Digital Capabilities – Constraints Assessment as a Service

AI-defined competitive landscape

AI has emerged as the dominant leverage for productivity and competitiveness. Industry leaders around the world are pushing Productivity Frontier with cutting edge AI use cases.

Falling behind creates strategic risks. Falling far behind creates existential risks.

Age of AI is here. Being an industry laggard has never been more dangerous.

AI at Scale is the only viable response

The only viable response to ongoing change is to embed AI in all aspects of value creation.

To discover tens or even hundreds of AI use cases for higher customer value, better customer experience and enhanced operational efficiency.

To integrate them into products, services, customer solutions, business processes and business systems.

Digital Capabilities needed to achieve AI at Scale

Article series on Digital Capabilities for AI at Scale documents in detail the required capabilities to achieve AI at Scale.

Necessary capabilities consist of technical and non-technical capabilities alike from Strategic Management to Data Integration and from AI Engineering to Data Culture.

Capabilities are systemic in nature: The whole is only as strong as its weakest link. AI at Scale calls for high performance across all capability areas.

Constraints: shortcomings, bottlenecks and roadblocks

Constraints are about missing or lacking digital capabilities. They can be minor shortcomings, major bottlenecks or downright roadblocks. To proceed, constraints have to be eliminated.

However, in order to effectively eliminate constraints, they need to be identified and understood. Understanding is about clarifying their nature and impact.

Elimination of most severe constraints at any given time provides a way to prioritize the effort over the entire digital capability build-up. This facilitates early wins while optimizing for the long-term whole.

Constraints Assessment – Service Overview

Constraints Assessment service results in 360° view of constraints on digital capabilities. The overarching perspective is this: What would it take to achieve AI at Scale and what is currently preventing that?

The service covers all eight digital capability domains from Strategic Management to Data Culture. Depending on selected service option, the depth varies from quick scan to in-depth investigation.

Applied methodology consists of preparations, checklists, interviews, workshops, assessment report with observations and conclusions, strategic recommendations, and team alignment. Exact contents depends on the service option chosen.

Constraints Assessment comes with three alternative service options: Basic, Standard or Premium, prepared as packages of 4, 8 or 14 consulting days, respectively.

Constraints Assessment comes with three service delivery options: remote, on-site or hybrid. That is, the whole service can be done remotely, fully on-site, or any combination of the two.

Constraints Assessment – Assessment Areas

Constraints Assessment covers eight areas. Assessment results in holistic landscape view over shortcomings, bottlenecks and roadblocks that prevent achieving AI at Scale. Assessment areas are as follows:

• Strategic Management – Strategic management appears as a constraint when organization lacks direction, focus or push to stay competitive in AI-defined business environment. Assessment covers areas like business strategy, digital strategy, strategic planning and change management.
• Digitalization – Digital characteristics of products, services, customer solutions, business processes and business systems create the foundation for AI integration. With the Age of AI, digitalization related technical debt becomes payable.
• Computing – Alongside data, computing is the bedrock of AI. Computing is needed for AI training and inference alike. Computing takes place in digitalized products, services and processes. Or from another perspective: In public and private cloud, on-premises and edge.
• Data Integration – Data is another bedrock for AI. Like computing, data is needed for AI training and inference. With advances in AI technology, requirements on data and data capabilities are growing fast. While data governance and data cataloging provide the necessary foundation, at the end of the day AI models call for batch and real-time data integration.
• AI Engineering – AI Engineering handles AI models through their entire lifecycle from interpreting business needs to AI model design and training, to AI model deployment and maintenance, and to eventual decommissioning. AI Engineering emerges as constraint when high value AI use cases cannot be implemented.
• Software Engineering – To embed AI in all aspects of value creation is to integrate AI models in enterprise applications running digitalized products, services and business processes. Once AI Engineering has finished with the AI model, Software Engineering takes over to do the final integration.
• Operating Model – While AI technology evolution creates the push, it is Operating Model that creates organizational pull to match that push. The way digital capabilities are organized and managed thru operating model design and deployment will ultimately determine resulting AI utilization scale.
• Data Culture – Alongside operating model, Data Culture creates organizational pull to match AI technology push. Strong data culture leads to initiative in AI utilization: Business capability owners seeking to leverage AI to improve performance on their own. Constraints Assessment aims to determine how far from ideal we currently are.

For detailed Digital Capability descriptions, see articles series on Digital Capabilities for AI at Scale.

Constraints Assessment is the foundation to build on

Constraints Assessment is the natural no-regret first step to take. In essence, Constraints Assessment creates the necessary foundation for subsequent planning, prioritization and decision-making.

Constraints Assessment may be followed by two additional assessment services:

• Use Case Assessment – To identify high-impact AI use cases adapted to industry context. With business relevance recognized and potential understood. Creating an outline of strong AI use case portfolio with consistency and coherence.
• Integration Assessment – To assess AI use case total integration requirements with comprehensive take on all integration aspects, including digitalization status, computing and data requirements, AI model and software engineering needs. Previously identified constraints’ impact on integration.

Let’s get started

With competitive landscape becoming AI-defined, it is high time for systematic take on AI utilization and enabling digital capabilities.

Let me help to build strong foundation for your future AI endeavors with Constraints Assessment service.

Contact me either via LinkedIn messaging or email: antti.pikkusaari (at) aedon.fi