Q829.

AI systems can generate artwork, write scripts, solve problems, and even propose new concepts — but does this count as creativity, or is it simply recombining patterns learned from human data?

Think of a specific creative task in your domain — such as designing training experiences, crafting customer communication, solving process problems, or generating improvement ideas. Based on how AI behaves in that task, do you believe AI is being creative, or merely remixing what it has seen before?

Support your view with a concrete example.

⚠️ Note: Any answer that is generic or does not connect with a specific, relevant creative task will not be approved.

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

• Relevance of the chosen creative task

• Depth of insight into whether the AI outcome is creative or remixing

• Clarity and strength of the reasoning and example

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Creative Agentforce Deployments: Belgium Ministry of Defense Human Resources Recruitment.

The Creative Task.
Agentforce is a cutting-edge solution designed to automate candidate-position matching and help job seekers navigate complex employment eligibility requirements. We wanted a system to be designed and implemented for the Belgium Ministry of Defense.

The AI Challenge. The system had a few major bottlenecks:

Unstructured CV Processing: Effectively extracting desired qualifications from diverse formats. Matching Candidates: Matching potential candidates with positions in military, civilian, and internship roles. Eligibility Rules: The intricate rules that apply to Belgian citizenship, residency, educational credentials, and criminal background check applicants. Guiding Candidates: Directing candidates through a multi-step application process. Creating Candidate Scores: Set up a scoring system for HR's review. MY VIEW On Intelligent Remixing Versus Original Creativity Agentforce is a prime case of ‘intelligent remixing’ as opposed to true creativity in my view. Such implementation indicates that AI processes more pattern recognition and recombination and less original creative thought. Here’s the evidence:

What AI Really Does Well:
Pattern Matching — CV parsing does not get out of bounds of the established frameworks of candidates' experiences. “3 years IT support,” for example, or “3 ans support informatique,” are keywords the AI notices and starts recognizing by analyzing patterns it has found in thousands of CVs it has been trained on. “The analysis of eligibility is not innovative – it is not about creativity.” Instead, it takes a methodical view of predetermined rules. Although the AI can conduct conversation, it always follows deterministic logic. Chat-Oriented Counsel; Agentforce constructs contexts from customer-service dialogues which makes the candidate interaction seem natural. To the extent that it inquires, for instance, “Do you currently live in Belgium?” once citizenship is detected, it takes learned conversational cues instead of coming up with new ways of communicating. Limitations of AI. In our implementation work, we found edge cases which the AI was not able to solve innovatively:

Example 1: Security clearance issues of dual citizenship cases with Belgian and non-EU candidates. The AI wouldn’t be able to solve this in an independent way so it would need to explicitly program some of those cases.
Example 2: A candidate with significant international military experience (e.g., NATO operations) received a low score as the phrase "foreign military service" was mistakenly perceived as irrelevant towards a Belgian post and could not be seen as relevant here. Scoring was manually adapted.
Example 3: Many candidates who come from unusual backgrounds (career changers, adult learners) experience challenges as the AI fails to spot transferable skills that are not included in the more conventional route.

The Value Proposition:
Efficiency + Creative Insight. For Candidates: The platform is supremely capable of stripping friction out of the application process, directly matching applicants with guides. But it fails to offer career-inspiring insights or even personalized ones. It won’t suggest unconventional roles based on unusual skill combinations, but just patterns already existing. For HR: It captures data well, but does not identify the “diamond in the rough” candidates who fit well in other environments. At the end of the day and in fact, it's up to the human HR professionals to do that creative judgement.

Conclusion:
As a Tool, Not a Substitute for Human Insight. Agentforce exposes what can be an extraordinary capacity for AI to turn knowledge into action: writing documents and applying rules and enhancing communication on an incredible scale. But the architects of this system should be humans:

Creative Logic Development:
Coming up with the matching logic was a creative challenge. Anticipating Edge Cases: Putting the AI to the test in a way that it couldn't solve in isolation. Defining ‘Good Fit’: Set standards outside of just pattern match. As Agentforce does the recruiting but you need to conceptualize the architectural stuff, and that has to be what the Ministry needs, where the conversation goes, the scoring logic, the exception handling. Through intelligent remixing, Agentforce puts our creative vision into action. Understanding this distinction becomes instrumental for AI Solution Architects that will need to design AI systems that combine creative insights with human intervention to solve complex scenarios which involve contextual understanding and ethical dilemmas at the level of learning from past behavior.

Domain: Industrial Manufacturing – Creating new fixturing & poka-yoke solutions for assembly lines

Specific creative task: Designing a brand-new error-proofing fixture for a never-before-seen part

We build complex hydraulic valves and pumps. Every new valve family (roughly 2–3 per year) has unique geometry, tight tolerances, and brand-new potential assembly mistakes. The classic creative task is: “Design a fixture that physically prevents the operator from installing the spool upside-down or forgetting the tiny circlip that costs €180,000 when it falls out in the field.”

