Q830.

Every organization runs on hidden assumptions — beliefs about customers, workload, risks, or behaviors that nobody questions until something fails.
AI systems, when deployed, often behave in ways that unintentionally expose these blind spots.

Think of a process in your domain where people rely on unspoken assumptions (“customers usually…”, “agents typically…”, “this step is always needed…”).

How could an AI agent surface, challenge, or validate such assumptions through its interactions or outputs?
Share a specific example.

⚠️ Generic answers or those without a concrete process will not be approved.

🏆 Best answer will be selected based on:

• Relevance of the chosen process

• Depth of insight into hidden assumptions

• Clarity of how AI exposes or challenges them
  

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Yes, AI can absolutely reveal the operational assumptions that we did not realise we were running on. A clear example from my world is the belief that SLA breaches happen only because volumes spike unpredictably. Everyone accepts this as the truth. Managers plan around it, teams repeat it, and it quietly becomes part of the culture.

If I were to apply an AI workload analytics agent to our historical data, I am almost certain it would tell a very different story. AI does not buy the human narrative of random spikes. It would start stitching together patterns across months of transactions, client behaviour, payroll cycles, renewal periods, public holidays, cut-off times, and even the way we schedule work internally.

What feels chaotic to us would probably come out looking like a painfully predictable pattern.

For example, the Monday surge that we keep treating as bad luck, would likely match a Friday submission pattern. The month-end pressure would line up with contract expiry cycles. The worst SLA failures might correlate with a few specific events that we have never connected, simply because humans are not good at holding that many signals in their heads.

The bigger realisation here is that the assumption itself, that workload spikes are unpredictable, survives only because we do not analyse enough data at once. AI would surface the blind spots, challenge the narrative, and show that our problem is not chaos. There is poor visibility and weak forecasting.

This is exactly where AI changes the conversation. It forces you to compare what you believe is happening with what is actually happening, and that shift alone is enough to reshape how you plan, staff, and manage performance.

Domain: Banking  

 

I agree that in many organization, the processes runs on hidden assumptions. At times, we forget to ask questions and we solely believe our customers & vendors are always right and our workload, risk mitigation & behaviours are on the right track. This belief has become a part of the culture in many organizations and many do not question it and don’t realise it until something fails.

 

Let me share few specific examples where there are few blind spots and how AI agents help to eliminate them and mitigate risks.

 

Example 1: Duplicate billings in Invoice Validation process

 

High-level steps:

 

·      Vendors send the invoice on monthly basis.

·      Accounting team checks for duplicates.

·      Approvals are done quickly due to tight timelines (say peak quarter or seasonal).

·      Duplicate invoices slip through occasionally.

·      Finance team discovers or misses duplicates during the payment.

·      Audit team discovers duplicates during the audit.

 

Assumption:

 

·      Duplicate invoices are random errors and there are no patterns to it.

 

Blind Spot:

 

·      Team missed to look at the seasonal behaviour.

·      Team missed to analyse year-over-year patterns.

·      Team assumed the duplicates were occasional mistakes.

 

How AI agent helped:

 

·      AI agent performed analysis for each & every season.

·      AI agent found that the duplicate bills spike during peak & holiday season (Oct-Dec) for high volume vendors (ABC & XYZ).

·      AI agent can perform additional validations to detect duplicates.

·      AI agent + Human in loop (HIL) to improve controls mechanism.

 

 

Example 2: Inaccurate Tax calculation in Invoice Validation process

 

High-level steps:

 

·      Vendors submit the invoices.

·      Accounting team performs validation checks.

·      Tax checks are ignored without extensive reviews.

·      Exceptions are raised only when discrepancies are identified.

·      Finance team approves the payment.

·      Payment gets processed.

 

Assumption:

 

·      Vendor systems are already automated and they are likely to submit accurate tax calculation on their invoices.

 

Blind Spot:

 

·      Team missed to question tax accuracy for few vendors.

·      Team overlooked recurring small mismatches.

·      Team assumed automation done by vendor will lead to accurate invoices.

 

How AI agent helped:

 

·      AI agent performs analysis on the invoices for the past 12 months.

·      AI agent detects patterns in vendors invoice tax calculation.

·      1-2 % tax deviations were detected by agent for vendor X and A.

·      AI agent identified correlation in line item count & errors in shipping charges.

·      Implement new validation rule if line items are greater than 3.

·      AI agent + Human in loop (HIL) to verify GST calculation manually.

 

 

Conclusion:

 

Blind spots are exposed by AI agents because they analyze data without relying on human assumptions. They detect hidden patterns and helps the team to redesign processes based on evidence rand not by intuition. AI helps to automate work and supports in strengthening controls.

