The enterprise version of that is I don't want a CRM unless at least two other giant enterprises have successfully used that CRM for six months. [...] You want solutions that are proven to work before you take a risk on them.
在企业环境中，作者强调需要经过验证的解决方案，而非仅凭AI快速生成的产品，这反映了企业对可靠性和风险管理的重视。
help large enterprises deploy AI responsibly across their core business operations
【令人震惊】「负责任地在核心业务流程部署 AI」——这句话意味着 Anthropic 正在承接以前由麦肯锡、埃森哲做的企业变革咨询工作。纯模型 API 商业模式的顶峰可能已过：Claude 的护城河从「技术优势」升级为「有金融资本背书的企业实施能力」，中间层 AI 集成商和咨询公司的生存空间被直接压缩。
Anthropic, Blackstone, Hellman & Friedman, and Goldman Sachs announced the formation of a new AI services company
🤝【洞察】Anthropic 联手 Blackstone + Goldman Sachs——这不是技术合作，而是资本结构的战略重组。Blackstone 管理 1 万亿美元资产，Goldman Sachs 是企业关系的顶级入口。Anthropic 用金融资本弥补了自己最大的短板：企业级销售网络。与 OpenAI「The Deployment Company」同周发布，两家公司的企业服务战争在同一时间点打响，这是 AI 行业从「技术竞争」转向「渠道竞争」的历史时刻。
52.5% reduction in hallucinations
🤖【令人震惊的数字】幻觉率降低 52.5%——这是 OpenAI 有史以来在单次模型更新中宣称的最大幻觉降幅。更重要的是这发生在医疗、法律等高风险领域。幻觉是 AI 在专业服务场景落地的最大障碍，这个数字若属实，意味着企业 AI 可信度的拐点正在到来。
The one real underlying asset, Workday's trillion-transaction dataset, is thinner than it sounds; what actually matters at runtime is how data connects to workflows, permissions, and integrations, and every layer of that stack is now a liability.
大多数人认为Workday的大量交易数据是其核心资产和护城河，但作者认为这些数据价值被高估，而连接层才是关键。这一观点挑战了数据规模作为企业软件护城河的传统认知，暗示数据连接方式比数据量本身更重要。
When customers renew at close to 100% every year, it's usually read as a sign the product is delightful. In Workday's case, it's a sign of something else: leaving is close to impossible.
大多数人认为高续约率意味着客户满意，但作者认为这实际上反映了客户被锁定在系统中难以离开。这一观点挑战了软件行业常见的假设，即高续约率等于产品成功，而揭示了Workday的防御性商业模式。
This dynamic forces a brutal new discipline in how enterprises deploy capital and architect their internal workflows.
这种动态迫使企业以全新的方式部署资本和架构内部工作流程，表明了人工智能对企业管理方式的深远影响。
Our professionals are using Codex to move from static requirements to working solutions in hours, not weeks. It's enabling rapid prototyping, real-time workflow redesign, and faster iteration across the development lifecycle.
Accenture首席AI官声称将开发时间从'周'缩短到'小时'，这是一个显著的效率提升声明，但缺乏具体数据支持。此处缺乏量化依据，无法验证这一断言的真实性或普遍适用性。
Today, those partners include Accenture, Capgemini, CGI, Cognizant, Infosys, PwC, and Tata Consultancy Services (TCS).
文章列出了7家全球系统整合合作伙伴(GSIs)，这些都是大型IT咨询和系统集成公司。这一合作策略表明OpenAI正在通过这些拥有丰富企业客户资源的合作伙伴来加速Codex在企业市场的渗透，但未提供这些合作伙伴的客户覆盖范围或预期增长数据。
Companies are using Codex across the software development lifecycle. Virgin Atlantic is using it to increase test coverage and increase team velocity - reducing technical debt and improving performance.
虽然文章提到了Virgin Atlantic使用Codex的具体应用场景，但没有提供任何量化数据来衡量其效果。此处缺乏量化依据，无法评估Codex实际带来的性能提升或技术债务减少程度。
The compliance-driven buyers improvising local AI out of retail Mac Minis because the product they need does not exist.
