美国劳工部发布职场人工智能使用原则，保护员工权益（附录原文）
今天5月16日，美国劳工部发布了一套针对人工智能（AI）在职场使用的原则，旨在为雇主提供指导，确保人工智能技术的开发和使用以员工为核心，提升所有员工的工作质量和生活质量。代理劳工部长朱莉·苏在声明中指出：“员工必须是我们国家AI技术发展和使用方法的核心。这些原则反映了拜登-哈里斯政府的信念，人工智能不仅要遵守现有法律，还要提升所有员工的工作和生活质量。”
根据劳工部发布的内容，这些AI原则包括：
以员工赋权为中心：员工及其代表，特别是来自弱势群体的代表，应被告知并有真正的发言权参与AI系统的设计、开发、测试、培训、使用和监督。这确保了AI技术在整个生命周期中考虑到员工的需求和反馈。
道德开发AI：AI系统应以保护员工为目标设计、开发和培训。这意味着在开发AI时，需要优先考虑员工的安全、健康和福祉，防止技术对员工造成不利影响。
建立AI治理和人工监督：组织应有明确的治理体系、程序、人工监督和评估流程，确保AI系统在职场中的使用符合伦理规范，并有适当的监督机制来防止误用。
确保AI使用的透明度：雇主应对员工和求职者透明地展示其使用的AI系统。这包括向员工说明AI系统的功能、目的以及其在工作中的具体应用，增强员工的信任感。
保护劳动和就业权利：AI系统不应违反或破坏员工的组织权、健康和安全权、工资和工时权以及反歧视和反报复保护。这确保了员工在AI技术的应用下，其基本劳动权益不受侵害。
使用AI来支持员工：AI系统应协助、补充和支持员工，并改善工作质量。这意味着AI应被用来提升员工的工作效率和舒适度，而不是取代员工或增加其工作负担。
支持受AI影响的员工：雇主应在与AI相关的工作转换期间支持或提升员工的技能。这包括提供培训和职业发展机会，帮助员工适应新的工作环境和技术要求。
确保负责任地使用员工数据：AI系统收集、使用或创建的员工数据应限于合法商业目的，并被负责地保护和处理。这确保了员工数据的隐私和安全，防止数据滥用。
这些原则是根据拜登总统发布的《安全、可靠和可信赖的人工智能开发和使用行政命令》制定的，旨在为开发者和雇主提供路线图，确保员工在AI技术带来的新机遇中受益，同时避免潜在的危害。
拜登政府强调，这些原则不仅适用于特定行业，而是应在各个领域广泛应用。原则不是详尽的列表，而是一个指导框架，供企业根据自身情况进行定制，并在员工参与下实施最佳实践。通过这种方式，拜登政府希望能在确保AI技术推动创新和机会的同时，保护员工的权益，避免技术可能带来的负面影响。
这套原则发布后，您认为它会对贵公司的AI技术使用和员工权益保护产生怎样的影响？
英文如下：
Department of Labor's Artificial Intelligence and Worker Well-being: Principles for Developers and Employers
Since taking office, President Biden, Vice President Harris, and the entire Biden-Harris Administration have moved with urgency to harness AI's potential to spur innovation, advance opportunity, and transform the nature of many jobs and industries, while also protecting workers from the risk that they might not share in these gains. As part of this commitment, the AI Executive Order directed the Department of Labor to create Principles for Developers and Employers when using AI in the workplace. These Principles will create a roadmap for developers and employers on how to harness AI technologies for their businesses while ensuring workers benefit from new opportunities created by AI and are protected from its potential harms.
The precise scope and nature of how AI will change the workplace remains uncertain. AI can positively augment work by replacing and automating repetitive tasks or assisting with routine decisions, which may reduce the burden on workers and allow them to better perform other responsibilities. Consequently, the introduction of AI-augmented work will create demand for workers to gain new skills and training to learn how to use AI in their day-to-day work. AI will also continue creating new jobs, including those focused on the development, deployment, and human oversight of AI. But AI-augmented work also poses risks if workers no longer have autonomy and direction over their work or their job quality declines. The risks of AI for workers are greater if it undermines workers' rights, embeds bias and discrimination in decision-making processes, or makes consequential workplace decisions without transparency, human oversight and review. There are also risks that workers will be displaced entirely from their jobs by AI.
In recent years, unions and employers have come together to collectively bargain new agreements setting sensible, worker-protective guardrails around the use of AI and automated systems in the workplace. In order to provide AI developers and employers across the country with a shared set of guidelines, the Department of Labor developed "Artificial Intelligence and Worker Well-being: Principles for Developers and Employers" as directed by President Biden's Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, with input from workers, unions, researchers, academics, employers, and developers, among others, and through public listening sessions.
APPLYING THE PRINCIPLES
The following Principles apply to the development and deployment of AI systems in the workplace, and should be considered during the whole lifecycle of AI – from design to development, testing, training, deployment and use, oversight, and auditing. The Principles are applicable to all sectors and intended to be mutually reinforcing, though not all Principles will apply to the same extent in every industry or workplace. The Principles are not intended to be an exhaustive list but instead a guiding framework for businesses. AI developers and employers should review and customize the best practices based on their own context and with input from workers.
The Department's AI Principles for Developers and Employers include:
[North Star] Centering Worker Empowerment: Workers and their representatives, especially those from underserved communities, should be informed of and have genuine input in the design, development, testing, training, use, and oversight of AI systems for use in the workplace.
Ethically Developing AI: AI systems should be designed, developed, and trained in a way that protects workers.
Establishing AI Governance and Human Oversight: Organizations should have clear governance systems, procedures, human oversight, and evaluation processes for AI systems for use in the workplace.
Ensuring Transparency in AI Use: Employers should be transparent with workers and job seekers about the AI systems that are being used in the workplace.
