Scaling Idea Management with AI:
BMW's enterprise-wide transformation
From a legacy system to an AI-native platform for 130,000+ employees.
About BMW
Sector
Automotive & Mobility
Employees
~154,500
Headquarters
Munich, Germany
Revenue
€133.453 billion
BMW Group is one of the world’s leading premium manufacturers of automobiles and motorcycles, with four brands — BMW, MINI, Rolls-Royce and BMW Motorrad. With a global presence across more than 140 markets, the company focuses on premium mobility, technological innovation and sustainable solutions.
Introduction
Artificial intelligence has transformed what is possible in idea management. Ideas can now be generated and refined through natural dialogue, language barriers are eliminated, and patterns and duplicates can be identified across thousands of submissions in seconds. As a result, idea management practices have become faster, more inclusive, and more intelligent than ever before.
Despite this potential, many large organizations continue to rely on systems whose foundations were built decades ago. Where AI is available, it is often bolted on as an isolated feature rather than embedded into the core of the idea management solution. The transition from these legacy systems to truly AI-native idea management; where artificial intelligence enhances every stage of the innovation process; remains a challenge that few enterprises have successfully mastered.
This is exactly where rready’s idea management solutions come in.
A collaboration with the BMW Group illustrates how AI-native idea management can overcome the limitations of traditional approaches in enterprise innovation.
The challenge
Modern idea management must do far more than simply collect suggestions or ideas. Especially in global organizations, international teams, multiple languages, and complex organizational structures place high demands on the submission, evaluation, and routing of new ideas.
Against this backdrop, the BMW Group explored how its existing idea management system could be advanced through the targeted use of artificial intelligence and prepared for the future. The business requirements and overall objectives of the program were defined by the BMW Group and implemented in close collaboration with its technology partner.
Multilingual by design
Employees should be able to submit ideas in their native language across all international locations.
Intelligent routing
Every idea should be automatically directed to the right evaluation team or individual, even within an organization comprising around 7,000 work groups.
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Higher-quality submissions
AI should guide employees during the submission process, helping them formulate clear, complete, and actionable ideas from the outset.
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AI at the core
Artificial intelligence should be a native part of the platform's architecture, not an add-on layered onto a legacy system.
An engaging user experience
Contributing ideas should feel creative, intuitive, and rewarding; not like completing an administrative task.
The search for the right partner
To advance idea management at the BMW Group, a modern, AI-native solution was required. The requirements were intentionally ambitious. The solution needed to support the established idea management process fully, while serving a wide range of user groups across the organization.
The defining requirement was the depth of AI integration. Rather than treating artificial intelligence as an add-on, the platform had to embed it natively into every stage of the process. From idea submission and validation to evaluation and implementation, AI was expected to enhance each step, creating a more efficient, higher-quality, and future-ready innovation process.
Why rready: A roadmap with impact
The selection process was driven not by a feature checklist, but by a fundamental insight: at its core, idea management is about working with text. Ideas must be articulated, structured, translated, and consistently compared across thousands of submissions. For Dr. Ulrich Stephany, Head of Idea Management at BMW Group, this clearly indicated where artificial intelligence could deliver the greatest value and became the benchmark for evaluating potential technology partners.
As discussions progressed, rready's vision became increasingly tangible. A decisive factor was the ability of rready’s platform to provide customer-configurable AI assistants, enabling the BMW Group to tailor AI capabilities to its own processes and requirements. This demonstrated that the ambition of embedding AI natively into every stage of the idea management process was not only achievable but practical. The result was the launch of SPARK, the BMW Group's next-generation idea management program.
Within BMW SPARK, artificial intelligence is embedded into the platform along a deliberate architecture. It first supports employees the moment an idea is created, and then continues to assist throughout the refinement, routing, and evaluation of ideas. A core principle applies at every level: AI provides guidance, while the final decisions remain with people.
