Episode 1: The Good the Bad and the Ugly of AI

Episode 1 starts the discussion of issues facing management today with respect to technology specifically AI, decision making, analytics for insight, and topics that may come up during the conversation. Some key points to watch for in the video are:

• Management traps- these are situations that develop as a result of technology change. Management uses their background, training and experience to address the issue and the results are not achieved. Why does this happen?
• Key point 1 to watch for – There are two concepts covered that deal with management capability in dealing with technology issues, one relating to awareness of the context and the other related to technology you are applying to that context. Some examples are discussed of what happens in this case.
• Key point 2 to watch for: Why are we not equipped to deal with some of these issues? The rate of change requires better more rapid capability to respond when organization survival is at stake.

Episode 2: Challenges of Applying Business Analysis for AI

Episode 2 picks up the discussion of issues facing management today with respect to AI technology and analysis efforts in general. In this case business analysis and AI. Business analysis is a broad discipline. At one time as part of a seminar in business analysis we listed over 30 types of business analysis including disciplines like financial , market, AI, decision, process, and operational analysis, plus automation, application, and other forms of requirements. Related topics came up during the conversation. Some key points to watch for in the video are:

• General versus Specific analysis - Analysis methodology steps contain analysis disciplines. They are part of many of the common methodologies such as Business Architecture, Structured Techniques, Enterprise Architecture, Requirements Development and so on. Within that you have analysis disciplines that make up the methodology, a type of orchestration of analysis techniques into a methodology.
• Issues with Using AI in Analysis – The idea of AI is to look for patterns in the data and use them for classification and prediction. There can be both data and algorithm bias. Also approaches like Agile techniques may bias the result by constraining the analysis.
• Business Analysis and Context Insight - Understanding the external environment as organization context is a key step in identifying change that impacts the organization.
• Analysis Integration Issues - There are large amounts of documentation including models, sets of data and text with few analytics and tools to integrate analytic results and ferret out the value of a major analysis effort. It is expertise dependent.
• Tools for Analysis – Answering the question of how all the analysis material is related together is difficult and there are few tools to help.
• Business Alignment and Decision Making - How to get from analysis material intake to making decisions and recommendation is not easy today. There are many approaches like strategy maps, business alignment, and other business models that can be used but they are not interrelated at all.