Q 756. In your work environment, what’s one task or decision that feels “too human” to hand over to AI — and how might you reimagine it to make AI a valuable contributor?

 

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The one thing i would like to focus upon is "Tacit knowledge". Currently, IMHO, feel that AI lacks that ability to possess that "Tacit knowledge", in several areas (in specific situations or in several industries). It is too human to hand over to AI 

 

Example:

Lets take an example in substantiating the claim that i made above.  As a workplace foundation coach and also an enterprise agile coach, i deal with many individuals and teams. The knowledge and experience that a coach have accrued over a period of time is difficult to be conveyed and replicated in AI in my purview. Especially when dealing with people on coaching, the emotional intelligence is a key aspect. IMHO, therefore it is not easy to train AI models on these aspects however sophisticated they be. 

 

How you might reimagine it to make AI a valuable contributor?

W.r.t examples related to the activities of say coaching/consulting (say Psychologist)/healing following can be useful steps

1. Build Capability/Train on emotional intelligence for the AI systems 

2. After capability is built, feed dummy problem statement (similar to the original problem where human beings were used for the activities) for coaching/consulting and check how results are coming out.

3. Compare the human based outcome vs AI generated results/outcome

4. If not satisfactory, then inspect where the gap is and adapt accordingly

 

In General, wherever 'tacit knowledge' is needed, you may need all these 4 steps. There are many areas in which AI is difficult to be leveraged. But nevertheless, i feel these steps could be by and large common - Build the corresponding capability, test the capability built with a sample data, compare that with existing ecosystem (human driven) and if satisfactory you move to AI based; else then inspect where the gap is and adapt accordingly. 

 

Also please do take a look at Polanyi paradox that gives some additional info on 'tacit knowledge' and 'emotional intelligence' aspect

 

"https://www.benchmarksixsigma.com/forum/topic/39795-polanyi%E2%80%99s-paradox/"

 

Conclusion:

IMHO, there are quite a few areas like 'Tacit knowledge' which are difficult to be achieved through AI. In general, any task or decision for which you are (made) accountable, you would want to manually do it and would not rely on AI. This may be due to a psychology fear (That there could be a backlash if things go awry and it stems from the fact that most of the people, do not trust technology especially when they do not have too much grip or understanding on that).  Therefore, IMHO, such tasks or decisions belong to this category of too human to handover to AI. So that kind of tasks/decisions are difficult to be reimagined in AI way. Better understanding of AI can increase people's confidence (for instance, where its power lies and where can be pitfalls in its usage - if that can be understand by people). This can help in transforming such manual works to AI based

 

 

 

 

 

 

As per my experience and knowledge, one task that i feel is too human is the 1-2-1 connects between the team members or the agents being done where there is a personal touch is i feel that is too human as of now and will be a difficult task to handle it through AI.

 

Having said that, AI can be implemented in this area with the specific rules and guidelines however we will still miss the personal touch.

Employee assistance programs, Mental health, etc. a task that continues to seem “too human” to delegate to AI is supporting employees during emotional or mental health difficulties, especially when they find it hard to express their emotions. Sometimes, people don't express even when it is clear, or they exhibit frustration that truly hides burnout, anxiety, or even early indicators of depression.

 

Although AI may not currently be able to kick off these conversations, I can imagine it as a discreet supporter operating in the background. For instance, AI could identify behavioral patterns such as a decrease attendance, or meeting attendance, alterations in communication styles, or numerous emails sent late at night and subtly notify HR or supervisors regarding these concerns. It might also suggest wellness resources or conversation starters tailored to the context, facilitating opportunities for meaningful human engagement. 

 

In this way, AI serves as a support system, not a substitute making certain that no one slips through the cracks, particularly when they are unable to express their emotions.

In your work environment, what’s one task or decision that feels “too human” to hand over to AI?

Answer: Preparing a consolidated report that meets leadership reporting expectations, alongside delivering proper presentations, can be challenging when they constantly evolve with each role and needs to be fetched from different systems. As individuals attempt to align numbers and insights with their specific targets, the data often needs to be broken down to its lowest level. This allows each person to see their individual performance data. However, it’s equally important that the overall data remains useful for leadership at the top level, enabling them to make informed decisions.

