Q831

AI solutions are often introduced to relieve existing constraints — speeding up decisions, reducing manual effort, or improving throughput.
But in many cases, removing one constraint simply shifts the bottleneck elsewhere — or even creates a new kind of constraint, such as data dependency, model latency, governance delays, or human trust issues.

Think of a specific process in your domain where AI has been introduced (or could be).
How might AI shift the constraint to another part of the process — or introduce a completely new constraint that didn’t exist earlier?
What signals would tell you that this new constraint has emerged?

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

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

• Relevance and clarity of the chosen process

• Depth of insight into how constraints shift or emerge

• Thoughtfulness in identifying early signals of the new constraint
  
  

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When AI removes one constraint ,it often creates another:

Taking the example of US healthcare insurance process , lets us understand the reasoning of this point

Reason - Artificial intelligence can streamline claims processing eligibility checks, reducing human error and speeding up approvals. But these incorporates new obstacles such as algorithmic bias ,data privacy concerns and dependency of technology infrastructure

Example - AI based solutions can eliminate delay in taking prior authorization caused by manual reviews. But this solution will face constraint If y if there are missing necessary details by the Claimant or non -standard data are technically unfairly denied coverage because the algorithm cannot interpret nuanced medical conditions

Solution -

So when AI removes operational bottlenecks, it parallelly throws the constraints such as fairness, transparency and compliance. Additionally, US health care process Insurance companies must implement explainable AI models, conduct Bias Audits, ensure compliance with HIPPA and CMS guidelines. Human in the loop or we can human oversight should complement aid decisions to maintain fairness. Thanks

Domain: Finance and Banking Processes

 

We all know that AI agents and solutions are getting implemented in Finance and Banking operations to remove existing constraints, reduce manual efforts, improve decision making, mitigate risks & improve the control mechanism.

 

But in many cases, by removing few constraints, AI has created new kind of constraints like data dependency, governance delays, trust issues or it has shifted the constraints to another process or function.

 

Let’s look at few examples from the Finance and Banking domain.

Example 1: Invoice Validation

In accounts payable processes, Invoice validation is a very common process where AI agents are deployed.

This is done to address issues like –

·      Manual data checks

·      Exception handling

By deploying AI agents and utilising capabilities like OCR, Machine learning –

·      Efforts are reduced

·      Speed of processing the invoices increase

·      Reduces the backlog

·      Straight throughput (STP) increases

 

However, once validation is taken care by AI, a new constraint often emerges downstream. The bottleneck shifts to data dependencies, data governance and exception handling and new constraints arises along with signals as below -

·      High volume of exceptions

·      Increase in Human in the loop

·      Inconsistent vendor master data

·      Ambiguity in tax rules

·      Interpretation issues

If the upstream master data quality is not good, AI agents likely to generate more exceptions and shift the issue to Exception handling teams.

Example 2: Loan Underwriting

In Banking processes, loan underwriting is a common activity where AI agents are deployed.

This is done to address issues like –

·      High turnaround time

·      Inaccurate and inconsistent efforts

By deploying AI agents, the models help in –

·      Automating the credit scores process

·      Automating the risk assessment

·      Improvement in speed, accuracy and consistency

However, once automation is taken care by AI and AI decisions lack transparency - new constraints arises like policy interpretation, policy explanation and trust. Also the constraints shifts to other teams or individuals like relationship managers, risk officers and compliance teams.

Due to this, there are issues along with new signals as below -

·      High rejections

·      Delay in approvals

·      Conflict with people

·      High volumes of manual review

·      Increase in escalations

·      Explanations to regulators for high rejections

·      Slow down in final decision making

 

Conclusion:

By going through the above examples, we understand that AI rarely eliminates constraints and also shifts the constraints from one team to the other.