大多数人认为企业AI采用需要专门的解决方案和供应商，但作者指出一些合规驱动的买家正在使用零售版Mac Mini自行构建本地AI解决方案。这挑战了企业AI市场的传统认知，暗示市场可能存在未被满足的需求，以及企业正在以非传统方式应对AI挑战。
Claude is now being deployed to NEC Group employees around the world
大多数人认为企业会谨慎地小规模试点AI工具，但作者认为NEC正在全球范围内大规模部署Claude，这表明企业对AI技术的信任度远高于预期，挑战了传统的技术采用曲线和变革管理理论。
over 100,000 customers now run Claude on Amazon Bedrock
10万客户在AWS上运行Claude，这是一个相当大的企业客户基础。这个数字表明Claude在企业市场已经获得了一定的采用率，但与OpenAI的数亿用户相比仍有差距。这一数据点反映了Anthropic在企业市场的定位和进展。
over 100,000 customers now run Claude on Amazon Bedrock
10万客户使用Claude是一个显著的用户基础，表明Anthropic的企业采用率正在快速增长。这个数字与OpenAI的数亿用户相比仍有差距，但对于一个专注于企业级AI模型的初创公司来说，这是一个有意义的里程碑，显示其市场渗透策略正在取得成效。
Admins can also manage who has access to use, build, and share agents.
在许多情况下，人们认为 AI 工具的使用和管理应该是完全自动化的，但作者提出管理员可以控制谁有权使用、构建和共享 agents。
The interest comes as Anthropic's annual revenue run rate has surged to about $30 billion, driven by strong demand from enterprise customers using its AI tools for coding, cybersecurity, and automation.
Anthropic年收入达到300亿美元的惊人速度展示了企业级AI市场的巨大潜力。这表明AI已从实验性技术转变为关键业务工具，特别是在代码编写、网络安全和自动化领域，反映了AI正在成为企业数字化转型的核心驱动力。
ChatGPT has 900 million weekly users, which means employees already know how to work with it. For enterprises, that reduces rollout friction and accelerates the point where every employee can delegate tedious tasks.
ChatGPT的9亿周活跃用户为企业AI采用提供了独特优势，消除了用户培训的障碍。这一惊人的用户基础表明，消费级AI应用已经培养了庞大的AI熟练劳动力，这将显著降低企业AI转型的实施成本和时间，加速AI在工作场所的普及。
Building on our consumer strength, enterprise now makes up more than 40% of our revenue, and is on track to reach parity with consumer by the end of 2026.
令人惊讶的是：OpenAI的企业业务在如此短的时间内就占据了公司收入的40%，并且预计将在2026年底与消费者业务持平。这表明AI在企业领域的采用速度远超预期，反映了企业对AI技术的迫切需求和巨大投资。
Microsoft Copilot, which leads paid AI usage among both work-oriented and personal-oriented users, illustrates this dynamic: its prevalence likely reflects bundling with Microsoft 365, a product widely deployed in workplaces through enterprise licensing.
微软Copilot的普及展示了企业捆绑策略如何推动AI工具在职场中的采用。这一洞察揭示了技术采用不仅关乎技术本身，还与商业生态系统和现有企业软件的整合密切相关。这表明AI工具的成功可能更多地依赖于与现有工作流程的无缝集成，而非独立功能。
76% among users with employer-provided subscriptions. As we would expect, paid access, especially when provided by employers, is associated with more intensive workplace use.
令人惊讶的是：由雇主提供付费AI工具的用户中，高达76%在工作场所使用AI，远高于免费用户的38%，这表明企业付费模式极大加速了AI在工作中的采用，反映了组织决策对技术采用的关键影响。
This level of penetration in such a short period of time is remarkable since Fortune 500 enterprises are not known to be early adopters of technology. Historically, many startups had to initially sell to other startups to get early momentum, and it was only after a few years that a startup would be able to land its first enterprise contract.
AI技术在财富500强企业中的快速采用打破了传统技术采用模式，这一现象揭示了AI可能正在重塑企业创新和采用技术的决策机制。大企业通常不是早期技术采用者，但AI却能在短时间内获得广泛采用，这可能意味着企业对AI的价值认知和风险接受度发生了根本性变化。
Based on our analysis, **29% of the Fortune 500 and ~19% of the Global 2000**are live, paying customers of a leading AI startup.