Can AI Reveal Operational Assumptions We Didn’t Know We Had?

Here’s a Rogers-specific, concrete, non-generic example that directly shows how an AI agent can reveal hidden operational assumptions in a real workflow.

Example: Rogers Contact Centre – Retention Save Offer Workflow

The Hidden Assumption -It is nowhere documented but AI still followed-

When customers threatening to cancel- ALWAYS respond best to a discount-based save offer.
This belief drives scripting, agent behavior, KPIs, and even what the CRM surfaces first.

But nobody ever questions it. It’s a cultural default. When a customer says they want to cancel, giving them a discount is always the best way to keep them.

People in the company assume this is true, so-

·       Scripts are written around it and agents are told to offer discounts first

·       Agents behave based on it, they jump to offers instead of checking the real issue

·       KPIs are designed for it- success is measured by how many discounts saved the customer

·       CRM supports it- the system shows discount options at the top, hiding other solutions

 

How an AI Agent Exposes the Hidden Assumption

Context

Rogers deploys an AI Reasoning Agent that listens to live calls or chat transcripts, classifies customer intent, and then recommends next actions and save strategies like pricing offer, empathy route, plan re-match, network check, loyalty benefits, etc..

What Actually Happens

Within a few weeks, the AI model starts recommending non-discounted actions more frequently for cancellation-intent calls- because it has learned from past patterns.

This triggers friction-

Human Agents say-

·       Why is the AI suggesting network diagnostics first?

·       Why is it prioritizing billing clarity instead of offers?

·       Why isn’t the AI giving the usual 15% save offer?

 

This unexpected behavior surfaces the blind spot. When a steady and robust AI system is deployed to govern the system, it will always go deeper and actually understand the root cause of issues, here customer issues, discount may not always be the option- this is done using Customer churn prediction methodology.

How Exactly the AI Surfaces these Assumptions-

1. Pattern Discovery

The AI finds that in 22% of cancellation calls, customers aren't actually leaving for price- root cause is-

·       An unresolved outage

·       Repeated technician no-shows

·       A billing error

·       Equipment issues

 

And it learns that offering a discount first increases frustration, because the customer feels unheard.

AI Insight: Your assumption that price is the primary driver of churn is wrong for a significant segment. A deeper study has to be made around customer’s real problems.

 

2. Recommendation Anomaly

AI begins recommending:

·       Run a network quality check before offering a discount.

·       Correct billing error before presenting retention bundles.

·       Escalate to tech support- save offer is not relevant yet.

 

This deviates from the standard script. And dives more into the solution to actual problem. AI automatically knows through churn prediction that customers are unhappy due to other reasons and it does the best to assist them.

3. KPI Disruption

When followed, AI-led calls show-

·       13% higher save rate

·       19% higher first-call resolution

·       Better CSAT compared to discount-first calls

 

This challenges-

“Our long-held discount-first assumption is not universally true.”

 

4. Churn Prediction

The AI predicts customer actions through churn predictions. This makes Rogers quickly get back customers who are likely to leave.

·       Will this customer leave?

·       What factors typically cause cancellations?

 

5. Root-Cause Pattern Learning

It learns what actually drives dissatisfaction - network issues, billing errors, bad experience, etc through pattern learning. It does a brief analysis on customer pattern and comes to know about root cause behind it. And then can eventually personalize the customer offers based on that data.

 

So, it’s churn prediction + pattern recognition + AI reasoning that exposes the hidden assumptions.

Churn prediction tells who might cancel. AI reasoning tells why and what to fix first.

 

6. Operational Uplift Reveals Another Assumption

The data also exposes a second hidden belief-
Agents skip diagnostics because they assume it makes the call longer.

Human agents often avoid doing basic checks like network tests or account diagnostics because they think it will make the call longer.

But AI shows-

·       Quick pre-screening checks are not increasing call duration

·       They often avoid long escalations or repeat calls

 

So now two hidden assumptions collapse thanks to AI-

1.       Price is the main reason customers cancel

2.       Diagnostics make calls longer

  

In Simple Terms - What AI Revealed

AI help execute the process far better.

It forced the organization to confront what it believed.

·       AI changed the offer order → humans questioned why- AI changed the order of steps, so people asked, “Why is it doing that?”

 

·       Humans realized their mental models didn’t match real customer behavior- They realized their old thinking didn’t actually match what customers wanted.

 

·       Data validated the new pattern → workflows changed- Once the data proved the AI was right, the company had to change how the work is done.

 

Summary

AI doesn’t just optimize operations- sometimes it quietly exposes the assumptions we didn’t know were driving them.