Protecting Labor and Employment Rights: AI systems should not violate or undermine workers' right to organize, health and safety rights, wage and hour rights, and anti-discrimination and anti-retaliation protections.
Using AI to Enable Workers: AI systems should assist, complement, and enable workers, and improve job quality.
Supporting Workers Impacted by AI: Employers should support or upskill workers during job transitions related to AI.
Ensuring Responsible Use of Worker Data: Workers' data collected, used, or created by AI systems should be limited in scope and location, used only to support legitimate business aims, and protected and handled responsibly.
美国联邦贸易委员会（FTC）FTC 宣布全国范围内禁止竞业协议，详细请看
美国联邦贸易委员会（FTC）于2024年4月23日发布最终规定，全国范围内禁止非竞争协议。此举旨在通过保护工人更换工作的自由来促进竞争，增加创新，并推动经济增长。根据FTC的预测，新业务的形成将每年增加2.7%，预计每年将新增超过8500家新企业。此外，预计工人的平均收入将增加524美元，未来十年内医疗费用预计将减少高达1940亿美元。同时，预计该规定还将在未来十年内每年新增17000至29000项专利。
详情以英文版为准：
FTC Announces Rule Banning Noncompetes
FTC’s final rule will generate over 8,500 new businesses each year, raise worker wages, lower health care costs, and boost innovation
Today, the Federal Trade Commission issued a final rule to promote competition by banning noncompetes nationwide, protecting the fundamental freedom of workers to change jobs, increasing innovation, and fostering new business formation.
“Noncompete clauses keep wages low, suppress new ideas, and rob the American economy of dynamism, including from the more than 8,500 new startups that would be created a year once noncompetes are banned,” said FTC Chair Lina M. Khan. “The FTC’s final rule to ban noncompetes will ensure Americans have the freedom to pursue a new job, start a new business, or bring a new idea to market.”
The FTC estimates that the final rule banning noncompetes will lead to new business formation growing by 2.7% per year, resulting in more than 8,500 additional new businesses created each year. The final rule is expected to result in higher earnings for workers, with estimated earnings increasing for the average worker by an additional $524 per year, and it is expected to lower health care costs by up to $194 billion over the next decade. In addition, the final rule is expected to help drive innovation, leading to an estimated average increase of 17,000 to 29,000 more patents each year for the next 10 years under the final rule.
Noncompetes are a widespread and often exploitative practice imposing contractual conditions that prevent workers from taking a new job or starting a new business. Noncompetes often force workers to either stay in a job they want to leave or bear other significant harms and costs, such as being forced to switch to a lower-paying field, being forced to relocate, being forced to leave the workforce altogether, or being forced to defend against expensive litigation. An estimated 30 million workers—nearly one in five Americans—are subject to a noncompete.
Under the FTC’s new rule, existing noncompetes for the vast majority of workers will no longer be enforceable after the rule’s effective date. Existing noncompetes for senior executives - who represent less than 0.75% of workers - can remain in force under the FTC’s final rule, but employers are banned from entering into or attempting to enforce any new noncompetes, even if they involve senior executives. Employers will be required to provide notice to workers other than senior executives who are bound by an existing noncompete that they will not be enforcing any noncompetes against them.
In January 2023, the FTC issued a proposed rule which was subject to a 90-day public comment period. The FTC received more than 26,000 comments on the proposed rule, with over 25,000 comments in support of the FTC’s proposed ban on noncompetes. The comments informed the FTC’s final rulemaking process, with the FTC carefully reviewing each comment and making changes to the proposed rule in response to the public’s feedback.
In the final rule, the Commission has determined that it is an unfair method of competition, and therefore a violation of Section 5 of the FTC Act, for employers to enter into noncompetes with workers and to enforce certain noncompetes.
The Commission found that noncompetes tend to negatively affect competitive conditions in labor markets by inhibiting efficient matching between workers and employers. The Commission also found that noncompetes tend to negatively affect competitive conditions in product and service markets, inhibiting new business formation and innovation. There is also evidence that noncompetes lead to increased market concentration and higher prices for consumers.
Alternatives to Noncompetes
The Commission found that employers have several alternatives to noncompetes that still enable firms to protect their investments without having to enforce a noncompete.
Trade secret laws and non-disclosure agreements (NDAs) both provide employers with well-established means to protect proprietary and other sensitive information. Researchers estimate that over 95% of workers with a noncompete already have an NDA.
The Commission also finds that instead of using noncompetes to lock in workers, employers that wish to retain employees can compete on the merits for the worker’s labor services by improving wages and working conditions.
Changes from the NPRM
Under the final rule, existing noncompetes for senior executives can remain in force. Employers, however, are prohibited from entering into or enforcing new noncompetes with senior executives. The final rule defines senior executives as workers earning more than $151,164 annually and who are in policy-making positions.
Additionally, the Commission has eliminated a provision in the proposed rule that would have required employers to legally modify existing noncompetes by formally rescinding them. That change will help to streamline compliance.
Instead, under the final rule, employers will simply have to provide notice to workers bound to an existing noncompete that the noncompete agreement will not be enforced against them in the future. To aid employers’ compliance with this requirement, the Commission has included model language in the final rule that employers can use to communicate to workers.
The Commission vote to approve the issuance of the final rule was 3-2 with Commissioners Melissa Holyoak and Andrew N. Ferguson voting no. Commissioners’ written statements will follow at a later date.
The final rule will become effective 120 days after publication in the Federal Register.
Once the rule is effective, market participants can report information about a suspected violation of the rule to the Bureau of Competition by emailing noncompete@ftc.gov.
The Federal Trade Commission develops policy initiatives on issues that affect competition, consumers, and the U.S. economy. The FTC will never demand money, make threats, tell you to transfer money, or promise you a prize. Follow the FTC on social media, read consumer alerts and the business blog, and sign up to get the latest FTC news and alerts.