这一数据揭示了企业AI采用率远高于公众认知，颠覆了传统技术采用模式。财富500强中近三分之一的企业已经实际部署AI应用，这一惊人的采用速度表明AI技术正在以前所未有的速度渗透传统企业，打破了企业技术采用通常需要数年才能达到大规模采用的规律。
Based on our analysis, **29% of the Fortune 500 and ~19% of the Global 2000**are live, paying customers of a leading AI startup.
令人惊讶的是：在短短三年多时间里，近三分之一的财富500强企业和五分之一的世界2000强企业已经成为AI初创公司的付费客户。这一采用速度远超传统技术，打破了大型企业历来是技术采用落后者的刻板印象，展示了AI在企业中的惊人渗透速度。
29% of the Fortune 500 and ~19% of the Global 2000 are live, paying customers of a leading AI startup.
令人震惊的渗透率：三年内，近三分之一的财富 500 强已经是 AI 创业公司的付费客户——而且是真实部署、而非试点。这打脸了 MIT「95% AI 试点失败」的结论。更值得注意的是「qualify」的定义：必须签署顶层合同、完成试点转化、在组织内上线。这三个条件滤掉了大量「假采用」，说明这 29% 是真金白银的生产级部署。
Shachar led the Amazon GuardDuty product, scaling the business to over 80,000 customers.
令人惊讶的是：亚马逊GuardDuty安全产品已经拥有超过80,000名客户，这表明企业级安全解决方案的市场规模和采用率远超普通人的想象。
Because of this, teams keep rebuilding the same integration layer. Even within the same company, similar integrations are often implemented multiple times in arbitrary code, leading to security risks, lack of traffic observability, and duplication of work.
令人惊讶的是：即使在同一公司内部，类似的集成也经常被多次实现，导致安全风险、流量可见性不足和工作重复。这种重复建设企业AI集成层的问题比人们想象的更为普遍，而Mistral的连接器旨在通过封装集成到单一可重用实体来解决这一问题。
They intentionally deploy two or three AI tools for the same use case. Not because of indecision—but by design. Redundancy is policy.
令人惊讶的是：大型金融机构故意为同一用途部署多个AI工具，这并非犹豫不决而是刻意为之。这种冗余策略反映了企业对AI应用成熟度的谨慎态度，以及对单一供应商依赖风险的担忧。这种做法与传统的效率至上的商业逻辑形成鲜明对比，展示了企业在关键业务流程中采取的'防御性多元化'策略。
Anthropic says Managed Agents is designed to cut the time it takes to move from prototype to production from months to days, with early adopters like Notion, Rakuten, Asana, Vibecode, and Sentry already using it across coding, productivity, and internal workflow automation.
将AI原型到生产的时间从几个月缩短到几天是一个惊人的加速，这将彻底改变企业采用AI的方式。这种快速部署能力可能加速AI在各行业的普及，但也带来了关于AI系统安全性和治理的紧迫问题，企业需要在快速采用和确保安全之间找到平衡。
gpt-oss-20B (high): 0.7%
gpt-oss-20B 的成绩是 0.7%——在 452 个专业任务中，只有不到 4 个通过了评测。这个数字与顶级模型的 33.3% 之间，存在近 50 倍的差距。这说明专业服务 Agent 能力不是「渐进改善」，而是存在明确的「能力阶梯」——低于某个规模的模型，在这类任务上几乎完全失效。这对企业 AI 选型的启示：在专业服务场景，「够用的小模型」可能根本不存在，只有「能用的大模型」和「完全不能用的模型」两种。
Gemma 4 models undergo the same rigorous infrastructure security protocols as our proprietary models.
「与专有模型相同的安全协议」——这句话针对的是企业和主权机构客户，暗示 Google 正在用开源模型打「安全牌」吸引政府和监管严格行业。对于不愿依赖 OpenAI/Anthropic 闭源 API 的企业，E2B/E4B 提供了一条「可审计、可部署、可监管」的路径，而 Google DeepMind 的安全背书是这条路的核心说服力。
in 2024, 47% of AI solutions were built internally and 53% were purchased; today, 76% of all AI is purchased rather than developed in-house.