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Field-specific AI
These AI capabilities are directly integrated into the idea submission process. They are activated when needed and operate within the specific input fields of the step currently being completed. Their purpose is to improve the quality of ideas entering the pipeline.
This is what makes the SPARK user experience feel less like completing a form and more like participating in an assisted, high-quality workflow.
Text AI
A writing assistant specifically optimized for BMW Group that supports grammar, clarity and professional phrasing - including helping users formulate ideas in their native language.
Image AI
Generates a visual representation of an idea in a defined style without requiring users to leave the interface.
Prompt and starting-point suggestions
Curated prompts from administrators and AI-generated starting points based on FAQs reduce the barrier to entry, while free-text input remains fully available.
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Field-specific AI improves the quality of ideas entering the funnel. However, the most demanding work in any idea management system begins afterwards: assessing quality, routing ideas to the right people, deciding which concepts should move forward, and identifying what is truly innovative.
This is where SPARK’s AI assistants create value.
The assistants focus on the idea itself. Each assistant analyzes a specific idea at a defined stage of the process and provides an assessment or recommendation. They identify weaknesses, suggest responsible evaluation departments, and highlight potential novelty. The assistants are intentionally designed as advisory tools: targeted guardrails that keep the process scalable and manageable at enterprise level.
The result is a review process that scales with volume. Across thousands of reviewers and approximately 7,000 possible organizational units, AI assistants help maintain consistent evaluation quality, accelerate decisions, and improve prioritization; precisely in the areas where innovation processes in large organizations often slow down.
The assistants perform four key functions:
Three coaches help before submission:
Idea Coach
Sharpens value and feasibility, and provides a starting point when the input field is still empty.
Creativity Coach
Encourages thinking beyond the obvious solutions.
Solution Finder
Guides users from identifying a problem toward developing a concrete solution proposal.
Ideas Partner
Flags weaknesses before an idea moves on through the funnel.
Plausibility Check
Tests claims and assumptions before they cost review time.
Expert-review and evaluation assistant
Structures technical and business assessments, enabling comparable evaluation across thousands of reviewers.
Department Suggestion Agent
Analyzes the content of an idea, matches it against the department descriptions and recommends the most suitable evaluation unit or evaluator.
Similarity Detection
Compares every submission against the existing idea database and highlights overlaps through a tailored, AI-generated comparison. It distinguishes genuine duplicates from ideas that are merely related in content.
Patent-relevance indicator
Provides early signals of patentability, helping ensure that the most original ideas move into development first.
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Cross-cutting Process Intelligence
Additional AI assistants operate across the entire platform, reducing friction and accelerating the flow of ideas through the funnel.
Chatbot
Rules and works-agreement assistant
Language-agnostic search and translation
The search function accepts any language and translates ideas in real time. A search term therefore finds relevant ideas across languages, enabling truly global knowledge sharing.
Ideas in every language
Results
Since the pilot launch, three consecutive studies have accompanied the development of BMW SPARK. Even within this relatively short period, a clear evolution has become visible: from initially limited usage and acceptance toward a more differentiated understanding of how AI-supported idea creation works and where it creates value.
1. Operational impact
AI supports the areas traditionally responsible for delays in idea management: formulating ideas, evaluating them, categorizing them, and routing them to the right people. It helps before an idea is submitted and continues to provide support as the idea moves through the funnel.
The key factor was how these capabilities were introduced: AI functions were integrated directly into the relevant points of the existing workflow rather than added as an additional layer on top. What initially saw limited adoption has become a valuable and established component of operational idea management.
For the ideas themselves, AI creates impact both in terms of quality and diversity. Before rollout, the team had concerns that generative AI could lead to standardized responses and reduce the variety of submitted ideas. An early study conducted by an external institution showed the opposite: with AI support available, the diversity of submissions increased, and ideas were less likely to be rejected during the initial review stage.