 

How might you reimagine it to make AI a valuable contributor?

Answer: AI tool may integrate multiple systems to enable user level access and can summarize the data across all levels to provide leadership with actionable, high-level insights that are essential for informed decision-making. By using AI-driven analytics, we can continuously adjust and optimize reports based on real-time data, ensuring both granular details for individual contributors and strategic insights for executives.

On 3/27/2025 at 7:24 AM, Vishwadeep Khatri said:

Q 756. In your work environment, what’s one task or decision that feels “too human” to hand over to AI — and how might you reimagine it to make AI a valuable contributor?

 

Entire PDCA(Plan Do Check Act) cycle is too human activity. Where AI can do the plan and Do like automating. But It will struggle with Check and Act with respect to real world context implementation.

However AI can partially support the PLAN and DO phase, Humans are needed for the Check phase for interpretation of results and making judgments. Based on this human input, AI can then Act by reworking/reprogramming automation to achieve smarter and more responsible Outputs.

 

 

 

On 3/27/2025 at 7:24 AM, Vishwadeep Khatri said:

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• Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error-prone because our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/

• We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be considered for winner selection.

 

Marking a situation as Major or Minor Non-compliance (NC) as part of ISO 9001 Internal Audits is one thing I feel is too human to hand over to AI.

 

The reason for stating the same is because the auditor has to review and interpret the situation holistically after reviewing evidence, matching it against the standards, talking to the auditee, looking the intent, etc., and then gauge the impact and decide if it is to be called as Major or Minor NC.

 

I believe AI can become a valuable contributor to some extent if we develop an Expert System (Non-ML) and utilize Knowledge Base, Inference Engine, and User Interface, to validate the given evidences against the standard and basis standard definitions of Major & Minor NCs, we can classify the non-compliances.

 

Again, not 100% but to a larger extent I believe it can help auditors to complete their audits in a much faster, accurate, and precise way.

Currently for any budget approvals, we refer to the budget files manually for the availability of it & along with that few other details to ensure the expense if justified. Then we go for round of approvals on emails. What We would love to see as AI involvement here :1) Is to have the information handy on the budget availability 2) To give logical explanation on acceptance or rejection of the budget request basis pre-defined combinations 3) To beforehand share any risks/threats with that transaction  

Background:

I am part of a team that facilitates Business Process and Excellence in the organization. One major wing of our team is to conduct audits for operating and support teams across the organization. These audits are based on the expectations of CMMI, Quality Management System and COPC (Customer Operations Performance Center).

 

We have a team of auditors who conduct varied audits (Business Unit, Enabling and Top Management). Planning, scheduling and utilizing the auditors' skills, experience and availability to conduct these audits in an efficient manner is at times is a challenging task and requires lot of effort and time.

 

Suggestion:

 

AI can definitely be a valuable contributor across all phases of the audit lifecycle:

 

• Pre-audit
  • Audit planning, scheduling, aligning the auditor). AI can study various aspects - annual target of audits to be conducted, plan and schedule the audits as per auditor skill matrix, experience and availability
  • Sensitizing/ orienting the auditee team on the expectations (Data, team involvement and commitment, timelines etc.) - The very moment the audit schedule and auditor alignment is ready, the AI can send out automated email to the Auditee team and copy interested parties with an Audio/ Video version of the Audit Orientation presentation which is currently involves the auditor's time and non-auditing effort
• During the Audit
  • Monitor audit progress and timelines cadence - AI can take up this aspect to send out auto reminders to both the Auditor, Auditor supervisor, Auditee team and interested parties
  • Support needed (to the Auditor and Auditee team) etc. If an AI agent is created it can throw responses from the repository of FAQs which can be very helpful to both the Auditor and the Auditee team
• Post Audit
  • Audit outcome discussion between Auditor and Auditee team - No AI here - this needs to be done between the two parties in-person
  • Publish report with timelines to mitigate non-conformities - AI can take up this part and relieve to pick up another audit
  • Seek commitment and send reminders as per the agreed timelines to the Auditee team on timely closures/ mitigation of gaps identified - AI can send reminders to interested parties and keep a close track until all non-conformities are mitigated
  • Support/ consultation needed to Auditee team post the - If an AI agent is created it can throw responses from the repository of FAQs, templates and formats along with some successful case-studies which can be very helpful to the Auditee team to mitigate the opportunities and gaps.