大多数人认为企业会越来越倾向于自主开发AI模型以保持竞争优势和控制权，但数据显示相反趋势——企业正加速转向购买第三方AI解决方案。这种转变表明企业可能更看重快速部署而非技术专长，但也可能导致组织失去对AI核心能力的理解和优化能力。
in 2024, 47% of AI solutions were built internally and 53% were purchased; today, 76% of all AI is purchased rather than developed in-house.
大多数人认为企业会越来越倾向于自主开发AI模型以保持竞争优势和控制权，但数据显示企业正迅速转向购买第三方AI解决方案。这一趋势与主流认知相悖，表明企业可能更看重快速部署和成本效益而非技术自主性。
over 500 business customers were each spending over $1 million on an annualized basis. Today that number exceeds 1,000, doubling in less than two months.
大多数人对AI企业客户的采用速度持保守态度，但Anthropic的高价值客户数量在短短两个月内翻倍，表明企业对AI的采用速度和投资规模远超行业预期，挑战了AI企业市场缓慢发展的普遍认知。
Sarah Anne Bendall
Sarah A. Bendall FRHistS is a senior lecturer at the Gender and Women's History Research Centre in the Institute for Humanities and Social Sciences. She is a material culture historian whose research examines the roles of women in the production, trade and consumption of global commodities and fashionable consumer goods between 1500-1800. She has particular expertise in seventeenth-century dress and recreative methodologies, such as historical dress reconstruction.
Sarah was awarded her PhD from the University of Sydney. During her doctoral research she was a visiting research student at Kings College London. Prior to joining ACU, she held positions at the University of Western Australia, the University of Sydney and the University of Melbourne. She has been awarded fellowships from The Bodleian Libraries at the University of Oxford, the Folger Shakespeare Library and the Powerhouse Museum. She was also co-investigator on the UK Arts and Humanities Research Council's Making Historical Dress Network grant (2023-5).
to ensure a competitive marketplace for publishing through enterprise publishing systems
enterprise publishing systems:
it is this architecture, the one which is in the heads of those writing the code, that is the most important. In adopting this decentralised approach, where the practice of architectural decision-making is much more dispersed, this problem is in many ways, mitigated
Only true in software architecture. But, in enterprise architecture - that spans domains decentralized decisions create fragmentations.
The same thing applies with lead assignment rules Salesforce – you can define which users will be assigned leads that come from your website and which users will be assigned leads that come from social media.
By automating this process, businesses can ensure that leads and cases are handled promptly and by the most suitable team members, improving efficiency and customer satisfaction.
5 ERP system examples (who benefits from ERP?)
The term EnterpriseResourcePlanning (ERP) system refers to a large number of integrated softwaresuites used by companies to manage day-to-day operations and business workflows, including datamanagement, inventory control, accounting, CRM, and projectmanagement. Thus, in order to remain an effective contender in an era of digital commerce, ERP_systems are an important part of the business information technology infrastructure.
Dafür wird der Wissensgraph um geeignete Tools erweitert. DasTechnologiespektrum reicht hier je nach Strukturiertheitsgrad der Daten von Methodender semantischen Textanalyse (vgl. [6]) über das Parsen von regulären Ausdrücken(s. Abschn. 6.4.2) bis hin zum (teil)automatischen Mappen mithilfe von Transforma-tionsvokabularen (z. B. D2RQ in [7], R2RML)
Beispiel für eine KG-Erweiterung
Verbesserungspotenzial im Bereich der Graph-Visualisierung, des Authorings von Ontologien und Regeln und der einfachen Anbindung weiterer Datenquellen.
Verbesserungspotential von EKGs
Für eine noch schnellere Verbreitung im Unternehmensumfeld müssen die zugrunde liegenden Technologien jedoch zugänglicher für Nicht-Techniker werden.
Zukunft: Zugang für Nicht-Techniker
Projektgraphen um die Fähigkeit erweitert, dem Projektleiter basierend auf seinen beschreibenden Texten relevante Themen und Technologien zur Übernahme vorzuschlagen.
Einsatz von Projektgraphen Erweiterung um die Fähigkeit, basierend auf seine beschreibenden Texten relevante Themen und Technologien zur Übernahme vorzuschlagen
Darüber hinaus ist ein wichtiger Trend Linked Data im Unternehmensumfeld zu etablieren, um eine neue Generation semantischer, vernetzter Daten-Anwendungen auf Basis des Linked Data Paradigmas zu entwickeln, zu etablieren und erfolgreich zu vermarkten. Im BMBF Wachstumskernprojekt „Linked Enterprise Data Services“ entsteht hierfür beispielsweise eine Technologieplattform, die es Unternehmen ermöglichen soll, neue Dienstleistungen im Web 3.0 zu etablieren.