From 45 minutes to a click
2. Adoption and implementation
The introduction of SPARK at BMW shows that the acceptance of AI applications is rarely linear. Immediately after launch, AI adoption ran more slowly than expected. While an almost immediate adoption had been anticipated, the reality was more nuanced: particularly in production environments, many employees initially did not recognize the role of AI or approached it with some skepticism.
The turning point came through iterative improvements to the user experience, targeted coaching, and a deliberate cultural change process.
This highlights an important lesson: technology alone does not create behavioral change. Making AI visible and accessible required focused enablement measures. Alongside multiple design improvements, weekly consultation sessions were introduced to support idea reviewers and gradually familiarize them with AI-supported workflows.
The central insight: capability only becomes impact when people are enabled to use it. The early experiences showed that once users discovered the value of AI, they continued using it. Initial curiosity quickly became routine, creating a stable user base for whom AI support during idea creation and refinement became a natural part of their daily work.
3. Impact in numbers
During the first four measured months, active AI usage was involved in almost every second idea: 47.9% of all ideas included AI-supported interactions. Around one quarter of all idea contributors (25.5%) used at least one AI function. This demonstrates that usage is intentional. Employees actively trigger AI capabilities and use them as practical tools in their workflow.
This shift did not happen overnight. Immediately after go-live, AI was used in only around one third of ideas. This highlights the importance of initial communication, training, and enablement. Afterward, adoption accelerated rapidly: Between November 2025 and February 2026, usage of Text AI increased by 131% (measured by the number of use cases) and the number of users increased by 127%. Image generation usage even increased by 297%.
Beyond these visible, user-driven interactions, a much larger AI layer operates automatically in the background. Translations and duplicate checks are performed automatically, supporting the processing of international ideas and reducing redundant work.
Together, these two dimensions tell the same story from different perspectives: The visible, user-initiated activity demonstrates employees actively choosing AI as part of their way of working. The automated layer demonstrates AI operating at a scale that no previous system or process could achieve. What began as an additional capability has become an integral part of the idea management process.
For the BMW Group, AI adoption was only the first milestone. The next phase is focused on depth, not just user adoption. The further development of AI within idea management is guided by a four-stage maturity model. This model serves as a roadmap for the evolution of BMW SPARK and also defines the direction of the joint roadmap with rready.
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Stage 1: Available
AI assistants exist within the platform. Users can discover them, activate them, and provide instructions through prompts.
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AI assistants become an active part of the workflow. They automatically appear at the right moment in the process, eliminating the need for users to find, activate, and instruct them manually. AI support becomes available precisely when it creates value.
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Stage 3: Dialogue-based
The traditional form-based input model disappears. Users describe their idea through a natural conversation, while AI structures the content in the background and transfers it into the appropriate fields. The capabilities of individual assistants become seamlessly embedded into the dialogue instead of appearing as separate functions within a form.
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Stage 4: Integrated / Connected
AI extends beyond the boundaries of the idea management platform and becomes connected across the enterprise. A company-wide copilot (or equivalent) can access BMW SPARK through APIs, understand the context of an idea, identify related projects, and suggest relevant experts across the organization. This forms the foundation for a fully integrated innovation ecosystem.
Over the long term, the focus will shift toward deeper integration of AI throughout the entire idea management process. This brings the original vision closer to reality: AI as a natural, embedded partner throughout innovation workflows. Thereafter comes Stage 4 - the seamless connection of idea management with the broader enterprise AI ecosystem. Intelligent assistants will understand the context of ideas, connect them with related initiatives across systems, and identify the right experts throughout the organization.
The BMW Group’s idea management transformation demonstrates how AI can enhance human creativity rather than replace it. The combination of people and artificial intelligence creates a new level of capability, one that neither employees nor technology could achieve independently.
A critical success factor was recognizing that employee enablement needed to stand alongside technical implementation as an equal priority. Rather than representing a simple software rollout, the transformation became a shared learning journey between technology, processes, and people.
This is the foundation of successful AI-driven transformation: not technology alone, but the combination of intelligent systems and empowered employees working together to create lasting impact.