Q849

As AI systems take on parts of analysis, pattern recognition, drafting, and even recommendation-making, the nature of human contribution changes.

We may no longer hire primarily for raw processing speed or memorized knowledge — but for judgment, framing, and oversight.

Think of a specific role in your domain where AI is now part of the workflow.

How should hiring criteria evolve for that role?

What qualities become more important — and which traditional requirements may become less critical?

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

💡 Participants are free to use AI tools while preparing their response — clarity of thinking and relevance will determine the best answer.

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

• Relevance of the chosen role

• Depth of insight into capability shifts

• Practicality of revised hiring criteria

Standard 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 Tuesday or Friday at 9:00 AM Indian Standard Time, depending on the launch day.

• Responses will not be visible until they are reviewed.

• 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 verify your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. 

• Participants are welcome to use AI tools while preparing their answers. However, selection of the winning response will depend on the quality of thinking, contextual relevance, clarity of reasoning, and practical insight demonstrated.

• 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.

Traditionally, in payment processing or alert adjudication, the role is built around high-volume alert handling, recall of regulatory knowledge, and manual investigation across multiple systems. This is typically a layered decision-making process, meaning the adjudication decision goes through a four- to six-eye check before the payment can be released. Today, AI penetration in this space is still primarily at Layer 1, where the complexity is relatively low, while Layer 2 and Layer 3 exist to validate the decision. Since AI is now being embedded in screening utilities to provide alert adjudication and risk probability scoring, the value proposition of the Layer 2 or Senior Investigator (L3) becomes even more significant.

In fact, their role becomes more critical in interpreting AI outputs, challenging model assumptions, and making defensible risk decisions. With this new operating model, hiring criteria should evolve to prioritize analytical judgment, risk contextualization, and the ability to collaborate effectively with AI systems, rather than focusing only on operational throughput or years of manual investigation experience.

New hires will increasingly function as risk analysts and AI validators, rather than just transaction processors. Therefore, recruitment should assess how candidates think through ambiguous alerts, identify potential blind spots in AI models, and explain decisions that may override AI recommendations.

 

In summary, the hiring model should shift from asking, “Can this person process alerts at different levels?” to “Can this person detect when the AI is wrong and make a defensible decision?” This shift ensures that organizations gain the full benefits of AI augmentation while maintaining strong compliance, integrity, and operational resilience.

Example: Supply Chain Demand Planner in AC Manufacturing Company

In AC manufacturing, demand is:

• Highly seasonal (summer spikes)

• Weather-sensitive

• Promotion-driven

• Capacity-constrained (compressor imports, copper price volatility, Free Trade Agreements etc)

 

Today, AI models already:

• Generate baseline forecasts (SKU × region × week)

• Detect Anomalies

• Suggest safety stock levels

 

How Hiring Criteria Should Evolve

1. Forecast Framing Ability (Problem Structuring > Calculation)

Can the candidate:

• Define forecast hierarchy correctly? (SKU vs model vs tonnage level)

• Identify structural breaks (new product launch, channel shift)?

2. Causal Thinking Over Correlation Acceptance

AI detects patterns. Humans must ask:

• Is this demand spike weather-led or distributor stocking?

• Is this repeatable or one-time?

3. Scenario & Risk Simulation Mindset

Instead of “What is next month’s forecast?”

The question becomes:

• What if IMD predicts a delayed summer?

• What if copper prices rise 12%?

• What if compressor imports delay 3 weeks?

4. Ai Literacy & Governance

Some Skills become important

• Understanding model bias

• Knowing when to override

• Recognizing overfitting patterns

• Monitoring forecast accuracy drift

Person should be supervisor, not just user.

 

As a part of LLM/NLP models, our businesses have deployed few AI capabilities in terms of level 1 data processing and organising.

The ops team used to manually enrich data extracted from the source systems which has now been taken over by AI assistance in terms of organising and enrichment. To build this model, few operation analysts were actively observed, and logics were built accordingly for organising data. For example, the debit entries are summated with a negative sign and in red font whereas the credit entries are summated in green colour.

Further the data entries are enriched from golden source viz. Master Data Maintenance/operational data store for items like address, date of birth, real time balances (ledger vs clear/statement).

Since the Level 1 is now done by AI in terms of organising and enrichment, the continuous focus has shifted from hiring starters/analysts to hiring techno functional resources, model owners and second line oversight.