Hi,

 

In my line of work, handling highly sensitive customer complaints/escalations which involves handling complex & emotional customers is a task that feels too human to hand over to AI

 

How I can reimagine the role of AI in this:

Analyse & Categorise:

AI can analyse the initial complaint, the tone on the call or email and categories the level of emotional distress and flag the case details along with a sentiment analysis for operators to be prepared to handle the customer appropriately

 

Providing real time support & information:

AI can extract info from back end DBs on the past history of the customer, the issues reported in the past and the policies that can help the operator to handle the issue.

 

Sentiment Analysis & Emotional Cues: 

AI can perform real time sentiment analysis to detect subtle changes in tone and call out the emotional changes during calls, thereby helping operators to tailor their responses.

 

Generating summaries for documentation:

 All of the customer handlings are recorded and operators spend time to document the interaction & record + save evidences of their responses/resolutin to the highly sensitive customer complaints. AI can generate these responses in parallel to the transaction, thereby eliminating the entire manual documentation process 

 

Future knowledge & best practice sharing

Using these documentations, better Training need analysis can be done. Best practice sharing by utilising the way other operators handled a complex customer can be used for enhancing training material. Also, AI can learn and evolve better to handle future customers with better insights over time. 

 

Vidhya R

 

 

Hello Sir, 

 

As an internal lead auditor I need to discuss our audit schedule with the respective stakeholders. We have, on an average 80 audits to be performed every year in my span. I feel creation of this audit schedule is too manual or human as this involves understanding the team's availability (there are many functions like Operations, Quality, Training etc), their client requirements (client deliverables like MBR, QBRs, client visits and client escalations), the closure of the findings from the previous audit. As all these are too dynamic in nature we need to speak with the respective stakeholders which makes this a manual job.

 

As this is a repetitive task, we should find a way to seek AI's help at least to some extent. By feeding the last 3 year's worth of audit plans, previous audit finding closure status we may ask the AI tool to come up with a generic audit plan where the client involvements are minimum, accounts are following a certain pattern and those accounts that have closed a certain % of previous findings. As AI can understand patterns basis the past data, at least 20 to 30% of the manual work can be minimised. Also, AI can be asked to predict how soon some of the processes may be able to close their current open findings basis their past behavior so that we can use these as recommended timelines for the audit plan. Although this can never be 100% foolproof, I think AI can contribute to reduce at least 40% of the effort. 

One task that feels too human to hand over to AI in a work environment is giving emotional or motivational feedback to a colleague. Whether it’s recognizing their hard work, helping them through a tough situation, or offering encouragement, there’s a level of empathy, intuition, and personal connection that AI just can’t replicate.

In many work environments specific to IT sectors supporting Data Collaboration Platforms (SaaS-based applications), there are certain tasks or decisions that feel inherently "too human" to delegate to AI. These tasks are typically handled by Customer Success Managers and Implementation Specialists, such as understanding customer needs, providing the right resolution at the right time in real-time, and delivering difficult feedback. These situations often require a deep understanding of client behavior, human emotions, empathy, and the ability to navigate complex interpersonal dynamics to improve CSAT and customer retention.

Building AI Solutions

To build AI solutions for these areas requires historic data trends, customer behavior patterns, employee skills and competencies, and customer feedback to train the LLM model. This process demands significant time and resources to make the solution robust and reliable. Many companies might hesitate to adopt such AI-enabled solutions due to ROI and other commercial factors.

