Privacy and Artificial Intelligence - 2.9 Cross-Functional Collaboration and Training

2.9 Cross-Functional Collaboration and Training

Introduction

Cross-functional collaboration and training are foundational to the responsible development, deployment, and governance of artificial intelligence (AI). As AI systems become more complex and integrated into every aspect of organizations, the need for diverse expertise and perspectives grows. Cross-functional collaboration means bringing together people from different departments—such as IT, data science, legal, compliance, security, and business units—to plan, implement, and oversee AI projects. Training ensures that everyone involved has the knowledge and skills to use AI safely, ethically, and effectively (Spiceworks, 2025; McChrystal Group, 2025).

AI is not just a technology challenge; it is also an organizational and cultural one. Successfully integrating AI requires breaking down silos, fostering open communication, and aligning goals across teams. Cross-functional collaboration helps organizations anticipate risks, address regulatory requirements, and innovate more quickly. Training programs tailored to different roles ensure that all employees understand how AI works, what the risks are, and how to use AI tools responsibly. Together, these practices build a culture of trust, accountability, and continuous learning, which is essential for long-term success in the AI era (Spiceworks, 2025; McChrystal Group, 2025).

Technical or Conceptual Background

Cross-functional collaboration in AI involves assembling teams that combine technical, legal, ethical, and business expertise. These teams work together to design, build, test, and monitor AI systems, ensuring that all relevant perspectives are considered. For example, data scientists develop models, IT specialists manage infrastructure, legal experts ensure compliance, and business leaders align AI initiatives with organizational goals (Spiceworks, 2025; McChrystal Group, 2025).

Collaboration is often formalized through governance bodies or task forces that oversee AI projects. These groups set policies, review risks, and provide guidance on best practices. They also serve as a forum for sharing knowledge, resolving conflicts, and making decisions that affect the entire organization (McChrystal Group, 2025; IAPP, 2024). Cross-functional centers for AI governance are particularly effective, as they provide a comprehensive view of AI initiatives and ensure that training is tailored to the needs of different departments and levels of AI users (McChrystal Group, 2025).

Training is an ongoing process that evolves as AI technologies and regulations change. It should be customized for different audiences: technical staff need deep knowledge of AI algorithms and security, while non-technical employees may only need to understand how to use AI tools in their daily work (McChrystal Group, 2025; Agile Business, 2025). Training programs can include workshops, online courses, simulations, and hands-on exercises. They should cover topics such as data privacy, bias mitigation, security, compliance, and ethical AI use (Spiceworks, 2025; Agile Business, 2025).

AI can also play a role in facilitating collaboration and training. For example, AI-powered collaboration tools can automate routine tasks, summarize meetings, and translate languages, making it easier for teams to work together across time zones and disciplines (Agile Business, 2025). AI-driven learning platforms can personalize training content, track progress, and recommend next steps for skill development (Agile Business, 2025).

Problems Being Solved or Best Practice Being Applied

Cross-functional collaboration and training address the problem identified in Sub-Point 1.3: Lack of Transparency and Explainability, and more broadly, the challenges of integrating AI into complex organizations. By fostering collaboration across departments and providing targeted training, organizations can ensure that AI systems are transparent, explainable, and aligned with business and regulatory requirements (Spiceworks, 2025; McChrystal Group, 2025).

One of the main problems in AI adoption is the risk of silos, where different teams work in isolation and do not share information or insights. This can lead to misunderstandings, duplication of effort, and gaps in oversight. Cross-functional collaboration breaks down these barriers, enabling organizations to identify risks early, share best practices, and respond quickly to new challenges (Spiceworks, 2025; Agile Business, 2025).

Training helps address knowledge gaps and ensures that all employees, regardless of their role, understand how AI works and what their responsibilities are. This is especially important for transparency and explainability, as employees need to be able to communicate how AI decisions are made and how data is used (Spiceworks, 2025; McChrystal Group, 2025). Training also supports compliance with regulations such as the GDPR, which require organizations to be transparent about AI use and to ensure that staff are properly trained (IAPP, 2024).

