A.Kumar

Voice of employee (VoE) refers to process of collecting, analyzing and acting on employee feedback to improve employee engagement, productivity and work place culture. It ensures employees feels valued, empowered leading to higher retention and organization success. Few component of VoE: Feedback collection: Through survey, suggestion box, open house and one to one discussion Data analysis: Identification of trends, concerns, opportunities for improvement, identification of pain areas and bottle necks. Implementation and follow up: Implementation of changes, communication with employee Example to understand the challenges and how it can be resolved: Low participation of employee for feedback, disengagement due to time constraints (to overcome we need to keep survey short and concise, clear communication about the purpose, offering incentives, survey through multiple channels) Lack of honest feedback: Employee withhold concerns due to fear of backlash (can be overcome by using third party to ensure anonymity) Vague feedback/ no follow up on feedback: Setting clear time lines, publicize progress Feedback/survey submission should be easy, safe and rewarding. Questions need to be clear and concise. By prioritizing VoE, organization foster a culture of continuous improvement and employee friendly workspace.

Key risk indicators helps in identification of potential risk, helps to take corrective action on time. Risk indicators signal/alarms potential risk which can become critical prevents damage. Risk can be evaluated based on experience or industry standards. Indicators helps to influence decision making. Industry tend to aim for growth, Key performance indicators are the suitable indicators which guides about performance whereas key risk indicators talks about the risk associated with it. Taking an example for manufacturing sector: production output talks about efficiency, is a key performance indicators however equipment failure rate, increase production load is a key risk indicators warns about potential breakdown which would lead to halt in production.

In today's changing world both the approaches are necessary and can complement each other. Let me share similarity and disimilarity between the two approaches: Disimilarity: BPR (Business Process Reengineering) LSS (Lean Six Sigma) Dramatic (sudden) improvement Combines the approach as lean (controlling waste) and variation reduction (Sigma) Complete change Incremental improvement Broad transformation Specific problems High risk, disruptive approach Low risk, slow improvement Implemented at once Implemented gradually Break through improvements Eliminates waste and reduces defect Similarity: Process improvement, use of data and it's analysis, involvement of different crossfunctional teams, enhances efficiency and reduces cost. Consider a situation where the organization first reduces man power, reduction in SKU for marketing team (BPR approach is used), then through gradual lean process (reduce reamianing inefficiencies) used of DMAIC approach (LSS method) to mesaure, analyze, improve and control the process. This is common approach observed in industries across the different segments.

What’s One Practice in Your Organization That Looks Efficient — But Isn’t? Being in manufacturing/ distribution industry, we have needs different codes for raw materials, finished product material, packaging material, engineering part, consumable etc. As a general practice we generate codes for each material for specific vendors for procurement, identification, same code to be aligned for creating internal specifications for testing, transfer of stock, taxation, stock evaluation, costing etc. In our organization, we have multiple site where they are implemented to have smooth execution (procurement, testing, stock transfer) and have traceability of material. In order to generate codes, we had standardized guideline to achieve harmonize, lean process (avoid multiple code generation) and automated process but with closer inspection over the time, we have observed multiple codes in the system. With closer look we have found that there are times were codes are generated only for procurement which is not linked to section/testing, codes generated with same name with slight modification (which goes unnoticed) which is not linked to all sites, individual sites create individual codes. Thus there are numerous code for same material. The actual practice for creating unique code applicable to all sites which would cater procurement, testing, valuation, stock transfer is not met and has created lot of confusion. To overcome this issue, we need to restrict new codes and trigger if same material code is required by any user. We need to check for completeness fro teh genrated code or else disable the code. To achive thhis integration with AI enabled system ican be planned which would restrict creating another duplicate code, message would be pop up to the user sharing the details for the already existing material code and if still required permission would be required as check box (notice to be shared to management). Individual ingredient item code would be similar across the sites following single specification for testing. This would limit the creation of duplicate codes and existing code to be filled all the required details.

With AI, indulging in creativity activities like creating paintings, poems, and literature will be joyful activity. AI can help to create the activities with personal touch as part of interaction (eg in conversational agent), we could personalize the interaction including emoji, animation, meme, humours, dialogue from movies or personality. Reminder can be more personal and interactive (like birthday wishes with animation) Greeting and interaction can be with additional features with AI generated tools, example giving compliments during interactions, use in social activities. AI can be more interactive in education tools as it can be fun for learning, such interaction makes learning and then user joyful.

