A C-suite guide to capturing the potential value of AI
How to turn AI pilots into measurable P&L impact
Artificial intelligence (AI) is reshaping the financial industry, yet many banks, insurers and asset managers struggle to turn pilots into measurable P&L impact. Deloitte’s State of Generative AI in the Enterprise highlights a common leadership question: how do you move beyond experimentation and embed AI to deliver sustained business value?
Subscribe to our newsletter
Meet Jorg Schalekamp, Partner & AI Lead at Deloitte
I help organisations to convert technology, data and AI into measurable revenue, cost and operational gains, drawing on 24 years' experience in transformations, board advisory and P&L leadership.
Your AI readiness roadmap
This page sets out a practical roadmap with key questions and deep dives to help senior leaders assess readiness, prioritise use cases and accelerate value realisation with AI.
Explore the five areas below to identify priorities and turn AI pilots into measurable business impact:
- Strategy
- Organisation and people
- Risk and compliance
- Technology and implementation
- Ecosystem and partnerships
Stay tuned for all (coming) deep dives.
1. Strategy: Solidifying your AI expectations
From accelerating revenue growth, realising 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.
2. 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.
3. 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.
4. 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.
5. 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.
End-to-end digital transformation
Tackling these strategy questions is not just about adopting technology; it is an end-to-end digital and business transformation roadmap that delivers sustainable value, competitive advantage and maximised ROI by aligning the C-suite. As CEO, present these strategy answers to the board to set the foundational phase for teams to build on.
