The competitive nature of AI development poses a dilemma for organizations, as prioritizing speed may lead to neglecting ethical guidelines, bias detection, and safety measures. Known and emerging concerns associated with AI in the workplace include the spread of misinformation, copyright and intellectual property concerns, cybersecurity, data privacy, as well as navigating rapid and ambiguous regulations. To mitigate these risks, we propose thirteen principles for responsible AI at work.

Love it or loath it, the rapid expansion of AI will not slow down anytime soon. But AI blunders can quickly damage a brand’s reputation — just ask Microsoft’s first chatbot, Tay. In the tech race, all leaders fear being left behind if they slow down while others don’t. It’s a high-stakes situation where cooperation seems risky, and defection tempting. This “prisoner’s dilemma” (as it’s called in game theory) poses risks to responsible AI practices. Leaders, prioritizing speed to market, are driving the current AI arms race in which major corporate players are rushing products and potentially short-changing critical considerations like ethical guidelines, bias detection, and safety measures. For instance, major tech corporations are laying off their AI ethics teams precisely at a time when responsible actions are needed most.

It’s also important to recognize that the AI arms race extends beyond the developers of large language models (LLMs) such as OpenAI, Google, and Meta. It encompasses many companies utilizing LLMs to support their own custom applications. In the world of professional services, for example, PwC announced it is deploying AI chatbots for 4,000 of their lawyers, distributed across 100 countries. These AI-powered assistants will “help lawyers with contract analysis, regulatory compliance work, due diligence, and other legal advisory and consulting services.” PwC’s management is also considering expanding these AI chatbots into their tax practice. In total, the consulting giant plans to pour $1 billion into “generative AI” — a powerful new tool capable of delivering game-changing boosts to performance.

In a similar vein, KPMG launched its own AI-powered assistant, dubbed KymChat, which will help employees rapidly find internal experts across the entire organization, wrap them around incoming opportunities, and automatically generate proposals based on the match between project requirements and available talent. Their AI assistant “will better enable cross-team collaboration and help those new to the firm with a more seamless and efficient people-navigation experience.”

Slack is also incorporating generative AI into the development of Slack GPT, an AI assistant designed to help employees work smarter not harder. The platform incorporates a range of AI capabilities, such as conversation summaries and writing assistance, to enhance user productivity.

These examples are just the tip of the iceberg. Soon hundreds of millions of Microsoft 365 users will have access to Business Chat, an agent that joins the user in their work, striving to make sense of their Microsoft 365 data. Employees can prompt the assistant to do everything from developing status report summaries based on meeting transcripts and email communication to identifying flaws in strategy and coming up with solutions.

This rapid deployment of AI agents is why Arvind Krishna, CEO of IBM, recently wrote that, “[p]eople working together with trusted A.I. will have a transformative effect on our economy and society … It’s time we embrace that partnership — and prepare our workforces for everything A.I. has to offer.” Simply put, organizations are experiencing exponential growth in the installation of AI-powered tools and firms that don’t adapt risk getting left behind.

AI Risks at Work

Unfortunately, remaining competitive also introduces significant risk for both employees and employers. For example, a 2022 UNESCO publication on “the effects of AI on the working lives of women” reports that AI in the recruitment process, for example, is excluding women from upward moves. One study the report cites that included 21 experiments consisting of over 60,000 targeted job advertisements found that “setting the user’s gender to ‘Female’ resulted in fewer instances of ads related to high-paying jobs than for users selecting ‘Male’ as their gender.” And even though this AI bias in recruitment and hiring is well-known, it’s not going away anytime soon. As the UNESCO report goes on to say, “A 2021 study showed evidence of job advertisements skewed by gender on Facebook even when the advertisers wanted a gender-balanced audience.” It’s often a matter of biased data which will continue to infect AI tools and threaten key workforce factors such as diversity, equity, and inclusion.