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2024年04月23日
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EEOC Issues Final Regulation on Pregnant Workers Fairness Act美国平等就业机会委员会（EEOC）发布了《怀孕工作者公平法案》（PWFA）的最终规则，该规则自2023年6月27日生效，要求15名以上员工的雇主为怀孕、分娩或相关医疗条件的员工提供合理的工作调整，除非这种调整给雇主带来过大困难。此规则进一步加强了1964年民权法案和美国残疾人法案下的保护措施，提供了关于合理调整、雇主责任及孕期工作者权利的更清晰指导。
Aids Implementation of Civil Rights Law Expanding Protections and Accommodations for Pregnant Workers
WASHINGTON -- The U.S. Equal Employment Opportunity Commission (EEOC) today issued a final rule to implement the Pregnant Workers Fairness Act (PWFA), providing important clarity that will allow pregnant workers the ability to work and maintain a healthy pregnancy and help employers understand their duties under the law. The PWFA requires most employers with 15 or more employees to provide “reasonable accommodations,” or changes at work, for a worker’s known limitations related to pregnancy, childbirth, or related medical conditions, unless the accommodation will cause the employer an undue hardship.
The PWFA builds upon existing protections against pregnancy discrimination under Title VII of the Civil Rights Act of 1964 and access to reasonable accommodations under the Americans with Disabilities Act. The EEOC began accepting charges of discrimination on June 27, 2023, the day on which the PWFA became effective.
The final rule will be published in the Federal Register on Apr. 19. The final rule was approved by majority vote of the Commission on Apr. 3, 2024, and becomes effective 60 days after publication in the Federal Register.
The final rule and its accompanying interpretative guidance reflect the EEOC’s deliberation and response to the approximately 100,000 public comments received on the Notice of Proposed Rulemaking. It provides clarity to employers and workers about who is covered, the types of limitations and medical conditions covered, how individuals can request reasonable accommodations, and numerous concrete examples.
“The Pregnant Workers Fairness Act is a win for workers, families, and our economy. It gives pregnant workers clear access to reasonable accommodations that will allow them to keep doing their jobs safely and effectively, free from discrimination and retaliation,” said EEOC Chair Charlotte A. Burrows. “At the EEOC, we have assisted women who have experienced serious health risks and unimaginable loss simply because they could not access a reasonable accommodation on the job. This final rule provides important information and guidance to help employers meet their responsibilities, and to jobseekers and employees about their rights. It encourages employers and employees to communicate early and often, allowing them to identify and resolve issues in a timely manner.”
Highlights from the final regulation include:
· Numerous examples of reasonable accommodations such as additional breaks to drink water, eat, or use the restroom; a stool to sit on while working; time off for health care appointments; temporary reassignment; temporary suspension of certain job duties; telework; or time off to recover from childbirth or a miscarriage, among others.
· Guidance regarding limitations and medical conditions for which employees or applicants may seek reasonable accommodation, including miscarriage or still birth; migraines; lactation; and pregnancy-related conditions that are episodic, such as morning sickness. This guidance is based on Congress’s PWFA statutory language, the EEOC’s longstanding definition of “pregnancy, childbirth, and related medical conditions” from Title VII of the Civil Rights Act of 1964, and court decisions interpreting the term “pregnancy, childbirth, or related medical conditions from Title VII.
· Guidance encouraging early and frequent communication between employers and workers to raise and resolve requests for reasonable accommodation in a timely manner.
· Clarification that an employer is not required to seek supporting documentation when an employee asks for a reasonable accommodation and should only do so when it is reasonable under the circumstances.
· Explanation of when an accommodation would impose an undue hardship on an employer and its business.
· Information on how employers may assert defenses or exemptions, including those based on religion, as early as possible in charge processing.
More information about the PWFA and the EEOC’s final rule, including resources for employers and workers, is available on the EEOC’s “What You Should Know about the Pregnant Workers Fairness Act” webpage.
For more information on pregnancy discrimination, please visit https://www.eeoc.gov/pregnancy-discrimination.
The EEOC prevents and remedies unlawful employment discrimination and advances equal opportunity for all. More information is available at www.eeoc.gov. Stay connected with the latest EEOC news by subscribing to our email updates.
头条
2024年04月19日
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The 10 golden rules for establishing a people analytics practice十大黄金法则：
战略适配性：确保人力资源分析项目与组织的战略目标对齐，以实现最大的价值和影响。
持续的员工倾听：通过整合员工和业务的声音，优先处理正确的战略人力资源问题。
证据基础的HR服务整合：将所有基于证据的HR服务整合到一个功能中，提升人力资源分析的交付速度和质量。
清晰的人力资源分析操作模型：建立一个目标操作模型，明确客户、可交付服务、服务水平和交付时间。
数据隐私合规性：遵守数据隐私法规，同时考虑数据分析在文化和业务连续性方面的影响。
数据驱动决策的HR能力提升：通过提升HR社区的数据和洞察力使用，将业务机会转化为分析服务。
管理HR数据：建立集中的企业级数据基础设施，改善数据的组合、共享和分析能力。
产品设计和思维：确保人力资源分析服务的用户设计友好，易于导航，并激励用户在决策中使用数据。
实验与最小可行产品：通过实验和最小可行产品，逐步评估和改进解决方案，避免大规模实施失败。
利用人工智能的潜力：构建和实施基于机器学习的AI功能，确保模型的性能和有效性，同时控制数据偏见和合法性。
这些法则展示了通过系统方法创建并采纳人力资源分析实践的重要性，强调了以数据和证据为基础支持人力资源功能的必要性。
It is time for an update on my previous posts on the 10 golden rules of people analytics, simply because so much has happened since then. For example, continuous employee listening, artificial intelligence (AI in HR), agile HR, employee experience, strategic workforce management, and hybrid working are just a few emerging topics in recent years listed in Gartner's hype cycle for HR transformation (2023).
