The Human Element in AI Governance
BS - Ben Saunders
Introduction
Artificial Intelligence (AI) has become a cornerstone of business transformation. However, whilst many organisations focus predominantly on the technical aspects of AI implementation, they often overlook a crucial element: the human factor. The success of AI initiatives hinges not on technology alone, but on an organisation's ability to build comprehensive AI literacy across all levels. This involves creating a workforce that can confidently and responsibly engage with AI technologies in their respective roles, thereby ensuring the effective adoption and usage of AI in a safe and secure way that unlocks business value.
Increasingly so, AI education programs are essential for building organisation-wide AI literacy. These programs should be designed to demystify AI concepts and their practical applications in business contexts, enabling employees to make informed decisions and use AI responsibly.
That said, the primary goal of AI education programs is not to transform every employee into a data scientist, but to foster a baseline understanding that facilitates collaboration and innovation. By investing in AI education, organisations can create a foundation for sustainable AI adoption that balances innovation with responsibility and technical capability with human understanding.
To that point, role based education is critical to ensure everyone in the organisation can consume the insights that are relevant to their specific contribution to business outcomes, value creation and risk mitigation.
Role-Based Training Approaches
Different roles within an organisation require varying levels of AI knowledge and understanding. For instance, executive leadership needs to grasp the strategic implications of AI initiatives to make informed decisions about investment and direction. Middle management requires practical knowledge to effectively oversee implementation and manage AI-driven changes within their teams. Whilst technical teams must possess in-depth understanding of AI systems and their governance frameworks. Whilst front-line staff need operational knowledge relevant to their daily interactions with AI-powered tools. Therefore, role-based training approaches should be tailored to meet the distinct needs of each colleague or business function, ensuring that employees have the necessary knowledge and skills to work effectively with AI systems.
Let’s unpack each of these role archetypes in a bit more detail:
Executive Leadership Training
Executive leadership plays a pivotal role in guiding AI initiatives within an organisation. As such, their training should focus on:
Strategic vision development and AI roadmap creation.
Risk assessment and governance framework oversight.
Investment decision-making and resource allocation.
Ethical considerations and corporate responsibility,
Regulatory compliance and legal implications.
Indeed, this a lot to cover. However, having this comprehensive understanding will enable executive leaders to make informed decisions regarding AI adoption and ensure that AI initiatives align with the organisation's overall strategy and objectives.
Middle Management Development
Middle management should be responsible for overseeing the implementation of AI initiatives and managing the resulting changes within their teams. Their training should therefore concentrate on:
The management of AI initiatives, team leadership in AI transformation.
Performance monitoring and evaluation of AI deployments.
Change management and stakeholder communication.
Resource optimisation and budget management. (i.e., realising when to start or stop AI investments)
By equipping middle management with these skills, organisations can ensure the successful execution of AI projects and the effective management of AI-driven change.
Technical Team Enhancement
Engineering and technical teams require a deep understanding of AI systems, including model development and deployment protocols, data governance and quality control, security and privacy considerations, and testing and validation procedures. Their training should be highly specialised, focusing on the technical aspects of AI to ensure that they can develop, deploy, and maintain AI systems effectively.
Front-Line Staff Education
Front-line staff interact with AI systems on a daily basis and therefore require practical training on basic AI concepts and terminology, the practical application of AI in daily operations, data input and quality maintenance, problem identification and reporting, and customer interaction protocols. This training should be designed to enhance their ability to work effectively with AI systems and provide excellent customer service.
Invariably, each of these resoles also needs to be made aware of considerations such as AI risks, ethical uses and their responsibilities when building, interacting with or consuming AI enabled services. Our recent publication, the Enterprise AI Governance Playbook mapped out a series of sample education pathways for enterprise employees.
Creating an AI-Aware Culture
Creating an AI-aware culture represents a crucial aspect of building organisation-wide AI literacy. This involves encouraging curiosity and continuous learning, creating safe spaces for questioning and feedback, and celebrating achievements in AI literacy. Organisations that successfully foster this environment often find their AI initiatives gain momentum naturally, driven by employees who understand and embrace the technology's potential. An AI-aware culture also embeds AI awareness in daily operations, ensuring that AI is not viewed as a separate entity, but as an integral part of the organisation's workflow.
Simulated AI Events for Awareness
One effective method for enhancing AI awareness is through simulated AI events. These events can mimic real-world scenarios, allowing employees to experience the challenges and opportunities presented by AI in a controlled environment. For example, a simulated email campaign using AI-generated content to spread misinformation can teach employees to identify and report fake content. Such exercises enhance critical thinking and awareness, equipping employees to better handle AI-driven information in real-world settings.