Best practices include establishing cross-functional AI governance bodies, developing tailored training programs, and using AI tools to facilitate collaboration and learning. Organizations should also encourage a culture of continuous improvement, where feedback from all levels is valued and acted upon (Spiceworks, 2025; Agile Business, 2025).

Role of Government and Regulatory Authorities

Governments and regulatory authorities play a crucial role in promoting cross-functional collaboration and training for AI. They set legal and regulatory frameworks that require organizations to be transparent, accountable, and responsible in their use of AI (AIGN, 2024; ITU, 2025). These frameworks often emphasize the importance of multidisciplinary teams and ongoing training to ensure compliance and mitigate risks.

Regulatory bodies such as the European Data Protection Board (EDPB), the UK Information Commissioner’s Office (ICO), and the US Federal Trade Commission (FTC) provide guidelines and best practices for AI governance, including the need for cross-functional collaboration and training (AIGN, 2024; ITU, 2025). For example, the EU AI Act and GDPR require organizations to document their AI processes, ensure transparency, and provide training to staff.

Governments also support the development of national and international standards for AI skills and collaboration. Initiatives like the ITU’s Global AI Skills Coalition provide accessible education and capacity-building resources, helping organizations and individuals develop the skills needed for responsible AI use (ITU, 2025). These programs are designed to be inclusive, reaching underrepresented groups and ensuring that the benefits of AI are shared equitably (ITU, 2025).

In addition to setting rules and providing guidance, governments raise public awareness about the importance of AI collaboration and training. They run educational campaigns, host workshops, and provide resources to help organizations and individuals understand their rights and responsibilities (ITU, 2025; AIGN, 2024).

International cooperation is also important, as AI risks and opportunities are global. Organizations like the OECD, G7, and UNESCO promote global standards for AI governance, including cross-functional collaboration and training (AIGN, 2024; ITU, 2025). These efforts help harmonize regulations and ensure that best practices are adopted worldwide.

Governments can also serve as model users of cross-functional collaboration and training, demonstrating best practices and creating demand for innovative solutions. By adopting these practices in their own operations, governments show leadership and help build a culture of trust and accountability in AI (ITU, 2025; AIGN, 2024).

Role of Organizations and Businesses

Organizations and businesses are responsible for implementing cross-functional collaboration and training in their AI initiatives. This involves creating multidisciplinary teams, establishing governance bodies, and developing tailored training programs (Spiceworks, 2025; McChrystal Group, 2025).

One key step is to establish a cross-functional AI governance body or task force. This group should include representatives from IT, data science, legal, compliance, security, and business units. The governance body sets policies, reviews risks, and provides guidance on best practices (McChrystal Group, 2025; IAPP, 2024). It also serves as a forum for sharing knowledge, resolving conflicts, and making decisions that affect the entire organization.

Training is another critical component. Organizations should develop training programs that are tailored to different roles and levels of expertise. Technical staff need deep knowledge of AI algorithms, security, and data management, while non-technical employees may only need to understand how to use AI tools in their daily work (McChrystal Group, 2025; Agile Business, 2025). Training should cover topics such as data privacy, bias mitigation, security, compliance, and ethical AI use.

Organizations should also encourage a culture of continuous learning and improvement. This includes providing opportunities for employees to give feedback, share insights, and participate in ongoing training and development (Spiceworks, 2025; Agile Business, 2025). AI-powered tools can be used to facilitate collaboration and learning, such as by automating routine tasks, summarizing meetings, and personalizing training content (Agile Business, 2025).

Transparency and communication are also important. Organizations should provide clear information to employees, regulators, and the public about how AI systems are developed, deployed, and monitored. They should also allow employees to exercise their rights, such as access to information and the ability to report concerns or incidents (Spiceworks, 2025; IAPP, 2024).

By adopting these best practices, organizations can reduce the risk of incidents, comply with regulations, and build trust with users and partners. Cross-functional collaboration and training also enable organizations to learn from past experiences and improve their AI systems over time (Spiceworks, 2025; McChrystal Group, 2025).

Role of Vendors and Third Parties

Vendors and third-party providers play a key role in supporting cross-functional collaboration and training for AI. They develop and supply tools, platforms, and services that facilitate collaboration, automate routine tasks, and deliver training content (Agile Business, 2025; ITU, 2025).