With testing automation in place using frameworks like cucumber, the role of business analysts have shifted towards that of data analysts/leads who are continuously involved in breaking down data, building controls for key data elements, applying dimensional data controls viz. accuracy, consistency, comprehensiveness, conformity. The data engineers are continuously engaged with quality testers and product owners play more of a business analyst role and requirement gathering.

The hiring criteria is including aspects of not only analysis, but also higher numeracy. The personas for hiring mandatorily include technical skills like Java, Selenium, Python along with statistical analysis.

 

The following skills have become necessary

1.     Numeracy and Statistical Analysis    

2.     Capability to breakdown work/functional decomposition

3.     Writing short requirements using INVEST criteria

4.     Understanding Machines

5.     Data Mastery

6.     Legal and Regulatory Compliance (to protect from machine doing wrong things)

 

The roles that are becoming redundant include

1.     Manual testers

2.     Business Analysts (not Data Analysts)

3.     Level 1 Ops/analysts

4.     Project managers/scrum masters

 

In the given example where AI does data organisation and enrichment, the hiring focus is more on data architects. Data team are continuously involved in two way consistency checks to protect organisation from regulatory penalties, thresholding data dimensions b/w 95%-100%. Reliance is more on technical knowledge such as credit cards follow luhn algorithm so that analysts know where machine can go wrong. Similarly focus is on framing error free algorithms like IBAN.

The hiring also focuses on the judgement and oversight meaning a stronger defence system which continuously monitors the models and work as model owners.

 

 

 

Assuming the role of a Data Analyst, the hiring conditions will now be:-

• Critical Analysis and Judgment: More focus is on pattern recognition.

• Problem Framing: Doing right first time, well it is believed that intelligence is required even if one must cheat. Therefore, human intelligence should be the core.

• Explainability: One of the critical criteria is model explainability like shaps. Understanding models in a more mature way will be required.

• Statistical Analysis: Data analysts are required to do result oriented statistical and mathematical analysis to study models.

 

 

Old Criteria

• Raw Data analytical speed: Models can handle this effectively without human intervention

• Manual Reporting: The Data Analysts do not have to do any manual reporting anymore as models are good enough to handle those.

• Data Organisation and Modelling – Analysts are no longer required to do data cleaning, organising and modelling which AI can handle well.

I am considering my own service as an example here,

Role: Senior Policy Review Analyst – Surplus Lines Tax (Insurance Operations)

In surplus lines insurance operations, a Senior Policy Review Analyst traditionally reviewed 70–100 page policy documents, verified insured details, interpreted endorsements, determined taxable states, calculated surplus lines tax, and prepared regulatory filings. The role rewarded reading speed, memorization of state rules, and manual accuracy.

Today, AI tools extract insured data, identify endorsements, flag multi-state exposure, calculate preliminary taxes, and even draft filing summaries. AI handles pattern recognition and first-level analysis. The human role shifts from processor to decision owner.

These changes hiring criteria fundamentally.

 

1. Judgment Over Processing Speed

Earlier, speed and accuracy in reading dense policies were critical. Now AI extracts key clauses instantly. What matters more is:

• Ability to validate AI output

• Recognizing when AI is technically correct but regulatorily risky

• Making override decisions in ambiguous scenarios

Revised Hiring Test: Provide an AI-generated tax computation with subtle regulatory errors. Assess whether the candidate questions it and explains the risk impact.

 

2. Framing and Problem Structuring

AI responds to how a problem is framed. Poor framing produces incomplete analysis.

The modern analyst must:

• Break down exposure logically

• Provide precise instructions to AI systems

• Define assumptions clearly

Revised Hiring Focus: Evaluate structured thinking through scenario breakdown exercises rather than testing memory of tax tables.

 

3. Exception Handling Capability

AI performs well in standard cases. Edge cases create compliance risk.

Hiring should prioritize:

• Comfort with ambiguity

• Ability to investigate anomalies

• Escalation judgment

Instead of asking, “How many years have you processed filings?” ask,
“What would you do if AI flags Texas exposure but stamping fee logic conflicts with policy wording?”

 

4. Risk Ownership Mindset

In regulated environments, mistakes have financial and legal consequences. When AI contributes to analysis, accountability becomes shared.

The new hire must:

• Think like a compliance custodian

• Understand downstream financial impact

• Take ownership rather than rely blindly on automation

Behavioural interviews should assess ethical judgment and responsibility under uncertainty.

 

5. AI Collaboration Literacy (Not Coding)

The role does not require programming skills. However, it requires:

• Understanding AI limitations

• Awareness of hallucination risks

• Ability to critically review automated drafts

AI fluency becomes more important than manual endurance.

