Leading the Charge on AI Tool Evaluation
The landscape of generative AI is expanding at an unprecedented rate, promising incredible leaps in efficiency for global brands. However, evaluating these tools for enterprise-wide deployment carries significant weight. When rolling out platforms to support teams and workflows far beyond the creative department, financing, procurement, and IT security must rightfully mandate rigorous evaluations for data privacy, infrastructural scale, and compliance.
While these enterprise deployment requirements are non-negotiable, prioritizing them without equal weight given to creative utility often results in adopting secure but feature-limited tools, creating a critical risk that our teams will lag behind the industry in their execution capabilities. Without a strategic, brand-centric evaluation to compliment IT security, organizations risk deploying tools that lead to inconsistent output, disjointed workflows, and a dilution of their core identity.
In my role as Global Executive Creative Director for Brand Experiences, I recognized the critical need for our creative team to lead the charge from the brand side in evaluating this rapidly emerging AI landscape. The goal was not simply to adopt new technology for its own sake, but to rigorously assess how these tools could integrate with our workflows, scale our capabilities, and ultimately serve the business while maintaining strict brand governance.
Early platform evaluation: Source brand image (left) used as compositional seed for Adobe Firefly image generation.
Navigating an Expanding Tool Landscape
To stay at the forefront of creative capability, our evaluation process was continuous and expansive. Over time, we pressure-tested a wide spectrum of platforms to understand their specific utility within a rigorous corporate ecosystem.
This included evaluating foundational visual models (such as Adobe Firefly, Google Gemini Nanobanana, ChatGPT Image Gen) for asset generation, specialized upscaling, character consistency, and aggregate tools (like Magnific and Leonardo AI), presentation, layout, and ad automation software (Beautiful AI, Plus Docs, Canva, AdCreative AI, and Pencil Pro), advanced language models for copy automation (ChatGPT, Writer), vibe coding and agentic workflows (Lovable, Figma Agent), as well as internally developed custom generative AI workflows. By mapping this diverse and constantly shifting landscape, we could pinpoint exactly which tools augmented our workflows and which fell short of enterprise-grade brand standards.
Why Creative Evaluation is Essential
The evaluation of creative AI tools cannot be solely an IT or procurement exercise. It requires the nuanced understanding of a creative team that intimately knows the brand's visual language, voice, and quality standards. By leading this evaluation, our team ensured that the focus remained on:
Brand Quality and Consistency: Assessing whether a tool's output met our rigorous design standards and could be seamlessly integrated into our existing asset libraries.
Workflow Integration: Determining if a tool enhanced our creative processes or created new bottlenecks, particularly within our established software stack.
Risk Mitigation: Ensuring that the platforms we explored complied with our legal and ethical guidelines, particularly concerning the generation of human likenesses and potential copyright issues.
Unlocking Opportunities Across the Creative Lifecycle
The most exciting aspect of this evaluation process was mapping the specific, practical opportunities where AI could augment our traditional workflows. We envisioned generative AI acting as a capability multiplier across four key phases of our production pipeline:
Conceptual & Strategic Tasks: We targeted AI to accelerate early-stage ideation, from generating style scapes, mood boards, and target audience personas to producing dozens of thematic campaign taglines. We also saw massive potential in crafting persona-specific copywriting and generating multiple creative variations for A/B performance testing.
Image & Design Production: Beyond basic generation, we evaluated workflows for in-situ ad mockups on realistic billboards, product photography mockups in AI-generated scenes, and the creation of abstract backgrounds and cohesive icon sets. For human elements, we explored character design sprints and generating synthetic headshots for internal talent mockups.
Video & Audio Production: For motion and sound, we put tools on our radar for shot-by-shot storyboarding, automated rough cuts, and generating UI sound design for interface interactions. We also anticipated leveraging AI for scratch tracks, demo voice-overs, and translating localized narration into multiple languages.
Post-Production & Content: To accelerate final delivery, we identified opportunities in automated object removal, scene cleanup, and seamless image outpainting and inpainting. Furthermore, we tracked AI's utility for upscaling low-quality assets, AI-powered transcription and subtitling, and automating content summarization and tagging for digital asset management systems.
The Emerging Economics of AI Production
Beyond creative capability and security, we identified a critical, often-overlooked operational challenge: the emerging economics of AI generation. Unlike traditional software subscriptions, many generative platforms operate on dynamic, credit-based models where every prompt, variation, and upscaled image carries a micro-cost. We recognized that scaling these tools across global teams would fundamentally shift how we budget for creative production.
To prepare the organization, we integrated AI credit budgeting into our evaluation framework. We began forecasting the cost-per-generation impact on future project planning, ensuring that as generative workflows become a required staple of our brand operations, our creative ambition remains financially sustainable and operationally predictable.
Smart workflows, not just more tools
By placing the creative team at the helm of AI tool evaluation alongside IT and Procurement, we ensured that technology served the brand, not the other way around. In the era of generative AI, the most successful brands will not be those with the most tools, but those with the smartest workflows that scale the brand appropriately while unlocking new potential. By bridging the gap between procurement's need for security and the creative team's need for capability, we built an AI infrastructure that is as operationally sustainable as it is creatively ambitious.