Here can be one of incidence where AI may sounds confident but can be totally wrong. Taking example in research domain. One discovery leads to another discovery. Researcher referred research article for the available knowledge. Traditionally research article were in paper format (thus mostly as research papers) but in current scenario all the articles are available digitally. There are numerous research article for a given problem statement which are in public domain, which can both genuine (Proved) and non-genuine (Ambiguous). Researched with help of AI agent, generally would refer both the internal knowledge base and external knowledge base to understand and predict their research work outcome. Technically based on the trained data sets and acquired knowledge, AI would be confident about the outcome based on its input data but it may happen that inclusion of unidentified/ misunderstood/ambiguous data would give another outcome far from the reality. Similar case study can be at diagnostic purpose at healthcare domain. Like all the symptoms may predicts some disease however it may happen that the symptoms are adverse reaction of a particular medicine which the patient is on.

Can AI Make the “Right” Call in an Ethical Dilemma ? Considering a situtation in BPO industry, where AI is trained on data sets (based on internal KB) to tackle situation or prompt the user to suggestion. In a scenario, where AI agent always suggests a particular brand. This could be due to biasness in the trained dataset as it inclined towards the brand or highlight the brand adbantages causing dilema in mind or fine tuned algorithim, where the agint drives to choose the particular brand. Here the AI agent may be in ethical dilema, but may not not take right descision due to the inbuilt restricted alogorithim steps. In a different scenario, where AI agent is exposed to unbiasness datasets and withno adjusted algorithim. AI agent would explore the different possibilities and also would suggest the best in business. This unbias approach will suggest the user with all the alternative possibilities , though AI would be in dilema but will not be biased on any particular items. In AI agent programming, boundaries need to be set to provide suggestion rather than prompting. Clean boundaries help in AI agent to set limits and be ethical correct. Here the decision would not be biased.

For any successful task, Mutiple units need to be performing as a one to achieve the goal. Taking scenario of Hospital setup: Different AI agent would be required to perform individual task To make repository for the patients: OPD/IPD/ ICU etc. Supply management of medicines/surgical items/ manpower attendance/ Doctor availability. payment record Insurance record inward/ outward record Medical record These are few areas where automation can help to bring efficient and smooth routine operations to run the hospital. Collaborations of these individual unit activities will bring the best of out come. Any missed activity can halt the routine operations. To achieve completeness of the activity, feed back mechanism, real time solution activity, other plan (plan B: which may not be as active) should be available.

How Can AI Earn Trust in Your Team? Goal of the any team is to complete task with strategic planning and execution, completeness, with ethics and within stipulated time. With proper utilization of differentiated AI in the working scenario, it can help in execution of activities in numerous ways. It would be fast and accurate. Activity based on AI would be non biased (considering the data is unbiased). Helping in undestanding multiple data irrespective of time zone and geopgraphy. It can be utilized with AI tool to provide suggestion and this would save time, gain extra time to recheck and finish the task. Final approval will be through human inferences. By this big load of data can be handled through AI tools with high speed, accuracy, completeness and which would directly help the team to grow and improve further. Example: If the team objective is in BPO bussiness which aims to provides help/assistance. Chat Bot/Vitual Assistance Referee are active 24/7 and can be utilized any part of world not confined to any geography. It is helpful as it covers numerous mutilple language. Developer/ assistance team will gain time to improve further. Routine task can be managed through AI agents.

In artificial intelligence domain, outcome of any question (process) depends on the input variables (existing references available datasets) and the algorithms (program) which is developed on available data sets. Incase of any goals clashes, firstly we need to retrospect the input data set for its accuracy, authenticity and purpose. We need to search within the developed algorithm (program) for any errors (e.g. syntax, semantics etc.), or any undesirable clause which can modify or influence the outcome. It may also happen that based on the data, AI agent is exploring new possibilities which has not been heard off and is completely new. Based on thorough human assessment, we can decide the actual outcome. If it still clashes, we can modify the algorithm (through advance discriminator) to achieve the target goal.