The reasons why it feels "Too Human" are:

• Logical Reasoning and Emotional Intelligence: Resolving conflicts or providing feedback involves reading subtle emotional cues, understanding the context of the situation, thinking out of the box, proactively communicating information to the client, and responding with empathy and sensitivity.
• Building Trust and Rapport: Agents are more likely to respond positively to feedback or conflict resolution efforts when they feel understood and valued by a human counterpart.
• Completeness and Correctness in Communication: These tasks often require a completeness and correctness in communication that AI might struggle with, such as tone, body language, and cultural context.

Reimagining AI's Role

While AI might not replace the human touch in these scenarios, it can certainly be a valuable contributor by supporting and enhancing the process. Here are some ways to reimagine AI's role:

• Data Analysis and Insights: AI can analyze patterns in employee behavior, feedback, and performance data to provide managers with insights that can inform their approach to sensitive issues.
• Training and Simulation: AI can be used to create training programs and simulations for managers to practice handling difficult conversations, improving their skills and confidence.
• Emotional Support Tools: AI-driven tools can offer employees a safe space to express their concerns anonymously, providing initial support and identifying issues before they escalate.
• Feedback Collection: AI can facilitate the collection of feedback from employees, helping to identify common concerns and areas for improvement without the pressure of face-to-face interactions.
• Resource Recommendations: AI can suggest resources, such as articles, videos, or training modules, to help managers and employees navigate difficult situations more effectively.

Giving feedbacks and one on one with the staff is "too human" to handover to AI. 

AI can be valuable contributor by providing data driven insights, identifying opportunities and can free up manager's workload to focus on more important things.

On 3/26/2025 at 11:24 PM, Vishwadeep Khatri said:

Q 756. In your work environment, what’s one task or decision that feels “too human” to hand over to AI — and how might you reimagine it to make AI a valuable contributor?

 

Note for website visitors -

• This platform hosts two weekly questions, one on Monday and the other on Thursday. 
• All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. 
• To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/.
• The question will be open until Monday or Thursday at 5 PM Indian Standard Time, depending on the launch day. 
• Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be considered for winner selection. 
• If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. 
• All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honourable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term.

• Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error-prone because our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/

• We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be considered for winner selection.

A task that comes to mind would be the process of putting together subassemblies to produce a JET engine. While this task is very repetitive and does appear to lend itself to a lot of automation at first glance, the process of putting together subassemblies involves complex engineering requirements and relies on the operator to build these parts by hand while verifying the requirements. A good way to leverage AI in this area would be using AI in post assembly inspections. By doing so, AI would be a good force of good in ensuring that current inspections that are done visually are replaced with AI, making the inspection process faster and of good quality.

In our current work environment being empathize to our customers concern is currently too human to hand over to AI. By using semantics and language patterns we can reimagine this to make AI as a valuable contributor.

Forum question 756

 

In a customer service environment when an customer service representative (Rep) is answering a query.on a voice call It is easy to determine if the Rep is using an unprofessional language or is curt or rude in a specific voice interaction. However, it is very difficult to identify if the Rep was sarcastic and fooling the customer and trying to develop a situation where he or she does not have to answer the query or help the customers. Politeness, Tone of voice is difficult to identify using AI or speech analytics. Sometimes the Speaker can sound very polite or genuine but might not necessarily be helpful in a transaction. 

E.g In a telecom customer service if the customer says he has relocated to a different address and now the internet dongle does not work in the new location. Instead of helping the customer if the Rep keeps asking unnecessary questions like: Oh have you never done this before? do you know how is device works? have you ever worked with  advanced Modems or Mesh routers before? - All under the pretext of probing questions -- some ignorant or insecure customers are ashamed to continue conversations that they are not tech savvy enough and better to get a paid network engineer on site rather than talk to tech support, just say thank you and hang up the voice calls. 

I think if we can train AI or Bots on how to recognise sarcasm patterns in a spoken language it will be an added advantage. In today's speech analytics, choice of words, tonality, intonations and voice modulation is used to draw heat maps and sentiment mapping. However, as technology, we have not yet empowered AI to understand a wide range of human emotions like sarcasm. AI takes every spoken or typed word on its face value and does not get the hidden meaning behind what was said. e.g. 1) if someone has written an unsatisfactory review of a product and mentioned. the words "Nothing-life-altering-about-it" might be taken as a positive review, or 2) If someone is love waiting in traffic and listening to horn music for hours might be misconstrued as positive when it is, infact, sarcastic and displays a negative sentiment 3) While commenting on a bad lecture or speech if someone says "oh that was an award winning performance" if we ask AI to analyse it, it might take the words on face value and denote it as positive. 