BMBF Wachstumskernprojekt „Linked Enterprise Data Services
Der Hauptanwendungspartner für die hier beschriebenen Lösungen war und ist der Sie-mens-Konzern. Die Lösungen wurden durch das KI-Start-up Giance, eine deutsch-chinesi-sche Ausgründung des DFKI, für den chinesischen Markt angepasst und weiterentwickelt.
KI-basierte Serviceplattform für Enterprise Intelli- gence
Unsere Global Enterprise Intelligence (GEI) Platform eignet sich nicht nur zur Beobachtung von Zulieferern, sondern wird auch in ande-ren Bereichen eingesetzt, in denen Firmen beobachtet werden müssen wie z. B. Wettbewer-beranalyse, Partnerbetreuung, Key-Account-Management oder Portfolio- Management.
Mehrwert
Knowledge Graph Check & UpdateMithilfe der Neo4J-Graphdatenbanktechnologie werden für die Anwendungen Wis-sensgraphen aufgebaut und ständig um neue Relationen und Eigenschaften der beob-achteten Firmen ergänzt. Die Wissensgraphen dienen nicht nur der Visualisierung der Ergebnisse, sie werden auch zum Entity Linking und zur Erkennung von bereits be-kannter Information verwendet
Neo4J-Graphdatenbanktechnologie werden für die Anwendungen Wissensgraphen aufgebaut und ständig um neue Relationen und Eigenschaften der beobachteten Firmen ergänzt.
Die Wissensgraphen dienen nicht nur der Visualisierung der Ergebnisse, sie werden auch zum Entity Linking und zur Erkennung von bereits bekannter Information verwendet.
Der im Projekt „Smart Data Web“ erstellte öffentliche Teil des Wissensgraphen wurde zudem zum Aufbau eines Siemens-internen Corporate Knowledge Graphen genutzt. Dazu wurden relevante Teilmengen des öffentlichen Wissensgraphen extrahiert und in das ge-schützte Siemens- Netzwerk transferiert. Die internen Datenbanken von Siemens wurden nach RDF konvertiert und zusammen mit dem SDW KG in eine geschützte Datenbank geladen. Weiterhin wurden vom Anwendungsfall getriebene Abfragen erstellt, welche in-terne und offene Daten kombinieren. Der Corporate Knowledge Graph (CKG) ermöglicht eine einheitliche, konsistente und elegante Verknüpfung interner und externer Informatio-nen, ganz im Sinne einer „Enterprise-Intelligence“-Lösung. Über den CKG können Infor-mationen, im konkreten Fall zu Zulieferern, aggregiert und konsolidiert abgerufen und für die Einkaufsabteilungen von Siemens dargestellt werden. Dabei werden interne Kennzah-len, z. B. zum Projektvolumen und zu Bewertungen einzelner Lieferanten, mit aktuellen, automatisch gesammelten, firmen-, produkt- und standortbezogenen Ereignissen aus Nachrichten und anderen Textdatenquellen verknüpft, sodass die Anwender eine Gesamt-sicht auf entscheidungsrelevantes Wissen erhalten
Projekt „Smart Data Web“ Corporate Knowledge Graph (CKG) - ermöglicht eine einheitliche, konsistente und elegante Verknüpfung interner und externer Informationen, ganz im Sinne einer „Enterprise-Intelligence“-Lösung
Semantische Verknüpfung/Ontologie:
Dabei werden interne Kennzah- len, z. B. zum Projektvolumen und zu Bewertungen einzelner Lieferanten, mit aktuellen, automatisch gesammelten, firmen-, produkt- und standortbezogenen Ereignissen aus<br /> Nachrichten und anderen Textdatenquellen verknüpft
Potential: eine Gesamt- sicht auf entscheidungsrelevantes Wissen erhalten.
Enterprise Knowledge Graphs (EKGs) mightbe considered as an embodiment of LED
Enterprise Knowledge Graphs (EKGs) als eine Verkörperung von LED
Sören Auer
The unified approachhas the advantage, that the enterprise has more control overthe data and quality, and the data querying is significantlyfaster.