In the last year, I have spoken to many people working in different organisations on establishing people analytics as an accepted practice. I have also joined some great conferences (HRcoreLAB, PAW London & Amsterdam) where I learned from excellent speakers. I also (re)engaged with some excellent people analytics and workforce management vendors, such as Crunchr, Visier, eQ8, AIHR, One Model, Mindthriven, and Agentnoon. Finally, I also enjoyed having multiple elevating discussions with some thought leaders who influenced my thinking (e.g., David Green, Rob Briner, Jonathan Ferrar, Dave Millner, Sjoerd van den Heuvel, Ian O'Keefe, Brydie Lear, Jaap Veldkamp, RJ Milnor, and Nick Kennedy).
These encounters and my ongoing PhD research on adopting people analytics resulted in a treasure trove of new ideas and knowledge that confirmed my experience and beliefs that it is all about creating an embraced people analytics practice using a systemic approach in supporting HR in becoming more evidence-based. So, like I said, it's time for an update. I hope you enjoy and appreciate the post, and I invite you to engage and react in the comments or send me a direct message.
Create a strong strategy FIT.
It is obvious but not a common practice that your people analytics portfolio needs to align or fit with your strategic organisational goals. A strong strategic FIT ensures you execute people analytics projects with the most value and impact on your organisation. It is, therefore, important to integrate the decision-making on where to play in people analytics with your periodic HR prioritisation process.
Strategic workforce management and continuous employee listening are pivotal in prioritising the right strategic workforce issues
The bigger picture is that two people analytics-related HR interventions, strategic workforce management and continuous employee listening, are pivotal in prioritising the right strategic workforce issues. By blending the insights from these HR interventions, you ensure you are prioritising based on the voice of the business and the voice of the employee. See also my previous post on strategic workforce management. Because people analytics is at the core of these HR interventions and provides many additional strategic insights, I argue we need a new HR operating model where the people analytics practice is positioned at the centre of HR.
I argue that we need a new HR operating model where the people analytics practice is positioned at the centre of HR
Grow and integrate evidence-based HR services.
Based on my experience and research, I strongly advise integrating all evidence-based HR services into one function. See also my previous post on establishing a people analytics practice. This integration will enhance the speed and quality of your people analytics delivery, make you a trusted analytical strategic advisor, and make you a more attractive employer for top people analytics talent. All other people analytics function setups seem like compromises.
With evidence-based HR services, I refer to activities such as reporting, advanced analytics, survey management, continuous employee listening, organisational design and strategic workforce management. It is hardly ever that a strategic question is answered by only one of these services. In most cases, you will need to combine survey management (i.e., collecting new data), perform advanced analytics (i.e., build a predictive model), and share the outcomes in a dashboard (i.e., reporting) or build new system functionality based on the models (e.g., vacancy recommendation).
You will need to combine various people analytics services to provide real strategic value
Create a clear people analytics operating model.
Because the people analytics practice is maturing, it deserves a clear target operating model. In a target operating model, you clarify to the organisation whom you consider your clients, what services or solutions you can deliver, what service levels your clients can expect, and when and how you will deliver the solution.
Being transparent about your target operating model will build trust and legitimacy in your organisation. Inspired by the work of Insight222, a people analytics target operating model consists of a demand engine (understanding and prioritising demand), a solution engine (e.g., data management, building models, designing surveys), and a delivery engine (e.g., dashboards, advisory with story-telling, bringing models to production), ideally covering all the evidence-based HR services mentioned under rule 2 in this post. Additionally, more practices are applying agile principles to increase time-to-delivery and are using some form of release management to balance capacity.
Built trust and legitimacy
Compliance with data privacy regulations has been an important topic since the early days of people analytics ten years ago. Even before the GDPR era, organisations did well to understand when personal data could be collected, used, or shared. Legislation such as GDPR offers guidance and more structure to organisations on how to deal with data privacy issues.
Being fully compliant is not where responsible data handling ends
However, being fully compliant is not where responsible data handling ends. Simply because you can, according to data privacy regulations, doesn't mean you should. There are also contextual and ethical elements to take into account. For example, being able and regulatory-wise allowed to build an internal sourcing model matching internal employees with specific skills with internal vacancies doesn't mean you should. From a cultural or business continuity perspective, creating internal mobility may not be beneficial or desired in specific areas of your organisation. Assessing the implications of using data analytics in a broader context than just regulations will also enhance the needed trust and legitimacy.
Upskill HR in data-driven decision-making
Having a mature people analytics practice that delivers high-quality, evidence-based HR services is not enough to ensure value creation for your organisation. Suppose your organisation, including your HR community, struggles to translate business opportunities into analytical services or finds it hard to use data and insights on a daily basis in their decision-making. In that case, upskilling is a necessary intervention.
HR upskilling in data-driven decision-making is a necessity in growing towards a truly evidence-based HR culture
Creating awareness of the various analytical opportunities, developing critical thinking, creating an inquisitive mindset, identifying success metrics for HR interventions and policies, evaluating these metrics, and understanding the power of innovative data services, such as generative AI, is essential. When upskilling, be sure to recognise the different HR roles and their needs and preferences. For example, your HR business partners will likely want to develop their skills in identifying strategic workforce metrics and strategic workforce management. However, your COE lead (i.e., HR domain leads) wants to develop their ability to collect and understand internal clients' feedback and improve their HR services (e.g., recruitment, learning programs, leadership development). So, diversify your learning approach to make it more effective.
Manage your HR data
There is enormous value in integrating your HR and business data in a structured matter. Integrated enterprise-wide data allows you to combine, improve, share, and analyse data more efficiently. More organisations are using data warehouse and data lake principles to create this central enterprise-wide data infrastructure based on, for example, Microsoft Azure or Amazon Web Services technology.
A mature people analytics team is best equipped to create an HR data strategy and manage the corresponding data pipeline.
