Insight
How the right operating model can turn AI ambitions into execution
Artificial intelligence (AI) – with generative AI (GenAI) at the fore – has the potential to deliver genuine innovation for customers, drive-up productivity and reshape your organisation along the way. But before you can capitalise on the promise of AI, you need the necessary foundations and surrounding operating model to deliver and execute successfully. What does an effective AI-ready operating model look like? How can you create the foundational capabilities before shifting up the gears into strategic acceleration?
Implementing AI at scale is unlike other tech transformations. There is a whole new set of challenges you might not have previously considered, from ethical issues and the need for responsible deployment to the impact on a potentially wary and unprepared workforce. Without the right foundations, these hurdles can slow or even stall progress.
A capabilities-driven target operating model is essential for success. The model should not only bring together the different capabilities needed to deliver your strategy, but also ensure that all these components from across the organisation are working together in harmony. No one function can achieve this alone. It’s also important to ensure that your workforce feels like they own, benefit from and can get behind the transformational changes ahead.
From a strategic perspective, this capability-driven approach to model design and implementation will help clarify the feasibility of use cases and strengthen the foundations for implementing AI as a transformational business tool. It will also guard against the pitfalls of implementation without preparation, including poor adoption, insufficient workforce buy-in and difficulties in measuring AI-driven outcomes and return on investment.
The operating building blocks
So what are the key components of this capability-driven target operating model? We define AI capabilities in two categories: foundational capabilities critical to harnessing the potential of AI tools, and transformational capabilities to drive long-term value through the development and use of AI technology.
Foundational AI capabilities
Definition: foundational capabilities form the basis of an AI-driven transformation, ensuring the right technology, governance and processes are in place to support sustainable AI adoption:
Articulating the vision for how AI will change your business. You can specify where it will be used, and where it won’t.
Ensuring your internal and external stakeholders’ needs are ethically and responsibly served using AI in your products and services.
Confirming you have the right partnerships, organisational alignment and talent to develop and drive value from an AI model.
The right capabilities to run and continually improve your AI models. Determine how AI tooling fits within your overall operating environment.
Checking you have the right level of transparency to both comply and lead your peers on AI performance. Determine whether you can deploy and manage AI successfully.
Establishing if your workforce and partners are aware and able to deploy, use and manage AI effectively and responsibly.
Transformational AI capabilities
Definition: transformational AI capabilities help organisations scale AI beyond one-off use cases, so it is embedded into core business processes to drive continuous innovation and efficiency:
Continually identifying ways that AI can improve your market positioning and opportunities.
The ability to proactively identify and mitigate threats to your risk profile, as well as the impact on your data and cyber security from employing AI tools. This is critical in retaining and building trust across your value chain.
Being able to identify, meet and improve AI use cases throughout the business.
Strengthening your ability to attract, develop and motivate talent.
Gathering feedback and measuring effectiveness, quality and alignment of AI performance to strategic and ethical goals. Priorities include coordination with voluntary or statutory reporting related to AI tools and usage.
Ensuring robust data pipelines and architectures are present to support AI initiatives.
Establishing and maintaining the necessary hardware and software infrastructure to support AI workloads, scalability and performance.
Creating compelling business cases for AI projects by aligning them to strategic goals, and identifying potential ROI, cost savings, and strategic benefits.
Ensure data is a valuable asset for AI applications by developing a comprehensive data strategy that aligns with AI objectives.
Designing and implementing effective AI solutions that are scalable, maintainable, and aligned with business needs.
Identifying market opportunities, developing go-to-market strategies, and ensuring regulatory compliance to support sales and marketing efforts.
Managing relationships with AI vendors and service providers to ensure the successful delivery of AI projects.
Set up to deliver
With the target components identified and assessed, the next big question is how to design and implement the operating model in practice. This is very much your model built around your strategic ambitions, existing capabilities and AI maturity. But there are three key considerations common to all.
1. Clarify your ambitions
The starting point is your vision. Key questions to ask include what are the business goals you are trying to achieve and how can AI help realise them? Where and how can your organisation use AI to increase productivity or reduce overheads? What current capabilities can AI augment or replace to drive up revenue?
AI implementation isn’t a strategy in its own right. Rather, it’s a tool for realising your objectives. By using a capability-driven approach, you can determine your differentiating strengths, how to play to them and how much to invest across each of these capability areas
2. Build capabilities around your AI maturity
Assessing your AI maturity is a key first step to prioritising your AI capabilities, articulating your ambitions and setting the direction of change. Have you identified the right set of capabilities for AI to drive real value for the business? You can then consider how to deploy AI ethically and embed that thinking into your operations and ways of working. Do you have the talent to customise models and structure data for specific use cases? How confident is your workforce that it can play a full part in harnessing AI and using it to drive value?
These strategic assessments will not only help you to identify the capabilities needed to support your ambitions, but also how they would best fit into your organisation and develop a roadmap for implementation and augmentation.
3. Mobilise your organisation around change
Building and honing AI capabilities is a long-term journey rather than a destination, though you can initially target the next level of maturity on the AI maturity scale. A critical element would be augmenting existing competencies and identifying new skills needed to deliver on an AI-enabled strategy. It’s also important to work out how those new capabilities will be embedded into day-to-day operations. This is likely to require a rethink of organisational design and then job descriptions, roles and responsibilities as you shift to AI-enabled ways of working.
Read more about skills, ethics and other key aspects of people and AI in our article ‘Mobilising your workforce behind ethical AI transformation’.
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