.

• Apple (n.d.). Careers at Apple: Machine Learning and AI. Available at: https://www.apple.com/careers/us/machine-learning-and-ai.html.
• C3.ai (n.d.). Best Practices in Prototyping: Cross-Functional Teams. Available at: https://c3.ai/introduction-what-is-machine-learning/cross-functional-teams/.
• Deloitte AI Institute (2021). Building Successful AI Teams. Available at: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/deloitte-analytics/us-ai-institute-building-successful-AI-teams.pdf.
• Gartner (2020, December 15). Staff AI Teams with Various Roles and Skills for Success. Available at: https://www.gartner.com/smarterwithgartner/how-to-staff-your-ai-team.
• General Services Administration (GSA) Centers of Excellence (CoE) (n.d.). AI Guide for Government. Available at: https://coe.gsa.gov/coe/ai-guide-for-government/understanding-ai-job-roles-career-path/.
• Google Quantum AI (n.d.). Careers. Available at: https://quantumai.google/team/careers.
• Graffius, Scott M. (2023, May 1). AI is a Team Sport: A Confluence of Diverse Technical and Soft Skills are Crucial for Success. Available at: https://www.scottgraffius.com/blog/files/successful-ai-teams.html.
• Graffius, Scott M. (2023). What Successful AI Teams Have in Common [Video]. Graffius’ talk at the 2023 Conf42 Quantum Computer Conference. Available at: https://www.youtube.com/watch?v=u2qRKFbSKYE.
• IBM (n.d.). Assemble the Team to Support a Data-Driven Project. Available at: https://www.ibm.com/garage/method/practices/culture/assemble-team-for-data-driven-project/.
• Intel (n.d.). Artificial Intelligence Teams: Careers. Available at: https://jobs.intel.com/en/search-jobs/artificial+intelligence/599/1.
• Intuit (2020, December 8). How to avoid conflicts and delays in the AI development process (Part I). Available at: https://blogs.intuit.com/2020/12/08/how-to-avoid-conflicts-and-delays-in-the-ai-development-process-part-i/.

• Twitter,
• Facebook, and
• LinkedIn.

• Implement and use the most popular agile framework―Scrum;
• Deliver products in short cycles with rapid adaptation to change, fast time-to-market, and continuous improvement; and
• Support innovation and drive competitive advantage.

• 🇦🇺 Australia
• 🇦🇹 Austria
• 🇧🇪 Belgium

• 🇧🇷 Brazil
• 🇨🇦 Canada
• 🇨🇿 Czech Republic
• 🇩🇰 Denmark
• 🇫🇮 Finland
• 🇫🇷 France
• 🇩🇪 Germany
• 🇬🇷 Greece
• 🇭🇺 Hungary
• 🇮🇳 India
• 🇮🇪 Ireland
• 🇮🇱 Israel
• 🇮🇹 Italy
• 🇯🇵 Japan
• 🇱🇺 Luxembourg
• 🇲🇽 Mexico
• 🇳🇱 Netherlands
• 🇳🇿 New Zealand
• 🇳🇴 Norway
• 🇪🇸 Spain
• 🇸🇪 Sweden
• 🇨🇭 Switzerland
• 🇦🇪 UAE
• 🇬🇧 United Kingdom
• 🇺🇸 United States

• 🌏 More countries

• 🇦🇺 Australia
• 🇦🇹 Austria
• 🇧🇷 Brazil
• 🇨🇦 Canada
• 🇨🇿 Czech Republic
• 🇩🇰 Denmark
• 🇫🇮 Finland
• 🇫🇷 France
• 🇩🇪 Germany
• 🇬🇷 Greece
• 🇮🇳 India
• 🇮🇪 Ireland
• 🇯🇵 Japan
• 🇱🇺 Luxembourg
• 🇲🇽 Mexico
• 🇳🇱 Netherlands
• 🇳🇿 New Zealand
• 🇪🇸 Spain
• 🇸🇪 Sweden
• 🇨🇭 Switzerland
• 🇦🇪 United Arab Emirates
• 🇬🇧 United Kingdom
• 🇺🇸 United States

