Enterprise Architectures for Artificial Intelligence (II)

A generic model for primary processes

Every organisation is unique but most organisations share some basic principles in the way they operate. Business processes have some form (between 5 and 100%) of support by online transaction systems (OLTP). Business drivers like consumer demand, government regulations, special interest groups, technological evolutions, availability of raw materials and labour and many others influence the business processes intended to deliver a product or service that meets market demand within a set of constraints. These constraints can range from enforcing regulatory bodies to voluntary self-regulation and measures inspired by public relations objectives.

This is a high level approach of how AI can support business processes

High level generic architecture

Business drivers are at the basis of business processes to realise certain business goals and delivering products for an internal or external customer.  These processes are supported by applications, the so-called online transaction processing (OLTP) systems.

Business process owners formulate an a priori scoring model that is constantly adapted by both microscopic transaction data as well as historic trend data from the data warehouse (DWH). Both data sources can blend into decision support data, suited for sharply defined data requirements as well as vague assumptions about their value for decision making.  The decisions at hand can be either microscopic or macroscopic. 

Introducing AI in the business processes

As an architect one of the first decisions to make is whether and when AI becomes relevant enough to become part of routine business processes. There are many AI initiatives in organisations but the majority is still in R & D mode or –at best- in project mode.  It takes special skills to determine when the transition to routine process management can provide some form of sustainable added value.

I am not sure if these skills are all determined and present in the body of knowledge of architects but here are some proposals for the ideal set of competences.

• A special form of requirements management which you can only master if the added value as well as the pitfalls of AI in business processes are thoroughly understood,
• As a consequence, the ability to produce use cases for the technology,
• Master the various taxonomies to position AI in a correct way to make sure you obtain maximum value from the technology (more on this in a next post),
• Have clear insights in the lifecycle management of the various analytical solutions in terms of data persistency, tuning of the algorithm and translation into appropriate action(s).

In the next post, I will elaborate a bit more on the various taxonomies to position AI in the organisation. 

Enterprise Architectures for Artificial Intelligence (I)

In the past three decades, I have seen artificial intelligence (AI) coming and going a couple of times. From studying MYCIN via speech technology in Flanders Language Valley to today’s machine learning and heuristics as used by Textgain from Antwerp University, the technology is here to stay this time.

Why? Because the cost of using AI has fallen dramatically not just in terms of hard and software but also in terms of acquiring the necessary knowledge to master the discipline.

Yet, most of the AI initiatives are still very much in the R&D phase or are used in limited scope. But here and there, e.g. in big (online) retail and telecommunications, AI is gaining traction on enterprise level.  And through APIs, open data and other initiatives, AI will become available for smaller organisations in the near future.

To make sure this effort has a maximum chance of success, CIOs need to embed this technology in an enterprise architecture covering all aspects: motivations, objectives, requirements and constraints, business processes, applications and data.

Being fully aware that I am trodding on uncharted territory, this article is –for now- my state of the art.

Introducing AI in the capability map

AI will enhance our capabilities in all areas of Treacy & Wiersema’s model, probably in a certain order. First comes operational excellence as processes and procedures are easier to describe, measure and monitor. Customer intimacy is the next frontier as the existing discipline of customer analytics lays the foundation for smarter interactions with customers and prospects.

The toughest challenge is in the realm of product leadership. This is an area where creativity is key to success. There is an approximation of creativity using what I call “property exploration” where a dimensional model of all possible properties of a product, a service, a marketing or production plan are mapped and an automatic cartesian product of all levels or degrees of each property with all the other properties is evaluated for cost and effectiveness. Sales pitch: if you want more information about this approach, contact us.

Capabilities where state of the art AI can play a significant role

Examples of capabilities where AI can play a defining role. Some of these capabilities are already well supported, to name a few: inventory management (automatic replenishment and dynamic storage), cycle time management (optimising man-machine interactions), quality management (visual inspection systems), churn management (churn prediction and avoidance in CRM systems), yield management (price, customer loyalty, revenue and capacity optimisation) and talent management (mining competences from CVs).

Areas where AI is coming of age: loyalty management and competitive intelligence, R & D management and product development.

In the next post I will discuss a generic architecture for AI in support of primary processes; Stay tuned and… share your insights on this topic!