Category: Artificial Intelligence

Mirakl Santa Quits

A Christmas campaign built with generative AI

Mirakl, the ecommerce software and marketplace platform provider, has launched a global Christmas campaign built around a 60-second brand film titled “Santa Quits”. The creative twist is not the plot. It is the production method. The film was created with AiCandy Australia, with every character and scene generated via AI, then shaped into a finished narrative through human creative direction and filmmaking craft.

The story. Santa resigns. The world panics. An elf restarts operations

In the film, Santa resigns under modern seasonal pressure, triggering worldwide protests as people demand Christmas be saved. The resolution is deliberately on-theme. An elf restarts the operation using “agentic commerce” powered by Mirakl Nexus, restoring gift delivery in time for Christmas Eve.

Why a #B2B brand film is suddenly the sharpest product demo

This is the part that matters for marketers. Mirakl is using AI to tell a story about AI-powered commerce. The plot is a metaphor for the underlying message. When expectations become impossible, legacy operations break. You need infrastructure designed for the age of AI agents, not just more people and more dashboards.

That is also why the film lands beyond the seasonal punchline. It frames “agentic commerce” as an operating model, not a feature. In a world where agents search, compare, and transact on behalf of customers, brands need systems that keep product data, availability, pricing, and promises coherent at scale.

The production lesson. AI does not replace craft. It changes the economics of craft

AiCandy’s claim is not that AI makes creativity optional. It is that AI filmmaking can deliver cinematic work faster and on tighter budgets, as long as human direction stays in charge of narrative, tone, and finishing. Mirakl, in parallel, positions the campaign as a proof point that it understands the AI shift in commerce deeply enough to build for it, and market it, at the same time.

The takeaway. Do not copy the gimmick. Copy the system

If you are tempted to reduce this to “AI-made ad”, you miss the strategic move. The film works because it connects three things into one coherent story:

  • A familiar cultural moment (Christmas pressure).
  • A clear operational failure mode (the system cannot scale).
  • A product truth (agentic commerce needs infrastructure).

That is the blueprint worth stealing. Make your narrative demonstrate the future you are selling. Then make the medium reinforce the message.

A few fast answers before you act

What is “Mirakl Santa Quits”?

“Santa Quits” is a Mirakl global Christmas campaign built around a 60-second brand film. Mirakl positions it as a story about modern commerce pressure and how agentic commerce can restore operations at scale.

Who created the film and how was it produced?

The film was created by AiCandy Australia. Mirakl states that every character and scene was produced via generative AI, then shaped into a finished narrative through human creative direction and filmmaking craft.

What does “agentic commerce” mean in this context?

In this story, agentic commerce refers to software-driven agents that can execute commerce operations with a degree of autonomy, such as coordinating tasks and workflows to restart and run delivery operations. In the film’s narrative, an elf uses agentic commerce powered by Mirakl Nexus to restore gift delivery.

Why is this campaign notable for marketers?

Mirakl intentionally uses AI to tell a story about AI-powered commerce, aligning message and medium. It is also a concrete example of generative AI being used for a brand film, paired with human creative direction, to reach a cinematic outcome under real constraints.

What’s the real business point behind the “Santa Quits” story?

The plot frames seasonal demand as an operational stress test. The resolution suggests that automation and agentic systems can restart and scale commerce operations quickly, restoring reliability when timelines are non-negotiable.

Does this prove generative AI can replace human filmmaking?

No. The campaign itself argues for a hybrid model. Generative AI produces characters and scenes, while human creative direction and filmmaking craft shape the final narrative and quality. The value is speed and efficiency with maintained editorial control.

What is a practical way to apply this idea without making “AI theatre”?

Start with one high-frequency content format and define clear quality criteria and approval checkpoints. Then measure cycle-time, cost, and consistency. If you cannot show repeatable outcomes, you are experimenting, not building a scalable capability.

AI Marketing Self-Service: Idea to Pipeline

Nas.io’s pitch. Type an idea. Get a pipeline

This video is, at its core, a marketing pitch for Nas.io’s Lead Forms product. It positions Lead Forms as a fast, self-serve workflow for marketers and solo operators to move from an idea to an active pipeline by simply typing what they want to sell.

In the demo, the product is presented as an end-to-end “just type” machine. A complete landing page and lead-capture form. Multiple promotional assets for social ads. Then, outreach support that claims to surface prospective customers and their email addresses. All in under a minute.

What makes this different. The workflow collapses into one prompt

The video does not explicitly discuss coding, but the repeated “just type” framing signals a zero-code, no-technical-knowledge approach. The promise is clear. Website generation, ad creative, and lead capture become a push-button process that almost anyone can run.

If tools like this keep evolving in the same direction, the operating model changes. Marketing becomes more like self-service infrastructure than a sequence of specialist tasks. The constraint shifts from execution capacity to offer clarity, positioning, and distribution.

In enterprise marketing organizations, the real constraint is rarely asset creation alone. It is governance, brand consistency, and compliance at iteration speed.

The strategic implication. Marketing becomes a productized loop

The interesting part is not that AI can generate assets. We have seen that already. The real move is the compression of the loop:

  • Define the offer in plain language
  • Generate the page and capture mechanism
  • Produce creative variants for distribution
  • Trigger outreach and follow-up workflows

When those steps become one continuous flow, the advantage goes to teams that can manage iteration speed, quality thresholds, and governance. Not just output volume.

