Energy Boost
AI Campaign
Marketing Design / AI creative / Motion Graphics︎︎︎ Campaign: Energy Boost AI Campaign
︎︎︎ Role: Senior Visual Designer
︎︎︎ Channels: Meta (static + motion), YouTube
︎︎︎ Focus: Multi-channel creative tested and iterated in a live performance environment
Background
I led a growth initiative to test how generative AI could increase creative velocity, reduce costs, and improve scalability for paid Meta and YouTube, without compromising brand quality or performance.
We deliberately ran this experiment against a high-stakes business initiative: Energy Boost (in the New Year 2026). The campaign targeted a broad audience across Meta and YouTube with a two-phase funnel:
- Drive marketing consent sign-ups at scale through switching
- Retarget engaged users with exclusive energy switching deals
To ensure learning was commercially meaningful, the project was underpinned by a rigorous testing plan. We designed a 28-day geo-based experiment to measure:
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Sales incrementality of YouTube energy investment
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Incrementality of creative strategy (multiple rotating creatives vs. a single hero)
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Halo impact of YouTube energy spend on Broadband, SIMO, and Handset sales
This ensured AI experimentation was directly tied to incremental revenue and cross-product growth, not vanity metrics.
I explored how to visualise the emotional burden of high energy costs in a simple, relatable way. Household appliances were shaped into the Uswitch “U” logo, serving as a visual metaphor for everyday frustration with energy bills. Ads followed a three-part storytelling structure:
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Problem state – Appliances malfunctioning or glitching
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Solution state – “Switch and save now at Uswitch.com” in the first 3 seconds
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Branded end-frame – Clear identity + CTA
Four distinct visual themes were developed and tested to support high rotation and variation across Meta and YouTube, preparing the creative for early performance insights.
Static assets
Developed a set of Meta-first static variants to complement YouTube video activity and increase creative diversity. Statics were designed for fast message absorption, scroll-stopping impact, and efficient rotation, supporting broader testing across formats to improve engagement, frequency efficiency, and overall campaign performance.
Challenges
- AI adoption risked inconsistent quality, brand drift, and unclear impact.
- I audited all marketing channels and built a Decision Framework scoring AI suitability by impact, risk, cost saving, and testability.
- YouTube paid media emerged as the highest-impact entry point, with a clear benchmark: 80% quality at 50% less effort, validated via rapid A/B testing.
- When a 20s ATL-style ad proved technically unfeasible, I pivoted to 8-10s YouTube non-skippable ads, aligning with platform best practices and delivery constraints.
Results
- Significantly reduced production time and cost vs. traditional agency workflows.
- Delivered ATL-quality paid video as established a repeatable AI testing framework for performance creative.
- Shipped multiple brand-safe YouTube variations, optimised for reach, engagement, and cost-efficiency, ready for early-2026 launch.
Process
From Zero to Scalable AI Performance Creative
Process work in FigJam
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Video development with iterations
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Storyboards for the 20S ATL-quality paid video before the brief change
Video experiment of the 20S ATL-quality paid video before the brief change
Video development with iterations
Storyboards for the 20S ATL-quality paid video before the brief changeVideo experiment of the 20S ATL-quality paid video before the brief change
Learnings:
AI is most effective as an acceleration layer, not a replacement. AI-assisted workflows significantly improved speed, volume, and flexibility, but creative quality and performance depended on human judgement, brand control, and iterative testing.
Platform best practices should act as the creative north star. Creative strategy must align with how platforms optimise delivery. For example, YouTube favours multiple creative variations over a single high-production asset, reinforcing the importance of designing for testing, rotation, and iteration rather than perfection.
What I’d do nex: Template and operationalise the AI video production workflow to fully leverage AI efficiency, enabling high-volume, performance-led creative production at scale for future campaigns.
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