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There are certain things that AI excels at like logical reasoning, for e.g. shortlisting candidate profiles based on qualifications, invoice processing, monitoring inventory, IT helpdesk chatbots etc. On the contrary there are certain subjective questions where AI struggles and is prone to decision making mistakes just like human beings, which is also known as the “Linda problem”, for e.g. decision making on HR policies pertaining to employee's behavior, issuing warning letters to employees or even termination letters in worst case scenarios. There is a stark distinction between traditional automation tools and AI, as AI is not dependent on fixed rule-based decision making, rather it learns from previous patterns and relationships to determine the best approach to address a given situation. A detailed example of area best suited for AI deployment in corporates is pertaining to IT helpdesk. Say for e.g. as per company policy all queries should be answered/resolved within 24hrs. AI can be extremely useful to categorize the queries correctly, route it to the appropriate team and even provide resolution to tier one queries without any human intervention. As AI is dependent on previous patterns to fetch the best answer, if previous patterns include bias, that bias will be carried forward by the AI in selecting the best approach. Therefore, subjective decision making like termination or issuing warning letter to an employee based on certain kind of employee behavior cannot be completely left up to AI. For e.g. There is a corporate rule pertaining to usage of social media sites in office. If an employee is found accessing social media site, AI cannot be fully trusted to pass a judgement in this scenario as there might be a lot of other factors to be considered before a warning letter can be issued.

Puneet is the winner for this question. Well done, Puneet.

Yes! While writing the problem definition in Define phase of DMAIC. There can be different opinions of different stakeholders. Here are some practical steps which can be utilized to avoid initiating a project with misunderstood problem: Root Cause Analysis: Use Techniques such as 5 Whys or Fishbone Diagram to identify the root causes of the issues by ensuring that each stakeholders actively participate in this analysis to provide their input Data Collection: Gather the volume number and defects data as well to understand the problem with the lens of data Facilitate workshops: Encourage the people to speak their opinions on the problem and group their thoughts based upon similarity Problem statement Drafting: Draft a problem statement based on the insights gathered Ensure that problem statement is Specific , measurable achievable relevant and timebound Validation and Consent buying from stakeholders: Present the drafted problem statement to all stakeholders for validation Encourage feedback and make necessary adjustments to ensure everyone agrees on the problem definition Documentation: Document the agreed-upon problem statement and ensure it is accessible to all stakeholders Include the logic and supporting documents of problem definition To avoid misunderstood Problem: Do a transparent and open communication throughout the Define Phase Regular Check ins Be prepared to refine the problem statement iteratively based upon the new insights or feedback