• 🌏 More countries

Tags: AI • #AI • AI Jobs • #AIJobs • AI Positions • #AIPositions • AI Research • #AIResearch • AI Research Scientist • #AIResearchScientist • AI Roles • #AIRoles • AI Software Engineer • #AISoftwareEngineer • AI team roles • #AITeamRoles • AI teams • #AITeams • AI tradecraft • Artificial Intelligence • #ArtificialIntelligence • Cognitive Computing • #CognitiveComputing • Data Engineer • #DataEngineer • Data Science • #DataScience • Data Scientist • #DataScientist • Deep Learning • #DeepLearning • Innovation • #innovation • Machine Learning • #MachineLearning • Machine Learning Engineer • #MachineLearningEngineer • ML • #ML • ML Engineer • #MLEngineer • #Science • Science • Team • #Team • Team Development • #TeamDevelopment • Teamwork • #Teamwork • Tech • #tech • Technology • #technology • Tradecraft • #Tradecraft • Technical Project Management • #technicalprojectmanagement • project management • Scott M. Graffius • #projectmanagement • #AITradecraft

Introduction

Methodology

• Humans alone perform best
• AI alone performs best
• Human-AI collaboration performs best
• Findings are inconclusive

Discussion

• Of the 42 studies, 26 (61.9%) favored human-AI collaboration, 7 (16.7%) favored humans alone, 4 (9.5%) favored AI alone, and 5 (11.9%) were inconclusive.
• Excluding the five inconclusive studies, there are 37 studies. Of them, 26 (70.3%) favored human-AI collaboration, 7 (18.9%) favored humans alone, and 4 (10.8%) favored AI alone.

1. What does the task require?
2. What does the human bring, and what does the AI bring?
3. Where do those differences help, and where might they cause errors or friction?
4. What workflow would let the strengths combine?
5. How does each configuration actually perform: human alone, AI alone, and the two together?
6. What does the data say?

Limitations

• The 42 publications are not methodologically uniform. Some directly compare humans, AI, and human-AI configurations. Others compare only two conditions or examine aspects of human-AI collaboration. The percentages reported here describe this particular evidence set; they are not pooled effect estimates.
• The studies cover different domains and tasks.
• AI capabilities are changing rapidly. A result obtained with an earlier model does not necessarily predict the performance of a current or future model. This is why the analysis prioritizes 2024–2026 publications and should be updated as technology and the research base develop.
• Different studies use different designs or performance measures. Accuracy, speed, quality, creativity, diagnostic performance, decision quality, and other measures are not interchangeable. A collaboration can improve one dimension while worsening another.
• Some studies examine laboratory or controlled settings rather than long-term real-world teams. Real-world collaboration introduces factors such as organizational culture, training, incentives, trust, workload, accountability, and learning over time.
• Publication bias is possible. Studies reporting interesting positive or negative results may be more likely to be published or noticed than studies finding little or no difference. Vaccaro et al. specifically identify possible publication bias and variation in study designs as limitations of the existing evidence base.
• Classifications in this article necessarily involve judgment. The intent was to classify the principal finding relevant to the core question while avoiding overstatement.

