Embedding Intelligence into the Business Process 2

#orgintelligence #entarch In my previous post, I talked about two aspects of Embedding Intelligence into the Business Process.

• Embedding business intelligence (BI) into the business process.
• Embedding Enterprise 2.0 into the business process. 

In this post, I'm going to talk about two further aspects of this.

• Embedding knowledge (content) into the business process.
• Embedding learning into the business process.

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Embedded Knowledge Content

There are various levels at which knowledge can be embedded in a business process. Some forms of procedural knowledge can be encapsulated as static rules, which can be either written into the process (as software or bureaucratic procedure) or stored in a form that can be easily and automatically referenced by software components or knowledge workers. There is a considerable software literature on so-called business rules - see for example my review of Business Rule Concepts.

More complex forms of knowledge can be represented as models. For example, the business processes associated with operating a complex industrial process or communications network require some representation of the physical structure and processes involved, while business processes in the finance world may use economic models that help to predict market trends and risks. These models may be buried within complicated algorithms, or represented visually in dashboards and control room displays. See my post on OrgIntelligence in the Control Room.

Thirdly there is contextual knowledge - an appreciation of the specific circumstances and general trends relevant to a business decision or action. This kind of knowledge is dynamic and typically requires human mediation and interpretation, although it may be possible to codify and even automate some limited kinds of contextual knowledge. When discussing the contribution of Enterprise 2.0 to the American security services, Dennis Howlett comments that "content without context in process is meaningless". (See my post on Connecting the Dots).

In her post on The Future of HRM Software, Naomi Bloom talks about embedded intelligence that integrates the rule-based and the contextual knowledge into a software agent she calls "Naomi". She claims that embedded intelligence can achieve several things.

• It "replaces what we lost" when we reduced or eliminated the interaction between experts and the rest of the organization. (In her piece, the experts are HR professionals.)
• It improves upon human embedded intelligence by removing human error. 
• Automated embedded intelligence improves compliance to rules/policies/regulations and reduces the organization’s exposure to risk.
• Commercial Web sites (Landsend.com, Amazon.com) and social Web sites (Wikipedia) set expectations of the embedded intelligence to be found in any self service environment. 

In my post Intelligent Knowledge Management, I pointed out the important step from collaborating-in-the-work (for example shared responsibility for decisions) and collaborating-in-the-knowledge (for example, shared responsibility for collecting and interpreting intelligence, connecting the dots). I also advocated a shift of emphasis from knowledge sharing to knowledge embedding - grounding the work in the best available and critically evaluated knowledge, as well as actively seeking well-grounded knowledge to support organizational learning.

One of the ways that enterprise architects can think strategically about business capabilities and business processes is in terms of knowledge intensity - in other words, the quantity and quality of knowledge required in a given capability or process to differentiate the enterprise from its competitors. The "core" activities of an enterprise are those requiring high levels of knowledge intensity and specificity, other activities can be regarded as "peripheral" and may be commoditized or outsourced.  See my post on Ecosystem SOA, which draws on the work of Amin and Cohendet.

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Embedded Learning

In my post on Learning by Doing, I pointed out that such characteristics as adaptability, agility, flexibility, responsiveness (supposed to be the benefits of various technologies including SOA) imply processes of adaptation and learning. So we need to ask: How do business systems (both organizational and technical) improve? Where is the learning located? What is the nature of the feedback loop?

1. The learning loop goes through the software developers. (The software development acts as a gate/brake on the learning process.)
2. A learning process is contained in the software or service. (Learning can take place in real-time, but only for things that have been explicitly anticipated in software development.)
3. The learning process is distributed across the community usage of the software or service.

In planning for organizational intelligence, we need to think about these learning processes, and how they may be accommodated in any sociotechnical system architecture. Advanced software (from SOA to Enterprise 2.0) gives us new and more flexible ways of implementing such learning processes, but only if we identify the learning requirements properly. We are going to need a business model that includes the learning capabilities as well as the operational capabilities, and an architecture that mobilizes these capabilities in a loosely coupled manner.

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Places are still available for my Organizational Intelligence Workshop on December 8th.