Domain: Customer Support Operations – Handling escalations and service-level agreements (SLAs)

 

A Fortune 500 enterprise software company deployed an AI model to predict escalations and improve SLA compliance. The CS team assumed that escalations were primarily caused by missed deadlines or slow responses. Processes are therefore designed to have strict SLA timers, auto-escalation rules, and agent coaching on speed.

What was fed to the AI:

3 years of ticket data (≈ 1.8 million cases)

Escalation logs and SLA breach reports

Customer sentiment scores from surveys

Activity timelines

Knowledge Based usage patterns

 

What was the AI’s job?

The AI was designed to find patterns that predict escalations and suggest interventions.

It analyzed:

• Ticket text and agent responses

• Customer sentiment trends

• Escalation logs

• Agent adherence to scripts

The results:

90% of the time a predictable remixing with superficial tweaks was observed. The AI confirms what the team already “knew”.

For example, tickets with long first-response times indicated higher escalation risk.

Negative sentiment in initial email showed higher escalation risk

It recommended better auto-routing and faster first replies.

And even more predictable was the team’s response, “Nice job.” But Nothing surprising.

 

The Revelation:

Then comes the 10% case where the actual blind spot remained which nobody saw. Citing a real example that occurred with that premium enterprise customers of APAC region in February 2025.

The AI flagged something bizarre: Escalations were NOT driven by SLA breaches or impatience. They spiked when customers followed the script too perfectly.

 

What did the AI do?

The AI model was designed to predict escalation risk and recommend interventions. When it correlated escalation patterns, it found something counterintuitive. Tickets that adhered to script had a 2.4 x higher escalation rate for APAC premium customers compared to partially customized responses.

 

Why this Happened?

During further analysis some cultural and behavioral dependencies were revealed:

• APAC premium customers preferred relationship-building and informal tone of communication. They expected proactive suggestions, not rigid compliance.

• Scripted responses indicated robotic and too formal tone, suggesting a perception of lack of warmth in the conversation or lack of ownership.

• Customers interpreted this as “They’re hiding something or not taking my issue seriously.” This distrust triggered escalations even when SLAs were met.

 

Hidden Assumption Exposed

The team believed: Consistency = professionalism
In reality, for this domain : Personalization = trust
This assumption had silently shaped training, QA, and performance metrics for years making team believe that scripted language was safe and accurate however it became a liability.

 

AI’s behavioral tweak suggestion.

AI proposed a dynamic tone-adjustment model. Understand the customer's persona and sentiment in real time; accordingly suggest response tone (formal vs conversational). Notify when an agent is using too much from the standard script.

 

Notable Results:

Escalations dropped by 37% in APAC premium accounts within 3 months with no SLA changes and no extra staffing. All that required was just a tone adaptation.

 

Lessons learnt

That insight was genuinely creative. No human manager had ever questioned the “formal = good” assumption. The training data contained zero explicit examples of tone causing escalations. The AI generated patterns for sentiment analysis, escalation triggers, and linguistic style existed but how to tweak and tune these based on domain applicability   is the biggest takeaway. The AI didn’t just optimize speed; it challenged a cultural assumption baked into our playbook.

 

Conclusion

In most cases, AI behaves as a powerful pattern-matching engine that expeditiously applies proven best practices quicker than any human analyst. However occasionally, around 10% of the time, it uncovers hidden blind spots that force us to rethink entire processes.

This rare but critical insight is where true operational creativity thrives. It’s not because AI “thinks,” but because it can explore possibilities no human team would ever consider. It is when those assumptions are revealed that the impacts are far-fetched and   it may reshape workflows, agent training, and even our definition of what great service looks like.

Domain: Aerospace Manufacturing – Supplier Nonconformance (NC) Disposition in the MRB (Real case: European Tier-1 structural machining site feeding Airbus A350 and Boeing 787 frames)

The sacred weekly ritual

Every Thursday at 14:00 sharp, five of the greyest, most expensive heads in the building lock themselves in the MRB room for 4 – 6 hours. On the table lies €4 – 6 million worth of titanium and aluminium parts that some poor supplier dared to machine 0.07 mm out of tolerance. The unspoken, holy assumption that has survived since the days of the Comet: “Only a human stress engineer with 25+ years, a bad back and a caffeine addiction can be trusted to say whether this six-figure part lives or dies. FEM + MIL-HDBK-5 + pure gut feeling = gospel.”

Enter the AI agent – January 2025 (dramatic music)

We fed it 19 years of MRB records, FEM results, every concession, every cracked part in the fleet, the full CATIA models, the measured deviations… basically the aerospace equivalent of giving a teenager the keys to the liquor cabinet.