HR would do well to improve its capability to manage the data pipeline by hiring data engineers. It is an interesting discussion about where to position this data management capability and related skill set. The first thought is to position this capability close to the HR systems and infrastructure function. This setup might work perfectly. However, based on your HR context and maturity, I argue that the people analytics practice is a good and sometimes better alternative. Mature people analytics teams are likely more able to think about data management and creating data products and services built with machine learning models. Traditional HR systems and infrastructure teams may tend to focus too much on the efficiency of the HR infrastructure (e.g., straight-through processing, rationalising the HR tech landscape).
Excel in product design and thinking
Successful people analytics or evidence-based HR services excel in product design. Whether built with PowerBI or vendor-led BI platforms (e.g., Crunchr, Visier, One Model), dashboards must be user-friendly, easy to navigate, and motivate users to work with data in their decision-making. The same applies to functionality based on machine learning models, such as chatbots, learning assistants, or vacancy recommendations. The user design, the functionality provided, and the flawless and timely delivery all contribute to maximising the usage of these analytical services and, ultimately, decision-making.
Strong product design and thinking requires product owners to have a marketing mindset
As important as the product design is product thinking by the product owner. A product owner for, e.g., recruitment or leadership programs, should be constantly interested in hearing what internal clients think about their products. This behaviour requires product owners to have a marketing mindset. As part of a larger continuous listening program, an internal client feedback mechanism should provide the necessary information to improve your products and services continuously. A product owner should be curious about questions like: Are your internal clients satisfied? Should we tailor the products for different user types? What functionality can we improve or add?
Allow yourself to experiment
When a solution looks good and makes sense based on your analytics, management tends to go for an immediate big-bang implementation. However, don't be afraid to experiment and learn before rolling out your solution to all possible users. Starting with a minimum viable product (i.e., MVP) allows you to evaluate your product among a select group of users early in the development process. Based on feedback, you can enhance your product incrementally (i.e., agile) manner.
It also enables you, when valuable, to compare treatment groups with non-treatment groups. These types of experiments (i.e., difference-in-difference comparisons) help you to evaluate the effect the new product intends to have. People analytics services can support this incremental approach, testing a minimal viable product (MVP) and obtaining feedback to provide additional insights that may avoid a big implementation failure of your new products.
Embrace the potential of AI in HR
Today, artificial intelligence (AI) is predominantly based on machine learning (ML). These AI-ML models provide powerful functionality such as vacancy and learning recommendations, chatbots, and virtual career or work schedule assistants. There is no need to fear these applications, but having a deeper understanding of them is necessary. However, implementing these types of functionality without checking and validating them is risky and, therefore, unwise.
A mature people analytics practice allows you to build your own machine-learning-based AI functionality
A mature people analytics practice allows you to create and build these AI functionalities internally. You can also buy AI functionality by implementing a vendor tool, but please ensure you do not end up with a new vendor for each AI functionality you desire. If you choose to buy AI functionality, the people analytics team should act as a gatekeeper. Internally built machine learning models are subject to checks and balances. And rightfully so. However, the same should apply to ML-based AI functionality from external providers. The people analytics team should check the performance and validity of the model and control for biases in the data and legal and ethical justification.
The people analytics leader can make the difference
If you are the people analytics leader within your organisation, it might be daunting or reassuring to hear that you can make the difference between failure and success. You bring the people analytics practice alive by reaching out to stakeholders, developing your team, understanding your clients, learning from external experts, and building a road map to analytical maturity.
A successful people analytics practice starts with the right people analytics leader
As a people analytics leader, you should excel in business acumen, influencing skills, strategic thinking, critical and analytical thinking, understanding the HR system landscape, understanding the possibilities of analytical services, project management, and, last but not least, people management (as all leaders should). The result of having all these capabilities is that a people analytics leader, together with the people analytics team, becomes a trusted advisor to senior management, understands the most pressing issues within an organisation, can effectively manage the HR data pipeline, and can build new analytical services to enhance decision-making and ultimately drive organisational performance and employee well-being.
I hope you enjoyed my update on the 10 golden rules for establishing people analytics practice. If you enjoyed the post, please hit ? or feel invited to engage and react in the comments. Send me a direct message if you want to schedule a virtual meeting to exchange thoughts one-on-one.
Thanks to Jaap Veldkamp for reviewing.
作者 ：Patrick Coolen
https://www.linkedin.com/pulse/10-golden-rules-establishing-people-analytics-practice-patrick-coolen-85use/
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2024年04月15日
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101 real-world gen AI use cases from the world's leading organizations在过去一年半的时间里，生成式人工智能（AI）在企业领域的应用迅速发展。Google Cloud的Next活动中展示了超过300家组织如何利用AI推动企业转型。这些企业已经从简单的问答助手，发展到能够进行预测和采取行动的AI代理，进一步扩展其业务功能和提升效率。
具体来说，AI代理在以下几个关键领域表现出显著的效益：首先是客户服务，AI能够帮助企业更好地理解和满足客户需求；其次是员工赋能，通过自动化日常任务和优化工作流程，AI提升了工作效率；在创意构思和生产领域，AI助力企业快速生成创新的解决方案；数据分析方面，AI通过高效处理和解析大数据，支持决策制定；在编码创建方面，AI简化了开发流程，提高了代码质量；最后在网络安全领域，AI加强了数据保护和风险管理。
这些应用不仅提高了生产力和操作效率，还极大地改善了客户体验和企业的创新能力。AI的多模态能力，即在文本、语音、视频等多种通信模式中的应用，使其能够更全面地满足不同行业的需求。通过这些先进的技术，企业正在开创一个智能、高效和互联的新时代。
我们一起来看看，是否有参考？
Since generative AI first captured the world’s attention a year and a half ago, there’s been a vigorous discussion about what, exactly, the new technology is best used for. While we all enjoyed those early funny chats and witty limericks, we’ve quickly discovered that many of the biggest AI opportunities are clearly in the enterprise.
