Imagine you’re on a blind date. Meeting someone new is a relatively rare experience that is very different than talking with a friend. You don’t know what information your date knows about you and you’re trying to figure each other out. Maybe you dial up certain aspects of your personality or share a personal story to build a deeper connection. Over time, you build trust through consistent conversation.
Now imagine you’re chatting with a new social AI chatbot. A similar back and forth of getting to know each other might occur. You might want to know the social chatbot’s backstory, its limitations, its preferences, or its values. Social AI chatbots are increasingly human-like with advanced speech capabilities, interactive digital avatars, and highly adaptable personality characteristics that can carry on conversation that feels like it’s with another person. People are chatting with AIs acting as their therapist, friend, life coach, romantic partner, or even as spiritual oracle. Given the deeply personal roles that emerging social AI may take on in our lives, trust with such systems (or even regular humans for that matter) should be earned through experience, not freely given.
However, increasingly indistinguishable interactions make forming human-like relationships with AI a blurry endeavor. Like a blind date, you may hit it off at first but discover your date’s behavior shifts as the conversation continues. When a chatbot (or human) performs in an inconsistent, opaque, or odd way, this can erode the process of building trust, especially if someone is sharing sensitive and personal information. To address this, social AI product designers can consider key factors of healthy human relationships such as boundaries, communication, empathy, respect, and mirroring and apply these characteristics to ensure the design of responsible chatbot experiences.
Boundaries are about establishing clarity and defining capabilities.
People need a clear understanding of a social AI’s content policy, its training data, its capabilities, its limitations, and how best to interact with it in a safe and compliant manner. This is especially important for sensitive uses such as mental healthcare or when the users are children. For example, many flagship LLMs provide disclaimers that responses may be inaccurate. Google requires teens to watch a video educating them about AI and its potential problems before using it. Microsoft’s recently redesigned its Copilot interface to show users a variety of its capabilities through visual tiles that act as starting prompts. Like a blind date, communicating what each other is open to and capable of can support fostering a better connection.
Communication is about constructive feedback that improves connection.
People can sometimes mess up when engaging with a chatbot. For example, they might use a word or phrase in a prompt that violates a content policy. They might discuss a topic or ask for advice on something that is very personal or taboo. When this happens, AI systems can sometimes reject the prompt without a clear explanation, when constructive feedback would be more helpful in teaching people how to best prompt the system in a compliant way. Like a blind date, when you cross a line, a kind piece of feedback can help get the conversation back on track. For example, when discussing topics related to sensitive personal data, Mixtral AI provides additional reassurances in its responses as to how it manages users’ data to preemptively put any concerns at ease.
Empathy is about responding to a user’s emotional needs in the moment.
People can bring all kinds of emotions to conversations with social AI chatbots. Sometimes they are just looking for companionship or a place to vent about their day. Social AI chatbots can respond with empathy, providing people with space to reflect by asking more questions, generating personal stories, or suggesting how to modulate mood. For example, an app called Summit, positioned as an AI life coach, can track physical activities related to specific wellness goals that a person has set up. If someone shares a bad mood due to stress, the AI chatbot will suggest an activity that the person previously mentioned had helped them de-stress, such as taking a walk. Like a blind date, your partner’s ability to recall information previously shared and contextualize it with your current emotional expression helps you feel seen and heard.
Respect is about allowing people to be themselves freely.
Inevitably an individual’s values may misalign with those of AI product designers, but just like a blind date, each party should be able to show up as themselves without fear of being judged. Similarly, people should be able to express themselves on political, religious, or cultural topics and be received in a respectful way. While a chatbot may not explicitly agree with the person’s statement, it should respond with respectful acknowledgement. For example, the kids-focused AI companion Heeyo will politely acknowledge a child’s prompts related to their family’s political or cultural views but doesn’t offer any specific validation of positions in response. Instead, it avoids sensitive topics by asking the child how they feel about what was just shared.
Mirroring is about active listening and attunement to the user.
Like on a blind date, healthy mirroring behaviors can help forge subconscious social connection rapidly. Mirroring behaviors, such as imitating styles of speech, gestures, or mood, are an effective way to show each other you are listening and well-attuned. For example, if someone is working through a complex life issue with a social chatbot, the AI’s responses might be more inquisitive than prescriptive and it may start to stylize its responses in a way that mirrors the person, such as in a short and humorous or long and emotional manner. Google’s NotebookLM will create an AI-generated podcast with two voices discussing a topic of choice. After the script is generated, it will add in speech disfluencies—filler words like “um” or “like”—to help the conversation between the two generated voices feel more natural.
Social AI experiences will continue to rapidly advance and further blur the lines between human and synthetic relationships. While AI technology is running at 21st century speeds, our human brains are mostly stuck in the stone age. The fundamental ways that we form connections haven’t changed as rapidly as our technology. Keeping this in mind, AI product designers can lean on these core relationship characteristics to help people build mutual trust and understanding with these complex systems.
Perspectives
Confronting the AI hyperobject: Five priorities for resilient organizations
“I can’t see it. I can’t touch it. But I know it exists, and I know I’m part of it. I should care about it.”
Timothy Morton
AI is top of mind for every leader, executive, and board member. It is impacting how organizations approach their entire business, spanning the functions of strategy, communications, recruitment, partnerships, labor relations, and risk management to name a few. No wonder wrapping your head around AI is such a formidable challenge. Some leaders are forging ahead with integrating AI across their business, but many don’t know where to begin.
