Tag: #GenAI

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We’ve always wanted to do it.

To create work that’s cohesive across every channel — from print to film to social — without having to brute-force it into consistency later.

To bring local teams into the creative process at the beginning, when it still matters, rather than tagging them in just before the deadline and asking them to “transcreate.”

To finally bring localisation into the heart of production — not bolted on at the end, but baked in from the beginning, so every market’s version can take shape as the content takes shape.

To design with adaptation in mind — not as an afterthought, but as a core principle.

But the truth is, until recently, the tools didn’t exist. Or they existed, but not at scale. And so, we got good at compromise. We got clever at fixing things late. We built processes around silos, because silos were safe.

Then came GenAI. And suddenly, the thing we’ve always wanted — that orchestration of content across markets, mediums, and moments — doesn’t seem impossible anymore. 

It may not be perfect yet, but it shows signs of possibility.

The instinct, of course, is to use the tech to speed up what we already do. Swap a synthetic voice in for a voice actor. Use AI to generate subtitles. Get three versions of a script instead of one.

It’s tempting. It’s useful. But it misses the point.

Because the real value of GenAI isn’t that it makes the existing machine faster. It’s that it lets us build a new machine entirely.

The Shift: From Tasks to Thinking

We don’t just need a more efficient workflow. We need a new kind of workflow — one that reflects how people consume content now.

Fragmented. Fast. Fluent across channels.
Personal, not just localized.
Relevant, not just repurposed.

In this new model, production isn’t linear — it’s layered.

Planning becomes platform-aware. Scripts are seeded with multilingual intent. Slogans written for print evolve into voiceovers. A shot designed for the hero film becomes a still for a product page, a loop for TikTok, or a background for a display ad.

The assets don’t just work harder — they work together.

AI doesn’t replace creativity here — it scaffolds it. It gives global teams a starting point, not a finish line. It lets us think modularly, culturally, and strategically at the same time. If anything, it puts the human imagination more firmly at the centre — because now we’re not just solving problems. We’re designing systems.

Start Where It Matters

So, I’ve started mapping out a living workflow — not a fixed blueprint, but a prototype. A draft for what global content production could look like when AI becomes a true creative partner.

It starts with integrated planning, where format, market, and message are aligned from the outset. Not just what to say, but where, how, and for whom. Not just one campaign, but all its potential versions. Not just global, but global-ready.

This framework breaks down the production cycle into four evolving stages — Planning, Pre-Production, Production, and Post-Production — with outcomes and roles clearly defined for each. It’s illustrative so it may not be perfect. But it’s adaptable. 

From there, pre-production becomes the foundation of adaptability. We use AI to generate multilingual script variants early, build asset libraries that are inherently cross-format, and design storyboards with different channels in mind. Every part of the creative process becomes an input into a wider system — a flywheel, not a funnel.

In production, we think in modules. A performance that works for the hero spot also works for the bumper. A product demo becomes a still image with a CTA. Synthetic voice tracks run alongside human ones — not to replace them, but to offer options. And AI tools help us localize visually in real time.

Then in post, we scale. Smartly. AI engines recompile edits by platform. Dubbing, subtitling, and cultural nuance are handled in hybrid — machine speed, human oversight. We don’t localise at the end. We finish at the end. And we feed what we’ve learned back into the machine for next time.

Because that’s the thing. The workflow itself isn’t static. It’s a work in progress — a living document. Because the tools are changing, the platforms are changing, and our ambitions should be changing too.

Build the Muscle, Not Just the Machine

If there’s one principle to hold onto, it’s agility.

No two projects will use the same tools in the same way. What works for a regional retail rollout won’t work for a global brand film. And that’s okay. The goal isn’t to lock in a perfect process. It’s to build a flexible one.

That means building cross-functional teams that speak the same language — creative, data, AI, strategy.
It means investing in brand-specific training data, so AI outputs aren’t generic but grounded.
It means testing new tools in low-risk environments — subtitling, B-roll, social variants — and then scaling what works.

And above all, it means thinking differently.
Not just faster.
Not just cheaper.
But better.

I’ve Never Felt So Excited About What Comes Next

I’ve said it to colleagues again and again: I’ve never felt so excited about the changes happening in global production.

We’re standing at the edge of a new kind of production — one that’s not just about making things, but about designing systems that make possibilities real.

If we get this right, GenAI won’t just help us do what we already do a little better.
It’ll help us finally do the things we’ve always dreamed of — the things we knew were right — but never had the tools to make happen.

And the best part?

We’ve only just begun.

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The past 12 months have been the exploration phase of Generative AI, with creatives across various disciplines experimenting with its capabilities, pushing boundaries, and envisioning possibilities. From AI-generated art to personalized marketing content, we’ve explored novel applications. While the initial “wow” factor of AI-generated creations captured attention, this year demands tangible value. This means focusing on how generated content impacts audiences, drives outcomes, and solves real problems. This year, creatives must transition from exploration to execution, shifting the focus from “what if” to “what works.” And to some brands, it means on a global scale.

Strategic Integration of GenAI Tools into Creative Processes

As we stand on the precipice of a new challenge – delivering tangible value through Generative AI tools – a strategic approach to integrating these tools into our creative processes is required. This demands not just innovative experimentation but also a clear articulation of the value they bring. Identifying areas within the creative process where Generative AI can enhance creativity, streamline workflows, and create production-grade, personalized content at scale is crucial.

