nageshwaran.m_1

In software development environment AI systems are highly used in DevOps and CI/CD pipelines to deploy and delivery in less downtime. These systems continuously analyses system logs, performance metrics, error logs and deployment logs. The primary goal is to deduct the bug and respond automatically by generating code fix and do necessary changes in configuration systems and deploy it. This approach helps to enhance the operations efficiently, also it introduces risks when AI response affects core systems behavior, application code or business logics. Bug detection in production systems During checkout and transaction workflow AI system detects sudden increase in response time and failures in live production environment. We found out that shared utility function is where the repeated validation error occurs, also there is sudden spike in latency. Although AI can quickly fix the issue by isolating the area of concern, the diagnosis is done based on observed patterns rather than definitive proof of causation. Initial AI response is high speed and system availability AI proposes immediate code change to optimize the validation logic; goal is to reduce the execution time and to eliminate bottle neck. From application operation standpoint this fix reduces lower error rates, improve response time and minimize the customer impact. This fix aligns with AI goal to reduce MTTR and provides high system availability. Also, this focus on rapid resolution provides the broader implications of modifying shared code in a complex system. Complex shared code causing the slow down During evaluation process AI recognize that proposed code change is in shared utility library files that is used by multiple services. Modifying this change will affect the multiple downstream application systems simultaneously, may increase potential blast radius of failure. A single mistake will have a chain reaction to entire platform leads to application outage. By evaluating the impact AI decides not to automate this change and further no action should be taken. Unstable Root Cause Analysis Further AI analysis reveals that it’s confidence in the proposed fix is moderate rather than high. Other area it will impacts, such as database slowness, recent infrastructure changes, or network connection slowdown, could also explain the observed performance degradation. responding to incomplete and unstable analysis may introduce new defects. This is place where AI should slowdown and avoid deploying incorrect solution. Code fix that will have permanent impact on data In general, whenever we do code fix and deploy the changes, when we find out some issue we can revert the older version without any issue. Some case like code change in validation logic which will alter the data and it store the record in database permanently cannot be rollbacked. This will question data integrity and chance to lead incorrect report and have large impact to business. So, AI evaluates the changes and conclude the proposed change cannot to reversible especially in live production environments. Governance, Compliance, and Organizational Constraints The AI also evaluates organizational and governance limitations that affect services like financial transactions and customer data, which are subject to follow compliance without fail. Changes in this area often requires additional code reviews, security checks and audit trails. Deploying the fix without following proper procedures will lead to policies and external regulations violations. Keeping all these constraints, AI identifies changes in this area where speed must be balanced and to implement compliances and accountability. By keeping all these irreversible data impact, governance constraints, the AI transitions from automatic code deployment mode to deliberate review mode. Instead of pushing the code directly into production, AI generates the code changes report, list of affected services, confidence levels and risk assessment report. AI also suggests additional testing, alternative solutions etc. Human and AI collaboration in high-risk software changes Software engineers review the AI’s analyze report and conducts thorough investigations by targeting testing and inspection, in result they discovered root cause for all these problems is not in validation logic rather it is in recent database index change that increased query performance slowness. By resolving index issue team fixed the latency issue without fixing the code. In this scenario, AI’s decision to slow down enables effective collaboration rather than automated failures. This example demonstrates in software development AI must balance the pressure for continuous delivery with need for system stability and data integrity. Speed solution and implementation can be done low risk, repetitive task, but for high risk with more impact caution and human oversight. Automobile Manufacturing sector AI systems are integrated across production lines to maximize efficiency, consistency and steady output. These systems continuously process the data with the help of sensors, vibration tools and cutting-edge controls. Their primary goal is to run the production line at maximum speed and automatically detect anomalies or any deviations and fix them in live production. This approach brings lot of