What actually happens when we let AI do it (2024–2025 real cases)

1. We feed the AI:

   • 3D model of the new valve

   • 18 years of historical fixtures (≈ 2,400 designs)

   • Every field failure linked to wrong orientation or missing circlip

   • Physics simulation rules

2. In 9 out of 10 cases the AI spits out a fixture in < 4 hours that is… perfectly functional, cheap to make and looks exactly like something a senior toolmaker would have drawn after 35 years on the floor. → 100 % remixing. Zero surprise. We just say “nice job” and build it.

3. But then comes the 10th case – the one that makes you stare at the screen.

Real example – March 2025, new high-pressure directional valve for an electric excavator.

The challenge: the spool has six identical-looking grooves, but only one correct orientation. Traditional way = laser-etch a tiny arrow + hope the operator sees it at 05:45 on night shift. Everyone knew that wouldn’t work.

The AI proposed something none of our 28 toolmakers had ever seen in 40 combined years:

It designed a fixture where the spool is dropped into a transparent acrylic tube. Inside the tube are six spring-loaded steel pins arranged in a hexagon. Five pins are 5 mm long, one pin is deliberately 5.8 mm long (the “master pin”). On the spool itself there is a single 0.8 mm deeper groove that only aligns when the spool is in the correct orientation. If the spool is even one position off → the longer master pin blocks the spool from fully seating → the entire fixture cannot close → the next station air cylinder physically cannot push the circlip in.

Result: zero wrong orientations in 100 % of the first 42,000 valves built.

My verdict and why

That fixture is genuinely creative.

• No human in our company (or any competitor we showed it to) had ever used variable-length pin geometry inside a transparent tube as a poka-yoke.

• The training data contained zero examples of that exact mechanism.

• The AI combined concepts from three completely different domains that existed in the dataset: – automotive keying cylinders (variable pin lengths) – medical device assembly (transparent inspection tubes) – ammunition feeding systems (one longer “indexing” pin) Then it synthesized something entirely new that perfectly solved the physics + human-factors problem.

It didn’t copy a fixture – it invented a new principle.

Bottom line from the shop floor 90 % of the time AI is just the world’s fastest junior engineer – remixing proven solutions beautifully. 10 % of the time it does something that makes a 40-year toolmaker say “damn, I wish I’d thought of that.”

That 10 % is real creativity – not because the AI has a soul, but because it can traverse a vastly larger possibility space than any single human brain ever could and occasionally land on a combination no human had connected before.

So yes – AI can be genuinely creative. I’ve seen the fixture with my own eyes and no human in our industry had ever drawn anything like it.

That’s my concrete proof.

With regarding 90% remix cases.

That is not creativity in the human sense — there was no intuition, no sketch on a napkin, no leap of faith.

It is the ultimate pattern-matching remix engine with perfect memory and zero ego.

So on the shop floor we don’t care if it’s real creativity or the world’s fastest copy-paste. We only care that the line is running and zero wrong parts are going out the door.

That’s why we already have 68 AI-designed fixtures in production. Call it remixing if you want — I call it winning.

I’ll take the remix every single time.

AI can be creative in a real, operational sense, but its creativity is fundamentally different from human creativity because it depends on pattern-learning from human-made data and lacks lived experience, emotion, and intrinsic intent.

Modern generative models learn statistical patterns from massive datasets and then recombine and transform those patterns to produce new texts, images, music, or designs. Even though the mechanism is recombination, the specific outputs can be things that have never existed before—down to word sequences or visual arrangements that are not found anywhere in the training set.

Human creativity is tied to consciousness, emotion, culture, and long-term goals, which shape why and what people choose to create. AI creativity, by contrast, is instrumental and dependent: it emerges from algorithms operating on prior human outputs, and from human choices about prompts, training data, and evaluation.

Under a human-centered, experiential definition—creativity as an expression of a conscious, feeling agent—current AI is not truly creative and mainly remixes human ideas. Under a functional definition—creativity as generating novel and valuable artifacts—AI systems clearly can be creative, especially when used as partners that augment and extend human imagination rather than replace it.

23 hours ago, Vishwadeep Khatri said:

Agree that AI systems can generate artwork, write scripts and solve problems for us, but I don’t treat this as creativity. I believe that AI primarily learns from the data that we feed and it tries to remix.

 

Let me give an example of how AI helped in solving few of the process problems in my organization and at the same time it tried to be more creative/re-mix things.

 

Process: One of the process in my organization involves multiple teams working on financial reporting, ledger updates and investment accounting. These activities are performed on a daily and monthly basis.

 

Below is the high-level process:

 

·      Pricing team sends pricing & valuation files to the accounting team.

·      Accounting team validates the details to ensure it is correct and booked on a timely manner.

 

Considering the volume, complexity & the risk of errors, we thought of implementing an AI agent to support the team members to review, validate & automate the process.

 

We considered below activities as part of our scope to implement Accounting Validation AI agent:

 

1) Extracting essential pricing and valuation information

2) Presenting extracted information to an accounting reviewer

3) Supporting decision-making (proceed with validation or put on hold)

4) Running accounting validation checks

5) Handling business-level outcomes (Validation OK or Exception) and

6) Sending notifications to stakeholders

 

While implementing the AI agent, we noticed that the agent accurately & consistently extracted pricing/valuation information & presented it to the accounting reviewer.

