Q839

Once leaders stop old habits that limit AI’s value, a new challenge emerges: what should replace them? AI-enabled organizations require different leadership behaviors — ones that focus on asking better questions, trusting data-driven signals, and designing decision frameworks rather than controlling every outcome.

Think of a specific leadership or managerial activity in your domain (reviews, approvals, prioritization, performance discussions, etc.).
What is one new behavior or practice leaders must consciously adopt to get the full benefit of AI — and how does it change decision quality or speed?

⚠️ Any answer that is generic or does not connect with a specific leadership activity or process will not be approved.

🏆 The best answer will be selected on the basis of:

• Relevance of the leadership practice proposed

• Depth of insight into how it enables AI value

• Practicality of adopting the behavior in real organizations

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AI-enabled shop floor managers and leaders need to stop involved in the daily matters like Production outputs, Breakdown time, Manpower absenteeism, Raw material availability etc. Instead they have to focus on the matters like:

1. Which process parameters are driving cell-to-module yield loss?-Interaction & Nonlinear Effects, DOE

2. Where is predictive maintenance signaling an impending bottleneck?-Time Series trend analysis, Control charts, Regression, Capability Analysis

3. Which operator or shift pattern correlates with higher defect density?-ANOVA
   Currently the manufacturing lines are equipped with AI-enabled MES systems which can gather and provide lots of data points that can help the leaders to analyze efficiently. They just need to ask the right questions to the subordinates who are equipped with right tools.

   Decision making by Generic discussions and previous experience can only delay the process and lead to less efficient results. Leaders need to use AI to store all the good and bad effects of every steps they have taken previously and generate Mistake proofing solutions to avoid failures in the future.

   During greenbelt projects AI-enables process need to used during FMEA discussions to achieve a faster ramp up target.

   AI can make all the analysis live on the shop floor, only the mindset of the leaders to implement this on the shop floor needs to be positive and involving.

The first step is leaders should lead by example and by learning and understanding what artificial intelligence is and focus on mainly learning the prompts that needs to be provided to get a much more accurate output. It should be used in conjunction with human logic and experience. While, Using AI should enhance us with improving the productivity and reduce manual intervention, it should be used carefully by not providing confidential information which might impact the business and company and causing legal issues.

AIs should be used by leaders for analyzing date, Automating, reports and drafting emails which can save significant time and the trends can help in making data driven outcomes and solutions. The primary use of AI should be for automating repetitive and redundant tasks and help in decision making.

One important thing to be followed by the leaders is to inform their team members about data privacy and use the AI responsibly, Educate team about the impact of misusing sensitive data. All the judgements should not be easily accepted provided by AI but making a conscious and cognizant effort to validate the data before using it.

leadership practice proposed - As part of the quality team. I asked my team to do a value stream mapping activity because the company in which I work has lot of smaller sub processes doing the same type of repetitive work. While, capturing the data in the old traditional method in excel was too time consuming and was not user friendly. I used AI and fetched an automated report where in the team members just need to add the description in the excel with the steps and mention VA and NVA along with the minutes, Automatically the report will calculate the PCT/TAKT time, VA/NVA and provides a detailed summary along with the time spent in VA and NVA activities additionally providing the AS is and TO be process map. This has helped the entire department impacting 500 QAs.

Domain: Aerospace Supply Chain Management - Tier-1 supplier for airframe components

(Turnover €1.2B, management of over 1,200 suppliers in Europe/Asia, supplies Airbus/Boeing assembly line just in time approach).

Specific leadership activity: Supplier performance review meetings every week (the "war room" where we analyze on-time delivery, quality escapes and cost)

This is the 2hr Friday ritual where the supply chain director and team grill the data, debate escalations, and determine interventions such as expedites, audits, and supplier changes. This was all done as gut-feel overrides in spreadsheets before the roll out of AI.

The single new behavior that leaders must adopt, Start designing and enforcing "AI-first" decision rules instead of leading every debate by themselves.

IMPACT ON DECRISION SPEED AND QUALITY

Rather than the director controlling with statements like "I think the supplier is bluffing regarding lead time," they would communicate the following rules:

·       If the AI indicates that OTD < 92% + Quality Trend Down > 3%, auto-trigger a 48-hour audit to SQE.

·       If the predictive cost model indicates >5% material cost drift, then auto-open RFQ to alternate suppliers.

·       If RiskScore > 45 (Geopolitics + finance data), cap new volume to 20% of current.

