Thoughtworks: Agile Innovation in the Digital Era
Bibliographic record
Abstract
本案例描述了Thoughtworks中国与跃龙汽车公司合力打造一款数字化产品过程中所经历的种种冲突与平衡：创新是为了超越竞争者还是满足消费者需求？数字化转型项目是新零售部门主导还是一把手工程？探索创新机会是否应当进行调研？应考虑所有的用户痛点和需求还是聚焦关键问题？关键问题排序时首先考虑高价值还是首先验证高风险的需求？形成最小可行性产品并发布之后，若遇到外部市场变化，企业到底要不要做出相应调整？创新产品到底应当由哪个部门来运营？面向未来，创新产品又当如何在时间维度和空间维度延伸与复制呢……沿着Thoughtworks的敏捷创新流程，本案例透过跃龙汽车的数字产品打造历程，将如上创新困境逐一陈述。 案例中，Thoughtworks和跃龙汽车在开放的“争论”中合作共创，使得跃龙汽车手机应用（App）项目，在推出一个月内取得了不错的市场反响。2021年1月底，跃龙汽车母公司腾飞集团的销售副总裁张敏，邀请了跃龙汽车总经理王捷和新零售总监李创，以及Thoughtworks首席项目经理赵新，来商讨如何将这款创新产品的经验在集团内复制。纵然数字化时代召唤着更多创新，赵新更希望引导客户企业先来复盘过去和认清发展现状。 基于此，案例引发了对企业推出创新产品的各种困境与探索的更深层次和更具普适性的讨论，并帮助学员理解Thoughtworks敏捷创新方法论的特点和适用场景。
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".