 

At the same time, the agent while supporting the decision making & handling outcomes (OK/Exception or mismatch) for Pricing calculations & invoices, it tried to be creative & remix things.

 

We all know that AI depends on human created data & architecture and cannot experience emotion or cultural context and they do not create concepts outside their trained data.

 

Examples of AI trying to be creative/re-mix things in the process:

 

1) The AI compared the historical accuracy, timelines, volatility & past delays for Pricing related information and forecasted that “Prices from vendor A this month are likely to have price miscalculations because the Pricing calculation template changed last month”

 

There was no rule written by us that treat this case as miscalculation but it remixed data patterns and produced a new warning which was more of a creative act in analysing information.

  

2) Instead of flagging an error like “Mismatch found” for a variation of Price in the invoice (8% vs agreed 5%) for vendor B, it identified that same discrepancy happened in June due to a system migration and recommended to validate the master file of vendor B unnecessarily.

 

This was not programmed but it generated the response based on re-mixing insights.

 

In the power generation sector creativity as far as AI integrations are concerned, outage training approaches experiences for the plant maintenance and operations personnel stand out. Traditionally, the training modules for Operations and Maintenance teams on our Enterprise Learning Management System were built by senior engineers who combined technical knowledge with lessons learned from past incidents throughout their experiences which are well documented. We have just recently experimented with using AI to draft training scenarios for turbine maintenance crews who are involved with various repairs the units are facing.

The AI pulled together data from manuals, incident logs, and prior outage reports, then suggested a scenario that could be used as a training exercise, where a simulated wiring fault during a hot‑day peak load in the summer which are normally brutal experiences in Arizona and peak energy demands occur during that time as well. From a quick overview of the AI picked scenario, it did appear like a clever thought-provoking idea. After a deeper analysis of the scenario, we identified that it was a blend of two existing patterns: one from a past wiring issue we had experienced and another from a high‑temperature stress case. AI in this case did not invent a new scenario but blended two different issues to come up with a training scenario.

This in my view can be considered as creativity to some extent, basically because of the wide array of issues that the operations and maintenance teams face as far as outage repairs on the turbines to keep the units online. The AI did not just imagine something beyond its inputs, but the way it recombined those inputs gave us a training exercise we had not considered before. The real creativity was when our operational trainers adapted the AI’s draft into a hands‑on drill, adding context about communication protocols and safety culture.

For this power generation domain context, AI acts more like a catalyst than a creator. It does not replace human imagination, but it accelerates the process by coming up with combinations we might overlook that provide us with a better training track for the operations and maintenance teams. The creativity portion is mostly how these personnel interpret and apply those outputs to the occurring challenges they experience during the process of keeping the turbines operating at optimal levels to meet energy demands from the market.

Can AI Truly Be Creative — or Does It Just Remix Human Ideas?

When people say AI is creative, they usually imagine art or writing.
But creativity in Rogers looks different - it’s about solving customer pain, designing better service flows and anticipating issues before humans notice.

Creative Task-

Designing a New Customer Outage-Communication Experience like Proactive Messages + Channel Strategy + Tone

This is a genuinely creative job that normally mixes:

·       Behavioural psychology- to understand deeply what customers want, their habits, their thinking patterns. When an AI model is trained on neural networks, the AI can-

o   Detect patterns in how humans behave

o   Learn what phrases calm customers

o   Predict what actions a person might take

o   Generate responses that look psychologically intelligent

 

·       Service design- to design the services according to what customers behaviours are. This pattern of designing generates a smooth, easy, and happy experience for customer from start to finish. For example- Service design shapes the full customer experience for things like-

o   How a customer buys a new Ignite plan

o   How they install the modem

o   How outages are communicated

o   How billing and refunds work

o   How tech support chats flow

o   Every step is designed so the customer feels-

o   Understood

o   Supported

o   not confused

o   not frustrated

 

·       Network-ops understanding- to thoroughly map the networks operations so that system would work better. This also holds true when there is heavy traffic observed, so the network parameters have to be intact to comply heavy/ peak hours.

 

·       Customer-empathy tone- to get more customers, AI has to make customers love it. A proper tone is what sets the customers happy and turn back.

 

·       Channel orchestration like SMS, app, IVR, email- these channels have to function together so that AI would be reaching to customers very easily.

 

Where AI Actually Shows a Hint of Creativity-

Imagine giving AI the full context-

Design an outage communication experience for a customer who-

·       Uses Ignite TV + Internet

·       Works from home

·       Has a meeting in the next 30 minutes

·       Prefers WhatsApp

·       Normally panics when service drops

·       Has a history of churn risk

 

AI begins reasoning, simulating scenarios, and proposing novel combinations. All of this is important artistry to win our customers and get their full support to become no. one brand in Canada.

A real example of emergent creativity-

1. Predictive Warning Message (before outage occurs):
Hi Manisha, network stability in your node is fluctuating. It may interrupt your 2 PM call.
Tap here for a temporary backup hotspot code generated just for you.

