Accelerate your AI transformation
Moving beyond experimentation

Around 80-90% of large corporations have started AI initiatives. However, less than 1 in 10 companies move beyond proof of concepts and experimentation according to executive surveys of MIT, BCG, Accenture and others. Adopting AI at scale requires transformational efforts on multiple fronts. Our AI Accelerator helps you identify and design the biggest AI opportunities to create value for your business and develop a plan to build the required capabilities.

How advanced is your company in AI solutions and platform capabilities?
Key ingredients of our AI Accelerator
Use cases

Develop a perspective on biggest opportunities of AI for your organization, translated into high level solution design.

Technology scan

Assess your technology starting point and translate use case requirements to priorities for AI platform capabilities.

People Scan

Create a perspective on reskilling priorities and develop a plan for building competencies on data and AI.

AI quick scan

Test how your company is performing on developing and implementing AI solutions and platform capabilities:


1.My organisation communicates a clear end-state vision on how AI can transform our business
2.AI initiatives are aligned with the strategic goals of my organisation
3.My organisation has a roadmap that aligns AI efforts with large scale business opportunities
4.My organisation succesfully builds high potential proof of concepts with AI
5.AI use cases are developed end-to-end from data to algorithm to an application, such that users can interact with the algorithm
6.AI use cases are automated and operationalized in a production environment
7.AI use cases are fully adopted by the business, without unintended retreat to own judgement
8.Business processes are fundamentally changed by implementing AI use cases
9.AI use case MVP's are continuously being improved, leaving MVP state
10.My organisation succesfully scales the impact of AI use cases
11.There is a clear infrastructure in place for users to approach (a selection of) data
12.There is a possibility to develop models in the same environment and language, for example in sandbox like environments
13.Algorithms are operationalized and automatically predict on new data
14.Your organisation has deployed one or more experiments with modern, for example cloud-based, infrastructure
15.AI platform standards are set and managed
16.Vision exists on target infrastructure for new solutions
17.Existence of legacy is accepted as a given, but migrations and dependencies are managed with an impact driven mindset
18.AI use cases are in production on scalable modern infrastructure
19.There are standardized processes to develop, deploy and maintain models (model management) in modern infrastructure.
20.There is a flexibility to access any data and use it to build solutions with diverse requirements, while still maintaining certain standards managed centrally