Our customers and partners at Google Cloud have found real potential for creating new processes, efficiencies, and innovations with generative AI. For proof, look no further than the 300-plus organizations who are featured at this week’s Next event in Las Vegas.
In a matter of months, organizations like these have gone from AI helping answer questions, to AI making predictions, to generative AI agents. What makes AI agents unique is that they can take actions to achieve specific goals, whether that’s guiding a shopper to the perfect pair of shoes, helping an employee looking for the right health benefits, or supporting nursing staff with smoother patient hand-offs during shifts changes.
In our work with customers, we keep hearing that their teams are increasingly focused on improving productivity, automating processes and modernizing the customer experience. These aims are now being achieved through the AI agents they’re developing in six key areas: customer service; employee empowerment; creative ideation and production; data analysis; code creation; and cybersecurity.
These special capabilities are made possible in large part by the new multimodal capacity of generative AI and AI foundation models, which allow agents to handle tasks across a range of communications modes, including text, voice, video, audio, code, and more. With human support, agents can converse, reason, learn, and make decisions.
The hundreds of customers who joined us at Next ‘24 to showcase and discuss early versions of their AI agents and gen-AI solutions have come to rely on Google Cloud technologies that include our AI infrastructure, Gemini models, Vertex AI platform, Google Workspace, and Google Distributed Cloud. We were also joined by more than 100 partners supporting the creation of AI agents and AI solutions, which you can read about in detail.Here’s a snapshot of how 101 of these industry leaders are putting AI into production today, creating real-world use cases that will transform tomorrow.
Similar to great sales and service people, customer agents are able to listen carefully, understand your needs, and recommend the right products and services. They work seamlessly across channels including the web, mobile, and point of sale, and can be integrated into product experiences with voice and video.
ADT is building a customer agent to help its millions of customers select, order, and set up their home security.
Alaska Airlines is developing a personalized travel search experience using advanced AI techniques, creating hyper-personalized recommendations that engage customers early and foster loyalty through AI-generated content.
Best Buy is using Gemini to launch a generative AI-powered virtual assistant this summer that can troubleshoot product issues, reschedule order deliveries, manage Geek Squad subscriptions, and more; in-store and digital customer-service associates are also gaining gen-AI tools to better serve customers anywhere they need help.
The Central Texas Regional Mobility Authority is using Vertex AI to modernize transportation operations for a smoother, more efficient journey.
Etsy uses Vertex AI training to optimize their search recommendations and ads models, delivering better listing suggestions to buyers and helping sellers grow their businesses.
Golden State Warriors are using AI to improve the fan experience content in their Chase Center app.
IHG Hotels & Resorts is building a generative AI-powered chatbot to help guests easily plan their next vacation directly in the IHG One Rewards mobile app.
ING Bank aims to offer a superior customer experience and has developed a gen-AI chatbot for workers to enhance self-service capabilities and improve answer quality on customer queries.
Magalu, one of Brazil’s largest retailers, has put customer service at the center of its AI strategy, including using Vertex AI to create “Lu’s Brain” to power an interactive conversational agent for Lu, Magalu's popular brand persona (the 3D bot has more than 14 million followers between TikTok and Instagram).
Mercedes Benz will infuse e-commerce capabilities into its online storefront with a gen AI-powered smart sales assistant. Mercedes also plans to expand its use of Google Cloud AI in its call centers and is using Vertex AI and Gemini to personalize marketing campaigns.
Oppo/OnePlus is incorporating Gemini models and Google Cloud AI into their phones to deliver innovative customer experiences, including news and audio recording summaries, AI toolbox, and more.
Samsung is deploying Gemini Pro and Imagen 2 to their Galaxy S24 smartphones so users can take advantage of amazing features like text summarization, organization, and magical image editing.
The Minnesota Division of Driver and Vehicle Services helps non-English speakers get licenses and other services with two-way real-time translation.
Pepperdine University has students and faculty who speak many languages, and with Gemini in Google Meet, they can benefit from real-time translated captioning and notes.
Sutherland, a leading digital transformation company, is focused on bringing together human expertise and AI, including boosting its client-facing teams by automatically surfacing suggested responses and automating insights in real time.
Target uses Google Cloud to power AI solutions on the Target app and Target.com, including personalized Target Circle offers and Starbucks at Drive Up, their curbside pickup solution.
Tokopedia, an Indonesian ecommerce leader, is using Vertex AI to improve data quality, increasing unique products being sold by 5%.
US News saw a double-digit impact in key metrics like click-through rate, time spent on page, and traffic volume to its pages after implementing Vertex AI Search.
IntesaSanpaolo, Macquarie Bank, and Scotiabank are exploring the potential of gen AI to transform the way we live, work, bank, and invest — particularly how the new technology can boost productivity and operational efficiency in banking. Watch the session to learn more.
Employee agents help workers be more productive and collaborate better together. These agents can streamline processes, manage repetitive tasks, answer employee questions, as well as edit and translate critical communications.
Avery Dennison empowered their employees with generative AI to enable secure, flexible, and borderless collaboration for enhanced productivity to drive growth.
Bank of New York Mellon built a virtual assistant to help employees find relevant information and answers to their questions.
Bayer is building a radiology platform that will assist radiologists with data analysis, intelligent search, and to create documents that meet healthcare requirements needed for regulatory approval. The bioscience company is also harnessing BigQuery and Vertex AI to develop additional digital medical solutions and drugs more efficiently.
Bristol Myers Squibb is transforming its document processes for clinical trials using Vertex AI and Google Workspace. Now, documentation that took scientists weeks now gets to a first draft in minutes.
BenchSci develops generative AI solutions empowering scientists to understand complex connections in biological research, saving them time and financial resources and ultimately bringing new medicine to patients faster.
Cintas is using Vertex AI Search to develop an internal knowledge center for customer service and sales teams to easily find key information.