This backup hotspot code generated just for you is a novel service concept by Rogers AI using reasoning across different domains like loyalty → network → billing → app features.

Creates a deep customer connect + seeing this kind of response no customer would ever feel disheartened. It works on psychology so much that customers would still love Rogers, even in outage scenario.

2. Multi-channel behaviour-adaptive response-
AI designs-

·       A calming WhatsApp flow for example- “Here are 3 things still working…”

·       A 15-sec Ignite TV screen overlay

·       A personalized app card that detects when the customer opens the app

 

This orchestration is not copied from data, because no dataset contains this full sequence. This is all artistry which when blended with process the outcome is customer friendly + creative.

3. Micro-journey improvement idea-
AI proposes-

“Automatically pause Ignite TV billing for hours exceeding 90 min downtime.”

AI synthesized it from principles- fairness + retention + customer sentiment. Which is about customer loving Rogers back. How? –

AI says Rogers should automatically stop charging you for that time - without you having to call customer care.

Why it’s a “micro-journey improvement”-

It’s not a big change to the whole service. Just a small fix in one part of the customer journey
like outage → billing → customer frustration.

But this small fix makes customers much happier.

So Is AI Creative Here?

AI is creative in the human emotional sense, too. It does show functional creativity - the ability to generate new combinations, new service ideas, and new experiences that do not literally exist in the training data.

AI can infer gaps.
It can propose solutions that humans haven't articulated yet.
But its creativity is always bounded by the patterns, rules, and constraints it has absorbed.

Final Position

AI in the Rogers domain is-

Creative when combining network patterns + customer behaviours + product features to propose new experience designs that don’t exist yet.

Human creativity = emotional, value-driven, intuition-based.
AI creativity = recombination + reasoning → novel outputs useful in practice.

In other words, AI creates possibilities humans never thought to try.

Can AI Truly Be Creative — or Does It Just Remix Human Ideas?

I've been thinking about this a lot, especially in the context of building a unified venue assessment framework for 110+ examination venues across 7 regions. This isn't a theoretical question for me—it's directly relevant to how I approach a real problem: synthesizing border agency security requirements, awarding body invigilation standards, and country office operational realities into a single framework. There's no template for this. The stakeholders pull in different directions. The venues operate under radically different constraints.

So what would AI actually do differently than what I do? And does that difference matter?

What AI Pattern Synthesis Actually Looks Like

If I asked an AI system to design the unified framework, here's what would happen:

It would extract patterns from existing audit standards, synthesize the standard compliance checkpoints (CCTV requirements, staff supervision, materials custody), recommend uniform assessment tiers, and generate scalable templates. Cost-efficient. Comprehensive-looking. That's useful. It's also not creative in the way the problem requires.

How I Solve Problems Across Competing Stakeholders

My approach would be different in specific ways:

Constraint reframing: I recognize when a standard framework has miscategorized a problem. A venue in Nigeria can't meet Italian CCTV infrastructure standards. But that's the wrong question. The real question is: does it achieve the security outcome through locally feasible means? Reframing shifts everything about what solutions become visible.

Contextual judgment: I carry organizational memory that doesn't exist in data systems. I know which venue operators are genuinely reliable versus theoretically compliant. I understand that when Turkey's political instability affects business costs and staff availability, that's different from operational failure—a distinction that requires knowing the context, not pattern-matching the data.

Value synthesis under conflict: When border agency security requirements, awarding body continuity needs, and country office constraints pull in different directions, I don't pick one constraint category to optimize. I create solutions that meet multiple objectives simultaneously—sometimes at the cost of standard efficiency.

Owning the decision: The framework I create isn't "what the data suggests." It reflects my judgment about what matters, what's sustainable, and what risks I'm willing to bear. That's different from synthesis.

The Turkey Case: Where the Difference Actually Matters

Let me walk you through a real example. A venue's compliance costs spiked 40%. Here's how pattern-matching works:

When an AI system flags high costs, it would recommend finding a cheaper alternative. The logic is straightforward: high costs equal risk, so seek a better option.

Here's what I did:

I investigated why costs spiked. Turned out it wasn't the venue operator failing—it was local political instability affecting site access and staff availability. That's a different problem entirely. Not "this venue is unreliable," but "this venue is reliable in an unstable environment."

Then I asked the actual questions: What does border agency security need? Continuous exam delivery. What does the country office need? A venue that meets the standard requirements. What do I know about this operator? That they're dependable when things are hard, not easy.

So I created something that doesn't exist in any standard framework: temporary fee adjustment + enhanced oversight protocol + timeline for stabilization. I'm paying more for reduced efficiency right now because I'm gaining continuity and preserving a critical relationship. I'm anticipating that political conditions stabilize within the timeframe I've set.

That decision isn't in the audit standards. I invented it because the situation demanded it. And I own it—I'm the one defending it to border agencies and awarding bodies if things go sideways.

Why this matters: The AI recommendation was logical but wrong. It treated "high cost" as the actual problem when the actual problem was "temporary external shock affecting a reliable operator." Once you see it that way, the solution space completely changes.