Vendors offer a wide range of solutions, including collaboration platforms, project management tools, and learning management systems. These products are designed to be integrated into existing workflows, making it easier for teams to work together and for employees to access training resources (Agile Business, 2025; ITU, 2025).

Third-party trainers and consultants provide specialized training and support, helping organizations develop the skills and knowledge needed for responsible AI use. They can also conduct assessments, identify gaps, and recommend improvements (ITU, 2025; Agile Business, 2025).

Vendors and third parties should be transparent about their own security and compliance practices. They should provide clear documentation, support regulatory requirements, and respond quickly to any identified vulnerabilities or incidents (ITU, 2025; Agile Business, 2025).

Collaboration between organizations and vendors is essential for advancing the state of the art in AI collaboration and training. Vendors can help organizations stay informed about emerging trends and best practices, while organizations provide valuable feedback and use cases that drive innovation (Agile Business, 2025; ITU, 2025).

Role of Employees and Internal Teams

Employees and internal teams are essential for the successful implementation of cross-functional collaboration and training. Technical staff, such as data scientists and IT specialists, are responsible for building and maintaining AI systems, ensuring that they are secure, reliable, and compliant (Spiceworks, 2025; McChrystal Group, 2025).

Legal and compliance teams review policies, ensure regulatory compliance, and provide guidance on best practices. They also manage incident response procedures and help resolve any disputes or concerns (IAPP, 2024; Spiceworks, 2025).

Business leaders align AI initiatives with organizational goals, prioritize resources, and communicate the value of AI to stakeholders. They also support a culture of collaboration and continuous learning (McChrystal Group, 2025; Agile Business, 2025).

Training and awareness programs help all employees understand the importance of AI collaboration and their role in maintaining security and compliance. Regular training ensures that staff are prepared to recognize and respond to risks, and that privacy and security protections are consistently applied (Spiceworks, 2025; Agile Business, 2025).

Internal teams also monitor and audit AI systems to ensure ongoing effectiveness. They review access logs, check for vulnerabilities, and update training and collaboration practices as needed. By maintaining high standards of data governance, employees help protect user privacy and build trust in AI systems (Spiceworks, 2025; McChrystal Group, 2025).

Role of Industry Groups and Professional Bodies

Industry groups and professional bodies develop standards, guidelines, and certifications to promote best practices in cross-functional collaboration and training for AI. They facilitate knowledge sharing, research, and advocacy to advance responsible AI use (IAPP, 2024; ITU, 2025).

Organizations such as the International Association of Privacy Professionals (IAPP), the International Organization for Standardization (ISO), and the National Institute of Standards and Technology (NIST) publish technical standards and best practices for AI governance, collaboration, and training (IAPP, 2024; ITU, 2025). These standards help organizations select, implement, and audit collaboration and training measures, and provide a common language for discussing risks and controls.

Professional bodies offer training and certification programs for privacy and security professionals, helping them develop the skills needed to implement and manage AI collaboration and training (IAPP, 2024; ITU, 2025). These programs cover topics such as data privacy, bias mitigation, security, compliance, and ethical AI use.

Industry groups also advocate for strong privacy and security regulations and support public awareness campaigns. They organize conferences, workshops, and working groups where experts can share insights, discuss emerging challenges, and develop new solutions (IAPP, 2024; ITU, 2025).

By setting industry-wide benchmarks and promoting ethical conduct, industry groups and professional bodies help build public trust in AI technologies and encourage widespread adoption of best practices in collaboration and training (IAPP, 2024; ITU, 2025).

Role of International and Multilateral Organizations

International and multilateral organizations play a key role in promoting global standards for cross-functional collaboration and training in AI. They develop frameworks, guidelines, and recommendations that influence national policies and industry practices (ITU, 2025; AIGN, 2024).

The OECD, G7, and UNESCO promote AI governance and collaboration best practices, encouraging countries to adopt robust risk management strategies and invest in skills development (ITU, 2025; AIGN, 2024). These organizations support capacity building, technical assistance, and research to help countries implement effective collaboration and training.