We need to train AI to understand the hidden meaning behind spoken words by feeding some books on how to understand sarcasm. Then, maybe to some level AI can start identifying negative sentiments wrapped in positive words and hidden meanings while we put things in context or perspective. 

Team connects including meetings with team members, one on one discussions, grievance discussions, Team events are 'too human' to hand over to AI. While AI will be helpful contributor to organize, provide important data inputs, manage the action items, help for ideas etc. 

Interesting to read the different perspectives on a task that is "Too Human" to be handed over to AI.

 

The best answer is given by Vikas Choudhary. Well done.

 

Answers from R Rajesh, Vidhya Rathinavelu, Swapnil Madhav Chaukar are also a must read.

The interaction, EI and dialogue needed for managing conflicts or coaching a person seems like 'too human' for AI to manage. 

However elements of fact gathering could be AI enabled for any of the above. 

Eg - in a murder investigation, we can depend on AI to visualize, gather data, use many models for analysis. But the questioning of the suspect would be better managed by humans

Interviewing auditees to uncover hidden issues seems too human to hand over to AI. A seasoned auditor possesses the intuition to know when to probe deeper, the skill to rephrase questions effectively, and the ability to detect when someone is withholding information. While AI can offer valuable data points, it is the human qualities of intuition, skepticism, and interpersonal skills that truly reveal risks that aren't evident in digital records.

In our work as assessors being able to probe deeper and investigate through interviews is an aspect of our work environment which feels too human to handover to AI. This technology might have progressed in leaps and bounds, experience and intuition of a seasoned auditor will still prevail. An auditor can also navigate through grey areas and come up to a conclusive decision. 

I want to reimagine the payroll variance check process. 

Gen-AI should able to analyse the payroll variance, investigate it and publish the variance report. 

 

In my work environment, the task or activities that related to emotional and related to personnel issue will be too hard to handle it and convert it to AI 

AI is based on algorithm and didn't understand the emotional and critical case

AI follow the instruction and KB that build it by developer.

we can use AI to enhancement the routine process and job like dealing with customer, supplier, internal process and operation activities

AI is powerful tool to increase the productivity and accuracy in a lots and different process

 

making employees understand the goal of AI it will help a lots to think creativity and improve the quality 

Currently we are always seeking opportunities to optimize the operations and leveraging support functions to ensure the end client receives an enhanced or elevated customer experience.

The way we do this is by deploying process improvement projects and initiatives based on Customer Experience Survey Scores, survey responses and subsequent Root cause Analysis (all completed by humans).

 

So the “as is” steps are:

1.     Trigger CX surveys to customers post a closure of a query on the CRM.

2.     Receive survey responses and have analysts do RCA based on a predetermined set of rules or criteria.

3.     CX Insights manager then reviews the outputs of the RCA and ranks it highest to lowest in terms of impact to customer.

4.     Once this is refined and documented, the Operations leads will connect the client for alignment and approval on agreed projects and initiatives that will potentially improve CX.

 

My “To be – AI integrated approach” would be as follows:

1.     (Automation): Trigger CX surveys to customers post a closure of a query on the CRM.

2.     (AI): Employ an AI bot to handle received surveys and generate Dash views based on customer sentiment that are curated by means of logic and criteria which informs management of the CX impact and articulate issues in a way that it is ranked by priority highest to lowest.

3.     (AI): Also rank dash views by order of positive impact if resolved, so show forecasted scores if top 5 issues are fixed.

4.     (AI): the AI agent to share suggestions of approach to be applied and share to operations and client.

5.     (AI): Use AI agent to track progress of progress and initiatives activities.

6.     (AI): Use AI to track progress/trend of scores based on impact of activities.

7.     (AI): Use AI to track progress/trend of scores based on impact of activities vs failure points where no projects and initiatives were actioned.