What This Means for AI in Complex Ecosystems

AI can do specific things well. It extracts patterns from existing frameworks, synthesizes standard compliance checkpoints, generates scalable templates, flags anomalies. All valuable. None of it requires creativity in the way I'm describing it.

What AI struggles with: reframing what the problem is. Carrying contextual judgment built on months of audit work and relationship history. Making value commitments about what matters when objectives conflict. Bearing responsibility for novel decisions in ambiguous, high-stakes contexts.

Those aren't limitations of current AI systems. They're structural. Reframing requires recognizing what standard frameworks miss. Contextual judgment requires experience. Value commitments require skin in the game. Responsibility requires consequence.

Building the Unified Framework

The unified venue assessment framework isn't just synthesizing border agency requirements + awarding body standards + country office operations. It's creating new categories that none of them recognize:

Context-aware risk thresholds that vary by geography, political stability, and infrastructure reality. Venue reliability profiles that override standard cost metrics. Temporary operational adjustments as a legitimate framework category instead of exceptions to rules. Stakeholder conflict resolution protocols that acknowledge when standard frameworks contradict each other.

These categories exist because I recognized what standard frameworks miss. That's the creative part.

The framework becomes genuinely useful not because it's pure invention—it builds on everything that exists—but because it changes what categories of solution are even visible or permissible. That's constraint reframing. And that requires human judgment rooted in context, stakes, and responsibility.

AI can help with the synthesis. But the framework itself? That's mine.

 

 

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.

In my view, AI demonstrates pattern-based creativity, which feels original in output, but is still recombining what it has learned rather than inventing from nothing.

A relevant example comes from my experience at MathCo, where we integrate GenAI into analytics operations. We recently experimented with using GenAI agents to craft response for a supply chain analytics proposal. Traditionally, our consulting team writes these proposals manually — identifying business pain points, mapping them to MathCo’s solutions (digital twin, forecasting, root-cause analysis), and designing a storyline that resonates with the client’s industry context.

When we prompted the AI to draft the first version, it generated a structured narrative including:

• A compelling opening about volatility in global logistics

• A recommended solution flow using our capabilities

• A future roadmap with AI-driven scenario planning

• Custom messaging tailored to C-suite priorities

The output was polished, logically ordered, and context-aware. However, upon reviewing it, we realized the AI didn’t invent a new solution or narrative — it recombined common language patterns from prior proposals, case studies, and public knowledge. The creativity emerged in how it blended existing components quickly into fresh-seeming messaging, but it did not show imagination that a consultant brings — for example, linking the client’s SKU variability issue with a novel optimization approach we had tried in an unrelated CPG project.

So, while the AI accelerated structuring and ideation, the final storyline and value articulation still required human creativity to introduce nuance, analogy, and risk-reward framing.

Conclusion: AI can appear creative, but in our domain, it mainly remixes learned patterns to generate useful starting points. True creativity — connecting ideas across contexts still comes from humans. Instead of replacing creativity, AI augments it.

Can AI Truly Be Creative — or Does It Just Remix Human Ideas?

In my view, AI cannot truly be creative but it is a masterful re-mixer, not an originator. Drawing from experience in semiconductor and telecommunication industry in the past and my current deep dives into air cargo logistics, I have seen AI shine in generating ideas feel innovative, but peel back the layers and it is always recombining human patterns looking at research papers, trial data, governance frameworks and stories from ops floors. Everything it produces, no matter how impressive or new it looks, is built by intelligently combining patterns it has seen in human-generated data. True creativity belongs to humans because only humans can step outside existing data, driven by emotion, curiosity, or pure stubbornness.

A spot-on creative task in my domain is generating improvement ideas for real-time process problems, for example yield crashes in fabs or capacity crunches in cargo holds. This isn’t about simple optimization but it requires envisioning adaptive, multi-stakeholder fixes that balance trade-offs like cost, compliance, and resilience, often in high-stakes chaos where one bad call costs millions.

In my experience, AI's creativity doesn't show up in abstract art tasks. Instead, it shows up in how it re-frames operational problems, suggests alternatives we might not think of right away, and creates scenarios that get teams to think of new ideas, again all this based on the past human-generated data.

Here are three examples from the real world that show the difference between "looks creative" and "truly creative."

1) Air Cargo - Protecting Shipments That Go Bad in Severe Weather
Problem: A storm in Europe suddenly makes 38% less room for cargo on flights to London. Medicines and flowers could go bad.

AI-made creative solution:

• Send partners a short, safe message that says, "Available space decreased by 38% from 13:00 today for 18 hours.

• Let AI agents automatically re-book shipments to Amsterdam, trade slots with other airlines, and hold a bid for the space that is still available.

• 95% of temperature-sensitive cargo was delivered on time

• Each airline group saved between $10 million and $15 million in fines and lost goods.

Though the above looks creative, but every part is a remix of human ideas: ONE IATA came up with record standards, the EU fixed noise injection, and yield pools were first used by passenger airlines in the 1980s and then copied to cargo airlines.