AI was good in increasing the speed, accuracy and consistency in the processing but it created other weaknesses and constraints like –

·      Data quality

·      Governance

·      Trust, Ambiguity & Interpretation

·      Readiness issues

and showed signals like

·      Increase in exception volumes

·      Increased escalation rates

·      Increase in cycle time in the downstream process

·      Increase in dependency on exception handling teams

Domain: Make-to-Print Aerospace Manufacturing – Precision machining of titanium and Inconel components for engine and structural programs (€380M turnover contract manufacturer, three plants, serving GE, Pratt, Rolls, Safran)

Specific process: Internal Sales Order Intake & Technical Review (from customer RFQ arrival to firm order release into production planning)

In TOC terms, for years the system constraint was clearly internal — our machining capacity (especially 5-axis milling Work centre of hard metals and creep-feed grinding). We were always full: queues at the machines, overtime every weekend, premium freight to recover late jobs. The plant was the bottleneck; sales could have sold twice as much if we only had the hours.

We introduced AI in 2024 to relieve that constraint:

• Generative toolpath optimization + predictive tool-wear models

• Real-time dynamic scheduling with AI drum-buffer-rope

• On-machine probing + auto-compensation loops

Result: protective capacity exploded. Throughput up 42 %, spindle exploitation from 68 % to 89 %, overtime hours almost zero. For the first time in 15 years, the machines were waiting for work.

We celebrated for about three weeks.

Then the constraint shifted completely — and a new internal one emerged that had been comfortably hidden.

The new system constraint is now internal order intake velocity: the time from customer RFQ drop to technically clean, priced, and contractually accepted order in the system.

Why this happened:

1. With capacity suddenly available, sales went hunting — RFQ volume tripled in six months.

2. Every RFQ for make-to-print parts needs detailed technical review: feasibility check on tolerances, material certs, NDT requirements, special processes (heat treat, shot peen, coatings), long-lead raw material verification, and cost build-up.

3. That review is done by only 8 senior application engineers (the ones who really understand the risk of a 0.012 mm wall thickness or Inconel 718 creep).

4. The process is still mostly manual: Excel sheets, emails to suppliers for raw-material quotes, phone calls to plating vendors, copy-paste into ERP.

5. No systematic follow-up on lost quotes (we never learned why we lost)

6. Pricing policy stuck in 2018 cost-plus mindset while customers wanted fixed-price multi-year deals

Suddenly we had machines starving because a €9M order was stuck for 11 weeks in technical review while the engineers drowned in 180 open RFQs.

The AI removed the production constraint… and exposed that our front-end commercial process was never designed for high velocity. It had been happily hidden behind “we’re full anyway.”

The completely new internal constraint that emerged

A knowledge & decision bottleneck in the application engineering team — plus a cultural one: engineers were rewarded for accuracy, not speed. Saying “yes, we can do it in 26 weeks” too fast felt risky when we used to be capacity-limited anyway.

Early signals that screamed “new constraint in the house”

1. Machine utilization plateaued and then started dropping despite zero technical problems.

2. Sales started complaining “we’re losing bids because we’re too slow to respond” — quotes going out in 10–14 weeks while competitors replied in 3–4.

3. Application engineers working weekends just to clear the inbox — same overtime pattern, different people.

4. Marketing brought in two huge new opportunities… that sat unsigned for months because “technical review pending.”

5. Cash flow dipped — we had capacity, but no new orders flowing into production.

What we’re doing now (still mid-journey)

• Built an AI-assisted RFQ triage: auto-feasibility check on 70 % of standard features, raw-material price pull from historical + supplier APIs.

• Created a red-tag “fast lane” for strategic customers (buffer management for quotes).

• Changed incentives: engineers now measured on quote velocity AND win rate.

• Hiring + cross-training three more seniors.

TOC reality check When you finally elevate the plant constraint you’ve chased for a decade, the system doesn’t thank you — it immediately promotes the next weakest link. In make-to-print manufacturing, once the machines stop being the bottleneck, the real fight moves upstream to how fast you can say a qualified “yes” to the customer.

We fixed production… and discovered our commercial process was the new silent killer.