Developing Internal Expertise
Developing internal expertise is vital for sustaining AI literacy programs. AI champions serve as bridges between technical and business perspectives, guiding teams through challenges and sharing best practices across departments. Their role extends beyond technical knowledge sharing; they become mentors who help colleagues navigate the complexities of AI implementation and usage. By cultivating AI champions, organisations can ensure that AI knowledge and skills are disseminated throughout the workforce, driving innovation and adoption.
Measuring and Improving AI Literacy
Measuring the success of AI literacy programs requires clear metrics. These might include knowledge assessment scores, implementation success rates, employee confidence levels, and reductions in AI-related incidents. However, these metrics should be viewed as guideposts rather than definitive measures, as the true value of AI literacy often manifests in subtle ways throughout the organisation. To improve AI literacy, organisations must establish a culture of continuous learning, providing regular training and updates to ensure that employees remain proficient in AI concepts and applications.
Change Management Strategies
Starting communities of practice, establishing centres of excellence, organising hackathons and game days, contributing to open source AI projects and research papers, running podcasts, and using other mediums such as newsletters and town halls are all innovative strategies to enhance AI awareness and literacy within an organisation. That said, each approach offers unique benefits and opportunities for engagement, learning, and innovation.
Here’s some suggestions I’ve found useful for organisations when starting their AI literacy journey:
Build Communities of Practice:
Communities of practice are groups of individuals who share a common interest in AI and come together to share knowledge, learn collaboratively, and develop their skills. These communities foster a culture of continuous learning and innovation, enabling members to stay updated on the latest AI trends and technologies. By encouraging cross-departmental participation, organisations can break down silos, promote interdisciplinary collaboration, and drive the adoption of AI best practices.
Establish a Centre of Excellence (COE):
Establishing a COE for AI within an organisation serves as a hub for expertise, resources, and support. These centres can provide strategic guidance, develop standardised processes, and facilitate training programmes. By centralising AI expertise, organisations can ensure consistent and efficient implementation of AI projects, while also fostering a culture of innovation and excellence. However, the secret is not to create an ivory tower that is dictatorial in nature. But more focussed on enablement, skills transition and embedding capability in a federated way so that the COE does not become a bottleneck to adoption.
Host Hackathons and Game Days:
Organising hackathons and game days can be an exciting way to engage employees with AI technologies. These events encourage experimentation, creativity, and problem-solving, allowing participants to work on AI challenges in a collaborative and competitive environment. Hackathons can lead to the development of innovative solutions and prototypes, while game days can simulate real-world AI scenarios, helping teams to test their skills and build muscle memory.
Contributing to Open Source AI Projects and Research Papers:
Encouraging employees to contribute to open source AI projects and research papers can enhance their technical skills and expand their professional networks. Participation in the open source community provides opportunities for learning from global experts, sharing knowledge, and gaining recognition for contributions. Additionally, research papers can showcase an organisation's expertise and thought leadership in AI, attracting talent and fostering partnerships.
Running Podcasts and Using Other Mediums:
Podcasts are an effective medium for disseminating AI knowledge and insights across an organisation. They can feature interviews with industry experts, discussions on AI trends, and case studies of successful AI implementations. Other mediums, such as newsletters and town halls, can also be used to promote AI awareness and keep employees informed about ongoing initiatives and achievements. These communication channels help to create a shared understanding of AI's role within the organisation and reinforce its strategic importance.
AI Agents for Colleague Education:
Perhaps one novel route to supporting your colleagues in their AI journey is by creating AI agents grounded in industry best practices that synthesises knowledge across various domains, including standards, ethics, architecture, and risk management. These agents can serve as assistants to up-skill and educate colleagues by disseminating knowledge, providing guidance, and enhancing understanding. This can be made easily accessible via a chat style experience which many employees will be familiar with given the advent of popular solutions like ChatGPT, Microsoft CoPilot and Claude as an example.
In Closing:
Enterprise AI governance is not simply a set of policies or guidelines, it starts with the awareness and education of humans about its risks and their responsibility when wielding its amazing power in their day to day operations.
Therefore, AI education programs are foundational to building organisation-wide AI literacy. These programs aim to demystify AI concepts, making them accessible and relevant to various business contexts. The primary goal is to equip employees with a baseline understanding that promotes informed decision-making and responsible AI use. By investing in comprehensive AI education, organisations can create a sustainable foundation for AI adoption, balancing innovation with ethical responsibility.
To ensure responsible AI adoption, it is essential to prioritise the human element of AI governance, with a central focus on educating and empowering people within the organisation.