 

What Becomes Less Critical

• Memorization of state tax percentages

• High manual processing volume

• Mechanical reconciliation repetition

• Pure tenure-based seniority

Experience still matters, but depth of reasoning outweighs years logged.

 

Capability Shift Summary

Before AI	After AI
Data Processing	Oversight & Validation
Memorization	Interpretation
Speed	Judgment
Task Execution	Risk Governance

 

Conclusion

When AI handles part of the thinking, we no longer hire for who can process the fastest. We hire for who can challenge the machine intelligently, frame problems clearly, and own the risk of final decisions.

In surplus lines tax operations, the future-ready analyst is not the best data reader — but the best decision reviewer.

 

Hiring of medical professional

AI used as a decision support tool

AI systems like IBM Watson Health are increasingly used in modern hospital system to assist doctors in diagnosing patients. Within seconds, these systems can analyse the X rays, MRI’s CT Scan etc ..It can analyse any abnormalities, suggest ,diagnoses and some time highlight risk based on the data available in historical records.

Here AI is not replacing the doctor. Though AI is providing support, the final medical decision is taken by the Doctor as the ethical and legal responsibility still with human professionals. AI can contribute to the analytical process but human is in charge of accountability. This has significantly changed for an organization what is the expectation from a medical professional.

Hiring criteria before AI integration

Earlier the hiring decision was taken based on the deep medical knowledge, memory recall and the skill to do independent diagnostic ability. Doctors had to check and interpret the scans manually. Connect the symptoms shown in the report to the disease and treatment completely based on the experience gained.

A strong candidate was someone who could(Before AI)

Detect any abnormalities in reports accurately.

Quick recall of the medical condition.

Confident decision making even under tough times.

Performance of the professional was based on the accuracy ,speed and technical competence. Ie, the reward based on individuals’ expertise and independent execution.

Performance was measured by diagnostic accuracy, speed, and technical competence. In simple terms, the system rewarded individual expertise and independent execution.

Doctor’s role after AI

After involving AI into daily work, the doctors role changes. Instead doing diagnosis from a blank slate, doctors review the AI generated suggestion.AI already show warnings in reports and scan and propose possible conditions and treatments. Here the role of doctor shift from a sole analyser to a reviewer and a decision maker.

Due to this, hiring criteria also changes. Instead of checking if the candidate can manually identify and interpret everything, the checking should be on whether the candidate can critically assess AI recommendation and take ownership of the outcome.

 

Importance of critical judgment and analysis

Eg:-Lets consider a case where a lung scan is flagged as potential cancer by AI due to a detected shadow. If the doctor reviewing this report is a skilled professional, they won’t believe it immediately. They do further check the medical history and identify the shadow matches scar tissue from a past infection. Here the doctors value lies in the reasoning, experience and sound judgement.

This example shows an important hiring shift. Organizations must assess if the candidate can detect AI errors, understand the limitation of system, confidence in override the AI recommendation if needed. The ability to think and take decision even when there is technology support becomes the main competency.

Ethical and practical decision making in an AI environment

AI give recommendation based on the statistics and clinical outcome. But the medical decisions are not purely mathematical eg: - An AI tool might recommend an expensive treatment because of the data showing high chance of survival. However, doctor should consider the patience financial capacity, physical strength to do the treatment, mental health and the personal value.

Since the doctors should balance data insight with empathy and ethical responsibility, hiring must be evaluated on emotional intelligence, communication skills etc. The human professional must ensure that decisions are not only medically good but practical as well.

Summary – A shift in hiring process

When AI is part of the thinking process, the hiring should not only be based on technical expertise to take independent decision, but someone who can collaborate with intelligent system, question and take a decision with full accountability.

Thus, the hiring process shift from criteria of testing memory, speed ,technical execution, judgment ,accountability ,reasoning and ethical maturing to thinking better, supervising technology wisely, owning the consequence of decisions.

Traditional written tests are no longer enough. They should be given scenarios and request to detect and decide during practical test. Basic understanding of AI limitations and comfort in working with intelligent systems becomes essential.

 

 

The integration of AI into professional workflows shifts the human role from "producer" to "editor-in-chief." This transition demands a fundamental re-evaluation of how we identify talent. As org chart evolves, we shift from a hierarchy of execution to a hierarchy of oversight and judgment.