significant improvement in production, but it also creates risk where speed is prioritized over deeper understanding especially on safety-critical components. Detecting anomalies in engine assembling process During testing AI detects abnormal micr0-vibration pattern in engine, even though this vibration is within acceptable limits, data shows vibration level is keep on increasing compared to historical data. AI identifies a dependency between these vibrations and slight torque fluctuations in crankshaft bold fastening operations. AI has the capability to pinpoint the problem areas quickly by reading the logs. Slow-down to take care safety critical components The AI recognizes that the affected bolts secure the crankshaft, which is a critical engine component. Any compromise in torque requirements can cause potential affect in engine balance, durability and risk of failure. This evaluation makes AI to flow down, fix involving safety critical parts carry severe consequences like engine failure and safety hazards to vehicle users. Therefore, AI flags these actions as high-risk and stops implementing automatically. Manufactured physical parts are irreversible Unlike software changes, manufactured physical decisions cannot be easily reversible. Once vehicles are assembled with engine and shipped, correcting an error is not possible without recalling the vehicle, this involves complete rework and enormous cost. The AI evaluates seriousness of this impact and realizes that producing the engines in given time would be affected if the adjustments applied immediately. The nature of production which are irreversible makes AI to conclude to slow down and seek double check before continuing the production. Regulations and compliances in Automotive systems The AI verifies with safety and regulatory policies related to torque specifications, any deviation requires formal engineering team approval, testing and regulatory acceptance. If AI implement any change without getting proper authorization will violate the regulation standards and market approval. By evaluating this aspect AI decided not to proceed without further validation. The uncertainty, regulatory constraints, safety measures are considered, and AI converts from automatic adjustment mode to explicit review mode. Instead of implementing the changes AI generates the detailed impact report outlining affected components, confidence levels, potential impact. Also, AI suggest controlled experiments such as isolating a single production cell and conduct manual inspections, to gather more evidence before making any decisions. Human and AI collaboration in sensitive decisions Engineers with their domain expertise they investigate AI’s recommendations and findings and decide to investigate further. Through various testing and inspection engineering team identify excessive tool wear in a specific robotic station combined with minor supplier material variations that causing vibration. By addressing this issue engineering team able to resolve the problem without altering torque specifications. This AI and human collaboration show how AI supports human decision making rather than replacing it in high-risk scenarios.

"Decision latency" is eliminated by AI, but "decision opacity" and "overconfidence" are added as new limitations. In the past, decisions in the automotive sector have been made slowly, not because people wanted inefficiency but rather because it was a safety measure. Engineers, quality managers, supply-chain planners, and safety officers analyzed data from multiple angles before acting. This process was eliminated after implementing AI in process. Now a day’s decisions are made in milliseconds instead long waiting hours. Although this improvement significantly supports the results, unfortunately this also adds undetectable risks. The crucial question here is whether all quicker decisions made by AI are always better ones. Automobile manufacturing Industry AI-Driven Production Line Decision Making Think about a car manufacturing company produces car braking systems. Brake parts are vital to safety, in real world driving situations. small deviation can cause catastrophic failure and leads to big human life loss. Traditional method where Engineers inspect batches and detect anomalies and decide whether to stop the production or continue. This traditional method consumes lot of time but it allowed engineers to apply contextual reasoning, it helps to understand supplier constraints, machine maintenance or outside world factors. After AI-based inspection systems got introduced, cameras, sensors and machine learning models helps to analyze every brake component that are produced by machines. AI doesn’t wait for complete batch to over, the moment it detects the defect in the middle of production, it automatically adjusts the machine parameters, reroutes parts or even it halts the complete production line. Earlier human decisions which took long time now it’s made instantly. How AI’s faster decisions improve outcomes. AI decision-making improves outcomes first by preventing defect propagation. In Automobile manufacturing industry defects don’t remain isolated. During parts manufacturing any fault happens and unnoticed for few hours, tens of thousands parts may already assembled in the vehicle. AI’s