Watch for idle capacity in a shop that used to be slammed. That’s the system whispering: “Congratulations. Your new constraint has arrived.”

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.

In my experience, AI was implemented in a retail banking credit card approval process to address what was widely perceived as the primary obstacle: inconsistent and sluggish credit decision-making. Particularly during periods of high application volume, manual rule checks, bureau analysis, and policy interpretation were taking hours or even days.

A machine learning–based credit scoring model was introduced to automate approvals and rejections in near real time. Initially, the impact looked impressive. Approval turnaround time dropped sharply, queues disappeared at the credit desk, and business teams declared the constraint “removed”.

However, within a few weeks, it became clear that the constraint had not disappeared — it had simply moved and transformed.

How the constraint shifted:

Once AI accelerated decisioning, two new constraints started emerging.

The first was data dependency. The AI model’s output was only as good as the input data it received. Upstream issues that were previously tolerable — delayed bureau pulls, inconsistent income fields, mismatched customer identifiers — suddenly became critical. Earlier, a human underwriter would pause, interpret, or correct such issues. Now, the system either failed silently or routed cases to manual review, creating a new queue that hadn’t existed before.

The second, and more subtle, constraint was human trust and governance. Risk and compliance teams were uncomfortable with fully automated approvals, especially for edge cases. As a result, new approval checkpoints were added: model explainability reviews, threshold validations, and periodic overrides. Ironically, while the model made decisions in seconds, files started waiting for sign-offs on whether the model should be trusted in those scenarios.

So while AI removed the visible credit processing bottleneck, it introduced constraints related to data readiness and organizational confidence.

Signals that the new constraint had emerged

The signals were not obvious at first, but they became clear over time:

A rising percentage of applications being diverted to “manual exception” queues

Increased complaints from operations teams about data issues rather than workload

Risk teams requesting frequent recalibration or tighter thresholds despite stable outcomes

Overall end-to-end TAT starting to creep up again, even though credit decision time remained low

More time being spent in governance reviews than in actual credit evaluation

These were strong indicators that the constraint had shifted from decision speed to data quality and trust in automation.

Reflection

What this experience reinforced for me is that AI rarely eliminates constraints — it reshapes them. In this case, the process moved from being people-constrained to being data- and governance-constrained. Without addressing upstream data discipline and downstream decision ownership, AI simply accelerated the process into a new bottleneck.

For process leaders, the key lesson is to watch not just the speed gains from AI, but also where work, waiting, and anxiety reappear elsewhere in the system. That is often where the new constraint is quietly forming.  

When AI Removes One Constraint — Does It Create Another?

 

AI are evolving programming environments, equipped with LLM and scenarios feed into the models and models at times do hallucinate and that’s an area that needs  fine tunning and reinforcement.

On the same lines when AI removes a constraint and offers solutions that may have not have earlier or ever identified by a Human programmed system. The inter linkages that AI solution may offer as part of its resolution can throw up scenarios that may be ideal from a solutioning standpoint but how practical and real time implementation can still pose a challenge.

 

Taking the Below example from a Customer Service Industry , which we discussed here earlier – Forum Q.No. 831

                                             AI Driven Constraint Identification and Advantages      

                     

Although the Above scenario, did identify the advantages that an AI may have over Human but the additional constraints that solution may impact the systems and resources involved can be explored through the below outcomes.

*Scenarios are highlighted in Yellow Shapes in the flowchart attached.     

        

Additional Constraints and Impact Areas

Scenario 1: Assist Registered users Basis their last Query

Additional Constraints: The new Customer Query may not be related to earlier issue.

Impact Areas:

Customer Experience – Customer may land in a queue or to a team that may not be able to service their issue/s the first time and may need to handover Customer over to a different department which was not what the customer would have expected and additional information or clarification the helping Expert or customer may have to provide to guide the customer to the correct department.

Volume Load – Incorrect routing may lead to additional callback and incoming call volumes to queues that are not staffed to handle such volumes.

