Apr 30, 2026 · HMZE · 5 min read

Ocell AI Transformation: Beyond Engineering

Ocell's Head of Engineering details the company's shift to an AI-first operating model. The discussion covers the elimination of granular dev tickets, the adoption of Claude across non-technical teams, and the strategic use of skills and plugins to standardize AI workflows.

Technology Business Strategy Product Management

The takeaways

Eliminate Granular Dev Tickets. Shift from breaking features into small engineering tasks to assigning full-stack ownership of product ideas. This leverages AI speed to allow engineers to verify complete features end-to-end without intermediate handoffs.
Standardize AI Across Departments. Adopt a single AI platform like Claude for all teams, including sales and customer success. Standardization reduces tool fragmentation and enables consistent context sharing across the organization.
Leverage Skills for Non-Tech Teams. Create reusable AI skills and plugins for non-technical staff to automate tasks like ticket triage and data structuring. This lowers the barrier to entry for AI adoption in operational roles.
Prioritize AI-First Daily Workflows. Make AI the primary interface for daily tasks, replacing traditional tools like Jira or HubSpot as the first point of contact. This ensures AI is integrated into the core workflow rather than used as an auxiliary tool.
Manage AI Cost Risks. Monitor token consumption and subscription costs as AI integration deepens. Prepare for potential price increases from providers by balancing usage with open-source alternatives where feasible.
Implement Knowledge Graphs for Context. Use graph databases and agentic layers to structure company knowledge for better AI retrieval. This enhances the accuracy of AI responses by providing rich, connected context beyond simple keyword search.

The Shift to AI-First Operations

Ocell, a Munich-based startup providing digital twins for forest management, is undergoing a radical transformation in its engineering and operational workflows. The company has moved beyond simple AI assistance to an AI-first operating model where Claude is the primary interface for all employees, from engineers to customer success teams. This shift is not merely about tool adoption but represents a fundamental rethinking of how work is structured and executed.

Eliminating Traditional Dev Tickets

A key strategic change is the elimination of granular development tickets. Traditionally, product managers break features into small engineering tasks, but Ocell is moving towards full-stack ownership where engineers take responsibility for entire product ideas. This approach leverages the speed of AI coding agents to allow developers to build, test, and verify features end-to-end without the overhead of constant handoffs. The goal is to reduce friction and increase the velocity of product iteration.

Standardization and Skill Sharing

To manage the complexity of AI tools, Ocell has standardized on Claude across the organization. This includes the use of custom skills and plugins that encapsulate specific workflows, such as ticket triage or user research brainstorming. These skills are shared via GitHub, allowing non-technical teams to leverage AI capabilities without needing deep technical knowledge. This standardization reduces the risk of tool fragmentation and ensures that best practices are easily disseminated across the company.

Cost and Risk Considerations

While the benefits of AI integration are clear, there are significant cost and risk considerations. Token consumption can lead to substantial expenses, especially as AI agents require large amounts of context. Additionally, there are security concerns regarding prompt injection and data privacy. Ocell is navigating these challenges by carefully managing access and monitoring usage, but the company acknowledges that the landscape is rapidly evolving and requires continuous adaptation.

Future Outlook

The future of Ocell's AI strategy involves further integration of AI into daily workflows, with the vision that AI will become the primary tool for task management and execution. The company is also exploring the use of knowledge graphs to enhance AI context, which could further improve the accuracy and relevance of AI responses. This transformation positions Ocell as a leader in AI-driven operations, but it also requires a culture of continuous learning and adaptation.

Key insights

Ocell is eliminating granular dev tickets in favor of full-stack ownership of product ideas. This leverages AI speed to allow engineers to build and verify features end-to-end.

Engineering Process →

Impact: Reduces handoff friction and increases product iteration velocity, allowing for faster market response.
The company has standardized on Claude as the primary AI tool across all departments, including non-technical teams. This reduces tool fragmentation and ensures consistent context sharing.

AI Strategy →

Impact: Enhances organizational efficiency and enables non-technical staff to leverage AI capabilities effectively.
Custom AI skills and plugins are being used to automate specific workflows for non-technical teams. These skills are shared via GitHub, lowering the barrier to AI adoption.

AI Adoption →

Impact: Democratizes AI usage across the organization, increasing overall productivity and reducing the need for specialized technical expertise.
AI is becoming the primary interface for daily tasks, replacing traditional tools like Jira or HubSpot as the first point of contact. This ensures AI is integrated into the core workflow.

Workflow Integration →

Impact: Improves task management efficiency and ensures that AI is used as a central tool rather than an auxiliary one.
Token consumption and subscription costs are significant concerns as AI integration deepens. The company is monitoring usage and preparing for potential price increases.

Cost Management →

Impact: Requires careful financial planning to balance AI benefits with potential cost increases, ensuring sustainable AI adoption.

Action items

Implement a pilot program to eliminate granular dev tickets and assign full-stack ownership of product ideas to engineers. Monitor the impact on development velocity and feature quality.

Impact: Can significantly reduce handoff friction and increase the speed of product iteration, leading to faster market response.
Standardize on a single AI platform across all departments, including non-technical teams. Provide training and support to ensure effective adoption.

Impact: Reduces tool fragmentation and ensures consistent context sharing, enhancing organizational efficiency and productivity.
Develop and share custom AI skills and plugins for specific workflows, particularly for non-technical teams. Use GitHub for distribution and version control.

Impact: Lowers the barrier to AI adoption for non-technical staff, increasing overall productivity and reducing the need for specialized technical expertise.
Integrate AI as the primary interface for daily tasks, replacing traditional tools like Jira or HubSpot as the first point of contact. Ensure that AI is used for task management and execution.

Impact: Improves task management efficiency and ensures that AI is used as a central tool, enhancing overall workflow integration.
Monitor token consumption and subscription costs closely. Develop strategies to manage costs, such as using open-source alternatives where feasible and optimizing prompt usage.

Impact: Ensures sustainable AI adoption by balancing benefits with potential cost increases, preventing unexpected financial burdens.

Quotes

“wir versuchen natürlich den Förstern oder die Waldbesitzer auch mehr Produktivität reinzubringen durch digitale Tools”

“wir nutzen wirklich alle Claude. Und das 100% für alles”

“warum bricht man am Ende runter? Warum bricht man das in kleinere Dev-Tickets?”