Conclusion

Note

Table Annotations

1. Vaccaro et al. (2024): Overall, AI was indicated. On average, human–AI combinations performed worse than the better of human-only or AI-only. Gains were more common in creation tasks; losses in decision tasks. When humans > AI, combinations often gained; when AI > humans, combinations often lost.
2. Hemmer et al. (2025): Overall, human-AI collaboration was indicated. Supports complementary team performance (CTP) when information or capability asymmetries exist and are properly leveraged; CTP is not automatic.
3. Liu et al. (2025): Overall, AI was indicated. Medical AI augments clinicians, but full complementarity (HMT > both alone) is rare. Simultaneous teaming mode and junior clinicians show more benefit; sequential mode less so.
4. Wang et al. (2026): Inconclusive was indicated. Mixed / non-significant or uncertain advantages for H+AI vs human-only on key metrics; H+AI did not universally outperform AI-only in three-arm settings; high prediction-interval uncertainty.
5. Zöller et al. (2025): Overall, human-AI collaboration was indicated. Collectives outperformed individual physicians, physician collectives, individual LLMs, and LLM ensembles by leveraging complementary error patterns.
6. Berretta et al. (2026): Overall, human-AI collaboration was indicated. AI-first then human workflow was best overall (faster than human-only, fewer errors than AI-only); human–AI improved certain psychological measures.
7. Vo (2025): Inconclusive was indicated. Human outperformed on novelty; AI on style in some stages; collaboration finished last on key CPSS ratings. Results varied by design stage and criterion.
8. Lai & Rau (2026): Inconclusive was indicated. Leadership effectiveness was comparable across human-only and AI-only; hybrid structures did not significantly outperform single-leader conditions.
9. Memmert et al. (2026): Overall, human-AI collaboration was indicated. Individual humans did not improve with GLM support, but the human–AI dyad collectively achieved superior/complementary performance on standard brainstorming metrics.
10. Akben et al. (2026): Overall, human-AI collaboration was indicated. Aggregated / collective human–AI intelligence outperformed either component alone.
11. Fügener et al. (2025): Overall, human-AI collaboration was indicated. Appropriate human–AI role allocation produced higher performance than alternatives.
12. Hua et al. (2025): Overall, human-AI collaboration was indicated. Conditional support for teaming; poor AI teammates can substantially deteriorate performance.
13. Flathmann et al. (2024): Overall, human-AI collaboration was indicated. Training and preparation influenced human–AI team performance positively under studied conditions.
14. Schmutz et al. (2024): Overall, humans were indicated. Human–AI teams can underperform when core mechanisms (trust, communication, coordination, shared cognition) are weak.
15. Krzywdzinski et al. (2026): Overall, human-AI collaboration was indicated. Team organization and communication influenced AI-assisted performance positively.
16. Mascareño et al. (2026): Overall, humans were indicated. Proximal AI collaboration hindered innovation under certain conditions.
17. Ong et al. (2026): Overall, AI was indicated. Collaboration improved human performance under some conditions but remained below LLM performance overall.
18. Mayer et al. (2026): Overall, human-AI collaboration was indicated. Effective collaboration is possible under appropriate AI adaptation strategies; performance–preference trade-offs exist.
19. Wang et al. (2026): Overall, human-AI collaboration was indicated. Hybrid workflow produced the highest analyst accuracy.
20. Gonzalez et al. (2026): Overall, human-AI collaboration was indicated. The framework identifies conditions supporting effective complementary teaming.
21. Senoner et al. (2024): Overall, human-AI collaboration was indicated. Explainable AI improved human performance in collaboration.
22. Wu et al. (2025): Overall, human-AI collaboration was indicated. Collaboration improved immediate task performance (motivation effects noted separately).
23. Winter (2025): Overall, humans were indicated. Human teams outperformed human–AI teams under the study’s competitive conditions.
24. Liel & Zalmanson (2025): Overall, humans were indicated. Participants sometimes performed better without AI recommendations.
25. Rojas et al. (2025): Overall, human-AI collaboration was indicated. Trust dynamics affected performance in human–human–AI teams.
26. Simpson et al. (2026): Overall, humans were indicated. Human-led teams generally outperformed AI-led teams.
27. Cristofaro & Giardino (2026): Overall, human-AI collaboration was indicated. Conditional synergy depending on AI-use intensity and cognitive engagement.
28. Ngo (2025): Overall, AI was indicated. AI augmentation was observed, but generally negative collaboration effects.
29. Kuang et al. (2026): Overall, human-AI collaboration was indicated. Tailored AI + human review produced the highest-quality results.
30. Li et al. (2025): Overall, human-AI collaboration was indicated. Human–AI approach exceeded both human-only and AI-only performance.
31. Kang et al. (2025): Overall, human-AI collaboration was indicated. The combined approach produced the highest accuracy and fastest processing.
32. Zeng et al. (2026): Overall, human-AI collaboration was indicated. Human–computer collaboration improved multiple decipherment measures.
33. Al-Ali et al. (2026): Overall, human-AI collaboration was indicated. Adaptive hybrid exceeded human-only and uncalibrated AI; calibrated AI was slightly higher on raw reward in some comparisons.
34. Tannoubi et al. (2026): Overall, human-AI collaboration was indicated. Hybrid human–AI lesson design generally produced the strongest outcomes.
35. Gerlich (2025): Overall, human-AI collaboration was indicated. Guided human–AI interaction produced stronger critical reasoning.
36. Luan et al. (2025): Overall, human-AI collaboration was indicated. Collaboration did not automatically improve joint creativity; guided co-creation supported better-designed collaboration.
37. Tang et al. (2025): Overall, humans were indicated. Human–human teams performed better on divergent thinking.
38. Jin & Rho (2025): Overall, human-AI collaboration was indicated. Explanations improved accuracy and reduced inappropriate reliance.
39. Choi et al. (2026): Inconclusive was indicated. Primarily examined perceptions of trust and fairness rather than comparative task-performance outcomes.
40. Raj et al. (2026): Overall, humans were indicated. Participants consistently devalued AI-generated creative writing compared with human-generated work (primarily a preference/perception finding).
41. Choung et al. (2026): Inconclusive was indicated. Primarily examined fairness and trust perceptions.
42. Chen (2025): Overall, human-AI collaboration was indicated. Human oversight affected efficiency and depth positively in qualitative inquiry.