REFERENCES[1] C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak,and S. Hellmann. Dbpedia-a crystallization point for the web of data.Web Semantics: science, services and agents on the world wide web,7(3):154–165, 2009.[2] D. Calvanese, M. Giese, D. Hovland, and M. Rezk. Ontology-basedintegration of cross-linked datasets. In Proceedings of the 14th Interna-tional Semantic Web Conference (ISWC). Springer, 2015.[3] X. Dong, E. Gabrilovich, G. Heitz, and W. Horn. Knowledge vault: Aweb-scale approach to probabilistic knowledge fusion. In Proceedingsof the 20th ACM SIGKDD international conference on Knowledgediscovery and data mining, pages 601–610, 2014.[4] P. Frischmuth, S. Auer, S. Tramp, J. Unbehauen, K. Holzweißig,and C. Marquardt. Towards linked data based enterprise informationintegration. In S. Coppens, K. Hammar, M. Knuth, and et al., editors,Proceedings of the Workshop on Semantic Web Enterprise Adoption andBest Practice (ISWC 2013), 2013. CEUR-WS.org, 2013.[5] R. Isele and C. Bizer. Active learning of expressive linkage rules usinggenetic programming. Web Semantics: Science, Services and Agents onthe World Wide Web, 23:2–15, 2013.[6] L. Masuch. Enterprise knowledge graph - one graph to connect themall. 2014.[7] P. N. Mendes, H. Mühleisen, and C. Bizer. Sieve: Linked data qualityassessment and fusion. In Proceedings of the 2012 Joint EDBT/ICDTWorkshops, pages 116–123, 2012.[8] J. Michelfeit, T. Knap, and M. Neˇcask `y. Linked data integration withconflicts. arXiv preprint arXiv:1410.7990, 2014.[9] A.-C. Ngonga Ngomo and S. Auer. Limes - a time-efficient approachfor large-scale link discovery on the web of data. In Proceedings ofIJCAI, 2011.[10] N. F. Noy. Semantic integration: a survey of ontology-based approaches.ACM Sigmod Record, 33(4):65–70, 2004.[11] T. Pellegrini, H. Sack, and S. Auer, editors. Linked Enterprise Data.X.media.press. Springer, 2014.[12] A. Schultz, A. Matteini, R. Isele, P. N. Mendes, C. Bizer, and C. Becker.Ldif-a framework for large-scale linked data integration. In 21stInternational World Wide Web Conference (WWW 2012), DevelopersTrack, Lyon, France, 2012.
In general, a federated approach will be advan-tageous if the enterprise aims to continuously ingest updatesand new additions from public LOD sources.
Nevertheless, acertain overhead for query expansion and entailment regimesis required.
Enterprise Knowledge Graphs
The unified approach has the advantage, that the enterprise has more control over the data and quality, and the data querying is significantly faster.
Areas of Integrated Governance
Major areas of integrated governance for enterprise architecture looks like Integrated Project Management Framework
Taylor, Charlie. ‘Ireland Ranked among Best for Covid-19 Innovative Solutions’. The Irish Times. Accessed 7 September 2020. https://www.irishtimes.com/business/innovation/ireland-ranked-among-best-for-covid-19-innovative-solutions-1.4233471.
"Off-line" vs "On-line". The RAT's focus is to get to a final state, and then ship it, all at once. During the working process, the thing we're working on is "off-line". It's not in the field and no one is using it
This is a common problem when trying to do agile with enterprise clients.
Can end up in a bubble where we are working on requirements that have been passed down - from how long ago? and then take even longer until users are actually using it.
In all cases, the surviving monarchies of Southeast Asia have power and influence that potentially or in reality exceed that described in constitutional terms. This has come about chiefly because of the continuity of the archaic sacred and cultural symbolism of monarchy, which the monarchs themselves have cleverly perpetuated—as well as the patronage derived from their considerable wealth.
This is another argument that led me to the skepticism of the argument that the constitutional monarchy is a dead governmental system and that the monarchy is nothing more than figureheads to the world when in reality, this is not the case with Southeast Asia. I find it fascinating that Japan does have an emperor that rules silently and he still is more authoritative than the UK monarchs.