In the domain of Payments Fraud Analysis, AI has moved from a back-end tool to a collaborative partner that identifies anomalies, drafts initial investigation reports, and suggests real-time block/allow actions.

Here is how hiring criteria should evolve for this role:

1. Qualities

As the "raw processing" is automated, the value of the human at the center shifts toward higher-order cognitive tasks.

• Contextual Framing and Problem Definition: AI is excellent at solving the problem you give it, but it cannot always identify if it's solving the right problem. Hiring should prioritize candidates who can frame a business challenge—such as distinguishing between a coordinated bot attack and a viral marketing surge—before the AI begins its analysis.

• Adversarial Thinking and Strategic Oversight: In fraud, the "enemy" is a human actor who adapts. We need hires who can anticipate how a fraudster might exploit the AI's logic itself. This requires a "human-in-the-loop" mindset that provides the oversight necessary to prevent model drift or systemic bias.

• Stakeholder Communication: As AI models become more complex (e.g., neural networks), the ability to translate an AI’s output into a narrative that executives or regulators can understand is vital. The hire must be a bridge between high-dimensional data and human-centric decision-making.

 

2. Requirements

Traditional benchmarks that once indicated high potential are now being mitigated by AI capabilities.

• Raw Quantitative Processing Speed: The ability to manually calculate risk scores or sift through thousands of rows in a spreadsheet is no longer a competitive advantage. AI does this instantly; hiring for manual "grind" is now inefficient.

• Memorization of Technical Syntax: Proficiency in the "mechanics" of coding (e.g., memorizing every SQL command or Python library) is less critical when AI can assist with drafting and debugging code. The focus shifts from syntax to logic.

• Standardized Drafting Skills: The ability to write a basic, factual investigative summary is now a "commodity" skill because AI can generate these drafts. The human value is in the nuance added to that draft, not the initial construction.

 

3. The New Hiring Profile

The ideal candidate for an AI-integrated environment looks less like a "Data Processor" and more like an "Analytical Orchestrator.

The candidate, instead of having mastery of software, should have fluency in prompt engineering – including auditing and refining outputs. They should be able to spot logic gaps and hallucinations. Education should now be more around Data Sciences, ethics and business.

Interview Approach

Instead of a standard technical test, consider a "Co-Pilot Case Study." Give the candidate a flawed AI-generated report and ask them to:

1. Identify the logical errors.

2. Adjust the "framing" to align with a specific business goal.

3. Propose a long-term strategy that the AI missed.

The traditional roles Analysts, Team Lead, Manager would also evolve and recruitment should also take that into account.

·        Analyst – Orchestration and Verification

No longer a Data gatherer, but an AI user with ability to drive AI tools to the correct problems and verification and integrity of outputs received. Iterative Prompting, Fact Checking, Audit. Critical thinking would be the most sought-after skill here

·        Team Lead - Workflow Optimization and Quality Control.

The Team Lead ensures the "human-AI swarm" is operating efficiently. They bridge the gap between individual analysis and departmental strategy. AI Workflow Design, Bias and Error monitoring, skill mentorship. Adaptive Leadership being the key here

·        Manager/Architect – Strategy, Ethics & Governance

With focus on the Why and the Should, they would manage the Dept/Org goals and the tools/tech used. Resource Allocation, Ethical alignment with requirements and standards, Translating the AI driven wins into Business value for Senior Leadership

Role Chosen: AI-Augmented Quality Analyst in a Customer Support BPO

Relevance of the Role

In a modern customer support BPO, Quality Analysts (QAs) historically reviewed 1–3% of total interactions due to capacity limits. With AI systems now transcribing 100% of calls, auto-scoring against compliance scripts, detecting sentiment shifts, and flagging potential regulatory breaches, the QA function is no longer sample-based policing - it is system-level oversight.

AI tools perform:

• Full-call transcription and keyword detection

• Automated compliance scoring

• Sentiment and escalation risk prediction

• Pattern recognition across thousands of interactions

However, AI cannot reliably interpret contextual nuance, cultural tone, ethical edge cases, or emerging failure patterns that fall outside training data. The QA role therefore shifts from “checking agent performance” to “validating system outputs, diagnosing systemic risk, and guiding performance strategy.”

This makes it an ideal BPO role to examine how hiring criteria should evolve when AI handles part of the thinking.

Capability Shifts in the Role

1. From Manual Review to System Oversight

Before AI:

• Listen to calls

• Fill scorecards

• Check script adherence

• Flag errors

With AI:

• Audit AI scoring accuracy

• Investigate false positives/negatives

• Identify drift in AI models

• Diagnose patterns across thousands of interactions

The QA now acts as a calibration authority between human agents and AI outputs.