International organizations also facilitate dialogue among stakeholders, helping to address emerging challenges and harmonize approaches to AI collaboration and training. They publish reports, host conferences, and provide platforms for collaboration and knowledge exchange (ITU, 2025; AIGN, 2024).

By fostering global cooperation and setting high standards, international organizations help ensure that cross-functional collaboration and training practices are consistent, effective, and aligned with global best practices (ITU, 2025; AIGN, 2024).

Role of Consumers and Users

Consumers and users play an important role in driving the adoption of cross-functional collaboration and training for AI. By demanding transparency, accountability, and robust privacy protections, they encourage organizations to prioritize collaboration and skills development (Spiceworks, 2025; ITU, 2025).

Users can exercise their rights under data protection laws, such as requesting access to their data, correcting inaccuracies, or reporting incidents. Feedback mechanisms, such as surveys, complaint channels, and public forums, provide valuable insights into user concerns and experiences (Spiceworks, 2025; ITU, 2025).

Educational initiatives help raise awareness about AI risks and the importance of collaboration and training. By understanding their rights and how their data is protected, users can make informed decisions and advocate for stronger protections (Spiceworks, 2025; ITU, 2025).

Ultimately, empowered consumers contribute to a market environment where collaboration and training are competitive advantages, motivating organizations to adopt best practices and innovate in AI (Spiceworks, 2025; ITU, 2025).

Role of Members of the Public

Members of the public influence the adoption of cross-functional collaboration and training practices through advocacy, education, and participation in policymaking. Civil society organizations promote awareness of AI risks and push for stronger privacy and security protections (ITU, 2025; AIGN, 2024).

Public consultations and participatory policymaking processes allow citizens to voice their concerns and contribute to the creation of balanced and effective AI governance frameworks. Media coverage and educational programs inform the public about the importance of collaboration and training (ITU, 2025; AIGN, 2024).

By holding organizations and governments accountable, members of the public help ensure that cross-functional collaboration and training practices are robust and effective. Public opinion and activism can influence the direction of innovation and policy, driving progress toward a more secure and trustworthy digital society (ITU, 2025; AIGN, 2024).

Role of Artificial Intelligence Itself

Artificial intelligence can support cross-functional collaboration and training by automating routine tasks, facilitating communication, and personalizing learning experiences (Agile Business, 2025; Worklytics, 2025). AI-powered tools can summarize meetings, assign action items, translate languages, and recommend training content based on individual needs.

For example, AI-driven collaboration platforms can help teams work together across time zones and disciplines, breaking down silos and enabling real-time information sharing (Agile Business, 2025). AI-powered learning management systems can track progress, recommend next steps, and adapt content to different learning styles (Agile Business, 2025).

AI can also analyze work patterns and suggest more efficient ways to collaborate, helping interdisciplinary teams stay aligned and productive (Agile Business, 2025; Worklytics, 2025). By automating administrative tasks, AI frees up employees to focus on creative and strategic work.

However, human oversight is essential to ensure that AI-driven collaboration and training are fair, transparent, and effective. Organizations must regularly review and validate the results of AI-powered tools, and involve human experts in interpreting findings and making decisions (Agile Business, 2025; Worklytics, 2025).

Role of Bad Actors

Bad actors, including hackers, cybercriminals, and malicious insiders, pose significant challenges to cross-functional collaboration and training for AI. They may attempt to exploit vulnerabilities in collaboration tools, manipulate data, or disrupt training programs (Spiceworks, 2025; Agile Business, 2025).

Robust security measures, continuous monitoring, and independent verification are necessary to protect against these threats. Organizations should implement strong access controls, encryption, and audit trails to prevent unauthorized changes to data or system configurations (Spiceworks, 2025; Agile Business, 2025).

Collaboration among organizations, governments, and industry groups is essential to share threat intelligence and develop effective countermeasures. By working together, stakeholders can identify emerging risks and respond quickly to protect system integrity and user trust (Spiceworks, 2025; Agile Business, 2025).

Bad actors may also target the technology underlying collaboration and training systems. Organizations must ensure that these technologies are implemented securely and that vulnerabilities are promptly addressed (Spiceworks, 2025; Agile Business, 2025).

