The AI-native Builder / AI for Us Lead will help Intelligent Industry work as agentified as possible. The goal is to reduce people- and effort-based coordination work, enable a lean core team and turn the NCE Intelligent Industry organization into a practical showcase for AI-native ways of working. This is not a generic AI adoption role and not a classic transformation office role. The role is accountable for building and scaling the first high-impact AI agents and workflows for the Intelligent Industry core team — starting with recurring use cases such as pipeline visibility, meeting preparation, knowledge reuse, leadership follow-up and decision support. In short: we want to “drink our own champagne” and prove internally what we want to bring to our clients.
This is a visible builder role at the heart of the NCE Intelligent Industry agenda. The person will work closely with business owners, IT and platform teams, data owners and selected champions across countries to turn practical use cases into reusable agent-enabled workflows. The role is not expected to deliver this alone or by building a fixed team; instead, it will flexibly pull together the right experts into small, purpose-driven squads as needed to move use cases from idea to working solution.
Key responsibilities include:
- Agentified Way of Working: Shape how the Intelligent Industry core team works with AI agents by default — starting with a focused set of high-value routines across pipeline management, deal support, knowledge reuse, reporting and follow-up.
- Real Agent Building & Deployment: Prototype, test and ship practical agents for recurring Intelligent Industry use cases, moving quickly from idea to working solution and scaling what proves useful.
- Lean Core Team Enablement: Minimize manual coordination, preparation and reporting effort by creating agentified workflows that allow a small core team to support a larger cross-country and cross-BU network effectively.
- Flexible Squad Orchestration: Mobilize the right mix of business, technology, data and platform experts for specific use cases, steer them pragmatically and release them again once the solution is built, tested and ready to scale.
- Knowledge Reuse & Decision Support: Connect relevant knowledge sources, collaboration spaces, proposals, assets, playbooks and expert input so that teams can find, reuse and act on information faster.
- Productivity by Design: Identify high-frequency work patterns and replace repetitive effort with AI-supported workflows that improve speed, quality, transparency and repeatability.
- Adoption through Usage: Drive adoption by proving value in daily work, not through generic training; create simple routines, examples and reusable patterns that make agentified working easy to use.
- Responsible Scaling: Define pragmatic guardrails for secure, compliant and responsible agent development and usage, ensuring that solutions can be reused and scaled safely across the organization.