2. From Error Detection to Risk Interpretation

AI can detect that a phrase was not used.
It cannot reliably assess:

• Whether omission was contextually appropriate

• Whether a compliant phrase was delivered in a misleading tone

• Whether customer vulnerability requires deviation from script

Judgment becomes more critical than detection.

3. From Individual Feedback to Systemic Insight

Instead of reviewing 20 calls per week per agent, AI enables full-population analysis. The QA must now:

• Identify training gaps affecting cohorts

• Recognize emerging customer friction themes

• Correlate sentiment trends with process changes

The skill shifts from micro-evaluation to macro-pattern framing.

Revised Hiring Criteria

Qualities That Become More Important

1. Systems Thinking Over Procedural Checking

New requirement: Ability to interpret interaction data trends and connect them to operational causes.

Example:
If AI flags a spike in “negative sentiment at billing explanation stage,” the QA must:

• Validate sentiment accuracy

• Review conversation context

• Determine whether the issue is agent behavior, unclear policy, or flawed scripting

Hiring implication:

• Assess analytical reasoning through scenario-based case exercises.

• Prioritize candidates who can explain cause–effect relationships across workflows.

2. Judgment Under Ambiguity

AI scoring often lacks nuance. For instance:

• An agent may skip identity verification for a repeat caller known to the system.

• AI flags it as non-compliant.

A strong QA must evaluate whether:

• It is a legitimate risk

• The policy needs updating

• The AI rule requires retraining

Hiring implication:

• Replace pure compliance knowledge tests with structured judgment simulations.

• Evaluate how candidates reason through gray-area scenarios rather than recall policy verbatim.

3. Data Literacy

The QA must interpret dashboards, confidence scores, and anomaly flags.

Required competencies now include:

• Understanding model confidence thresholds

• Recognizing statistical anomalies

• Differentiating signal from noise

Hiring implication:

• Include basic data interpretation exercises.

• Favor candidates comfortable with dashboards, trend charts, and metric-based storytelling.

Not full data scientists - but analytically fluent operators.

4. AI Calibration Mindset

AI models drift over time due to:

• Language evolution

• New product launches

• Process updates

The QA must:

• Periodically audit AI scoring reliability

• Identify bias patterns (e.g., accent misclassification)

• Escalate retraining needs

Hiring implication:

• Seek candidates who demonstrate curiosity about “why the system thinks this way.”

• Prior experience with automation tools or workflow systems becomes valuable.

5. Coaching in an AI Environment

When agents see AI scores daily, they may:

• Over-optimize for keywords

• Game sentiment triggers

• Follow scripts mechanically

The QA must coach agents on:

• Authentic communication

• Balancing compliance with empathy

• Understanding the intent behind metrics

Hiring implication:

• Behavioral interviews should test coaching capability, not just evaluation ability.

• Emotional intelligence increases in importance.

Traditional Requirements That Become Less Critical

1. Raw Listening Stamina

Previously:
Ability to listen to 6–8 hours of calls daily was a core requirement.

Now:
AI handles bulk review; human sampling is strategic.

Reduced importance:

• Endurance-based manual review

• Speed of scorecard completion

2. Memorization of Scripts

AI flags script deviations instantly.

Reduced emphasis:

• Perfect recall of wording

• Rote compliance knowledge

Instead:
Understanding policy intent matters more than memorized phrasing.

3. Tenure as a Senior Agent (as Primary Filter)

Previously:
High-performing agents were promoted to QA roles based on operational experience.

Now:
Top agent performance does not automatically translate into:

• Analytical capability

• Systems interpretation

• AI oversight ability

Operational experience remains useful - but should not be the primary qualification.

Practical Hiring Redesign

Instead of traditional hiring filters:

Old Model

• Years of call center experience

• Script knowledge test

• Listening accuracy assessment

• Basic communication evaluation

Revised Model

1. Scenario-based case: AI flags 20% non-compliance spike - diagnose root cause.

2. Dashboard interpretation test: Identify trend anomalies.

3. Ambiguity judgment interview: Evaluate gray-zone compliance cases.

4. Coaching role-play: Provide feedback to an agent gaming AI scoring.

5. Technology adaptability assessment: Prior exposure to automation tools.

This moves hiring from “can you check?” to “can you govern?”

