Develop and scale AI solutions faster
Your AI prototype is ready, but wider adoption is stalling? Organisations across all industries are facing this challenge. Initial AI use cases are easy to identify and implement in isolation, but moving them into production at scale often proves difficult – whether due to budget constraints or limited development capacity.
AI @ Scale offers a way forward. The approach relies on central steering and the reuse of patterns from existing use cases. This accelerates the delivery of new AI solutions while reducing development effort.
Drawing on our strategic and hands-on AI experience, we help you build a scalable AI organisation – so your AI use cases do not remain stuck in pilot mode, but deliver real impact across the business.
“Successfully bringing AI applications into production and scaling them requires the right balance of organisational and technical parameters.”
What sets PwC’s AI scaling services apart
Why scaling AI solutions is so difficult for many organisations
The step from a successful proof of concept (PoC) to enterprise-wide implementation is the critical threshold at which many AI initiatives fail. Key obstacles include the rapid pace of innovation in the market and the realities of day-to-day operations. Running AI in live environments introduces complex risks – from data protection and compliance issues to unpredictable model behaviour. Many organisations lack the processes needed to manage these risks effectively.
On top of this, the organisational framework is often unclear. Responsibilities between IT, business functions and data science teams remain vague, and defined ways of working for AI project teams are rare. This leads to a “silo mentality”: different departments work in isolation on use cases that pursue different goals but are built on similar technical foundations. As a result of this fragmentation, crucial synergies are lost. Teams repeatedly reinvent the wheel, tying up resources and preventing efficient scaling.
Central control meets reusable components
To move AI use cases from experimental pilots into broad-based deployment, a shift in mindset is needed. The answer lies in a platform-based approach focused on reusability. Just as prompts and entire agent configurations can be shared in modern AI ecosystems, organisations can identify reusable elements in more technical dimensions of their use cases – from cleaned data sources and user authentication to specific interfaces. These should not be rebuilt for every new use case, but provided as modular building blocks.
This requires standardisation and strong central governance. Only when it is clearly defined how these components are created, maintained and shared can real efficiency gains be achieved. This is where the AI @ Scale concept comes in. It provides the technological connective tissue that standardises the development, deployment and scaling of AI solutions.
“AI @ Scale accelerates time-to-market. It enables innovations to be rolled out securely and at scale across the entire organisation.”
Bring AI solutions into production faster and more efficiently
We help you scale AI across your organisation.
How AI @ Scale helps you scale AI
- Use case selection
- AI @ Scale MVP
- Operating model
- Scaling with patterns
- Technical enablement
Use case selection
Based on best practices, we identify use cases with quick-win potential that align with your AI strategy. They demonstrate the value of (Gen)AI immediately and contain generalisable principles and reusable elements. Additional use cases are prioritised according to value potential, strategic fit and implementation feasibility.
AI @ Scale MVP
As the foundation for AI @ Scale, the first use case is developed and deployed as a Minimum Viable Product. This creates key building blocks such as the required technical architecture as well as initial process and governance guidelines. We help you make the right design choices and, where needed, accelerate MVP delivery with an external development team.
Operating model
To enable scalable use case delivery via AI @ Scale, an appropriate operating model is essential. It defines processes, roles and responsibilities across the data platform and development environment. We support you in designing these fundamentals, successfully rolling out AI @ Scale in your organisation, and embedding the new operating model in your existing governance structures.
Scaling with patterns
Scaling AI solutions with AI @ Scale goes hand in hand with the gradual expansion of use case patterns. Your repertoire of use cases grows, while the patterns they contain are identified and made available for reuse. This allows additional use cases to be rolled out across an ever broader range of applications – and at increasing speed – enterprise-wide.
Technical enablement
From a technical perspective, operating a modern AI development and orchestration platform on flexible cloud infrastructure is essential. AI pods can be scaled up and down dynamically to respond to changing demand. We support you in implementing the required technical infrastructure, upskilling your workforce, and fully integrating AI into your value chain.
Contact us
Director, Data & Analytics, Operations Transformation, PwC Germany
Tel: +49 151 15535019
