AI in the Adobe marketing stack: A business case for Figma-to-code automation

Executive summary

While your competitors are debating their AI strategy, the gap between design and code is costing you weeks in every release cycle. AI-powered Figma-to-AEM automation can shrink that gap to days, all while improving the quality and consistency of your digital experiences. As we enter the 2026 budget planning cycles, this technology represents a strategic inflection point. Organizations that act now to invest in tokenized design systems and AI-ready workflows can achieve radically faster time-to-market and reallocate significant budgets from manual coding to high-impact strategic initiatives. The window to lead this transformation is narrowing, and the time to act is now.

Why automate? The business case for automating Figma to AEM

Building on our exploration of design system tokenization as the foundation for AI-powered workflows, this article examines the business case for Figma-to-AEM automation. From consumer retail to highly regulated industries like finance, healthcare, and pharmaceuticals, AI-powered design-to-code automation delivers measurable value even in the most complex, multi-brand, and compliance-heavy AEM environments. The question isn't whether this applies to your organization - it's how quickly you can capture these benefits.

Cost savings and budget reallocation

Realistic development gains

Realistically, while some headlines promise unbelievable AI efficiency gains, the reality is more nuanced. Your developers do more than just code, they meet, collaborate, and solve complex problems. Automating Figma-to-AEM component creation delivers tangible 30-40% time savings on coding tasks, which translates into meaningful and realistic productivity gains. For a global brand producing 50 new components per quarter, this can mean hundreds of development hours saved annually … and a budget that can be redirected into UX research, personalization, and experimentation.

Third-party research confirms these types of gains; Forrester’s 2024 Total Economic Impact study of Adobe Experience Cloud found that organizations achieved:

• A 20% reduction in developer time on digital experience projects.
• Up to 45% faster campaign creation.
• A 333% ROI within three years.

AI-driven design-to-code automation builds on these same efficiencies, compounding the value.

Initial strategic investments

To unlock these efficiencies, organizations first need to invest in robust, tokenized design systems — the foundation that makes automation possible. As Nielsen Norman Group’s “Design Systems 101” explains, a strong design system ensures consistency and scalability across teams, which in turn enables automation tools to reliably produce on-brand, high-quality outputs.

Alongside AI tooling, there’s integration development, and upskilling and training the team to work effectively with AI-enhanced workflows. Most organizations find that these upfront investments pay for themselves within 6-12 months through downstream efficiency gains and reduced technical debt.

Shifting budgets for long-term impact

Once the foundational investments are made and automation begins delivering savings, budgets can shift from repetitive development work toward high-value initiatives, such as advanced personalization, new digital features, and deeper user research, further accelerating competitive differentiation.

Team structure evolution

Enhanced collaboration across disciplines: AI automation creates a common language between design, development, and content teams, reducing ambiguity and fostering alignment. This cross-functional clarity enables faster decision-making and more coherent project execution.

Evolved developer roles: Developers shift from repetitive coding to strategic problem-solving, architectural oversight, and innovation, becoming system architects rather than just coders.

Greater focus on UX and research: As repetitive coding tasks diminish, resources must be reallocated to UX strategy, user research, and testing. This ensures that user needs and behavioural insights directly inform design and development decisions.

Shift toward business and marketing alignment: Technical teams increasingly collaborating with business and marketing stakeholders to ensure that every digital experience aligns with brand objectives, campaign goals, and measurable KPIs.

Strategic specialisation: Teams can concentrate on accessibility, performance optimization, personalization logic, and other specialised skills that truly differentiate the brand.

Quality and consistency gains

• Design token integration: AI consistently applies brand values during code generation, ensuring a unified identity across all digital touchpoints and eliminating human error.
• Predictable outcomes: Automated generation follows predefined rules, creating fewer QA issues and more reliable results.
• Brand cohesion: Consistency in design and code translates to a trustworthy brand presence, directly impacting customer loyalty.

Scalability and speed advantages

Project scaling: New components integrate into AEM environments faster, enabling rapid expansion of digital properties and feature launches.

Timeline reliability: Automation reduces rework and manual errors, making project timelines more predictable and budgets more accurate.

Competitive edge: When coding efficiencies are combined with reduced rework and faster QA, overall time-to-market can improve by up to 40% - a decisive edge in industries where speed determines success.

Proven in practice: At Cognizant Netcentric, we’ve shown how AI agents can analyze Figma designs - including structure, naming, and layout intent - to generate production-ready AEM component scaffolding directly in IDEs. As Dragan Filipovic explains in his article on AI-Driven Code Generation, this speeds coding while ensuring architectural consistency and reducing technical debt.

Learning from fast fashion: Balancing speed and quality

To connect this analogy directly to the digital delivery context, in the same way fast fashion brands rapidly design, produce, and distribute new styles, AI-powered coding allows organizations to quickly generate and deploy new digital components. The "fast coding" parallel to fast fashion helps illustrate the trade-offs of accelerated production.

Just as fast fashion enables brands to respond to trends but risks compromising garment quality without strong design and quality controls, AI-powered coding enables faster market responses, but can lead to technical debt and inconsistent experiences if governance and oversight are weak. As the effort required to create code quickly decreases, maintaining order, standards, and consistency becomes even more critical.

This includes technical governance, ensuring architectural integrity, security compliance, performance standards, and UX governance, which establishes design consistency, accessibility, and user-centric decision-making. The lesson is clear: success comes from pairing speed with a robust design system, disciplined governance processes, and skilled oversight, ensuring that acceleration enhances quality rather than undermining it.

Strategic timing: Why now?

So, with all this potential, is it better to wait until the technology is more mature? The data says no. As McKinsey’s “Navigating the Generative AI Disruption in Software” highlights, early adopters of AI in software delivery are already reshaping release cycles, capturing operational efficiencies, and gaining market share ahead of competitors. The risk is not oversaturation, letting rivals build AI muscle while you’re still in the planning phase.

Early mover advantage: Widespread Figma-AEM AI integration is still emerging. The real risk is not market saturation, but missing out on the early benefits, allowing competitors to build experience, optimize processes, and capture efficiencies while you’re still evaluating options.

Budget planning window: Now is the critical time to align 2026 budgets with AI infrastructure, tooling, and team development needs.

Market leadership: Organizations that act now will set new standards for digital experience delivery while competitors are still planning.

Your next strategic move

The organizations that will dominate digital experiences in 2027 are making infrastructure and team decisions today. As you finalise your 2026 budgets, consider how AI-powered design-to-code automation fits your strategic roadmap.

We help brands assess readiness, design AI-enhanced workflows, and integrate Figma-to-AEM automation, so you realize the benefits in your next release cycle, not your next fiscal year.

It’s time to tackle your organization's AI automation readiness and implementation pathway.