Broader Implication for BPO

As AI absorbs mechanical cognition (pattern recognition, transcription, surface scoring), the human layer becomes:

• Ethical arbitrator

• Contextual interpreter

• System corrector

• Behavioral coach

• Process re-framer

The competitive advantage of a BPO will no longer be how many interactions it reviews - but how intelligently it governs AI-driven workflows.

Therefore, hiring criteria must evolve from operational compliance expertise to judgment-centric oversight capability.

In AI-augmented BPO environments, the most valuable hires are not the fastest processors of information - but the most reliable custodians of judgment.

 

Domain: Solar Cell & Module Manufacturing Sector

( Capacity: 1.3 GW Solar Cell & 1.6 GW Solar Module Production and Supply per annum)

As this has been observed regularly in Solar PV Module Manufacturing, AI is involving increasingly into: In-process Inspection systems (EL imaging, IV curve Analysis, AOI defect detection etc.), SPC monitoring & anomaly alerts, Yield loss Pattern recognition, Predictive maintenance Models, Reliability test analysis the human role shifts significantly.

In Solar PV Module Manufacturing as the above mentioned domains are getting AI enabled, hiring should be focusing on following competencies:

1. Root Cause reasoning using AI outputs.

2. Decision Making under uncertain signals.

3. AI Model interpretation and validation.

4. Risk Based thinking instead of following rules.

As an example:

Interpretation of AI-driven Inspection Result:

AI detects the increasing micro cracks in Post Lamination EL inspection with some patterns.A lean practitioner must determine whether the root cause is:

1. Handling Issue?

2. Lamination Pressure Profile imbalance?

3. Cell Supplier change?

4. Hidden stress or vibration from automation?

Hence, the hiring process should test the candidate for competency of failure mode reasoning, hypothesis testing. AI detects pattern, Human detects correcting pathway.

BORM rejection pattern collaborated with ERP Data:

AI is now capable of:

1. Detecting rejection clusters across shifts, machines, operators.

2. Linking rejection trends to specific BORM codes (EVA lot, glass batch, cell supplier, ribbon type)

3. Identifying correlations between material lots and downstream failures

4. Generating automated Pareto and trend dashboards from ERP + MES data

But correlation doesn't mean causation. So the executives responsible for improvement must have Data-to-Process translation capability, Multi-layer pattern interpretation (Material, Machine, Operator, Shift) ability, Risk-weighted decision making knowledge, ERP-MES data integrity validation capability, Statistical causality assessment capability.

Hence the Hiring must asses:

1. Structured root cause tree building capability.

2. Ability to challenge data before acting

3. Understanding of process-material interaction

4. DOE experience

Since this part of ERP System is governed by PPC team, the hiring of PPC team should be evolved also. To govern an AI driven PPC process, a candidate should have competencies of:

1. validating model assumption periodically for optimization of noises.

2. Recalibrating after process changes.

3. Cost-of-poor-quality modeling

4. Supplier negotiation data framing

5. Risk-based prioritization

As the Solar PV Module Process and quality part is becoming AI enabled rapidly, the hiring process must be evolved around that accordingly. Without a proper governing knowledge there will be wastage of AI tool resources and chances of catastrophic affects in module processes and unwanted breakdowns in critical machines.

AI is no longer a novelty in Software Engineering field. Tools like GitHub Copilot, ChatGPT, and Amazon CodeWhisperer now write boilerplate code, suggest refactors, generate tests, and even explain legacy systems. If AI handles part of the thinking, then hiring software engineers based on pre-AI assumptions is a mistake. The job hasn’t disappeared. It has shifted. The question is not “Can you code?”, The question is now “Can you think clearly in a system where AI also codes?”

Let’s analyse what that means.

The Traditional Software Engineer Profile

Historically, hiring focused on:

• Syntax mastery in one or more languages

• Data structures and algorithms recall

• Speed of coding in whiteboard interviews

• Framework familiarity

• Debugging through manual inspection

• Years of experience as a proxy for competence

Engineers were valued for their ability to translate requirements into working code line by line.

Precision and individual output were the differentiators.

What AI Now Does in the Workflow

AI tools can:

• Generate CRUD APIs in seconds

• Scaffold full-stack templates

• Write unit tests

• Suggest performance improvements

• Identify common security vulnerabilities

• Refactor repetitive logic

• Convert code between languages

What Becomes More Important

1. Problem Framing Ability

AI generates solutions. Humans must define the right problems.

Hiring should assess:

• Can the candidate clarify ambiguous requirements?