Why it Enables the Value of AI:

·       Quality increases because the rules are consistent and data-driven. No more forgetting a trend or bias for the "favorite" suppliers. We’ve seen false escalations decrease 40% in our pilot.

·       Speed doubled: Meetings reduce to 45 minutes when exceptions are discussed, and 80% of regular calls are addressed by AI. Decisions communicated through alerts mid week and not wait till Fridays.

·       AI learns faster because each override of the rules turns into feedback for learning, refining the model without any of the leader ego in the way.

Practical adoptation in a real-life organization (how we did it)

1.      Keep it small – pilot on one commodity category (example: fasteners), for 3 months, and establish 5-7 rules collaborative with the team.

2.      Train the shift: Director models the role of “facilitator” in a meeting, using “what does the rule say?” rather than “what do I think?”.

3.      Measure Behavior: Measure percentage of decisions made by rules versus manual decisions (target 70%+ Assign to Director’s OKR).

4.      Build guardrails: The rules will be reviewed on a quarterly basis by a cross-functional team to prevent “stupid AI” moments.

5.      Celebrate Early: When the first auto-escalation saves a line from stoppage, shout it out from the rooftops this will builds buy-in from all departments.

The bottom line from the supply office

Leaders who self proclaim themselfs as heroes in every review will turn artificial intelligence into a sophisticated charting tool.

By accepting rule design as their new jam, they allow AI to handle the heavy lifting for consistency (data backed) and speed, freeing themselves work on strategic wins like new supplier ecosystems.

“We are 8 months in, now the meetings are more concise, the decision making is more crisp and the director actually has some spare time for the strategic long term thinking.”

It is not about less control, it all about smarter control.

My team is responsible for preparing 5882 filings annually for 913 companies\nonresident entities registered in 64 different countries for ESS (non-resident) filings manually. Ideally 490 filings will be prepared manually by a team of 10 i.e. each of them has to prepare 49 filings. These 490 filings will be reviewed and approved by myself.

Briefing about preparing the ESS (non-resident) filings steps manually at high-level, the first step is: Analysts (preparers) make a copy of prior filing spreadsheet to current period folder and rename the file to current month name. Analysts will delete all prior period data from current obligation spreadsheets and do necessary edits in all six tabs. Overriding errors identified during this phase which impacted the quality of the filings during Output (incorrect calculations) being ended in re-work.

Then each of my team member downloads both summary and transaction detail reports for their respective entities based on country\entity\period\registration\ using specified parameters.

Post downloading, the raw reports were saved in respective obligation folders.

Considering huge volumes of data my team faced a lot of waiting time (min – 20 minutes to max 90 minutes) to download summary and transaction detail report. Moreover, time-out error is also a bigger challenge while downloading the reports. The second challenge is the file size (the transaction detail – minimum file size is 30 MB and maximum as 2.5 GB) not excel compatible and was crashing the spreadsheets.

The transaction details reports are needed for General Ledger Account Reconciliation and for Audit purposes to ensure that the invoices are recorded in the respective General Ledger accounts as per chart of accounts. Preparers must provide explanation why few of the Sales invoices were excluded (based on country laws exceptions). Preparers has to investigate the reasons for invoices that were also missed to record in the General Ledger (most cases incorrect GL booked due to mapping of incorrect GL or missed out posting or cut off time in the systems). Preparers must provide information to accounting team to post re-class journals or correction journals accordingly.

Once the reports are ready and available, the preparer copy and paste the transaction details, followed by ‘refresh’ pivots in ‘analysis’ tab. During this phase identified multiple human\manual errors while copying paste or while refreshing the pivots. (Usually pivot data is not fully captured based on incorrect selection in the raw data).

Tax analysts must validate (check) the taxable products and tax codes (PTC’s), the tax rates, service indicators (B2C or B2B), destination, currency codes, net amount, net VAT amount and any other required columns in transaction detail reports.

Tax analysts also ensure to validate the data refreshed in ‘pivots’ with the source data. Tax analyst also considers the revenues from external sources (apart from regular reports) and will add to the main revenues.

Tax analysts will check the formulas in filings spreadsheet and update them accordingly to pick the correct values (control check on excel formula’s).  The finalized taxable revenue will be copied to ‘revenue analysis’ tab.

The revenue analysis tab has rolling twelve months’ data which helps the tax analysts and reviewers to compare the revenue trends with prior periods (during this manual preparation time there weren’t any graphical data representations).