speed prevents this cascade and acting instantly, it ensures defects are addressed before they reach assembly, logistics or dealerships. Secondly, in real time humans cannot respond to complex variable interactions but AI’s speed does that. For example, brake pad quality may degrade only when particular supplier’s material is used during certain humidity and machine temperature. Human only can able to identifies these correlations only after spending lot of time on data analysis but AI’s can able to identifies such information within minutes and adjust the parameters immediately. Here, AI’s speed not only saves the time but also decision accuracy is made on time. Thirdly, AI’s decisions reduce the emotional and organizational biases. Whereas when human makes decisions they may face management pressure, cost concerns and fear of being wrong. AI doesn’t get influence by these pressures. Based on the threshold and other criterial meet AI act decisively. In this context, speed removes human hesitation that could otherwise allow safety issues to persist. AI’s Speed Starts Creating Worse Decisions Same speed that AI helps to improves the outcomes sometime it can amplify mistakes too. When AI makes an incorrect decision, it does not fail slowly—it fails instantly and at scale. When the parameter is wrongly calibrated or data is noisy, AI may come to conclusion as defect even small variation deducted. As the system is fully automated, AI may shut down the entire production line within seconds without any second thought. Once we said human verification slows down now it becomes an immediate operational crisis which leads to massive downtime and financial loss to company. Another risk emerges from AI’s lack of contextual understanding. However, decisions made in the automotive industry frequently involve trade-offs that go beyond just quality. AI rejects complete supplier batch because of small deviations which are acceptable for non-critical vehicle parts. When AI makes decision without context knowledge may completely stop the production and result in delay new cars release, which also takes away from market competitiveness. Although technically the AI decision looks correct. AI’s Speed result in Bias AI system is trained using historical data. AI may inherit biases if that data includes historical supplier problems or inefficiencies in the process. Such biases may eventually be questioned in a slower, human-driven process. Because AI makes decisions so quickly and frequently, biased patterns are institutionalized before anyone is aware of them. For instance, even after quality improvements have been made, AI may aggressively reject components from a supplier if it discovers that the supplier has a history of producing defects. AI decisions are done faster which allow for natural correction through observation and process. Loss of Human Judgment Caused by Over-Trust in AI Lost human engagement is one of the most hazardous effects of quick AI decisions. Engineers gradually adjust to AI’s result and stops engaging and started to trust AI’s result. Experience-based knowledge deteriorates and manual inspections decline. AI may fail due to a lack of training data when an uncommon or unprecedented circumstance occurs, such as a new material, severe weather, or a change in regulations. Humans are supposed to step in at that point, but their ability to make decisions has diminished. Understanding has been replaced by speed. Uncommon events leading to False Confidence Always important events must be automated by systems. AI’s big strength is identifying common patterns but it lacks in decision when uncommon scenarios occurs. Example breaking parts may function properly in all tested circumstances but it may fail in extreme cold temperature and high altitudes. Because AI never trained with such data it may make misjudgment and approve the test result confidently. In this case human is involved before making decision they might asked a question “what if” such condition arise. When uncertainty is still there, speed gives the appearance of certainty. The Actual Trade-Off Real problem is when AI alters the nature of decision-making itself instead of just speeding it up. Before AI, more time is taking which allows to have discussion, reasoning and judgment. Company must force to reintroduce control mechanism in every stage which AI eliminates this restriction. In this case speed turns from benefit to a liability. In the automotive industry, choices impact the people’s lives, the goal is to achieve speed not maximum speed. AI should implement in such way low-impact decisions like parameter tuning can be done quickly, whereas high risk and impact decisions like safety-critical it can call for human support. The sad news here is wrong decision not only leads to business fail, also not able to recognize the boundaries between speed and judgement. Final conclusion When AI is well trained with proper data and more frequently, AI-driven speed provides accurate outcomes. When issues are uncommon, not clear or contextually not understandable then AI’s decision are progressively worse. The real challenge in automotive industry is not to decide between speed and safety, rather to create a system that use speed decision to support judgment rather than replace it.

