Introduction: Digital Capabilities for AI at Scale

Key marker of Age of AI is the competitive landscape becoming AI-defined: Industry leaders pushing Productivity Frontier with cutting-edge AI use cases integrated seamlessly into their business operations.

The only viable response is to do AI at Scale – defined as tens/hundreds of versatile AI use cases integrated into business operations, covering processes, systems, products, services and customer solutions.

However, such integration calls for extensive Digital Capabilities from Data Management to AI Engineering and from Computing to Software Engineering. What’s more, to do all that at scale requires Operating Model and Enterprise Architecture designs that facilitate scaling up without organizational or technical bottlenecks.

This is an introduction to article series exploring digital capabilities needed to achieve AI at Scale.

Five themes of exploration

Exploration will be based on five themes:

Alignment – How business strategy would need to provide guidelines for digital capabilities build-up. Conversely, how digital capabilities’ current state and outlook would instruct strategic planning. Digital Capabilities for AI at Scale - Alignment

Identification – How to point out required digital capabilities using AI Use Cases as business need proxies, both individually and as an aggregate. How to assess constraints emerging from shortcomings in capabilities.

• Strategic Management for AI at Scale - Identification part 1 of 8
• Digitalization for AI at Scale - Identification part 2 of 8
• Computing for AI at Scale - Identification part 3 of 8
• Data Engineering for AI at Scale - Identification part 4 of 8

Acquisition – How to gain access to digital capabilities thru in-house build-up, outsourcing, or otherwise.

Configuration – How to organize and structure digital capabilities. For example, in terms of Operating Model or Enterprise Architecture.

Management – How to manage digital capabilities once in place. For example, managing and maintaining data assets and AI models.

Article series will investigate the five themes from several complementary angles:

• What is important, what needs to be taken into account, requirements, Do’s and Dont’s
• Applicable methodologies, practises and processes
• Examples of useful tools, platforms and other technical enablers

Since the topic is so vast, the prime objective is to create navigational aid for strategic capability planning and change management rather than implementation details. However, created structure and concepts should be such that adding details later becomes straightforward.

The prime objective is to create navigational aid for strategic capablity planning and change management

Examples of exploration

Exploration is about identifying the right questions and outlining answers to them. Here are some examples:

• In terms of overall company configuration, would decentralized Operating Model be de-facto requirement to enable AI at Scale? If yes, what would be the implications? On data management? On capability ownership? On organizational design?
• What would be the Enterprise Architecture methodologies and practises to optimally support AI at Scale? How EA would be used to identify and assess synergies and dependencies across digital capabilities? How to identify potential bottlenecks in terms of AI at Scale?
• How to determine what to build in-house versus what can and should be outsourced instead? What would be useful strategy practise to identify outsourcing potential.

AI at Scale Workshop:

AI at Scale workshop is a compact one-day event for business and technology executives and managers. Workshop seeks answers to the question: What should we as a business and as an organization do to secure our success in the Age of AI?

Next article in the series:

Digital Capabilities for AI at Scale - Alignment