References

About Scott M. Graffius

• Paperback ISBN-13: 9781533370242
• Kindle ASIN: B01FZ0JIIY

• Paperback ISBN-13: 9781072447962
• Kindle ASIN: B07R9LJLPJ

• Kindle ASIN: B0F2SJ83WT
• Audible ASIN: B0DJG163R5

• Adobe,
• American Management Association,
• Amsterdam Public Health Research Institute,
• Bayer,
• BCG,
• BMC Software,
• Boston Consulting Group (BCG),
• Boston University,
• Broadcom,
• Cisco,
• Coburg University of Applied Sciences and Arts - Germany,
• Computer Weekly,
• Constructor University - Germany,
• Data Governance Success,
• Deimos Aerospace,
• DevOps Institute,
• Dropbox,
• EU's European Commission,
• Ford Motor Company,
• Gartner,
• GoDaddy,
• Harvard Medical School,
• Hasso Plattner Institute - Germany,
• IEEE,
• Innovation Project Management,
• Johns Hopkins University,
• Journal of Marketing,
• Journal of Neurosurgery,
• Lam Research (Semiconductors),
• Leadership Worthy,
• Life Sciences Trainers and Educators Network,
• London South Bank University,
• Microsoft,
• MSN,
• NASSCOM,
• National Academy of Sciences,
• New Zealand Government,
• Ohio State University,
• Oracle,
• Pinterest Inc.,
• Project Management Institute,
• Mary Raum (Professor of National Security Affairs, United States Naval War College),
• Round Square Global Educational Network,
• Royal Australasian College of Physicians (RACP),
• SANS Institute,
• SBG Neumark - Germany,
• Singapore Institute of Technology,
• Torrens University - Australia,
• TBS Switzerland,
• Tufts University,
• UC San Diego,
• UK Sports Institute,
• University of Galway - Ireland,
• US Department of Energy,
• US National Park Service,
• US Soccer,
• US Tennis Association,
• Verizon,
• Wrike,
• Yale University,
• and many others.

• The Standard for Artificial Intelligence in Portfolio, Program, and Project Management.
• Agile Practice Guide — Second Edition.
• A Guide to the Project Management Body of Knowledge (PMBOK Guide) — Eighth Edition.
• A Guide to the Project Management Body of Knowledge (PMBOK Guide) — Sixth Edition.
• The Standard for Program Management — Fourth Edition.
• Practice Standard for Work Breakdown Structures — Second Edition.
• The Practice Standard for Project Estimating — Second Edition.

• Certified SAFe 6 Agilist (SA),
• Certified Scrum Professional - ScrumMaster (CSP-SM),
• Certified Scrum Professional - Product Owner (CSP-PO),
• Certified ScrumMaster (CSM),
• Certified Scrum Product Owner (CSPO),
• Project Management Professional (PMP),
• Lean Six Sigma Green Belt (LSSGB), and
• IT Service Management Foundation (ITIL).

Booking

Sign up for Miro—it's free!

(Want more features? You can always upgrade to a paid plan.)

List of Additional Articles

How to Cite This Article

Digital Object Identifier (DOI)

Content Acknowledgements

Tags and Hashtags

• Artificial Intelligence
• Future of Work
• AI Research
• Decision Making
• Technology
• Human-AI Collaboration
• Human-AI Synergy
• Complementarity
• Human-AI Teams
• Human-AI Teaming
• Workflow Design
• Exotic Team Dynamics

• #AI
• #ArtificialIntelligence
• #FutureOfWork
• #Research
• #DecisionMaking
• #HumanAI
• #HumanAITeams
• #HumanAICollaboration
• #HumanAISynergy
• #HumanAITeaming
• #AugmentationVsAutomation
• #ExoticTeamDynamics
• #AIResearch

Post-Publication Notes

Copyright

Tags: Artificial Intelligence • Future of Work • AI Research • #AI • #ArtificialIntelligence • Exotic Team Dynamics • #FutureOfWork • #HumanAI • #AugmentationVsAutomation • #HumanAITeams • #ExoticTeamDynamics • #HumanAITeaming • #AIResearch • #HumanAISynergy • #HumanAICollaboration • #DecisionMaking • #Research • Workflow Design • Human-AI Teaming • Human-AI Teams • Complementarity • Human-AI Synergy • Decision Making • Human-AI Collaboration • Technology