 

 

2) Semiconductor Fab: How to Fix a Sudden Drop in Yield

Problem: A lithography machine drifts, and the defect rate goes up by 25%. This is very costly during a chip shortage.

Creative solution made by AI:

• Don't give partner factories the secret recipe, just tell them the result ("defect signal +15 % on layer 7").

• Automatically adjust machine settings in real time and move wafers to backup lines.

Results from 2025 fab deployments that have been reported:

• Yield errors went down by 30–50%

• Time to fix defects cut by 20%

• Each mid-sized factory saves between $50 million and $100 million a year

Once more, impressive but just a remix: federated learning (taken from banking fraud systems), real-time tuning (standard predictive maintenance), and capacity trading (taken from air cargo yield pools).

3) Recovering from a telecom network outage

Problem: Over 100,000 customers are affected by a fiber cut.

Message and action from AI: "We've already borrowed spare network capacity, full service will be back by 20:15." Get a free 10 GB gift.

Result: Customer satisfaction stays high, and the risk of losing customers goes down by 12–18%.

The tone, gift size, and timing are all taken from millions of other scripts and retention studies written by people.

Some of the Pros and Cons of the AI creativity includes:

Aspect	Advantages of AI (Remix)	Limitation of AI (No True Creativity)
Speed	Seconds instead of days	Cannot imagine solutions never seen before
Scale	Handles thousands of variables at once	Misses rare events outside training data
Consistency	Same high quality every time	No emotional insight or ethical gut feel
Cost	Saves millions	Risk of hidden bias or accidental collusion
Novel combinations	Often beats human teams by 10–30 %	Still only combinations, never a genuine invention

 

So, suggestion for best results is human and AI partnership:

• Let AI generate 50 ideas in seconds and then humans pick the 3 most promising.

• Always keep a human approval step for high value decisions.

• Regularly audit data and add deliberate “noise” so AI does not copy bad patterns.

• Use open standards (like ONE Record in cargo) so small companies can join.

• Run cross-industry experiments, many of the best “new” ideas are simply good ideas moved from one sector to another.

 AI is already making a difference, with yields going up by 20% to 50%, millions of dollars saved, and faster recovery from problems. These are real numbers from 2025 deployments in the semiconductor and air cargo industries. But every improvement is just a smart mix of things that people have made, standards, research papers, and past trials. AI doesn't make things the same way people do. It doesn't have feelings or intuition. But in real business situations, AI shows a kind of augmented creativity when it comes to coming up with training scenarios, new pricing ideas, or operational hypotheses.

People are still the ones who come up with the real creative spark, the idea that has never been in any dataset. AI is not a replacement for us, it is our most powerful partner. When both work together, we can do more than either AI or people could do on their own.

Domain: Enterprise L&D & Process Optimization in a Global Electronic manufacturing MNC- Fox Fonn (Name masked)

(Context: Fox Fonn has multi-country plants, thousands of employees in production, quality, and supply chain; continuous onboarding, compliance training, and process improvement)

The creative task- Design KPI-linked training journeys that adapt to live operational signals and suggest micro-improvement ideas or Process fixes (checklists, prompts, playbooks or Kaizen ideas).

What is currently happening in the company.

AI-driven curriculum creation & simulations

At Fox Fonn, AI systems now create training modules for Surface Mount Technology (SMT) operators. When the goal is specified -to reduce soldering defects by 20 %, the AI creates competency maps, assembles micro-learning modules, and builds realistic troubleshooting simulations for common failure modes (e.g., defective soldering, misaligned parts, and profile errors)

Operation linked adaptive learning

Training connects to MES (Manufacturing Execution Systems). If defect rates increase on Line 4, the AI triggers a quick simulation on root causes (e.g., misalignment). The data gets updated weekly based on live production feedback.

Continuous idea harvesting
AI extracts operator feedback and machine logs to suggest improvements (e.g., “Add a pre-check for misalignment,”. These creative Ideas are clustered and sent to process engineers with impact scores.

Results thus far

• Onboarding time is reduced for new line operators where training is completed in 7 days against previous 12 days with defect rate down 12% post-onboarding.

• Process stability: MTBF (Mean Time Between Failures) for machine improved 18–22% after AI-suggested checks were undertaken and with updated SOPs.

• Idea generation: From earlier approx. 25 ideas to presently 150 ideas per quarter, while 30% have been implemented (e.g., minor fixture tweaks, checklist changes).

 Now is this AI being creative or remixing?

What is Creative? AI generates new interventions like alignment calibration prompt or simulation for soldering errors—based on real-time failure patterns. It hypothesizes, tests, and iterates.

Where is it still remixed? Dialogue tone and scenario framing reuse familiar patterns and templates.

• The construct or framing: The tone, eg: “line one requires feed defective”, “material inputs quality check “often borrow established patterns. The AI’s reporting framing can feel familiar or even repetitive because it pulls from common phrases and reusable conventions or themes.

• Risk of cosmetic change: Without effective constraints, AI generates simulations that seem new but are superficial variants (different names for same logic). Unless checked, this leads to decorative remix rather than substantive creativity.