Post applying month end FX rates (Denominational currency), the taxable revenue is updated in ‘Return submission’ tab. Tax analyst will provide a detailed analysis in ‘GST payment process’ tab explaining the payment, payment approval due dates,

Preparers need to provide the reasons for fluctuations in trends and notify the reviewer to review.

The above-mentioned steps are similar while preparing each obligation manually, apart from specific exceptions being followed based on country\Geo-based tax rules i.e.

Reports Generation,

Compilation and Consolidation

Data Validation

Reconciliation

Return Preparation

Download Invoice copies for review

Submit the final work papers for reviewer (myself) and approvals

The above was the process and pain points presented to the leadership through one year time allocation survey and quality impact at 77% i.e. based on inputs provided by each preparer and error count by each preparer during preparation (documented by reviewer).

I conducted continuous brainstorming sessions with in the team and with my first level manager. I presented a six pager documents metrics covering for a time period of one year. Initially there was a push back due to budget and other priority project constraints, later, they approved and supported this initiative.

Post leadership approvals – I engaged my team and started working with Technical team support and through multiple AI tools automated most of the process steps through such as (1). Auto downloading of reports with the parameters based on each country and entity at desired date and time. (2). Auto-roll forward of workpapers with required naming conventions and saving to respective shared drive under each country\period (Year\Month format) (3). The current period data will be auto populated under respective six tabs (4). FX rates will be auto updated in FX tab for multiple currencies along with conversion (5). Pivots will be auto refreshed (6). Trends are updated with graphical representation and highlights which products need leadership and business team attention (7). Check and validate the report parameters like tax rate, jurisdictions, taxable PTC, Net Amount and Net VAT Amount calculations, valid registration details, taxable Product code or not and as per law or not. (8). In addition, based on the report, top 25 invoices (based on amount criteria) will be auto downloaded and saved in the drive for review mechanism.

These eliminated manual intervention in areas like roll over work papers, compilation & consolidation, and data validations (formula’s, refreshing pivots, inserting or deleting rows in work papers, links). Pre-defined ESS tax logic's were established based on country specific tax laws \ entity jurisdiction rules that provided accurate taxable revenue and GST\VAT payable Easy comparison on MoM, QoQ, YoY trends and variance analysis. Easy to highlight the areas where focus is needed and make specific notes for reviewers and final approvers (this helped to understand what customer focus or approach is). Grabbed quick attention through data representation. Post analysis, it was identified that the quality of the filings improved from 77% to 98% and helped to reduce 3 of the headcount to move them to different streams based on their interest and the existing work load was able to manage by 7 headcount with stress free - maintaining work life balances.

 

At each step, myself and my leadership were engaged in continuous discussions with the preparers and tech team during testing phase, checking the desired outputs. The ownership was clearly distributed between my technical team and my team. My team and myself took full ownership while designing the inputs to the technical team while updating in AI tool. We didn’t onboard all countries and entities at one stretch, this was completed at multiple phase level understanding the errors during pre and post launches for each 15 countries. It was a difficult phase to present and share the automated work papers to tax and audit departments where they were equipped with manual work papers. Even though the budget for this entire project is estimated at $850K, it went up to $ 1100K which my leadership approved the excess based on the phase 1 and phase 2 successful impact.

The real challenge is the patience and the commitment of both Technical as well as my team to get this successful even though we slightly crossed the budget. Even now if we face any new tax law change or new rules implemented, we update the technical team to update the new laws or rules and make the changes according to which is an ad-hoc support basis and was approved by the leadership.

The above case study explains the roles of leadership while leveraging while automation

Domain: Construction Chemical Plant

To answer the question, "what new habits should AI-driven organisations and leaders should adopt?", i will state the example of Construction chemical industry.

The main challenges generally faced in construction chemical industry is Low forecast, lower margins, higher transportation costs, continuously changing plans to name a few. To give an example, during the beginning of the week, generally on a monday, Supply and Demand planning calls are held. The topic of discussion in these calls involves the below points:

• What was the previous week dispatch? did it match the plan? If not, why did it vary and what better could have been done.

• What is this week's plan? What volume has been distributed across which factory. What is the accuracy of plan.

• Do we have sufficient Raw materials and packing materials to support this?

• Are there sufficient vehicles been assigned for dispatch to customer? will the vehicles be placed by the customer or by us?

• Do we have sufficient manpower deployed? is the crew plan done according to order?

• What is the capacity vs actual order for this week? was there a carry forward from last week? if yes, how will it be handled.

• Are there any urgent orders?

• And many other queries.