Introduction

The Nine Lectures

Textbook Provides Additional Information

About Stanford University

About the Instructors

About Scott M. Graffius

• Paperback ISBN-13: 9781533370242
• Kindle ASIN: B01FZ0JIIY

• Paperback ISBN-13: 9781072447962
• Kindle ASIN: B07R9LJLPJ

• Kindle ASIN: B0F2SJ83WT
• Audible ASIN: B0DJG163R5

• Adobe,
• American Management Association,
• Amsterdam Public Health Research Institute,
• Bayer,
• BCG,
• BMC Software,
• Boston Consulting Group (BCG),
• Boston University,
• Broadcom,
• Cisco,
• Coburg University of Applied Sciences and Arts - Germany,
• Computer Weekly,
• Constructor University - Germany,
• Data Governance Success,
• Deimos Aerospace,
• DevOps Institute,
• Dropbox,
• EU's European Commission,
• Federal Reserve Bank of New York,
• Ford Motor Company,
• Gartner,
• GoDaddy,
• Harvard Medical School,
• Hasso Plattner Institute - Germany,
• IEEE,
• Innovation Project Management,
• Johns Hopkins University,
• Journal of Marketing,
• Journal of Neurosurgery,
• Lam Research (Semiconductors),
• Leadership Worthy,
• Life Sciences Trainers and Educators Network,
• London South Bank University,
• Microsoft,
• MSN,
• NASSCOM,
• National Academy of Sciences,
• New Zealand Government,
• Ohio State University,
• Oracle,
• Pinterest Inc.,
• Project Management Institute,
• Mary Raum (Professor of National Security Affairs, United States Naval War College),
• Round Square Global Educational Network,
• Royal Australasian College of Physicians (RACP),
• SANS Institute,
• SBG Neumark - Germany,
• Singapore Institute of Technology,
• Torrens University - Australia,
• TBS Switzerland,
• Tufts University,
• UC San Diego,
• UK Sports Institute,
• University of Galway - Ireland,
• US Department of Energy,
• US National Park Service,
• US Soccer,
• US Tennis Association,
• Verizon,
• Wrike,
• Yale University,
• and many others.

• The Standard for Artificial Intelligence in Portfolio, Program, and Project Management.
• Agile Practice Guide — Second Edition.
• A Guide to the Project Management Body of Knowledge (PMBOK Guide) — Eighth Edition.
• A Guide to the Project Management Body of Knowledge (PMBOK Guide) — Sixth Edition.
• The Standard for Program Management — Fourth Edition.
• Practice Standard for Work Breakdown Structures — Second Edition.
• The Practice Standard for Project Estimating — Second Edition.

• Certified SAFe 6 Agilist (SA),
• Certified Scrum Professional - ScrumMaster (CSP-SM),
• Certified Scrum Professional - Product Owner (CSP-PO),
• Certified ScrumMaster (CSM),
• Certified Scrum Product Owner (CSPO),
• Project Management Professional (PMP),
• Lean Six Sigma Green Belt (LSSGB), and
• IT Service Management Foundation (ITIL).

Booking

Sign up for Miro—it's free!

(Want more features? You can always upgrade to a paid plan.)

List of Additional Articles

How to Cite This Article

Digital Object Identifier (DOI)

Content Acknowledgements

Tags and Hashtags

• Stanford AI lectures
• Free AI lectures
• Stanford CME 295 (CME295)
• Transformers
• Large language models
• Artificial intelligence
• Generative AI
• Machine learning
• LLM training and fine-tuning
• AI agents

• #ArtificialIntelligence
• #AI
• #StanfordUniversity
• #StanfordCME295
• #LargeLanguageModels
• #LLM
• #GenerativeAI
• #MachineLearning
• #AIResearch
• #AIAgents

Post-Publication Notes

Copyright

Tags: Stanford AI lectures • Free AI lectures • Transformers • Generative AI • AI agents • #ArtificialIntelligence • #AI • #StanfordUniversity • #StanfordCME295 • #MachineLearning • #AIAgents • #AIResearch • #LLM • #GenerativeAI • #LargeLanguageModels • LLM training and fine-tuning • Machine learning • Artificial intelligence • Large language models • Stanford CME 295 (CME295)