Discriminatory employment practices may be only one of a cocktail of legal risks that generative AI exposes organizations to. For example, OpenAI is facing its first defamation lawsuit as a result of allegations that ChatGPT produced harmful misinformation. Specifically, the system produced a summary of a real court case which included fabricated accusations of embezzlement against a radio host in Georgia. This highlights the negative impact on organizations for creating and sharing AI generated information. It underscores concerns about LLMs fabricating false and libelous content, resulting in reputational damage, loss of credibility, diminished customer trust, and serious legal repercussions.

In addition to concerns related to libel, there are risks associated with copyright and intellectual property infringements. Several high-profile legal cases have emerged where the developers of generative AI tools have been sued for the alleged improper use of licensed content. The presence of copyright and intellectual property infringements, coupled with the legal implications of such violations, poses significant risks for organizations utilizing generative AI products. Organizations can improperly use licensed content through generative AI by unknowingly engaging in activities such as plagiarism, unauthorized adaptations, commercial use without licensing, and misusing Creative Commons or open-source content, exposing themselves to potential legal consequences.

The large-scale deployment of AI also magnifies the risks of cyberattacks. The fear amongst cybersecurity experts is that generative AI could be used to identify and exploit vulnerabilities within business information systems, given the ability of LLMs to automate coding and bug detection, which could be used by malicious actors to break through security barriers. There’s also the fear of employees accidentally sharing sensitive data with third-party AI providers. A notable instance involves Samsung staff unintentionally leaking trade secrets through ChatGPT while using the LLM to review source code. Due to their failure to opt out of data sharing, confidential information was inadvertently provided to OpenAI. And even though Samsung and others are taking steps to restrict the use of third-party AI tools on company-owned devices, there’s still the concern that employees can leak information through the use of such systems on personal devices.

On top of these risks, businesses will soon have to navigate nascent, varied, and somewhat murky regulations. Anyone hiring in New York City, for instance, will have to ensure their AI-powered recruitment and hiring tech doesn’t violate the City’s “automated employment decision tool” law. To comply with the new law, employers will need to take various steps such as conducting third-party bias audits of their hiring tools and publicly disclosing the findings. AI regulation is also scaling up nationally with the Biden-Harris administration’s “Blueprint for an AI Bill of Rights” and internationally with the EU’s AI Act, which will mark a new era of regulation for employers.

This growing nebulous of evolving regulations and pitfalls is why thought leaders such as Gartner are strongly suggesting that businesses “proceed but don’t over pivot” and that they “create a task force reporting to the CIO and CEO” to plan a roadmap for a safe AI transformation that mitigates various legal, reputational, and workforce risks. Leaders dealing with this AI dilemma have important decision to make. On the one hand, there is a pressing competitive pressure to fully embrace AI. However, on the other hand, a growing concern is arising as the implementation of irresponsible AI can result in severe penalties, substantial damage to reputation, and significant operational setbacks. The concern is that in their quest to stay ahead, leaders may unknowingly introduce potential time bombs into their organization, which are poised to cause major problems once AI solutions are deployed and regulations take effect.

For example, the National Eating Disorder Association (NEDA) recently announced it was letting go of its hotline staff and replacing them with their new chatbot, Tessa. However, just days before making the transition, NEDA discovered that their system was promoting harmful advice such as encouraging people with eating disorders to restrict their calories and to lose one to two pounds per week. The World Bank spent $1 billion to develop and deploy an algorithmic system, called Takaful, to distribute financial assistance that Human Rights Watch now says ironically creates inequity. And two lawyers from New York are facing possible disciplinary action after using ChatGPT to draft a court filing that was found to have several references to previous cases that did not exist. These instances highlight the need for well-trained and well-supported employees at the center of this digital transformation. While AI can serve as a valuable assistant, it should not assume the leading position.