Glossary

Term	Meaning and Example Sentence
Cross-functional collaboration	Working together across different departments or disciplines. Example: “Cross-functional collaboration ensures that all perspectives are considered in AI projects.”
Training	Providing knowledge and skills to employees. Example: “Training helps employees use AI tools safely and responsibly.”
Governance body	A group that oversees AI policies and practices. Example: “The governance body reviews risks and sets guidelines for AI use.”
Multidisciplinary team	A team with members from different fields. Example: “A multidisciplinary team includes data scientists, legal experts, and business leaders.”
Continuous learning	Ongoing education and skill development. Example: “Continuous learning ensures that employees stay up to date with AI trends.”
Transparency	Openness about how AI systems work. Example: “Transparency helps build trust in AI.”
Explainability	The ability to understand how AI decisions are made. Example: “Explainability is important for accountability and compliance.”

Questions

1. What is cross-functional collaboration, and why is it important for AI systems?

2. How does training support the responsible use of AI in organizations?

3. What roles do governments and regulatory authorities play in promoting cross-functional collaboration and training?

4. What responsibilities do organizations and businesses have in implementing these practices?

5. How can consumers and users contribute to the adoption of cross-functional collaboration and training?

Answer Key

1. Suggested Answer: Cross-functional collaboration is the process of bringing together people from different departments or disciplines to plan, implement, and oversee AI projects. It is important for AI systems because it ensures that all relevant perspectives are considered, risks are identified early, and decisions are aligned with business and regulatory requirements (Spiceworks, 2025; McChrystal Group, 2025).

2. Suggested Answer: Training provides employees with the knowledge and skills they need to use AI tools safely, ethically, and effectively. It helps organizations ensure that all staff understand how AI works, what the risks are, and how to comply with regulations (McChrystal Group, 2025; Agile Business, 2025).

3. Suggested Answer: Governments and regulatory authorities set legal and regulatory frameworks, provide guidelines and best practices, and support education and capacity-building initiatives. They also promote public awareness and international cooperation to ensure that cross-functional collaboration and training are aligned with global standards (ITU, 2025; AIGN, 2024).

4. Suggested Answer: Organizations and businesses are responsible for creating multidisciplinary teams, establishing governance bodies, and developing tailored training programs. They must also encourage a culture of continuous learning and ensure transparency and accountability in AI use (Spiceworks, 2025; McChrystal Group, 2025).

5. Suggested Answer: Consumers and users can drive adoption by demanding transparency and accountability, exercising their rights under data protection laws, providing feedback, and participating in educational initiatives. Their actions encourage organizations to prioritize collaboration and skills development (Spiceworks, 2025; ITU, 2025).

References

Spiceworks. (2025). Effective AI cybersecurity: Cross-collaboration and proactivity. https://www.spiceworks.com/tech/artificial-intelligence/guest-article/effective-ai-cybersecurity-cross-collaboration-and-proactivity/
McChrystal Group. (2025). AI integration is a team sport: A strategic guide for leaders. https://www.mcchrystalgroup.com/insights/detail/2025/06/10/ai-integration-is-a-team-sport--a-strategic-guide-for-leaders
Agile Business. (2025). Using AI to empower cross-functional teams. https://www.agilebusiness.org/resource/using-ai-to-empower-cross-functional-teams.html
IAPP. (2024). Building strong AI governance: Collaboration between privacy, security, and governance teams. https://iapp.org/news/a/building-strong-ai-governance-collaboration-between-privacy-security-and-governance-teams
Worklytics. (2025). What it means to be an AI-first organization in 2025. https://www.worklytics.co/blog/what-it-means-to-be-ai-first-organization-in-2025
ITU. (2025). ITU launches global AI skills coalition to bridge expertise gap in developing nations. https://dig.watch/updates/itu-launches-global-ai-skills-coalition-to-bridge-expertise-gap-in-developing-nations
AIGN. (2024). How can collaboration between governments, industry, and academia be promoted to achieve effective AI governance? https://aign.global/ai-governance-consulting/patrick-upmann/how-can-collaboration-between-governments-industry-and-academia-be-promoted-to-achieve-effective-ai-governance/