Kerstin Ludwig
Kerstin Ludwig · going→fwd

Fractional Brand & Design Lead | Brand Systems Strategy

Closing the gap between brand strategy and execution across teams, touchpoints, and AI

 

I solve gaps between strategy, systems, and execution so brands become clearer, more coherent, and easier to act on — backed by 25+ years (Scout24/ImmoScout24, Shopware, Berlin Brands Group, DaWanda) leading across categories, markets, and transformation contexts.

 

 

 

 

 

AI / Agentic Governance

AI at scale without brand drift: Governance that holds

 

Teams adopt AI tools fast. Output volume goes up. Brand coherence breaks. The tools work — but without governance, they accelerate drift rather than execution.

Two organisations proved AI can scale brand operations without fragmenting identity. One integrated AI governance into existing brand infrastructure for self-service workflows. The other built decision architecture for agentic AI generating campaigns at speed. Both solved the same core problem: AI multiplies output — governance makes it brand-safe.


Case Study · B2B SaaS platform
AI-Enabled Brand Operations
Problem
Teams using AI tools without governance. No quality thresholds. Brand-unsafe outputs. Platform guidelines didn't extend to AI generation.
Build
  • Structured AI tool evaluation framework with decision criteria
  • Pilots in production contexts (what ships, what needs review)
  • Custom GPTs for brand validation and tone of voice
  • AI governance integrated with existing brand infrastructure
  • Quality thresholds + review logic defined
  • Legal and compliance alignment
Outcome
Brand-safe self-service workflows operational. Manual production steps reduced. Faster campaign and content iteration. AI tools integrated with brand infrastructure — no drift at scale.
Case Study · AI employer branding platform
Agentic AI Campaign Governance
Problem
Agentic AI translates brand and employer value propositions into recruiting campaigns at scale. Output drifts — brand fragments at AI speed, trust breaks between campaign promises and process reality, critical requirements miscommunicated. Clients had comprehensive brand platforms but no governance layer for AI-generated content.
Build
  • Decision architecture for agentic AI output (what AI decides autonomously, what requires human review)
  • Governance framework: quality thresholds, brand-safe boundaries, review logic
  • KPI system for AI output quality beyond volume metrics
  • Repeatable implementation blueprint
Outcome
Framework adopted as 'repeatable strategic pattern' across client implementations. AI generates campaigns at scale while brand coherence holds.

 

The pattern

 

AI without governance is just faster chaos. Both cases proved the same architecture works: define what AI decides autonomously, what needs review, and what constitutes brand-safe output. Then integrate that logic into existing systems.

The infrastructure case embedded AI governance into the brand platform — evaluation frameworks, review thresholds, custom GPTs aligned with brand standards. The agentic case built decision architecture for autonomous AI — quality boundaries, human escalation triggers, and KPIs beyond volume.

The shared principle: governance isn't about slowing AI down. It's about making speed safe. Clear boundaries. Quality thresholds. Review logic that scales.


 

When this applies

 

You need this if:

  • Teams are using AI tools but output quality is inconsistent
  • Brand coherence is breaking as AI content volume increases
  • No governance layer exists for AI-generated content
  • Agentic AI is generating campaigns or content at scale without quality boundaries
  • Speed is up but trust in AI output is down

If AI is increasing output but fragmenting brand coherence, the missing layer is governance — not less AI.


 

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