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A C-suite guide to capturing the potential value of AI

According to Deloitte’s research State of Generative AI in the Enterprise, organisations are having challenges capturing the full potential value AI brings. C-suite leaders are dealing with the same question: How to transition beyond the experimentation phase and start realising actual business value and P&L impact?

In conversations across industries and regions, we are hearing C-suite leaders dealing with similar topics around AI. To that end, we present below a comprehensive, holistic set of questions, divided into five areas that leaders should systematically address:

  1. Strategy
  2. Organisation and people
  3.  Risk and compliance
  4. Technology and implementation
  5. Ecosystem and partnerships

This article introduces the questions per each of these five categories. In the next weeks we will bring deep-dives for these categories to support answering these questions.

Strategy: Solidifying your AI expectations

From accelerating revenue growth, realizing cost efficiencies, strengthening core competencies to redefining business models, CEOs want to understand how AI is relevant to their sector and organisation. A clear and explicit strategy towards AI, as integral part of the business strategy, is an essential starting point.

The questions below will help determining how far and how deeply AI will permeate the business. The answers to these questions will give input to the questions in the other categories. As an example, if AI is going to impact the core of the business model, the operating model will most likely change significantly and technology investments will have to be material.

  1. What are the bigger societal challenges and developments driven by AI, surrounding the organisation, and how does this inform our strategy?
  2. How relevant is AI for our industry and sector? Is AI fundamentally changing the core of our business model, or does it mainly offer potential to improve non-core domains?
  3. How can we use AI to strengthen our competitive advantage?
  4. What are the most impactful use cases we should invest in and which concrete applications are relevant for us?
  5. What is the ROI of AI for our organization and how do we create, measure and capture the full value?
  6. What role does AI play in our innovation strategy?
  7. What are the risks if we do not act fast enough?
  8. What is our explicit choice regarding being an early adopter, fast follower or laggard?

Organisation and people: Gauging your organisation’s readiness for AI transformation

AI’s effects are felt throughout workplace cultures, the labour market, education and skills training – demanding that you prepare your organisation and employees to accept and keep pace with how AI will be (or is) fundamentally transforming their work. This involves considering how you’ll integrate AI into your culture, how you prepare employees for new tasks and what new leadership qualities are needed.

Typically, the following questions are asked to understand the implications of AI for the workforce: 

  1. What organisational, process and cultural changes are necessary to capture the full potential of AI?
  2. How is AI impacting the labour market and workforce demand-supply curves?
  3. Who will lead the AI transformation and to whom do they report?
  4. How do we integrate AI into our processes and make our people adopt a new way of working?
  5. Which roles will change or may disappear?
  6. How do we prepare employees for an AI-driven future?
  7. What new skills and leadership qualities are required in an AI-driven organisation?
  8. How do we create acceptance of AI, and support for it, within our organisation?

Risk and compliance: Governing responsible AI use

With new technologies come new risks, meaning that privacy issues, ethical dilemmas, cyberthreats and reputational risks demand a robust governance model. This area of AI focus is about creating clear frameworks that comply with laws and regulations and safeguard the trust of customers and stakeholders.

Typically, the following questions fall under the responsibility of the Chief Financial Officer or Chief Risk Officer:

  1. Which compliance framework should we use for global AI compliance?
  2. How do we control risks and protect our reputation when using technologies that lack clear risk & compliance requirements?
  3. How to strike the right balance between risk controls and innovation?
  4. How do we handle privacy and data ethics in AI?
  5. What ethical risk controls do we need to prevent AI from leading to unethical behavior or legal issues?
  6. How do we protect our AI models and data against cyberattacks?
  7. How do we prevent company data used for AI from leaking to third parties?
  8. How do we give confidence to key stakeholders (e.g. supervisory board, shareholders, etc.) that we are in control of AI risks & compliance?
  9. How do we understand and meet regulatory expectations from supervisors on AI and signal that we are in compliance?
  10. What new or additional risks does AI pose to our reputation?

Technology and implementation: Building a safe and robust AI infrastructure

The technological foundations of AI require a well-thought-out architecture, careful choices when it comes to internal development and external solutions, and an approach that guarantees data quality and reliability. The speed and effectiveness of implementation depend, in part, on how centralised AI capabilities are; big-picture thinking is needed to find opportunities to connect and align for a centralised structure.

Typically, the following questions fall under the responsibility of the Chief Information Officer and Chief Digital Officer:

  1. Which AI technologies and foundational models do we need to deliver the portfolio of value cases that are defined as part of the AI strategy?
  2. How do we ensure the quality and reliability of GenAI technology?
  3. How do we prevent errors, bias and unpredictable behaviour of our AI models?
  4. What infrastructure and technical architecture are required?
  5. Do we need a sovereign cloud to manage our risks?
  6. How do we weigh in-house development versus purchasing off-the-shelf solutions?
  7. How do we organise the delivery of AI solutions?
  8. How do we ensure high quality data and what is the appropriate data governance to enable this?

Ecosystems and partnerships: Keeping AI relevant into the long term

Choosing and applying AI technologies is about discovering the right option for each purpose. But AI innovation rarely occurs in isolation. Building a dynamic ecosystem of partners is essential to maintain continuous access to the latest developments and expertise.

Typically, the following questions fall under the responsibility of the Chief Commercial Officer:

  1. Which AI trends should we monitor now and in the future? 
  2. How do we keep track of the latest developments relevant to our industry and organisation? 
  3. How can we collaborate with startups, knowledge institutions and other partners?

Navigating with confidence

Addressing this comprehensive set of questions – and accepting what it reveals – goes beyond adopting a technological innovation: It’s a navigation guide for an end-to-end business transformation that creates sustainable value and a competitive advantage. It’s also a way to make each C-suite leader aware of the overall vision, and empower them to make aligned decisions to maximize the return on investment.

As a CEO, you can feel confident approaching your board armed with the answers to the strategy-focused questions – a necessary first phase that the rest of the teams can build on when answering their own questions.

Stay tuned for the deep-dives per category.

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