Scenario 2: Routing to Last Service Rep if available and Suggest to Customer the option of the availability.

Additional Constraints: Wait time to for the Customer to reconnect with the last serviced Expert.

Impact Areas:

Experience Vs Cost  – In the effort to customize the Customer experience , the capacity and cost that we need to negotiate between the ideal wait time and the customer expectations and would be hard to measure and evaluate in case the system is either not able to handle the expert tagged routing vs the cost involved to design systems around this process.

 

 

Scenario 3&4 : AI can identify the Agent Skill constraints and provide Agent Assist capabilities

Additional Constraints: Agents will have to cross trained and multiskilled to handle additional transfer volumes

Impact Areas:

Agent Experience – Agent fatigue and overload can be a metric that is not typically measured but felt during an interaction, which not only impacts the customer experience but also the agent longevity in an organisation and process.

Staffing Needs Vs Cost – With the constant leveraging  between the expert skills and customer query type , the staffing needs will have to be more dynamic and real time across queues, that may involve additional cost to meet the service level agreements.

 

Scenario 5: Agent Opportunities to Optimize AHT and Productivity

Additional Constraints: At times ACW is a value add activity where the case notes needs to be updated with details and inter-case output for an interaction , which if not investigated and captured correctly can lead to incorrect or incomplete documentation

Impact Areas:

v  Query Completeness – AI Driven solution and process may lead to additional human intervention and extended effort and time requirement if critical details are missed to be captured automatically.

v   Data accuracy – AI driven constraint and any human intervention inaccuracy may lead to data set getting corrupted over a period of time .

Yes, AI often makes new limits when it takes away one. This is a common reality in systems design and solution architecture, not a flaw. Here is a clear, architect-level explanation with real-world examples, written in the way that solution architects and senior engineers think. 1. The Main Idea (Systems Thinking) Taking away one restriction moves the bottleneck to a different place. This goes along with: The Theory of Constraints (TOC) Design of distributed systems How socio-technical systems work AI isn't magic; it changes the way constraints work across: Tech Information People Management Price Morality 2️⃣ Example 1: AI Takes Away Manual Work and Makes Data Dependency; ❌ Old Constraint Human analysts look over 10,000 records by hand every day. Bottleneck = time for people ✅ AI Answer The ML model works with a million records every day. 🆕 New Rule Quality and labeling of data The accuracy of the model depends on: Get rid of old data Continuous training Controlling bias Insight into Architecture Old bottleneck → Human capacity New problem: Data pipelines and model management AI didn't get rid of the limit; it just moved it up. 3️⃣ Example 2: AI Speeds Up Decisions → Makes Trust & Explainability Constraints ❌ Old Constraint It took days to make decisions (manual approval, analysis) ✅ AI Answer AI making decisions in real time (like credit scoring and fraud detection) 🆕 New Limits Explainability (Why did you make this choice?) Following the rules (GDPR, AI Act) Requirements for human override Case in the Real World AI in Banking Quick loan approval ✔ Regulators want to know: "Explain why this was rejected" "Show no bias" New Type of Constraint ➡ Legal and moral limits take the place of time limits 4️⃣ Example 3: AI lowers the level of skill needed, which leads to gaps in oversight. ❌ Old Constraint Needed skilled engineers and designers ✅ AI Answer AI copilots make: Code Diagrams of architecture Content for marketing New Limits Users don't understand very well Too much trust in AI output Hallucinations that are hard to find Risk in Architecture It takes less skill to make something than it does to check it. AI makes it easier to get in, but harder to prove who you are. 5️⃣ Example 4: AI cuts costs, which makes computing and energy more expensive. ❌ Old Constraint High costs of labor AI Solution Automated tasks (AIOps, chatbots) New Limits GPU availability Cloud costs go up How much energy you use Latency on a large scale For example Teaching a big model: Work ↓ Cost of infrastructure goes up Carbon footprint goes up New Blockage ➡ Calculate the cost of computing 6️⃣ Example 5: AI Makes Things Bigger, Which Makes Governance More Complicated ❌ Old Constraint Systems couldn't get any bigger after a certain point. ✅ AI Answer Automation powered by AI makes huge scale possible. 🆕 New Limits Control over model versions Trails of audits Confusion about who owns what across teams Who is responsible when AI doesn't work? This is what solution architects call it: "Spreading responsibility" 7️⃣ Architect View of the Constraint Shift Map AI Takes Away But Makes TimeDependence on data Labor Compute cost ExpertiseValidation load Latency Demand for Explainability Control by handComplicated governance 8️⃣ The Key Insight (Very Important) AI doesn't get rid of limits; it moves them around. Bad teams say: "AI will take away all limits" People who are good at architecture say: "Where does the limit go next?" Great builders: Plan for the next limit before it happens. 9️⃣ How Smart Architects Deal with This ✅ Design Patterns People in the loop Observability of the model Contracts for data Systems that fall back Budgets for cost and latency Layers of explainability Example Architecture: User → AI → Confidence Score ↓ Human Review (if low confidence) 🔚 Final Thought AI changes the rules, but it doesn't break them. The actual competitive edge is not: "Who uses AI" It is: Who knows what the next AI bottleneck will be?