Principles for Responsible AI at Work

To help decision-makers avoid negative outcomes while also remaining competitive in the age of AI, we’ve devised several principles for a sustainable AI-powered workforce. The principles are a blend of ethical frameworks from institutions like the National Science Foundation as well as legal requirements related to employee monitoring and data privacy such as the Electronic Communications Privacy Act and the California Privacy Rights Act. The steps for ensuring responsible AI at work include:

• Informed Consent. Obtain voluntary and informed agreement from employees to participate in any AI-powered intervention after the employees are provided with all the relevant information about the initiative. This includes the program’s purpose, procedures, and potential risks and benefits.
• Aligned Interests. The goals, risks, and benefits for both the employer and employee are clearly articulated and aligned.
• Opt In & Easy Exits. Employees must opt into AI-powered programs without feeling forced or coerced, and they can easily withdraw from the program at any time without any negative consequences and without explanation.
• Conversational Transparency. When AI-based conversational agents are used, the agent should formally reveal any persuasive objectives the system aims to achieve through the dialogue with the employee.
• Debiased and Explainable AI. Explicitly outline the steps taken to remove, minimize, and mitigate bias in AI-powered employee interventions—especially for disadvantaged and vulnerable groups—and provide transparent explanations into how AI systems arrive at their decisions and actions.
• AI Training and Development. Provide continuous employee training and development to ensure the safe and responsible use of AI-powered tools.
• Health and Well-Being. Identify types of AI-induced stress, discomfort, or harm and articulate steps to minimize risks (e.g., how will the employer minimize stress caused by constant AI-powered monitoring of employee behavior).
• Data Collection. Identify what data will be collected, if data collection involves any invasive or intrusive procedures (e.g., the use of webcams in work-from-home situations), and what steps will be taken to minimize risk.
• Data. Disclose any intention to share personal data, with whom, and why.
• Privacy and Security. Articulate protocols for maintaining privacy, storing employee data securely, and what steps will be taken in the event of a privacy breach.
• Third Party Disclosure. Disclose all third parties used to provide and maintain AI assets, what the third party’s role is, and how the third party will ensure employee privacy.
• Communication. Inform employees about changes in data collection, data management, or data sharing as well as any changes in AI assets or third-party relationships.
• Laws and Regulations. Express ongoing commitment to comply with all laws and regulations related to employee data and the use of AI.

We encourage leaders to urgently adopt and develop this checklist in their organizations. By applying such principles, leaders can ensure rapid and responsible AI deployment.

As business leaders, today we find ourselves in a place that’s all too familiar: the unfamiliar. Just as we steered our teams through the shift to remote and flexible work, we’re now on the verge of another seismic shift: AI. And like the shift to flexible work, priming an organization to embrace AI will hinge first and foremost on culture.

The pace and volume of work has increased exponentially, and we’re all struggling under the weight of it. Leaders and employees are eager for AI to lift the burden. That’s the key takeaway from our 2023 Work Trend Index, which surveyed 31,000 people across 31 countries and analyzed trillions of aggregated productivity signals in Microsoft 365, along with labor market trends on LinkedIn.

Nearly two-thirds of employees surveyed told us they don’t have enough time or energy to do their job. The cause of this drain is something we identified in the report as digital debt: the influx of data, emails, and chats has outpaced our ability to keep up. Employees today spend nearly 60% of their time communicating, leaving only 40% of their time for creating and innovating. In a world where creativity is the new productivity, digital debt isn’t just an inconvenience — it’s a liability.

AI promises to address that liability by allowing employees to focus on the most meaningful work. Increasing productivity, streamlining repetitive tasks, and increasing employee well-being are the top three things leaders want from AI, according to our research. Notably, amid fears that AI will replace jobs, reducing headcount was last on the list.

Becoming an AI-powered organization will require us to work in entirely new ways. As leaders, there are three steps we can take today to get our cultures ready for an AI-powered future:

Choose curiosity over fear

AI marks a new interaction model between humans and computers. Until now, the way we’ve interacted with computers has been similar to how we interact with a calculator: We ask a question or give directions, and the computer provides an answer. But with AI, the computer will be more like a copilot. We’ll need to develop a new kind of chemistry together, learning when and how to ask questions and about the importance of fact-checking responses.