• Can they break down complex business problems into logical components?

• Do they ask better questions before jumping to implementation?

Garbage problem definition leads to garbage AI output.

2. System Design & Architectural Thinking

AI can generate functions. It struggles with long-term system evolution.

Engineers must:

• Design scalable architectures

• Anticipate failure modes

• Understand trade-offs (performance vs cost vs security)

• Make technology choices aligned with business strategy

Architecture thinking becomes more critical than raw coding speed.

3. AI Collaboration Skills

Not everyone who uses AI uses it effectively.

Strong candidates:

• Craft precise prompts

• Validate generated output

• Detect hallucinations or logical flaws

• Iterate efficiently with AI tools

AI is an amplifier. If the engineer lacks discernment, it amplifies mistakes.

4. Code Review & Quality Judgment

When AI writes large portions of code, quality control becomes essential.

Hiring must evaluate:

• Can the engineer review AI-generated code critically?

• Do they understand security implications?

• Can they maintain readability and maintainability?

The skill shifts from “Can you write it?” to “Can you judge it?”

5. Accountability & Ownership

AI suggestions are easy to blame.

But production incidents don’t care who wrote the code.

Engineers must:

• Take ownership of AI-assisted output

• Understand the full system impact

• Think beyond “it works” to “it works safely at scale”

Responsibility becomes a defining trait.

6. Continuous Learning Mindset

AI tools evolve rapidly.

Hiring criteria should favour:

• Adaptability

• Curiosity

• Willingness to unlearn outdated practices

• Comfort experimenting with emerging workflows

Static skillsets will age fast.

What Becomes Less Critical

Let’s be direct.

1. Memorizing Syntax

Documentation and AI fill gaps instantly.

2. Writing Boilerplate from Scratch

That’s automation territory now.

3. Speed of Typing Code

Velocity is less about keystrokes, more about clarity of intent.

4. Pure Algorithm Trivia Interviews

Unless the role is research-heavy, excessive puzzle-solving may not reflect real-world performance anymore.

5. Years of Experience as a Primary Filter

An engineer who knows how to leverage AI well may outperform someone with more years but rigid habits.

How Interviews Should Evolve

·       Instead of asking: “Reverse a binary tree on the whiteboard.”, Ask: “Use an AI coding tool to scaffold a service. Now explain what it got wrong.”

·       Instead of testing memory, test judgment.

·       Instead of testing isolation coding, test human-AI collaboration.

The Bigger Shift

When AI handles part of the thinking, software engineering becomes less about production of code and more about:

• Systems thinking

• Critical evaluation

• Risk management

• Ethical implementation

• Business alignment

AI reduces friction in writing code. It raises the bar for thinking clearly.

Conclusion

Hiring criteria must move from: “Can you code without assistance?” to: “Can you design, evaluate, and own solutions in an AI-augmented environment?”. The engineers who thrive will not compete with AI. They will orchestrate it. And organizations that hire for that mindset will move faster with fewer expensive mistakes.

As AI systems increasingly handle data-heavy, repetitive, and analytical tasks in reverse logistics — such as identifying return patterns, forecasting refurbishment demand, or automating disposition recommendations — the role of humans shifts from performers to judges, framers, and orchestrators.
Traditional requirement:
• Strong Excel/ERP skills
• Ability to manually process and clean data
• Knowledge of return codes, material movement, SLAs
• Ability to create static reports and forecasts
• Basic problem-solving around exceptions

These reflect the rising need for human judgment, oversight, and business framing.
1. Systems Thinking & Process Understanding
2. Critical Judgment & Exception Handling
3. Data Interpretation & Storytelling
4. Collaboration & Change Management

Qualities That Become Less Critical
1. Raw Data Processing Skills e.g. manual Excel crunching, pivot-heavy reporting etc
2. Memorizing Return Codes or Policies
AI systems can: auto-suggest disposition, surface relevant warranty rules, detect mismatches in claim reasons, Knowledge still matters — but recall speed becomes less critical.
3. Repetitive Operational Execution
Roles that used to rely on: high-volume RMA processing, manual case triage

In Reverse Supply Chain Operations:
As AI takes over analysis, pattern recognition, and rule-based decision-making, recruitment must prioritize:
• Judgment
• Systems thinking
• Interpretation & business framing
• Ethical oversight
• Human-AI collaboration
And rely less on:
• manual data work
• memorization-heavy tasks
• repetitive operational processing

In the world of scientific publishing, especially when you’re handling a massive volume like 500,000 articles per year, the transition from manual labor to AI-augmented workflows isn't just a convenience; it’s a total transformation of our DNA. This is a fascinating shift that we are living through right now.

