rashmi gavas

Strategic choosing of Business Process Reengineering (BPR) and Lean Six Sigma (LSS) must be done by organizations. They can also consider integrating both if required based on requirement. While LSS uses a DMAIC approach which is data driven, incremental approach and focused on eliminating waste and reducing defects, BPR us used when a complete transformation of the process, or organization restructuring is required.

Yes -AI can spot hidden patterns across processes, by way of checking the data volumes, patterns across processes, and analyzing the data. One of the techniques which can be used is unsupervised learning which is great for discovering unknown or emergent patterns without labeled data.

AI depends on data which is fed into the system. It is not human to understand emotions. Hence the everything is decided by human whether it is ethical or unethical. AI only gives the output as data rules AI.

If AI agents worked as one team it would lead to smarter planning, data analysis and reporting. Multiple AI agents handling different departments like Operations, Finance, Quality, Risk leading to lesser human intervention. However, there could be some negatives outcomes like coordination issues between AI agents if not programmed correctly ,conflicting goals.

Accountability if an AI going wrong is a joint responsibility right from the designers, reviewers to the leadership and heads. There coukd be a mistake in the design, no proper line of sight and weak controls and flaw in approval mechanism, no governance /review from the leadership, leading to the AI system going wrong. Some of the ways this could be backtracked is audit logs, document control, RACI matrix t check where and what went wrong

When goals clash AI should be able to understand which goal gives maximum benefit along with minimizing any issues or disrupt the overall project. AI should be able to follow some logics like, what is the core priority, expected benefits, what are the constraints, check regulatory compliance requirements, inputs from stakeholders

Our team performs organization wide internal audits, the scoping of accounts, processes etc for the various business units (BU's) happens in the beginning of the financial year. At times there are exceptions requested by BU's to postpone the audits, or an account sunset's and no longer active, In such scenarios the new planned audit numbers are required to be changed manually. AI that operates in changing environments should learn from exceptions as part of its ability to adapt to new patterns over time. The AI model should be designed to differentiate between genuinely valuable exceptions and noise

One rule where i would fully trust AI to make the call — i would trust AI to check compliance checks, simply because AI can efficiently monitor systems and validate if compliance is met or not met. AI can be used where rules are defined clearly to get an apt response. One rule where i would never trust AI - AI may not be a suitable option to use where human behavior is involved example disciplinary issues by employee, appraisal discussions etc. This is best judged by relevant teams like HR who are trained to judge and understand situational behavior, emotions of employees, facial expressions etc.