Fear is a natural reaction to change, so it’s understandable for employees to feel some uncertainty about what AI will mean for their work. Our research found that while 49% of employees are concerned AI will replace their jobs, the promise of AI outweighs the threat: 70% of employees are more than willing to delegate to AI to lighten their workloads.

We’re rarely served by operating from a place of fear. By fostering a culture of curiosity, we can empower our people to understand how AI works, including its capabilities and its shortcomings. This understanding starts with firsthand experience. Encourage employees to put curiosity into action by experimenting (safely and securely) with new AI tools, such as AI-powered search, intelligent writing assistance, or smart calendaring, to name just a few. Since every role and function will have different ways to use and benefit from AI, challenge them to rethink how AI could improve or transform processes as they get familiar with the tools. From there, employees can begin to unlock new ways of working.

Embrace failure

AI will change nearly every job, and nearly every work pattern can benefit from some degree of AI augmentation or automation. As leaders, now is the time to encourage our teams to bring creativity to reimagining work, adopting a test-and-learn strategy to find ways AI can best help meet the needs of the business.

AI won’t get it right every time, but even when it’s wrong, it’s usefully wrong. It moves you at least one step forward from a blank slate, so you can jump right into the critical thinking work of reviewing, editing, or augmenting. It will take time to learn these new patterns of work and identify which processes need to change and how. But if we create a culture where experimentation and learning are viewed as a prerequisite to progress, we’ll get there much faster.

As leaders, we have a responsibility to create the right environment for failure so that our people are empowered to experiment to uncover how AI can fit into their workflows. In my experience, that includes celebrating wins as well as sharing lessons learned in order to help keep each other from wasting time learning the same lesson twice. Both formally and informally, carve out space for people to share knowledge — for example, by crowdsourcing a prompt guidebook within your department or making AI tips a standing agenda item in your monthly all-staff meetings. Operating with agility will be a foundational tenet of AI-powered organizations.

Become a learn-it-all

I often hear concerns that AI will be a crutch, offering shortcuts and workarounds that ultimately diminish innovation and engagement. In my mind, the potential for AI is so much bigger than that, and it will become a competitive advantage for those who use it thoughtfully. Those will become your most engaged and innovative employees.

The value you get from AI is only as good as what you put in. Simple questions will result in simple answers. But sophisticated, thought-provoking questions will result in more complex analysis and bigger ideas. The value will shift from employees who have all the right answers to employees who know how to ask the right questions. Organizations of the future will place a premium on analytical thinkers and problem-solvers who can effectively reason over AI-generated content.

At Microsoft, we believe a learn-it-all mentality will get us much farther than a know-it-all one. And while the learning curve of using AI can be daunting, it’s a muscle that has to be built over time — and that we should start strengthening today. When I talk to leaders about how to achieve this across their companies and teams, I tell them three things:

• Establish guardrails to help people experiment safely and responsibly. Which tools do you encourage employees to use, and what data is — and isn’t — appropriate to input. What guidelines do they need to follow around fact-checking, reviewing, and editing?
• Learning to work with AI will need to be a continuous process, not a one-time training. Infuse learning opportunities into your rhythm of business and keep employees up to date with the latest resources. For example, one team might block off Friday afternoons for learning, while another has monthly “office hours” for AI Q&A and troubleshooting. And think beyond traditional courses or resources. How can peer-to-peer knowledge sharing, such as lunch and learns or a digital hotline, play a role so people can learn from each other?
• Embrace the need for change management. Being intentional and programmatic will be crucial for successfully adopting AI. Identify goals and metrics for success, and select AI champions or pilot program leads to help bring the vision to life. Different functions and disciplines will have different needs and challenges when it comes to AI, but one shared need will be for structure and support as we all transition to a new way of working.

The platform shift to AI is well underway. And while it holds the promise of transforming work and giving organizations a competitive advantage, realizing those benefits isn’t possible without a culture that embraces curiosity, failure, and learning. As leaders, we’re uniquely positioned to foster this culture within our organizations today in order to set our teams up for success in the future. When paired with the capabilities of AI, this kind of culture will unlock a better future of work for everyone.