The risk. Speed amplifies compliance and brand debt

One element in the demo deserves a responsible lens. Any promise around finding “prospects and email addresses” must be treated as a compliance topic, not a growth hack. Data provenance, consent, regional privacy requirements, and outreach legitimacy will determine whether this is scalable or brand-damaging.

For enterprise marketers, the right question is not “can we do this fast”. It is “can we do this safely, consistently, and on-brand”.

The takeaway. The future is now, but it needs guardrails

This video is a strong preview of what marketing and entrepreneurship could look like when the path from idea to pipeline becomes self-service. The differentiator will be who can combine speed with signal. Clear offers, clean data, disciplined testing, and brand-safe governance.

A few fast answers before you act

What does “from idea to pipeline” mean in a marketing context?

It describes the full path from a raw concept to an executed, measurable marketing workflow. The emphasis is on turning ideas into repeatable production, not one-off campaigns.

What does “marketing self-service” actually mean?

Marketing self-service means teams can create, test, adapt, and ship marketing outputs without waiting on long queues. The goal is faster throughput with guardrails, not uncontrolled decentralization.

How does AI enable self-service without creating chaos?

AI reduces effort in drafting, variation, localization, and repurposing, but self-service only works when standards are built in. That typically means defined inputs, reusable templates, approval checkpoints, and measurement from the start.

Which workflows are best suited for AI-driven self-service?

High-frequency workflows with repeatable patterns tend to win first. Examples include content variations for paid and social, product copy and enrichment, campaign briefing drafts, translation and localization, and first-pass analytics summaries.

What is the biggest risk when marketing becomes AI-enabled self-service?

The main risk is inconsistency. Brand voice drifts, claims become sloppy, and teams flood channels with low-quality variations. Without governance and quality criteria, speed turns into noise.

What guardrails should teams define before scaling?

Define who owns the workflow, what inputs are allowed, what must be reviewed by humans, and which outputs are prohibited. Set brand and legal checks, define escalation paths, and log what is generated so issues can be traced and corrected.

How do you make AI outputs measurable and finance-credible?

Start with baselines and a small number of outcome metrics that matter, such as cycle time, cost per asset, conversion uplift, and quality measures. Instrument the workflow so improvements are attributable, not anecdotal.

What is a practical first step to move from pilots to a pipeline?

Pick one workflow with clear demand and measurable output. Standardize the pattern, including prompts, templates, checkpoints, and KPIs. Prove repeatability, then scale the same pattern across adjacent use cases.

Lovart AI: Photoshop, Now as Simple as Paint

The Lovart AI ‘designer for everyone’ moment just got real

For decades, creative software demanded expertise. Layers. Masks. Rendering. Color theory. Not because it was fun, but because the tools were built for specialists.

Lovart frames a different future. Instead of learning the tool, you describe the outcome, and an AI design agent orchestrates the work across assets and formats.

What Lovart is really selling. Creative output as an agent workflow

The shift is not “design got easier”. The shift is that the workflow collapses into intent. You type what you are trying to achieve, and the system produces a coordinated set of outputs.

In the positioning and demos around Lovart, the promise is that you can move from a prompt to a usable bundle of creative. Brand identity elements. Campaign assets. Even video outputs. Without tutorials, plugins, or the classic “maybe I will learn Photoshop someday” hurdle.

In enterprise brand teams, the main unlock from agentic design tools is faster option generation while governance and taste still decide what ships.

Why Photoshop starts to feel like Microsoft Paint

This is not a diss on Photoshop. It is a reframing of value.

When an agent can produce a coherent set of assets quickly, the advantage shifts away from operating complex software and toward higher-order thinking:

  • What is the offer.
  • What is the story.
  • What is the differentiation.
  • What should the system optimize for. Consistency, conversion, memorability, or speed.

If everyone can generate assets, the edge belongs to people who can direct the system with clarity and taste, not just execute.

The real constraint moves upstream. Taste, strategy, and governance

The future hinted at here is not “more content”. It is content creation that behaves like a pipeline.

That raises two practical questions that matter more than the wow factor:

  1. How do you keep quality high when output becomes abundant.
  2. How do you keep brand coherence when anyone can spin up campaigns in minutes.

This is where the craft does not disappear. It relocates. From hands-on production to creative direction, guardrails, and decision-making.

The takeaway. The future is here. Are you ready to direct it?

Lovart is a signal that creative tooling is becoming agentic. The barrier is no longer the interface. The barrier is how well you can articulate what “good” looks like, and how consistently you can repeat it across channels.

The future is not coming. It is already here. Are you ready?

A few fast answers before you act

What is Lovart in one sentence?

Lovart is a design-oriented agent experience that turns a brief into a guided workflow. It plans, generates, and iterates across assets, rather than handing you a blank canvas.

How is this different from using Photoshop plus AI tools?

The difference is orchestration. Instead of switching between tools and prompts, the workflow becomes “brief to deliverables” with the system managing steps, versions, and outputs.

Does this replace designers?

It can replace some production tasks and speed up concepting. It does not replace taste, direction, brand judgment, and the ability to decide what is worth making.

What should brand teams watch closely?

Brand safety, rights and provenance, and consistency. Faster creation increases the need for clear guardrails, review, and a shared definition of “good.”

What is the simplest way to test value?

Pick one repeatable asset type, run the same brief through the workflow, and compare speed, quality, and revision cycles against your current process.