Covered California, the state’s healthcare marketplace, is using Document AI to help improve the consumer and employee experience by automating parts of the documentation and verification process when residents apply for coverage.
Dasa, the largest medical diagnostics company in Brazil, is helping physicians detect relevant findings in test results more quickly.
DaVita leverages DocAI and Healthcare NLP to transform kidney care, including analyzing medical records, uncovering critical patient insights, and reducing errors. AI enables physicians to focus on personalized care, resulting in significant improvements in healthcare delivery.
Discover Financial helps their 10,000 contact center representatives to search and synthesize information across detailed policies and procedures during calls.
HCA Healthcare is testing Cati, a virtual AI caregiver assistant that helps to ensure continuity of care when one caregiver shift ends and another begins. They are also using gen AI to improve workflows on time-consuming tasks, such as clinical documentation, so physicians and nurses can focus more on patient care.
The Home Depot has built an application called Sidekick, which helps store associates manage inventory and keep shelves stocked; notably, vision models help associates prioritize which actions to take.
Los Angeles Rams are utilizing AI across the board from content analysis to player scouting.
McDonald’s will leverage data, AI, and edge technologies across its thousands of restaurants to implement innovation faster and to enhance employee and customer experiences.
Pennymac, a leading US-based national mortgage lender, is using Gemini across several teams including HR, where Gemini in Docs, Sheets, Slides and Gmail is helping them accelerate recruiting, hiring, and new employee onboarding.
Robert Bosch, the world's largest automotive supplier, revolutionizes marketing through gen AI-powered solutions, streamlining processes, optimizing resource allocation, and maximizing efficiency across 100+ decentralized departments.
Symphony, the communications platform for the financial services industry, uses Vertex AI to help finance and trading teams collaborate across multiple asset classes.
Uber is using AI agents to help employees be more productive, save time, and be even more effective at work. For customer service representatives, they’ve launched new tools that summarize communications with users and can even surface context from previous interactions, so front-line staff can be more helpful and effective
The U.S. Dept. of Veterans Affairs is using AI at the edge to improve cancer detection for service members and veterans. The Augmented Reality Microscope (ARM) is deployed at remote military treatment facilities around the world. The prototype device is helping pathologists find cancer faster and with better accuracy.
The U.S. Patent and Trademark Office has improved the quality and efficiency of their patent and trademark examination process by implementing AI-driven technologies.
Verizon is using generative AI to help teams in network operations and customer experience get the answers they need faster.
Victoria’s Secret is testing AI-powered agents to help their in-store associates find information about product availability, inventory, and fitting and sizing tips, so they can better tailor recommendations to customers.
Vodafone uses Vertex AI to search and understand specific commercial terms and conditions across more than 10,000 contracts with more than 800 communications operators.
WellSky is integrating Google Cloud's healthcare and Vertex AI capabilities to reduce the time spent completing documentation outside work hours.
Woolworths, the leading retailer in Australia, boosts employees’ confidence in communications with “Help me write” across Google Workspace products for more than 10,000 administrative employees. It’s also using Gemini to create next-generation promotions, as well as for quickly assisting customer service reps in summarizing all previous customer interactions in real time.
Box, Typeface, Glean, CitiBank, and Securiti AI discuss developing AI-powered apps across the enterprise, with measurable returns on investment for marketing, financial services, and HR use cases.
Highmark Health and Freenome join Bristol Myers Squibb to explore how AI can improve efficiency and innovation across care delivery, drug discovery, clinical trial planning, and bringing medicines to market.
Creative agents can expand your organization with the best design and production skills, working across images, slides, and exploring concepts with workers. Many organizations are building agents for their marketing teams, audio and video production teams, and all the creative people that can use a hand. With creative agents, anyone can become a designer, artist, or producer.
Belk ECommerce is using generative AI to craft better product descriptions, a necessary yet time-consuming task for digital retails that has often been done manually.
Canva is using Vertex AI to power its Magic Design for Video, helping users skip tedious editing steps while creating shareable and engaging videos in a matter of seconds.
Carrefour used Vertex AI to deploy Carrefour Marketing Studio in just five weeks — an innovative solution to streamline the creation of dynamic campaigns across various social networks. In just a few clicks, marketers can build ultra-personalized campaigns to deliver customers advertising that they care about.
Major League Baseball continues to innovate its Statcast platform, so teams, broadcasters, and fans have access to live in-game insights.
Paramount currently relies on manual processes to create the essential metadata and video summaries used across its Paramount+ platform for showcasing content and creating personalized experiences for viewers. VertexAI Text Bison is now helping to streamline this process.
Procter & Gamble used Imagen to develop an internal gen AI platform to accelerate the creation of photo-realistic images and creative assets, giving marketing teams more time to focus on high-level planning and delivering superior experiences for its consumers.
WPP will integrate Google Cloud’s gen AI capabilities into its intelligent marketing operating system, called WPP Open, which empowers its people and clients to deliver new levels of personalization, creativity, and efficiency. This includes the use of Gemini 1.5 Pro models to supercharge both the accuracy and speed of content performance predictions.
Data agents are like having knowledgeable data analysts and researchers at your fingertips. They can help answer questions about internal and external sources, synthesize research, develop new models — and, best of all, help find the questions we haven’t even thought to ask yet, and then help get the answers.
AI21 Labs offers a BigQuery integration called Contextual Answers that allows users to query data conversationally and get high-quality answers quickly
Anthropic has partnered with Google Cloud to offer its family of Claude 3 models on Vertex AI — providing organizations with more model options for intelligence, speed, cost-efficiency, and vision for enterprise use cases.
The Asteroid Institute is using AI to discover hidden asteroids in existing astronomical data. This is a major focus for astronomers researching the evolution of the Solar System, investors and businesses hoping to fly missions to asteroids, and for all of us who want to prevent future large asteroid impacts on Earth.
Contextual is working with Google Cloud to offer enterprises fully customizable, trustworthy, privacy-aware AI grounded in internal knowledge bases.