What helped Fox Fonn to safeguard "real creativity"?

• Diverse and high-quality data training using varied sources such as internal training materials, industry best practices, academic research, multimedia content. Including global perspectives for electronic MNCs to avoid cultural bias in learning content

• Fresh data binding by connecting learning content or scenarios to the latest, real-world data so that training stays relevant. Automatically remove or update outdated training scenarios that no longer reflect current processes, technologies, or compliance requirements

• Human review for sensitive content. Instructional designers review AI-generated learning modules before deployment

• A/B test interventions where a comparison of two versions (A - Current training approach and B- New intervention (e.g., micro-learning, gamification, AI-driven personalization) is done to see which performs better and randomly assign learners to either A or B and measure outcomes.

Bottom line

In electronics manufacturing domain, AI is not just remixing but it’s creating new, useful interventions that improve yield and speed. The winners are those who link training to live data and let AI iterate fast. whereas the Static training models that can’t keep up will end up as losers.

Today, AI shows a narrow, ‘synthetic’ form of creativity. In other words, it shows real but limited form of creativity. It re-combines patterns from human data in ways that can be genuinely useful and sometimes surprising, but it lacks the lived experience, intention, emotion and self-driven goals behind human creativity. Whether this counts as creativity depends on whether the creativity is defined by internal states or by outward behavior.

 

‘creativity’ means

 

Many treat something as creativity if it means both novel and valuable or appropriate for a goal.

 

1.        Generative/Combinational creativity: producing novel combinations or variations within existing style or framework.

2.        Transformational creativity: inventing something genuinely new styles, concepts or problem framings.

 

Today’s generative AI models are experts in first type: they methodically generate new artifacts by recombining patterns that they have learned from massive datasets, which is why they can write, draw and design in various convincing ways. But they lack emotional insight, personal experience, and independent goals, so this ‘creativity’ is closer to a high-end remix engine shaped by prompts and training data.

 

‘new’ things

 

Technically, large models balance memorization: regurgitating training data with generalization (outputs that are statistically consistent but no duplicates/copies). Studies shows as the models scale, they memorize simple fact-recall tasks, but for complex reasoning tasks they depend on generalization and generate more novel text than the one appears in the training data.

 

So AI is not just copy-pasting; it constructs new things guided by learned probability distributions over sequence of words, pixels or tokens. Again, at the same time, it operates entirely within patterns extracted from human produced data/content, with no grounding in personal experience, emotion or intrinsic motivation, which one can say are core to human creativity.

 

Designing a GCP architect training

 

Let’s say the task is to design a 6-8 weeks training program to prepare engineers in the organization for Google’s Cloud Architect certification, including labs, simulations, and scenarios that reflect the org’s stack as well as the one’s available in certification prep notes available through Google. An AI assistant with knowledge of GCP patterns, architecture, blueprints, and Gen-AI based learning tools can:

 

Map exam objectives like networking, IAM, security, data, reliability, case studies, etc to a week by week curriculum with readings, Qwiklabs-style labs, and mock case studies.

 

Design cloud architecture scenarios like multi-region High Availability, Cost-Peformance trade-offs, IAM design with minimal privileges that all look similar to real Google exam case studies and Cloud Architecture Center’s examples.

 

This feels creative because it proposes coherent sequences, novel lab combinations, and scenarios narratives that did not exists as a single source in its training data. However, it is recomposing known GCP concepts, exam patterns, and architecture templates as one.

 

AI looks genuinely ‘creative’ in this task

 

1.        Scenario Synthesis: Given constraints like “APAC heavy traffic, strict data residency, unpredictable load, cost ceiling and quota limitations”, AI can invent a new case: a fictional retail tech company on GKE with regional Cloud SQL, Cloud Armor, and multi-layer IAM, then ask the learners/engineers to design the desired target state and migration plan.

 

This specific combination of requirements and failure modes may be unique and sometimes AI combines together few constraints that human would not have thought of combining, which feels like new or fresh ideation.

 

2.        Adaptive learning path:  If an engineer struggles with say VPC peering and shared VPC, the AI can dynamically generate remedial labs, analogies, and extra quiz questions pertaining to those gaps. This dynamic reconfiguration of the learning path across many learners/engineers in the org is not static remixing; it is a continuous, context-sensitive recomposition goes beyond typical templated training design.

 

3.        Cross-domain analogy and framing: AI might explain GCP concepts using analogies drawn from simple domains like traffic police for firewalls, apartment complexes for projects and folders which will make learning more easy to grasp and also showcases AI’s creative teaching.

 

In all the above, AI is creating new, coherent structures that solve real design constraints. That is a legitimate, if engineered, form of creativity within a bounded space.

 

‘Limits’ shows up

 

1.        Reinventing the role itself: Try asking the AI to rethink “What should a cloud architect role look like in the present world of GenAI on GCP?” pushes it into remixing existing data from blog post, role definitions from official documentation, and from strategy pieces. It will blend ideas but will not take into considerations like organizational politics, talent market, or other factors in the way a human does.