塔塔首席數字官C.R. Srinivasan表示：「《發展周期》像是對企業發出的警告，提出了由於革新的出現組織中不同層面開始浮現的『理想與現實』間的差距，他還表示：「這種差距表明，企業決策者和各個部門主管應該主動向企業 CEO提出其面對物聯網和人工智能技術時遇到的困難。」
<big>评：</big><br/><br/>Tata 释出的这份调查报告《发展周期（The Cycle of Progress）》枚举了企业在区块链应用上面临的主要障碍，但这并非警告——我们有必要正视不同层面的差距。事实上无论是在公司内部还是在更广的社会层面，这种异步感早已存在，甚至可以说，恰恰是这种异步感造成了人们认知上的差别，庞大的生态体系因此得以建立、维系。<br/><br/>如今，这些巨型组织的内部孵化出了一股新生力量——他们面对新技术的诱惑蠢蠢欲动，又无法轻松甩掉旧资产的包袱，还要和那些持不同意见的高管和股东们做对抗。但这样的拉扯并不一定是零和游戏，在这争斗中不同派别也能射出良性互动的微光，亦向现状抛出问题——既然我们做好了迎接新技术到来的外部战略准备，为何不改变自下而上的内部交互方式？
Inputs: the investment dollars and employee time devoted to innovation, along with the number of ideas that are generated internally each month or sourced from customers, suppliers, and other outsiders. Throughputs: the number and quality of ideas that enter the pipeline after initial screening, the time it takes for those ideas to move from concept to prototype to reality, and the notional value of the innovation pipeline. Outputs: the number of innovations that reach the market in a given period, the percentage of revenue derived from new products and services, and the margin gains that are attributable to innovation. Leadership: the percentage of executive time that gets devoted to mentoring innovation projects, and 360-degree survey results that reveal the extent to which executives are exhibiting pro-innovation behaviors. Competence: the percentage of employees who have been trained as business innovators, the percentage of employees who have qualified as innovation “black belts,” and changes in the quality of ideas that are being generated across the firm. Climate: the extent to which the firm’s management processes facilitate or frustrate innovation, and the progress that is being made in removing innovation blockages. Efficiency: changes over time in the ratio of innovation outputs to inputs. Balance: the mix of different types of innovation (product, service, pricing, distribution, operations, etc.); different risk categories (incremental improvements versus speculative ventures); and different time horizons.
Some nice metrics for innovation in enterprise.
an environment unlike anything they will encounter outside of school
Hm? Aren’t they likely to encounter Content Management Systems, Enterprise Resource Planning, Customer Relationship Management, Intranets, etc.? Granted, these aren’t precisely the same think as LMS. But there’s quite a bit of continuity between Drupal, Oracle, Moodle, Sharepoint, and Salesforce.
institutional demands for enterprise services such as e-mail, student information systems, and the branded website become mission-critical
In context, these other dimensions of “online presence” in Higher Education take a special meaning. Reminds me of WPcampus. One might have thought that it was about using WordPress to enhance learning. While there are some presentations on leveraging WP as a kind of “Learning Management System”, much of it is about Higher Education as a sector for webwork (-development, -design, etc.).
This has much in common with a customer relationship management system and facilitates the workflow around interventions as well as various visualisations. It’s unclear how the at risk metric is calculated but a more sophisticated predictive analytics engine might help in this regard.
Have yet to notice much discussion of the relationships between SIS (Student Information Systems), CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), and LMS (Learning Management Systems).
Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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Competitive Enterprise Institute
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“An individual building, the style in which it is going to be designed and built, is not that important. The important thing, really, is the community. How does it affect life?” I.M. Pei
Engineers who worked on a lot of open source projects had high levels of creativity
Developers felt more ownership over their work, and pride in it
Open source developers work well together because of their similar ways of thinking
Peer pressure from GitHub—having their name on a project—was a big motivator for engineers to work harder and not let the community of users down.
If they leave, they're likely to keep working on the project, so you're still getting value for free!
Any contributor to our open source projects is already familiar with a bit of software that we use internally and would require less training if they joined the company.
suffers from an over protective legal organization
respecting open source licenses to making it easier for engineers to open source code and ensuring we’re giving back to the open source projects we depend on