The strategic role of AI in scaling Design Systems across channels
by Graphite Digital 16 February 26AI is now firmly on the agenda for pharma digital teams. The request often sounds simple: “How can we add AI to this?”
But adding AI without structure rarely solves the real problem.
In a recent presentation at the NEXT CX & AI Conference, Graphite’s Head of Design, Ed Hart, and independent pharma CX expert Nicolas Barcza explored why scaling AI successfully depends less on the tool itself and more on the system that governs it.
Pharma operates within layers of complexity. Multiple brands, markets, languages and regulatory requirements all shape how digital assets are created and approved. Introducing AI into that environment without a clear framework doesn’t reduce friction. It risks multiplying it.
The real opportunity isn’t to add AI for the sake of innovation. It’s to use AI to remove bottlenecks, reduce repetitive reviews, accelerate localisation, and improve the experience for healthcare professionals (HCPs) and patients. To do that safely, AI needs context. That context comes from Design Systems.
Why Design Systems matter more in an AI-enabled world
AI is powerful, but directionless. Without guardrails, it generates variation without understanding brand, compliance or intent.
Design Systems provide the structure that AI can operate within. They act as a shared source of truth for how a brand looks, behaves and stays compliant across channels. More importantly, they can translate human guidelines into structured, machine-readable rules.
Traditional brand guidelines were written for people. Design Systems can be structured so machines can interpret them too.
When tokens, components, patterns and rules are clearly defined — and supported by metadata such as:
- Market scope (EU, US, global)
- Audience type (HCP or patient)
- Channel (web, email, banner, app)
- Claim logic and safety requirements
AI moves beyond generating content to applying rules. It stops guessing and starts operating within defined boundaries.
This is what makes scale realistic. AI can generate or adapt assets that are on-brand and compliant by default, because it is drawing from the same source of truth as your design and compliance teams.
From guidelines to machine-readable governance
Scaling AI across channels requires more than good creative prompts. It requires structure.
Design tokens define how something looks. Metadata defines when and why it can be used. Together, they turn expert knowledge into structured data.
That shift is critical. It means compliance rules, approval logic and market constraints can be embedded directly into components and patterns. Instead of reviewing assets at the end of the process, teams build within guardrails from the start.
This changes the role of compliance. Rather than acting as gatekeeper at the final stage, compliance becomes part of the system architecture. The result is fewer review loops, fewer repeated corrections, and more predictable delivery across markets.
Scaling across channels without losing control
Pharma teams rarely create a single asset. Content is adapted across websites, emails, banners, eDetails, apps and social formats. Each channel introduces variation and risk.
When Design Systems underpin AI workflows, cross-channel adaptation becomes safer and more consistent. AI can adjust layouts, formats or content variations while respecting channel constraints, brand rules and market-specific requirements.
The outcome isn’t just operational efficiency. It’s a more cohesive experience. When every component and rule comes from one governed system, navigation feels familiar, hierarchy is consistent, and tone aligns across touch points. The structure behind the scenes translates into confidence for the end user.
The more complexity you manage, the more valuable that structure becomes.
Making it practical: audit, tokenise, build, refine
Turning Design Systems into AI-ready infrastructure doesn’t happen overnight. It requires a deliberate approach:
- Audit reality. Map how work actually gets done. Identify repeated design or compliance decisions that could be formalised into rules.
- Tokenise knowledge. Extend tokens beyond visual styles to capture intent, conditions of use and regulatory logic.
- Build compliance into components. Embed approval logic and guardrails into the system so humans and AI use the same rules.
- Measure and refine. Start with one high-volume workflow, define success metrics, and expand gradually.
Over time, AI shifts from experimental tool to operational capability. Asset creation speeds up. Review cycles reduce. Teams spend less time correcting and more time thinking strategically.
The key insight is simple: you don’t scale AI in isolation. You scale the Design System that governs it.
Questions to ask your team
If you’re exploring AI in your organisation, start with structure:
- Where are teams fixing the same design or compliance issues repeatedly?
- Which recurring approval patterns could be formalised into rules?
- Does your Design System capture decision logic, or only components?
- How machine-readable is your system today?
These questions aren’t about technology. They’re about governance and trust.
The more structure you give AI, the more freedom you give your people to focus on judgement, storytelling and innovation — and ultimately on improving healthcare experiences rather than managing process.
