TechTimes interviewed Tim Gareev, Chief Creative Officer at JETA, on what his team uses for research, orchestration, and rapid visualization. Gareev is a market-agnostic creative leader, building brand systems and go-to-market engines and aligning strategy, product, and performance creative into end-to-end campaigns and accountable growth. Grounded in JETA's Electric Pawn Shop case, Gareev explains where AI accelerates creative work, where it fails, and the practical steps brands can take to adopt it safely and effectively.
1. How has AI changed the way your team approaches creative strategy today?
AI spans our process, but we pair tools to tasks. On the thinking side, we run Claude through the Model Context Protocol so the model can securely reach approved data and utilities without "stuffing" the prompt. That makes early research, synthesis, and first-pass ideation faster and more auditable. And then a human verifies anything we'd rely on, in line with NIST's AI RMF.
On the visualization side, the shift is tempo. We can stand up convincing motion and look-and-feel studies in hours: Veo 3 gives us text-to-video with native audio for quick scene energy and pacing. Kling, at the time, helps explore longer, smoother clips and camera language before we commit to heavy craft. The result is earlier and better conversations because we're reacting to moving frames instead of hypotheticals.
We also run two flows. With time, a creative immerses first (research, sketches, rough frameworks), then we widen and structure with Claude-via-MCP and spin quick Veo/Kling studies. Under deadline, we flip it to accelerate context, then iterate like vibe-coding: strip clichés, tighten references and constraints, and keep steering until it's on-brand. Either way, the prompt is the new brief. Cleaner structure, references, and constraints give us outputs that stay on course and let us test multiple routes without burning a week.
2. Which parts of a campaign (idea creation, production, release) benefit the most from AI?
All three benefit, just in different ways. Upstream, AI is a fast research companion: it helps us parse long reports, cluster audience signals, and frame a point of view, with human filtering to keep it brand-true. In production, generative models compress the time from thought to something on screen. We use them to explore style, build boards, and rough-in simple animation passes so the team can compare routes before committing heavy craft. Tools like Runway's Gen-3 give us text-to-video and image-to-video control modes that are great for motion studies, useful as scaffolding, not a substitute for art direction.
Release is where things are evolving fastest. We're seeing agentic capabilities help set up, test, and optimize campaigns more autonomously inside the ad platforms. Google has publicly introduced agentic features aimed at reducing manual lift in building and running campaigns.
3. Can you share an example where AI accelerated your process without compromising originality?
Our work with the Electric Pawn Shop in Dubai is a good illustration. The brief was to create a unified social aesthetic steeped in '90s cultural codes. It's like pixel art and console-game storytelling that would resonate with millennials and spark "borrowed nostalgia" for Gen Z. We turned weekly events into an episodic "story cinema," with DJs and performers appearing as recurring characters. To move quickly without losing authorship, we generated base images in Midjourney, ran motion tests in AI video tools such as Runway, then brought everything into manual motion design to add event-specific details, typography, and timing. That hybrid pipeline lets us maintain a distinct voice while shipping fresh assets every week.
For context, Electric Pawn Shop itself is a real venue and cultural fixture, so keeping the vibe authentic mattered as much as speed.
4. Do you think AI will ever lead a campaign, not just support it?
On the idea level, no. We haven't seen AI originate a platform idea that truly surprises or unlocks a brand's emotional truth. It's excellent at simulating, scaling, and stress-testing a human direction, but left to run, it tends to drift off-brief (stylistically impressive, strategically fuzzy), so you spend cycles pulling it back.
Where AI may "lead" is in the mechanics of delivery: agentic orchestration of channels, tests, and optimizations once a human-made idea is in place. Even though the platform roadmaps point in that direction, the insight, taste, and cultural nuance still belong to people.
5. With the Electric Pawn Shop case, what did AI make possible creatively, and what were its limitations?
AI made practical what would otherwise have been prohibitively time-consuming: new visual stories for each event, fast logo or graphic flourishes for one-offs, and a consistent series look across a high volume of posters and motion snippets adapted to social formats on the fly. Because we could audition multiple visual directions quickly, we kept the feed vibrant and aesthetically coherent.
The limitations were just as instructive. AI didn't generate the core idea; it supplied variations at speed. It couldn't feel the brand, catch the unscripted energy of the room, or edit itself for cultural nuance. It also occasionally wandered stylistically, so human art direction had to steer, curate, and finish.
6. What advice would you give to brands jumping into AI for the first time?
Begin with a sharp brief. Your input quality determines output quality; treat prompts like you would a creative brief: objectives, audience, tone, references, constraints, and examples. That rigor is echoed across mainstream prompting guidance and pays off immediately. From there, encode your brand world in the tools: build tonal guardrails, visual references, and reusable prompt "patterns" so internal teams and partners can scale consistently.
Pilot across the stack, but keep a human review loop. Use LLMs for research and formatting, GenAI for visualization and rough motion, and keep humans verifying claims and making taste calls. As you mature, explore agentic delivery in the ad stack, and let the system handle orchestration while you decide what matters. And look at how big brands are industrializing this: Coca-Cola's Fizzion is a public example of turning brand guidelines into adaptive, asset-aware systems to speed creativity while keeping control in designers' hands.
Tim Gareev is Chief Creative Officer at JETA, a Dubai-based agency combining traditional marketing and Web3 growth marketing strategies. Tim is a market-agnostic creative leader who builds brand systems and go-to-market engines across sectors. He aligns strategy, product, and performance creative to deliver end-to-end campaigns, clear playbooks, and accountable growth.
ⓒ 2025 TECHTIMES.com All rights reserved. Do not reproduce without permission.
