Case Details
BYD’s Vision Center: Scaling Global Brand Storytelling with Agentic AI
BYD, a global new-energy-vehicle and technology company, had just launched the internal beta of an agentic AI-powered creative platform in its Vision Center, part of the Brand and Public Relations Division. As BYD expanded rapidly across products and markets, the Vision Center faced mounting pressure to produce more brand, advertising, and overseas communication materials in less time. Employee experiments had shown that generative AI could cut costs and shorten some project cycles. They also revealed inconsistent output, visually inaccurate vehicles, and workflows that were hard to share and reuse. AI had boosted individual productivity without yet becoming a dependable organizational capability.
The beta was only the beginning. Would employees adopt the platform in their daily work? Could its outputs meet the accuracy standards of automotive advertising? How should its contribution be measured? The case asks students how the director should redesign roles, workflows, decision rights, incentives, and performance measures. The goal is for the platform to become trusted production infrastructure rather than an experimental tool for a few AI enthusiasts.
The authors teach this case in courses on Digital Innovation and Digital Transformation. It was designed for modules covering AI strategy, platform-enabled transformation, technology adoption and business-value realization.
Learning Objective:
- Make the right AI investment decision: Evaluate whether a company should build an internal AI platform, rely on general-purpose external tools or adopt a hybrid approach by assessing strategic fit, expected value, organizational readiness, risks and opportunity costs.
- Deploy the right AI platform: Translate business problems into platform requirements and design a scenario-driven platform combining proprietary knowledge, reusable assets, workflow orchestration and risk-based human review.
- Facilitate user acceptance: Apply technology-acceptance, task–technology fit and change-management frameworks to address concerns about usefulness, usability, reliability, professional autonomy and accountability.
- Maximize return on investment: Prioritize use cases according to value, feasibility and risk; create reinforcing cycles of adoption, asset reuse and organizational learning; and evaluate returns using measures of cost, speed, quality, rework, adoption, reuse and communication effectiveness.
- Integrate these four elements into a staged AI innovation strategy in which continued investment depends on evidence generated through implementation and use.