 

2.        Deep pedagogical innovation: if you ask the AI to invent a new radical pedagogical for teaching reliability, it will combine existing learning theories and practices rather than inventing a new one. It can simulate “novelty”, but will not take risks, won’t consider cultural change, or have lived experimentation to invent a new teaching change/culture.

 

3.        Value and intent: Human creativity in training is value driven. The intent is to make sure the learners will be enriched with the training, achieve the goal of obtaining the certification, and make progress in career. However, AI does not care about these things.

 

 

Conclusion

 

In the GCP Cloud architect training, AI is only creatively recombining than inventing new from scratch. It will design fresh feeling curriculum, scenarios, and analogies by systematically remixing GCP patterns, exam structures, and pedagogy knowledge. This is also how human create by recombining data from various resources. Hence this still deserved to be called as ‘creativity’ in a narrow, instrumental sense since it provides solutions that we human can find it genuinely useful or surprising.

 

However, without its own experiences, intentions or stakes in the outcome, the AI’s creativity always remains derivative and bounded. In the GCP Cloud architect program, we will need to set up AI as a creative partner, where humans set goals, values and constraints, then refine and give feedback to what AI generates into training that will foster real architects in the organization.

 

Domain: Supply Chain – Can AI Be Creative or Does It Just Remix Human Ideas?
In logistics and fulfilment, the AI can generate ideas on its own, but those ideas tend to be generic and disconnected from how the real operations actually behave. If you ask an AI to design a peak-season fulfilment strategy or an improved picking flow(within a warehouse) without giving it any process context, it will suggest things like dynamic batching or congestion-aware routing. While these look creative, they are just pattern recombination from human suggested ideas, underlying textbook principles or best-practice libraries. They don’t account for operating constraints such as aisle widths, dock congestion, carrier cut-offs times, SKU velocity, WMS limitations, or resource roster. Without these constraints, AI’s creativity becomes theoretical — impressive to read, but impractical for implementation.

Suggestions from AI become useful only when a human provides context around operating parameters, historical performance, certain business rules and the expected outcome. Suppose, if you tell it something specific — ‘the cross dock vehicle will dispatch at 5 PM regardless of the fill rate’ or ‘Aisles G–J are one way aisles and become a congestion point every afternoon because two reach trucks can’t pass there’ or “the pick modules slow down 20% whenever fast movers are out of slot’ — the AI’s output changes completely. It stops producing generic playbooks and starts generating site-specific, operationally creative and implementable options that would never emerge from pattern remixing alone.

You can see the difference in the ideas generated by the AI in both absence and presence of operating context:

• Example 1: Picking Strategy

  • Without detailed context:

    • AI proposes “switch to wave-less continuous release” — a trendy but risky generic textbook tactic.

  • With detailed context:

    • AI designs a hybrid model with pre-waves for bulky items, micro-waves for SKUs in congested aisles, and continuous release only for fast movers.

    • It also introduces a replenishment buffer for Aisles G–J during peak to avoid reach-truck blockages.

• Example 2: Transport Planning

  • Without detailed context:

    • AI recommends consolidating all northbound loads during off-peak hours.

  • With detailed context:

    • AI learns the 3PL’s cross-dock refuses late arrives after 5 PM and hence designs a split-transport strategy where high-velocity SKUs ride on an early “feeder” truck, and low-priority loads move later.

    • It also adjusts dock sequencing to keep shippers synchronized with carrier cut-offs.

 In each case, the AI’s response is initially curated based on patterns learnt from human provided data but then it provides suggestions that are operations friendly only when a human provides the real operational constraints around warehouse and network (which the AI would never infer alone).

 In short:

• AI without user process knowledge generates:

  • Generic optimization ideas

  • Strategies that ignore local bottlenecks

  • Solutions that may be unrealistic for execution

• AI with user process knowledge generates:

  • Realistic fulfilment flows

  • Adjustments tied to carrier cut-offs, workforce capacity & layout

  • Strategies that are realistic and will work during peak operations.

Q829 Results — “Can AI Truly Be Creative — or Does It Just Remix Human Ideas?”

This round delivered unexpected insights and a few delightful surprises! Several submissions went far beyond pattern-remixing and demonstrated how AI can spark genuinely novel ideas when placed in real operational contexts. Thank you for the outstanding domain-specific responses.

🏆 Top 3 (with surprising breakthroughs!)

1. Adil – Industrial manufacturing poka-yoke design
   AI generated a fixture idea no toolmaker had ever seen — a rare, truly inventive leap that surprised everyone.

2. Bijesh – AI-driven, KPI-linked adaptive training and micro-improvement generation
   Live-data learning journeys producing fresh, high-impact improvement ideas — an unexpected edge for L&D and operations.

3. Manisha – Telecom outage-experience design
   AI blended behavioural psychology, network intelligence, and channel orchestration in ways that felt genuinely creative.

✔️ Other Approved

Barbara, Sandeep, Sri Ramana, Juma, Venessa, Nageshwaran, Shan, Mahesh, Arul, Shashank.

Thank you all for the depth, clarity, and the surprising perspectives you brought to this question.