In our Department of Revenue, we’ve been experimenting with using AI to help catch fraudulent tax returns. Before this, most of the work was a mix of manual checks and a handful of rule-based filters which the customer support tax agents needed to go through. It wasn’t terrible, but it was slow and easy to overwhelm, so a lot of the more subtle erroneous cases slipped right past the tax agents. Once we brought in AI models, the biggest change was speed which suddenly allowed the agency to run through huge volumes of filings almost immediately and pick out odd patterns that would’ve taken forever to notice manually.

What caught us off guard, though, was that the bottleneck didn’t disappear; it just moved. The main challenge became keeping all the data that feeds the models clean, consistent, and on time. These systems depend on information from payroll, banks, older tax records, and a handful of other sources. If any of that data shows up late or isn’t formatted the way the models expect, the whole workflow slows down, even though the analysis itself runs quickly.

Another sticking point has been getting people to trust the results. Auditors and legal teams don’t want a mystery “flagged” label since they want to understand the logic behind it so they can defend their decisions if challenged. When the system can’t clearly explain itself, the human side of the process grinds to a halt.

There are a few things which tipped us off that these new constraints were forming:

-We started seeing queues pile up even though computing power was not the issue, which pointed to delays before the data even reached the model.

-Auditors pushed back more often, asking for overrides because they weren’t comfortable relying on decisions they couldn’t trace.

-Even with faster detection, cases weren’t closing quicker, which made it obvious that review and governance had become the new slow points.

If anything, the whole experience has made it clear that speeding up one step just exposes whatever the next constraint is. AI can make detection faster, but that doesn’t matter unless all the ecosystem  surrounding processes such as data handling, oversight, and human judgment are able to keep up the pace.

🏆 Q831 Results — When AI Shifts (or Creates) the Constraint

Strong, system-level thinking across responses. Clear theme: AI accelerates flow → the bottleneck migrates to data, governance, trust, or upstream decision velocity.

Top 3
🥇 Adil — Aerospace: AI removed the shop-floor constraint and exposed a front-end order intake & decision bottleneck. Classic TOC shift, brilliantly articulated.
🥈 Apoorv — Retail banking: Decision speed improved, but the real constraint became data readiness + governance trust. Clear signals and balanced AI vs human view.
🥉 Juma — Revenue & tax: AI sped up detection, but data pipelines and explainability became the new limiting factors.

✅ Also approved (worth reading): Sri Ramana, Puneet, Nageshwaran, Mohammad Riyadh, Sundeep Kailwoo.

Key takeaway:
AI rarely “breaks” constraints — it reveals the next weakest link faster. Watch for rising exceptions, governance queues, idle capacity, or trust-related delays.