An article from LBBonline titled “Is Generative AI Proving to be ‘Too’ Creative?” offers a nuanced perspective on integrating and utilizing Generative AI in the creative process. Each expert contributes a unique lens to the discussion, highlighting both potential benefits and challenges of leveraging AI in creative work. While some emphasize the necessity for critical thinking and contextual awareness, they also discuss the rapid advancement of AI technology, urging a differentiated approach to content creation based on the need for accuracy and quality. Others view AI’s imaginative output as a form of creativity, suggesting it could evolve alongside human creativity.

Across these viewpoints, common themes emerge: the need for critical evaluation, the balance between leveraging AI’s creative potential and recognizing its limitations, and the importance of human oversight and contextual understanding. However, I feel there are a few aspects that are missing.

From “Human-in-the-Loop” to “Cultural Expert-in-the-Loop”

Integrating Generative AI into the creative process requires more than just technical know-how; it demands a deep understanding of the content’s context, purpose, and audience. We often hear about the need for a “human-in-the-loop”. In the article mentioned above, Alex Hamilton from Dentsu Creatives advises, “Critical thinking, verification, and a healthy dose of scepticism are therefore essential.” He emphasizes considering the context in AI-generated content to ensure relevance and mitigate misleading outputs.

But I propose we go one step further: to ensure there is “cultural expertise” in the loop.

Involving cultural expertise in the process signifies a pivotal evolution in leveraging Generative AI for global creativity. In this advanced paradigm, human experts don’t just play a supervisory role but lead the initiative from inception, setting the standards for what constitutes high-quality output. This leadership encompasses everything from crafting nuanced prompts that guide AI in generating content, to defining and refining the brand’s tone of voice during the initial training and subsequent fine-tuning of Large Language Models (LLMs). The involvement of human expertise from the start ensures that the AI’s outputs are not only technically competent but also deeply infused with the brand’s identity and ethos.

Crucially, this expertise incorporates a profound understanding of cultural nuances, making it indispensable in today’s global marketplace. This approach mandates the inclusion of cultural consultants or experts who possess an intimate knowledge of the target audience’s cultural context. Their role is to ensure that AI-generated content is culturally congruent, sensitive, and capable of resonating positively with diverse audiences worldwide. These cultural experts provide insights into the societal norms, values, and taboos of different communities, helping to steer the content away from potential cultural faux pas and toward more inclusive, respectful, and engaging narratives.

As the AI undergoes iteration and improvement, the contribution of both subject matter and cultural experts becomes increasingly vital. They offer invaluable insights into refining the solution, effectively expanding the scope of feedback from purely technical or content-specific to encompassing broad cultural feedback. This richer, more diverse input is instrumental in further fine-tuning the algorithm, enhancing its ability to produce content that is not only of high quality but also culturally nuanced and relevant.

The iterative process of improvement facilitated by the involvement of cultural expertise ensures that the AI’s learning trajectory is aligned with evolving cultural trends and sensitivities. Regular quality assurance checks, informed by both expert critique and cultural insights, are integral to this process, helping to maintain and elevate the content’s quality, relevance, and cultural appropriateness over time. 

This model cultivates a dynamic and synergistic partnership between human experts and algorithms. It leverages the scalability and efficiency of AI while grounding its outputs in the rich, complex tapestry of human culture and expertise. Experts guide the AI, imbuing it with a nuanced understanding of cultural intricacies and brand-specific directives, thus enabling it to generate content that not only meets the technical criteria of quality but also embodies the values, tones, and sensitivities required to truly engage a global audience.

In essence, this approach represents a holistic and forward-thinking strategy for content creation on a global scale. It recognizes the indispensable role of human expertise in navigating the complexities of cultural diversity and brand identity, setting a new standard for AI-generated content that is as culturally informed as it is creatively inspired. Through this collaborative model, the potential of Generative AI is fully realized, offering content that is not just innovative and efficient but also deeply resonant and culturally attuned, continually improving to meet the highest standards of quality and relevance.

The Creatives X Machine Era

Generative AI represents a transformative force in the creative industry, offering tools that can augment human creativity in unprecedented ways. However, its effective integration into the creative process requires a nuanced approach that considers the importance of expertise, cultural sensitivity, and collaboration. By grounding the technology’s application in expert knowledge and a deep understanding of the audience, creatives can harness AI’s potential without compromising on content quality and relevance. This model emphasizes the collaborative nature of AI in creativity, where technology enhances human expertise, and together, they produce outputs that are not only innovative but also deeply resonant with the intended audience. In navigating the exciting possibilities of Generative AI, adopting a thoughtful, expert-guided approach is key to creating content that truly matters.

Note that what I have covered here focuses on the advertising and production use cases of Generative AI. In reality, the relationship between creatives across different disciplines and Generative AI is influenced by the distinct challenges and opportunities of each field. While the underlying technology might be similar, its application and impact vary widely, reflecting the unique creative processes, ethical considerations, and ultimate goals of each discipline.

So, no matter which creative disciplines you practice – be it advertising, architecture, fashion, art, music, gaming, or beyond – I would like to hear about the unique challenges, opportunities, and goals inherent to your specific field.