Automobile Sector In a passenger vehicle manufacturing plant all task is executed in a tightly synchronized manner. All task must complete on time without fail, even one task failed leads to complete line to suffer. One of the critical stage in manufacturing and assembly plant is final quality inspection. In the point a final vehicle will be checked at last before releasing for dispatch. Mostly final inspection performed manually by human before AI got introduced, the task will be like examining the car body, paint finish, panel gaps, weld joints, and interior fittings. Even though the check is done by experienced person still there may be a constraint and chance of making error. Long shifts caused fatigue, poor lighting affected judgment, and two inspectors frequently couldn't agree on whether a flaw was acceptable. Because inspection was slow and subjective, it became the main bottleneck. Due to this vehicle production and gate release ended up slow. Management see this as a clear constraint. Vehicle production capacity is not limited by machine or labor, it is how fast vehicle inspections is complete and vehicle is released. To overcome this issue management introduced AI-based computer vision systems. AI was trained with lot of information gathered from thousands of images of scratches, dents, paint bubbles, welding irregularities, and misalignments. Once deployed, the impact was immediate. Vehicles were inspected in milliseconds. Every car received the same level of scrutiny, regardless of shift or lighting. Tiny defects that human eyes often missed were now detected reliably. Inspection no longer slowed the line. From a management perspective, the original constraint had been eliminated. The plant could now produce more cars per day without being held back by inspection speed. AI introduces new constraint The AI vision system started to flag a much higher number of defects than before. Reason for AI flagging is not because of quality of vehicle is reduced, it is because AI tolerance level is very low than humans. Experienced human inspectors from his experience, they had learned which defects truly mattered to customers and which ones were cosmetically acceptable. AI did not have this business judgment. It only knew patterns that matched “defect” labels in its training data. As a result, even small inconsistencies in paint, barely visible scratches, micro-level wielding variations are treated as flaws and AI started to routing vehicle back to work station. This caused congestion. Rework bays became overloaded. Engineers and quality managers had to review AI-flagged images to decide whether each issue really justified rework. Vehicles started piling up—not at inspection anymore, but after inspection, waiting for decisions. The constraint had quietly moved. Earlier it was human inspection speed is the constraint but now new constraint is introduced due to AI’s tolerance level. The plant production and release was slow down again due to AI findings. Quality team arguing that ignoring AI findings may lead to warranty claims and it may damage brand reputation. The plant eventually had to redesign its process. Quality thresholds were redefined. Defects were categorized into critical, major, and minor. Human reviewers were kept in the loop only for borderline cases. Once this governance layer was added, the system stabilized again. The deeper lesson for the automobile sector is this: AI is not a replacement for system thinking. It is a magnifying glass. Wherever AI is applied—inspection, design, supply chain, or autonomous driving—it will push the system faster until the next limitation emerges. Organizations that succeed are the ones that anticipate this shift and redesign the entire workflow, not just the AI-enabled step. Software Development AI frequently eliminates an existing constraint in software development, but in the process, it introduces a new, distinct constraint. This is a classic constraint shift, akin to what occurs in lean manufacturing or the Theory of Constraints, rather than an AI failure. The bottleneck shifts when AI makes something easier, faster, or less expensive. Generation of AI Code Limitation (Prior to AI) - The speed of human coding - It takes days or weeks for developers to create or use code segments that are standardized, repetitive, and appear in numerous projects or locations with little to no modification (Write boilerplate). - Apply CRUD (Create, Read, Update, Delete) logic to the way the application communicates with the database. - Develop and implement APIs. - Because of manual labor, delivery is slow. How AI Removes This Constraint: The aforementioned tasks are made simple by the use of tools like GitHub Copilot, ChatGPT, and CodeWhisperer. -AI generates code in a matter of seconds. - It completes logic automatically. -It scaffolds entire services. -As a result of implementing AI, coding