In general the answers to these queries are based on personal experience rather than on Data. The approach an AI driven Organisation or Leader needs to take is the

• Establishment of an integrated real time data module which directly collects information in the field

• Asking data driven questions rather than gut feel

• Development of system which can take faster decisions

• Working on building a team with a data first mindset

• Coaching teams to utilise AI but at the same time not blindly trust the output

Going point by point:

• Establishment of an integrated real time data module which directly collects information in the field: Use of systems like IOT to connect directly to data platforms can help us to get real time data. for example, an automated packing line connected to an MES can help us to monitor the actual packing time, the tolerance of packing, if packing is slowed then at what juncture, which product etc., This would eventually allow us to map the process of packing end to end and convey clearly the capacity per batch, per material. It can also help us to project real time based on the material parameters as to how long the batch would take.

• Asking data driven questions rather than gut feel: With this data we will be able to analyse patterns. We can ask questions like if i fulfill an urgent order of "X" material, then "Y" would be my downtime, "W" would be my cleaning time, "Z" would be changeover, "V" would be my total loss. Is this loss okay for sales? this will also allow me to prioritize which batch to be taken first, set the cleaning matrix etc.,

• Development of system which can take faster decisions: with this data in hand, the system can calculate and redo the the entire production plan based on an GO or NO-GO input from the plant team. What would generally take a production manager 2 hours can be adjusted by AI in seconds. Development of a right decision making framework is needed.

• Working on building a team with a data first mindset: AI is only as good as the people who use it. It is necessary to invest in training of teams to build their capability of asking the write questions. They should understand the logic behind the output.

• Coaching teams to utilise AI but at the same time not blindly trust the output: Along with training, it is necessary for the teams to understand the limitations of AI and also understand the data accuracy. For example, The production calculations can be possible if we check what kind of data is considered. Has the manpower needed for the activity been considered? if yes, what hours have they already worked. Is it needed for them to work Overtime? Can we deploy the same manpower or a different manpower is needed? Are we complying with the Overtime Time restrictions? Basically we need to check if the right parameters are set.

 It is necessary for the leader to let go of the bias and move towards a data driven approach.

🏆 Best Answer: Adil Khan
Very specific leadership activity (weekly supplier “war room”), and a sharp new behavior: design/enforce AI-first decision rules. Clear link to faster cycles (exceptions-only meetings) and better quality (consistent triggers, fewer bias-driven escalations). Practical adoption steps included.

✅ Approved: Ankit Kulkarni
Excellent, concrete example (SAP IBP + inventory/MRP reviews). Strong shift: sign off on decision logic, not numbers, plus guardrails/thresholds. Clear impact numbers and a realistic leadership mechanism change.

✅ Approved: Vijay Yivaturi
Highly specific process (ESS filings) with a strong leadership practice: phase-wise rollout + sustained governance with tech + process ownership. Clear outcomes and demonstrates how leadership behavior enables scale.

✅ Approved: Suman Acharjee
Relevant manufacturing context and a good shift toward question-led, data-driven management. Slightly broad, but still tied to shop-floor leadership activities (yield loss, bottlenecks, FMEA/FMEA discussions).

✅ Approved: Vijay Gonsalves
Has a specific example (AI-assisted value stream mapping/report automation) and a practical leadership angle (data privacy + validation). Could be tighter on one leadership activity, but acceptable.

✅ Approved: Abhinandan Kunder
Good domain anchoring (weekly S&OP / planning calls) and clear behavior shift: data-driven questioning + decision frameworks. Relevant and practical.

⚠️ Not Approved: Domz D
Too generic; lacks a specific leadership activity/process and concrete behavior change mechanism.

⚠️ Not Approved: Taby Sheikh
Interesting narrative, but not anchored to one clear leadership practice with a crisp “new behavior” and measurable effect.

⚠️ Not Approved: Bharath CN
Framework-heavy and generic; not tied to one specific leadership activity/process where the new behavior changes speed/quality.

⚠️ Not Approved: Rabiya Bronekar
Doesn’t answer the question directly (says leaders shouldn’t stop habits). Lacks a specific leadership activity and the “one new practice” requirement.

⚠️ Not Approved: Aditya Bhavsar
100% AI-Generated Content — Excluded from Evaluation

⚠️ Not Approved: Aloke Biswas
100% AI-Generated Content — Excluded from Evaluation

Note: Responses identified as fully AI-generated are excluded from evaluation by design. Approval requires original thinking, domain/process linkage, and direct engagement with the question.