Steering the organisation towards strategic goals

Many organisations are still in the early stages of AI transformation, but there’s increasing momentum for action. Deloitte surveyed 700 board directors and executives from across the world and found that almost a third of them (31%) say AI is not on the board agenda yet. However, the results also indicate that boards are starting to see the importance of AI education and adoption, and are working to include it in their agendas and discussions with C-suite executives. But what should be discussed about AI in board meetings? How should the board evaluate, support and, if needed, challenge management’s perspective?

“It all starts with the company’s strategy ambition”, says Antti-Veikko Vuorikoski, Senior AI, Analytics and Business Development professional at Deloitte. He goes on to say, “The strategy should determine the extent to which GenAI is implemented or discussed in the board meetings. From the board member’s perspective, it is essential to understand that AI is not just a technological upgrade but a strategic enabler. Their role is to ensure that the organisation can identify and leverage AI to unlock new revenue streams, reshape cost structures and drive business-model evolution.” 

Vuorikoski emphasizes the importance of understanding how GenAI can act as a growth lever, tapping into new profit pools and enhancing capex/opex efficiency: “The top-line impact includes the creation of innovative products, services and customer experiences. On the bottom line, AI can automate workflows, optimise procurement, enable intelligent forecasting and drive internal productivity gains. By comprehending these facets, board members can steer the organisation towards harnessing the full potential of GenAI, positioning it for sustained competitive advantage.” 

Scaling GenAI means finding and creating more repetitive use cases, the ability to handle a growing amount of work and simply moving from experimentation to implementation in a sustainable, secure way. According to Vuorikoski, evaluating the organisation’s AI readiness in relation to its strategic ambition is essential for scaling GenAI. This includes investigating the organisation’s capability to advance ideas and deciding which capabilities to leverage and improve. It also involves understanding the implications of different approaches and choices related to the architecture, such as developing an in-house tech stack versus acquiring external solutions, with the ultimate goal being to seek scalable capabilities.

After determining where the firm stands in terms of AI maturity, the next step is to decide whether to build or buy related capabilities. Should the company develop these capabilities internally or acquire them from external sources?” Vuorikoski asks and continues: “Naturally, this leads to investment decisions: Where should investments be directed? Should they focus on internal development or other strategic areas? These decisions should come from the strategy, distinguishing between strategic choices and operational ones.” 

Trustworthy and sustainable GenAI

Although AI offers significant opportunities, there are concerns about the risks associated with it. Before an organisation can consider moving to the next phase with AI, the foundation must be solid. Business leaders are prioritising the trustworthy deployment of AI and safeguarding their organisations against potential risks. According to Deloitte’s State of Generative AI in the Enterprise - Nordic cut report, one of the main barriers to scaling is data. “Organisations have struggled to move their generative AI experiments into production because they face difficulties in accessing or cleaning all the data needed to run AI programmes. Data is foundational to generative AI, for both training the AI model and for using the tool itself. We recommend that organisations invest in data management before progressing further with AI”, Vuorikoski says. 

According to Vuorikoski, generative AI can be a game changer in risk management by automating regulatory compliance monitoring, allowing organisations to stay updated and ensure adherence to evolving requirements. “AI systems can analyse regulatory documents and identify changes impacting organisational operations, thus supporting executives in maintaining compliance effortlessly”, he explains.

“While organisations must comply with established regulatory frameworks, such as the EU AI Act, trustworthy AI extends far beyond mere compliance, and organisations face more complexity in ensuring their AI’s output can be trusted. Clear governance is an enabler of trust, speed, and scale. The board’s role in this process is crucial, and they should have clear visibility in relation to how AI is utilised across the company and who’s accountable”, Vuorikoski summarises.  

Deloitte has developed the Trustworhy AI framework which is designed to help organisations minimise risks and optimise GenAI’s potential in a safe and secure way. The framework outlines the essential criteria that AI systems must meet to earn trust. 

“Another consideration for board governance related to GenAI is its environmental impacts. When we think about the environmental impact of GenAI, we tend to fixate on just the electricity you consume when you plug the computer in. However, training GenAI models also uses a huge amount of electricity, which also leads to higher carbon dioxide emissions as well as putting pressure on the electric grid where the data centres reside”, Vuosikoski says.  

Vuorikoski suggest several strategies to mitigate these impacts. These include avoiding unnecessary AI usage by employing traditional technologies for simple tasks, limiting high-volume automation and opting for smaller, lighter models for tasks like summarisation and classification. Additionally, organisations should track and report AI usage, establishing KPIs and integrating this data into ESG reporting to assess environmental impacts. “By adopting these measures, companies can align their AI deployment with sustainability goals, promoting responsible and efficient technology use”, he adds. 

What’s next for GenAI? 

The advancement of AI is moving faster than expected. Leading organisations are already thinking about the next phase of AI. Last year, many basic AI agents, with relatively simple functionalities, were created and launched. Today, the trend has shifted towards more advanced and complex multi-agent systems. These sophisticated multi-agent systems consist of multiple AI agents working together within an ecosystem. This evolution enhances the way AI models communicate with each other and improves their ability to execute tasks more efficiently and effectively.  

AI agents are changing how technology teams and the business units they support operate. So, what is an AI agent? “To put it simply, while GenAI focuses on creating, agentic AI focuses on actions or making decisions – on doing”, Vuorikoski explains, going on to say: “Agentic AI is a broader concept that solves problems with minimal supervision, and an AI agent is a part of this system that handles tasks independently.” 

AI agents imitate human abilities, such as language processing, planning and reasoning. Organising these agents by specific business areas and assigning clear roles can improve efficiency. In a business setting, AI agents are similar to human employees; they require careful selection, thorough training and the right tools to work effectively. “Effective deployment and continuous management are essential to ensuring efficiency and adding value”, Vuorikoski emphasises. 

As with GenAI, board members should understand the impact of agentic AI and prepare their organisations to use these technologies in an efficient and secure manner. “We are not merely discussing chatbots but AI that acts and automates workflows, pushing boundaries with agents and multimodal AI”, Vuorikoski says. “This requires viewing a multi-agent AI system as a cohesive network of capabilities rather than as isolated solutions. Consequently, boards should consider organisational redesign, job role evolution, and partner and tech stack decisions. Technology is starting to take initiative – not just answer questions”, he concludes. 

Conclusion

As GenAI continues to evolve, board members find themselves at the helm of a transformative journey. Embracing AI’s strategic significance is crucial, it opens up transformational use cases and prompts reconsideration of how work is organised, how talent is deployed and which capabilities should be owned versus outsourced. This journey, from initial experimentation to full-scale implementation, demands a robust governance framework, strategic oversight and unwavering commitment to trustworthy and sustainable AI practices. The key to harnessing AI’s full potential lies in proactive governance, beginning right in the heart of boardroom discussions.

Strategic AI use is becoming a differentiator. The key question for the board is: Are we capturing the upside or just watching others do it?