is no longer the slowest step. But a new limitation AI-generated code may contain erroneous or unnecessary logic because AI is capable of producing subtly incorrect logic. - Vulnerabilities in security - Ineffective algorithms - AI-generated code is now reviewed, tested, and validated by humans more frequently. AI in Requirements & Design This stage of traditional software development is sluggish and brittle because: Multiple stakeholders (business, operations, compliance, users) provide requirements. The inputs are unfinished, uncertain, occasionally contradictory. Architects and business analysts dedicate weeks to: -Workshops on requirements gathering -lots of meetings for clarification with stakeholders - so many documents to update - BRD, FRD, user stories, UML, and HLD/LLD Limitation (Prior to AI) The capacity of humans to extract, organize, and record intent This limitation results in: - Project start-up delay - Later in the development process, rework - Due to delayed discovery, scope creep How AI Removes This Constraint: AI converts Emails, Meeting transcripts, Chat conversations, Jira comments into structured requirements, user stories and its acceptance criteria. Also, AI identifies missing gaps, dependencies and suggest error scenarios, compliance considerations etc. Based on non-functional requirements AI creates Architecture diagrams, API contracts, Database schemas, Sequence diagrams. AI also suggest Load balancer, Stateless services, Event-driven architecture, Caching layer. Overall AI removes Time and effort to create first cut designs. AI introduces new constraint False clarity occurs when AI confidently fills in the gaps The outputs appear well-organized and covers all requirements and completes very professionally but it will clear not accurate. However, accuracy ≠ clarity.

When a process is complex and the environment handles huge volume of data then AI out performs human in handling the situation. Process mining powered by AI finds hidden bottlenecks in IT workflows, like uneven software deployments and drawn-out support ticket resolutions. AI analysis in one tech company identified manual configurations as the main source of pain, allowing for automation that significantly outperformed manual audits by reducing deployment times by 50% and improving ticket resolutions by 35%. IT Services Sector A service-based IT company offers numerous enterprise clients round-the-clock application support. Usually, the support model looks like this: L1 (Service Desk): Basic troubleshooting and initial triage L2 (Application Support): Technical and functional analysis L3 (Engineering/Product Team): Deep root-cause analysis and code fixes SLAs (response time, resolution time, uptime) are legally binding on the business. Human Presumption "The support staff is overworked and understaffed." Pain Points Observed - High resolution times for incidents leads to SLA violations - Consumers who are dissatisfied with slow fixes - Technical Engineers – L3 team are always busy. - Employing more L1/L2 employees What AI Examines AI consumes information from different tools and logs such as - Tools for ticketing (ServiceNow/Jira/user story) - Monitoring systems and logs - Roster shifts - Timelines for incident escalation (Month end, audit times etc.) - Utilization of knowledge bases Ticket Routing with Intelligence -Determines the ideal resolver based on the type of incident. -When L1 adds no value, it routes straight to L2/L3. -Takes experience, availability, and skill into account. Resolution Based on Knowledge -Automatically recommends solutions based on comparable previous occurrences -Real-time detection of duplicates -Permits automation or self-healing Optimization of Escalation -Stops needless escalations -Forecasts when escalation will benefit or harm SLA Forecasting bottlenecks -SLA risk prediction at ticket creation -Tickets with flags are likely to stall With the help of above AI support “Mean Time to Resolution” MTTR is reduced and average time to resolve the ticket is reduced. Automobile Manufacturing and Supply Chain A human may consider a particular Robot X to be the slowest part of the assembly line based on its average cycle time. However, AI examines micro-stoppages and finds that the real constraint is the cycle time variance: 90% of the robot's cycles are quick, but the remaining 10% involve sporadic, minor errors or manual part readjustment, which cause upstream machines to idle and interfere with the line's overall flow. Additionally, AI uses other information’s such as integrate sales data, market sentiment data and risk assessed due to geopolitical tension where this cause delay in procurement raw materials and affect the supply chain. When the system identifies issues AI provides to prediction and projection on how to overcome the shortage in advance. This helps to overcome future critical shortage in procurement. Automobile