Here is how we have evolved our hiring criteria and internal operations to meet this new reality.

From "Typesetters Factory" to "Quality Guardians"

In the past, our typesetting department was a numbers game. We managed thousands of typesetters whose primary value was speed and high-volume manual entry. We hired for "raw processing power"—the ability to turn a manuscript into a PDF as quickly as possible.

Today, everything has changed. We have integrated AI that handles more than half of the standard typesetting tasks. We no longer need a "factory" of thousands; instead, we have pivoted to a leaner, elite team of specialists. Here is how we've adjusted our hiring:

1. Shift from Data Entry to Domain Expertise

We used to hire for technical proficiency in layout software. Now, we hire for domain knowledge. Since AI handles the "easy" layouts, our humans only touch the complex cases—articles filled with intricate chemical structures, multi-line LaTeX equations, and high-resolution spatial data.

What we did: We stopped testing for typing speed. Now, we test for the ability to spot a misplaced radical in a complex equation or a misaligned axis in a scientific chart.

2. Prioritizing "Human-in-the-Loop" Oversight

We realized we cannot risk our brand value on fully autonomous AI. A single error in a medical journal or a physics paper can derail research and damage our reputation.

What we did: We redefined the typesetter’s role as an Auditor. We hire people who have a "skeptical eye"—those who can verify AI output against the original source material with 100% accuracy. We look for high-level judgment rather than just execution.

3. Hiring for AI Orchestration

Our modern typesetters need to be "AI-fluent." They aren't just using tools; they are guiding them.

What we did: We started looking for candidates who understand how to "frame" a task for the AI and identify exactly where the automation is likely to fail (like in non-standard symbol rendering). We need people who can troubleshoot the technology, not just work alongside it.

Why Quality Over Quantity is Our New Mantra,

Factor	Old Hiring Criteria (Pre-AI)	New Hiring Criteria (Post-AI)
Primary Skill	Manual Speed & Software Proficiency	Scientific Literacy & Technical Auditing
Focus	Throughput (Pages per hour)	Accuracy (Zero-error tolerance)
Education	General Technical Background	Domain-specific (STEM) backgrounds
Role	Executioner of tasks	Overseer of AI-generated workflows

The "Risk Mitigation" Filter

We are extremely candid with our new hires: We cannot risk the business with AI alone. Because we are responsible for the integrity of global research, we’ve made "Ethical Responsibility" a core hiring pillar. We hire people who understand that their role is the final firewall between a machine-generated error and the scientific community.

1. ✅ WINNER - Taby Sheikh — Very strong: specific QA role in AI-enabled customer support BPO, clear shift to system oversight, and practical hiring tests (scenario diagnosis, dashboard interpretation, coaching role-play).

2. ✅ Himanshu Lohani — Excellent regulated role (Senior Policy Review Analyst – surplus lines tax), strong “judgment over speed,” and very practical revised hiring tests using AI-generated errors.

3. ✅ Jinad Padiyath — Strong software engineer hiring shift with clear “more/less critical” criteria and modern interview redesign (AI-assisted build + critique).

4. ✅ Anil Kumar (CAISA) — Strong fraud analysis role framing (“editor-in-chief”), good new criteria (framing, adversarial thinking, explainability) and solid interview approach with flawed AI output.

5. ✅ Abhishek Chaudhary — Strong demand planner example; good emphasis on framing, causal thinking, and scenario/risk simulation with AI in place.

6. ✅ Kush Singh — Clear L2/L3 screening/investigator role; good judgment-centric shift; could be stronger with explicit revised hiring tests/criteria list.

7. ✅ Aditya Bhavsar — Strong publishing/typesetting context; good shift to complex exception handling and auditing; role title could be sharper.

8. ✅ Suman Acharjee — Strong solar manufacturing grounding with concrete inspection/ERP examples; slightly scattered across multiple roles but still relevant and practical.

9. ✅ Preethi Bijesh — Relevant doctor + AI decision support; good focus on judgment, ethics, empathy; slightly more generic than top entries.

10. 🟡 Dhruva Kapur — Has a process, but becomes broad and role-sprawl; needs one specific role focus and clearer revised hiring criteria/tests.

11. 🟡 Manish Gupta — Right direction, but reads template-like; needs a specific role title and a concrete hiring scenario/test to avoid “generic.”