When it launched GPT-4, in March 2023, OpenAI touted its superiority to its already impressive predecessor, saying the new version was better in terms of accuracy, reasoning ability, and test scores—all of which are AI-performance metrics that have been used for some time. However, most striking was OpenAI’s characterization of GPT-4 as “more aligned”—perhaps the first time that an AI product or service has been marketed in terms of its alignment with human values.

The idea that technology should be subject to some form of ethical guardrails is far from new. Norbert Wiener, the father of cybernetics, proposed a similar idea in a seminal 1960 Science article, launching an entire academic discipline focused on ensuring that automated tools incorporate the values of their creators. But only today, more than half a century later, are we seeing AI-embedded products being marketed according to how well they embody values such as safety, dignity, fairness, meritocracy, harmlessness, and helpfulness as well as traditional measures of performance, such as speed, scalability, and accuracy. These products include everything from self-driving cars to security solutions, software that summarizes articles, smart home appliances that may gather data about people’s daily lives, and even companion robots for the elderly and smart toys for children.

As AI value alignment becomes not just a regulatory requirement but a product differentiator, companies will need to adjust development processes for their AI-enabled products and services. This article seeks to identify the challenges that entrepreneurs and executives will face in bringing to market offerings that are safe and values-aligned. Companies that move early to address those challenges will gain an important competitive advantage.

The challenges fall into six categories, corresponding to the key stages in a typical innovation process. For each category we present an overview of the frameworks, practices, and tools that executives can draw on. These recommendations derive from our joint and individual research into AI-alignment methods and our experience helping companies develop and deploy AI-enabled products and services across multiple domains, including social media, health care, finance, and entertainment.

The first task is to identify the people whose values must be taken into account. Given the potential impact of AI on society, companies will need to consider a more diverse group of stakeholders than they would when evaluating other product features. These may include not only employees and customers but also civil society organizations, policymakers, activists, industry associations, and others. The picture can become even more complex when the product market encompasses geographies with differing cultures or regulations. The preferences of all these stakeholders must be understood, and disagreements among them bridged.

This challenge can be approached in two ways.

Embed established principles.

In this approach companies draw directly on the values of established moral systems and theories, such as utilitarianism, or those developed by global institutions, such as the OECD’s AI principles. For example, the Alphabet-funded start-up Anthropic based the principles guiding its AI assistant, Claude, on the United Nations’ Universal Declaration of Human Rights. Other companies have done much the same; BMW’s principles, for example, resemble those developed by the OECD.

Articulate your own values.

Some companies assemble a team of specialists—technologists, ethicists, human rights experts, and others—to develop their own values. These people may have a good understanding of the risks (and opportunities) inherent in the use of technology. Salesforce has taken such an approach. In the preamble to its statement of principles, the company describes the process as “a year-long journey of soliciting feedback from individual contributors, managers, and executives across the company in every organisation including engineering, product development, UX, data science, legal, equality, government affairs, and marketing.”

Another approach was developed by a team of scientists at DeepMind, an AI research lab acquired by Google in 2014. This approach involves consulting customers, employees, and others to elicit AI principles and values in ways that minimize self-interested bias. It is based on the “veil of ignorance,” a thought experiment conceived by the philosopher John Rawls, in which people propose rules for a community without any knowledge of their relative position in that community—which means they don’t know how the rules will affect them. The values produced using the DeepMind approach are less self-interest-driven than they would otherwise be, focus more on how AI can assist the most disadvantaged, and are more robust, because people usually buy in to them more easily.

Beyond establishing guiding values, companies need to think about explicitly constraining the behavior of their AI. Practices such as privacy by design, safety by design, and the like can be useful in this effort. Anchored in principles and assessment tools, these practices embed the target value into an organization’s culture and product development process. The employees of companies that apply these practices are motivated to carefully evaluate and mitigate potential risks early in designing a new product; to build in feedback loops that customers can use to report issues; and to continually assess and analyze those reports. Online platforms typically use this approach to strengthen trust and safety, and some regulators are receptive to it. One leading proponent of this approach is Julie Inman Grant, the commissioner of esafety in Australia and a veteran of public policy in the industry.

Generative-AI systems will need formal guardrails written into the programs so that they do not violate defined values or cross red lines by, for example, acceding to improper requests or generating unacceptable content. Companies including Nvidia and OpenAI are developing frameworks to provide such guardrails. GPT-4, for instance, is marketed as being 82% less likely than GPT-3.5 to respond to requests for disallowed content such as hate speech or code for malware.

Red lines are also defined by regulations, which evolve. In response, companies will need to update their AI compliance, which will increasingly diverge across markets. Consider a European bank that wants to roll out a generative-AI tool to improve customer interactions. Until recently the bank needed to comply only with the EU’s General Data Protection Regulation, but soon it will need to comply with the EU’s AI Act as well. If it wants to deploy AI in China or the United States, it will have to observe the regulations there. As local rules change, and as the bank becomes subject to regulations across jurisdictions, it will need to adapt its AI and manage potentially incompatible requirements.

V alues, red lines, guardrails, and regulations should all be integrated and embedded in the AI’s programming so that changes to regulations, for example, can be keyed in and automatically communicated to every part of the AI program affected by them.