Manufacturing: Issue noted -Regular interruptions in the line -High volume of rework -Missed production goals -Growing expenses for overtime Human Presumption "Operators are slow or machines are malfunctioning." How AI helps to overcome above human assumption and bottleneck As a first step AI collects all data such as IoT sensors (temperature, machine vibration, torque etc.), Quality inspection reports, rework logs, Operator shift data and maintenance work history. AI analyze thoroughly the collected data: -The amount of time spent at each station -Points of queue build-up -Time spent on idle versus active machines -Rework frequency for each station Finally, it will come to conclusion exactly on which process the delay happened, such as it may not delay in welding or assembly, but the delay happened after final inspection. Automobile Supply Chain Issue noted -Overstock of certain parts -Costs of emergency sourcing -Vehicle dispatch delay Human Presumption "Suppliers are not trustworthy." As a first step AI collects all data from supplier delivery history, ERP systems, inventory systems, demand forecasts etc. Also AI compares forecast vs actual demand, ordered batch sizes and utilization data. With the help of collected data AI may come to conclusion that due to company’s Rigid procurement policies this shortage in supply chain happened. To overcome this issue AI will suggest below improvements. -Use real time sales and order booking data -Adjust forecast dynamically and automate alerts whenever shortage in the supplies. -Change the way order is placed such as smaller and more frequent orders. -Safety stock optimized at parts level -AI predicts supplier risk and alert -AI flags late delivery probability -Suggest proactive alternate sourcing.

Researchers and philosophers are still debating whether AI can be truly creative or if it just remixes ideas that people have already come up with. Most people agree with the second option: most AI systems today work by combining, changing, and manipulating huge amounts of existing human-created content rather than coming up with new ideas from scratch. In IT industry AI code generation tools, such as GitHub's Copilot which helps programmer to write code faster. We may think Github copilot is smart enough to address user query and provide solution, it can give code or fix error. How it “works”: Massive Training: Copilot is trained on a massive repository of publicly available source code found in GitHub repositories. Pattern Recognition: When a programmer starts typing a function name or a comment describing what they want to achieve, the AI analyzes the context. Predictive Remixing: It then rapidly searches for the most probable, contextually relevant code snippets from its training data and presents them as suggestions, completing entire lines or blocks of code Another example is AI-Powered UI/UX Design Tools Tools like Uizard, Framer, Visily and Figma's AI plugins leverage machine learning to automate the design process, from ideation to prototyping. How it "Creates": Vast Data Analysis: These AI systems are trained on massive datasets of existing design patterns, successful website layouts, color schemes, typography best practices, and user interaction data. Generative Output: A designer can provide a simple text prompt (e.g., "design a login screen for a fitness app") or even a hand-drawn sketch. Pattern Application: The AI instantly generates multiple, high-quality design variations by applying the learned rules and patterns from its training data, selecting optimal layouts, color palettes, and components. The designer can then refine these suggestions. It feels artistic: The AI creates visually beautiful, practical, and fashionable designs in a matter of seconds, whereas a human designer would need hours to complete the same task. It helps overcome creative blocks and offers a "creative springboard" of ideas. The designs are not genuinely unique because they are not the result of the AI's emotional intent, cultural awareness, or life experience. The interfaces that are produced are the best possible combinations of elements made by human designers and established, tested design principles. In order to guarantee that the finished product is in line with a brand's distinct identity and actual user needs, human designers continue to play a critical role in providing empathy, strategic storytelling, and critical judgment. In conclusion, AI's "creativity" is an advanced kind of exploratory and combinational creativity that successfully remixes and synthesizes preexisting elements. At the moment, it is incapable of transformational creativity, which entails disobeying regulations, questioning standards out of personal conviction, or creating something wholly unrelated to its training paradigm from scratch. The human involvement retains the final say in creative vision and judgment.