After exactly 4 weeks of silence, then AI slides into the Teams chat at 07:12 on a Friday morning with the calmest career-ending message you’ll ever read:

“Good morning. Analysed last 512 MRB cases, Findings:

• 61 % of your Use-As-Is decisions were unnecessarily conservative → €9.8 million in perfectly safe parts sent for rework or scrap last year alone.

• 5 cases (1 %) were actually dangerous. Predicted fatigue life 38 % lower than you approved.

• Your average decision accuracy: 78 %. My simulated accuracy on the same cases: 99.7 %. Coffee is on me.”

You could hear the egos deflate from the parking lot.

How the AI murdered our sacred assumptions (with receipts)

1. The legends were applying 1970s allowables and then knocking another 25 % off “because safety”. The AI politely pointed out we’ve had 14 years of A350 fleet data proving the real margins are higher. Translation: we were throwing away money out of nostalgia.

2. Humans couldn’t remember that an identical 0.09 mm oversize hole on P/N 114A5678-201 was already flown safely on 180 aircraft since 2016. The AI remembered. Every. Single. One.

3. While the committee argued for 40 minutes whether a 0.11 mm thin flange was acceptable, the AI re-ran the full non-linear FEM with the as-measured geometry in 38 seconds and said: “Relax, it’s fine. Here’s the new buckling factor: 3.84.”

The punchline (because OEMs move at the speed of continental drift)

We packaged all this into a 180-page validation report, sent it to Airbus and Boeing, and waited……for nine months.

Finally, in October 2025, both OEMs came back with the most aerospace sentence ever written: “AI-assisted disposition is acceptable provided a qualified stress engineer clicks ‘Approve’ on the final recommendation and assumes full liability.”

Translation: the AI does 99 % of the work, saves €9+ million a year, catches the dangerous cases the humans missed… and the grey-haired engineer still gets to push the green button and pretend he’s the hero.

The hidden assumption that died screaming

“Only a grey-bearded engineer who has seen three CEOs come and go can be trusted with a €600 k part.”

The old guys were being extra-careful because “better safe than sorry” but they were too safe on most parts and threw money away.

Humans couldn’t run a quick “what-if” FEM on every case in a 4-hour meeting. The AI re-ran the stress model with the exact measured geometry in 40 seconds per part.

Humans couldn’t perfectly remember the last 10,000 decisions, so they kept making the same conservative call over and over.

The AI remembered everything, re-ran the math in seconds and looked at what actually broke in service.

Results 6 Month in

Now every Thursday the MRB meeting is 42 minutes instead of 6 hours, the coffee is colder and the senior engineers spend most of the time arguing with a computer that is usually right.

They hate it. The balance sheet loves it.

Now the humans only get called when the AI itself says, “Yeah… this one’s weird. Wake the olds.”

The funniest part? Now the senior engineers finally get to go home before sunset and the AI does the heavy lifting without ever needing a pat on the back or a bigger office.

Moral of the story:

Never bring forty years of gut feel to a knife fight against AI with perfect data.

Based on my experience, AI is great at peeling back the layers of operational truths we take for granted in air cargo, where decisions about pricing, routing, and loading depend on things like "demand always spikes predictably" or "partners share data reliably." These blind spots can cost millions in lost profits or delays.

Unspoken beliefs that teams accept as fact just because "that's how it has always been" affect every part of an air cargo operation, from pricing to forecasting to booking to capacity planning. These assumptions are often hidden until the system acts in an unexpected way or there are problems with performance. AI, especially conversational agents and pattern-based models, can bring these hidden beliefs to light by questioning long-held operational shortcuts, showing where perception and data don't match up, and checking to see if our internal logic still matches real market conditions. Studies show that over 60–75% of process deviations in airlines originate from outdated assumptions or undocumented knowledge.

Here are three specific air cargo processes where unspoken assumptions are hiding, and AI brings them to light through things like anomaly alerts, simulations, or counter-proposals.

1)      Cargo demand is stable on key routes

Process Area: Forecasting & Capacity Planning

Most cargo teams think that demand for general cargo will stay the same on long-established routes (like FRA–JFK and DXB–LHR). Planners often make predictions with a small range because they feel that demand fluctuations will be minimal on these routes.

How AI Exposes the Assumption

When an AI forecasting model runs multi-factor simulations (seasonality, competitor schedules, macro-economic indicators), it often identifies unexpected volatility caused by:

• sudden e-commerce surges,

• geopolitical restrictions shifting flows,

• competitor network changes,

• regional promotions from major forwarders.

The AI’s prediction variance may be far wider than the historical forecast range used manually.