Cox 2M, the commercial IoT division of Cox Communications, is able to make smarter, faster business decisions using AI-powered analytics.
Essential AI, a developer of enterprise AI solutions, is using Google Cloud’s AI-optimized TPU v5p accelerator chips to train its own AI models.
Generali Italia, Italy's largest insurance provider, used Vertex AI to build a model evaluation pipeline that helps ML teams quickly evaluate performance and deploy models.
Globo, one of Brazil’s largest media networks, is using Service Extensions and Media CDN to fight piracy during live events by blocking pirated streams in real time.
Hugging Face is collaborating with Google across open science, open source, cloud, and hardware to enable companies to build their own AI with the latest open models from Hugging Face and Google Cloud hardware and software.
Kakao Brain, part of Korean technology company Kakao Group, has built a large-scale AI language model that is the largest Korean language-specific LLM in the market, with 66 billion parameters. They’ve also developed a text-to-image generator called Karlo.
Mayo Clinic has given thousands of its scientific researchers access to 50 petabytes worth of clinical data through Vertex AI search, accelerating information retrieval across multiple languages.
McLaren Racing is using Google AI to get up-to-the-millisecond insights during races and training to gain a competitive edge.
Mercado Libre is testing BigQuery and Looker to optimize capacity planning and reservations with delivery carriers and airlines to fulfill shipments faster.
Mistral AI will use Google Cloud's AI-optimized infrastructure, to further test, build, and scale up its LLMs, all while benefiting from Google Cloud's security and privacy standards.
MSCI uses machine learning with Vertex AI, BigQuery and Cloud Run to enrich its datasets to help our clients gain insight into around 1 million asset locations to help manage climate-related risks.
NewsCorp is using Vertex AI to help search data across 30,000 sources and 2.5 billion news articles updated daily.
Orange operates in 26 countries where local data must be kept in each country. They are using AI on Google Distributed Cloud to improve network performance and deliver super-responsive translation capabilities.
Spotify leveraged Dataflow for large-scale generation of ML podcast previews, and they plan to keep pushing the boundaries of what’s possible with data engineering and data science to build better experiences for their customers and creators.
UPS is building a digital twin of its entire distribution network, so both workers and customers can see where their packages are at any time.
Workday is using natural language processing in Vertex Search and Conversation to make data insights more accessible for technical and non-technical users alike.
Woven — Toyota's investment in the future of mobility — is partnering with Google to leverage vast amounts of data and AI to enable autonomous driving, supported by thousands of ML workloads on Google Cloud’s AI Hypercomputer. This has resulted in resulting in 50% total-cost-of-ownership savings to support automated driving.
Broward County, Florida, and Southern California Edison are using geospatial capabilities and AI to improve infrastructure planning and monitoring, generate new insights, and create regional resilience for communities facing climate challenges today and tomorrow.
Kinaxis and Dematic are building data-driven supply chains to address logistics use cases including scenario modeling, planning, operations management, and automation.
NOAA and USAID are among the U.S. government agencies using Google Cloud AI to unlock critical data insights to streamline operations and improve mission outcomes — all with an emphasis on responsible AI. Watch the session to learn more.
Code agents are helping developers and product teams to design, create, and operate applications faster and better, and to ramp up on new languages and code bases. Many organizations are already seeing double-digit gains in productivity, leading to faster deployment and cleaner, clearer code.
Capgemini has been using Code Assist to improve software engineering productivity, quality, security, and developer experience, with early results showing workload gains for coding and more stable code quality.
Commerzbank is enhancing developer efficiency through Code Assist's robust security and compliance features.
Quantiphi saw developer productivity gains of more than 30% during their Code Assist pilot.
Replit developers will get access to Google Cloud infrastructure, services, and foundation models via Ghostwriter, Replit's software development AI, while Google Cloud and Workspace developers will get access to Replit’s collaborative code editing platform.
Seattle Children's hospital is using AI to boost data engineering productivity and accelerate development.
Turing is customizing Gemini Code Assist on their private codebase, empowering their developers with highly personalized and contextually relevant coding suggestions that have increased productivity around 30 percent and made day-to-day coding more enjoyable.
Wayfair piloted Code Assist, and those developers with the code agent were able to set up their environments 55 percent faster than before, there was a 48 percent increase in code performance during unit testing, and 60 percent of developers reported that they were able to focus on more satisfying work.
Security agents assist security operations by radically increasing the speed of investigations, automating monitoring and response for greater vigilance and compliance controls. They can also help guard data and models from cyberattacks, such as malicious prompt injection.
BBVA uses AI in Google SecOps to detect, investigate, and respond to security threats with more accuracy, speed, and scale. The platform now surfaces critical security data in seconds, when it previously took minutes or even hours, and delivers highly automated responses.
Behavox is using Google Cloud technology and LLMs to provide industry leading regulatory compliance and front office solutions for financial institutions globally.
Charles Schwab has integrated their own intelligence into the AI-powered Google SecOps, so analysts can better prioritize work and respond to threats.
Fiserv’s security operations engineers create detections and playbooks with much less effort, while analysts get answers more quickly.
Grupo Boticário, one of the largest beauty retail and cosmetics companies in Brazil, employs real-time security models to prevent fraud and to detect and respond to issues.
Palo Alto Networks’ Cortex XSIAM, the AI-driven security operations platform, is built on more than a decade of expertise in machine-learning models and the most comprehensive, rich, and diverse data store in the industry. Backed by Google's advanced cloud infrastructure and advanced AI services, including BigQuery and Gemini models, the combination delivers global scale and near real-time protection across all cybersecurity offerings.
Pfizer can now aggregate cybersecurity data sources, cutting analysis times from days to seconds.
To find even more customers using our AI tools to build agents and solutions for their most important enterprise projects, visit the Google Cloud customer hub and watch the Next ‘24
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