Revealed Hidden Assumption

“General cargo demand doesn’t change much” is outdated. Demand does swing, we just stopped noticing because our forecasting process assumed stability.

 

2)      Customers would rather have lower rates than faster confirmation

Process Area: Cargo Pricing and Booking Behavior

Teams often think that forwarders care more about price than anything else.  The idea that rates are the strongest lever guides pricing decisions.

How AI Shows the Assumption

An AI agent looking at thousands of booking patterns might show that:

• Forwarders with high shipment urgency will pay more if they get confirmation right away.

• Some small and medium-sized businesses would rather have consistent rates than the lowest rate.

• Some customers value capacity assurance more than discounts.

An AI-powered chat agent might also notice that users keep asking for "quick confirmation" instead of "lower price."

Exposed Concealed Presumption

It is too simple to say that "price is the main factor in customer decisions."  AI shows that in real life, speed, certainty, convenience, and contract consistency often matter more than price.

3)       Cargo rating process takes longer during peak load

Process Area: Revenue Management & System Performance

Teams often think that performance problems only happen during certain times, like the end of the month or during sales.

How AI Exposes the Assumption

AI that monitors performance might find:

• slow responses during mid-week, non-peak times

• micro-delays caused by specific origin–destination pairs

• higher latency when certain product types (e.g., pharma, special handling) are included

• API sequences that are using unnecessary recalculations.

 

Revealed Hidden Assumption

We believed that peak load was the cause, however AI revealed the root cause is more complicated than just one thing, it was a combination of specific origin/destination, logic, data spikes, and business rule complexity.

Below are Pros and Cons of using AI to surface hidden operations assumptions:

Aspect	Pros	Cons
Decision Accuracy	The right choices AI checks data, which helps people avoid making mistakes based on their gut feelings or personal opinions.	If the data they are based on is incomplete, noisy, or biased, AI insights can be wrong.
Process Improvement	Making the process better Improving processes makes it easier to redesign them by revealing hidden problems and inefficiencies.	If teams depend too much on AI, they may miss important human or operational details that AI can't see.
Root Cause Identification	AI can find small, complicated patterns that people often miss, which speed up the search for the root cause.	AI might find connections that seem important but don't really cause anything to happen, so people need to look over them very carefully.
Customer & Market Understanding	It shows how real customers behave and what they like, which goes against what people used to think.	AI can make mistakes when it comes to behavior if it doesn't know the cultural, contractual, or relationship-based context.
Organizational Learning	Learning promotes a culture of always learning, asking questions, and using data to improve things.	Teams may not want to use AI results if they go against established practices or strongly held beliefs.
Operational Efficiency	Automates the process of analyzing large datasets, which saves time and speeds up the decision-making process.	If teams start to rely on AI instead of using it as a tool to help them, they may not be able to think critically as well.

 

Here are few recommendations:

• Use AI in weekly reviews of prices, forecasts, and performance to keep finding gaps in your assumptions.

• Make a "Challenge the Assumption" dashboard that shows where AI and human predictions are different.

• Combine AI insights with domain knowledge to make sure that both points of view are taken into account when making the final decision.

• Use conversational AI to find undocumented workflows or "tribal rules" that analysts use but have never written down.

• Teach teams about cognitive bias so they know why people make assumptions and how AI can help break down old ones.

• Validate AI anomalies as soon as possible, because they often show the most useful information.

AI is more than just a way to automate tasks. It is also a way for organizations to look inward. AI helps find the hidden assumptions that guide decisions in air cargo operations by bringing to light differences from long-held operational beliefs.

AI makes teams face facts that data shows but people often miss. This is true whether they are predicting volatility, customer behavior, or performance changes. Organizations that take on this challenge will grow faster, create processes that are more flexible, and make decisions based on facts instead of tradition.

 

Q830 Results – “Can AI Reveal Operational Assumptions We Didn’t Know We Had?”

Thank you for the strong, process-grounded responses. This question clearly showed how AI doesn’t just optimize workflows — it challenges beliefs teams didn’t even realize they were operating on.

🏆 Winner
Adil Khan – Aerospace MRB decisioning: AI dismantling long-held assumptions around human-only judgment, conservatism, and risk in multi-million-euro disposition decisions.

🥇 Top 3

1. Adil Khan – MRB non-conformance decisions

2. Manisha B – Telecom retention workflows exposing “discount-first” bias

3. Bijesh – Support escalations revealing the hidden cost of over-scripted responses

✅ Other Approved
G Sri Ramana, Akkul Dhand, Mahesh Vemula,

Well done to all contributors for making hidden assumptions visible, testable, and debatable.