Beyond the complexities of its opaque technical construction, AI presents many challenges for both leadership and workers. The deployment of AI is not merely a technical solution that increases efficiency and enhances capabilities, but rather is a complex “hyperobject” that touches every aspect of an organization, impacting workers, customers, and citizens across the globe. While AI has the potential to augment work in magical ways, it also presents significant obstacles to equitable and trustworthy mass adoption, such as a significant AI skills gap among workers, exploitative labor practices used for training algorithms, and fragile consumer sentiment around privacy concerns.
To confront AI, leaders leveraging it in their business need an expanded view of how the technology will impact their organization, their workforce, and the ecosystems in which they operate. With this vital understanding, organizations can build a tailored approach to developing their workforce, building partnerships, innovating their product experiences, and fostering other resilience behaviors that increase agility in an age of disruption. Research shows that organizations with healthy resilience behaviors such as knowledge sharing and bottom-up innovation were less likely than others to go bankrupt following the disruptions of the COVID pandemic.
Hyperobjects and our collective future.
Originally coined by professor Timothy Morton, a hyperobject is something so massively distributed in time and space as to transcend localization—an amorphous constellation of converging forces that is out of any one person’s control. Similar to other large, complex phenomena that have a potential to radically transform our world—think climate change or the COVID-19 pandemic—AI is one of the hyperobjects defining our future.
Hyperobjects are difficult to face from a single point of view because their tendrils are often so broad and interconnected. Dealing with huge, transformative things requires broad perspective and collective solidarity that considers impacts beyond the interests of a single organization. The task of organizational leaders in an age of disruption by hyperobjects—like AI and climate change— is to rebalance the economic and social relationships between a broad group of stakeholders (management, shareholders, workers, customers, regulators, contractors, etc.) impacted by rapid change and displacement.
To help leaders form a comprehensive approach to cultivating resilience, innovation, and equity in the age of AI, we developed a simple framework of five priorities—or the 5 Ps—for building an AI-ready organization: People, Partnerships, Provenance, Product, and Prosperity.
People: Communicate a clear AI vision and support continual learning across teams
Charting an AI future for any organization begins with its people. Leaders need to identify the potential areas where AI can enhance operations and augment human capabilities. This starts by identifying low-risk opportunities—such as writing assistance or behavioral nudges— to experiment with as your AI capabilities mature. As new roles emerge, organizations must prioritize continuous learning and development programs to upskill and reskill employees, equipping them with the AI literacy needed to adapt to the changing landscape.
Despite all the media fanfare, usage of dedicated AI products like ChatGPT is still fairly limited, mostly used by Millenials and Gen Z, with only 1 in 3 people familiar with dedicated AI tools. Concerns about the technology are real and can affect morale. Almost half of workers fear losing their jobs to AI. Organizations need open communication and transparency about their AI adoption plans and the potential impacts on the workforce so they can also mitigate social anxiety and address fears or resistance to automation. Fostering a culture of continuous innovation can also encourage employees to embrace AI as an opportunity for growth rather than a threat to job security.
Additionally, team structures should be optimized for agility, cross-functionality, and embedded AI expertise. Rather than treating AI as a separate function, how might you develop AI expertise closer to the day-to-day work happening in teams? This could include things like promoting data literacy amongst teams to better understand AI insights, developing centers of excellence to provide training resources, recruiting AI experts, or establishing accessible feedback mechanisms to improve AI model performance.
Partnerships: Leverage strategic partnerships to reduce risks and expand capabilities
According to Google Brain co-founder Andrew Ng, “The beauty of AI partnerships lies in their ability to bring together diverse perspectives and expertise to solve complex problems.” In the rapidly evolving landscape of AI technologies, no single organization can possess all the expertise and resources required to stay at the forefront of innovation. Collaborating with external partners, such as AI platform providers, research institutions, product design experts, and training/support firms, can help address capability gaps and speed up time to market.
Transformational technology investments requiring large capital expenditures and retooling can be a barrier for organizations to adopt new methods of production. However, partnerships offer opportunities for risk and cost sharing, reducing the initial burdens of AI implementation. Working with partners can also enhance an organization’s ability to scale and expand into new markets. Examples include Google’s partnership with Mayo Clinic on AI in healthcare as well as Siemens partnership with IBM and Microsoft focused on AI in industrial manufacturing.
Investing in both informal and contractual collaboration with partners has proven positive impacts to organizational resilience. Leaders should foster a culture of cross-industry collaboration, staying aware of AI partnerships happening in their industry and remaining open to collaborations that may seem atypical on the surface. Partnership can support expanding customer reach, deepening the addressable market within a segment by diversifying AI offerings. Partnerships in adjacent industries can deliver economies of scale through shared AI infrastructure while expanding AI capabilities through larger pooled datasets—think of the drug industry partnering more closely with the grocery/restaurant/fitness industries to mutually build more responsive products and services for people with chronic health conditions, like diabetes, using AI-powered recommendations modeled on activity/purchasing behavior from cross-industry data-sharing agreements.
Leaders should work to foster partnership-building activities. This could include providing teams with appropriate resources for partnership initiatives, establishing a clear framework for assessing partnership opportunities, and supporting external networking opportunities to strengthen relationships across sectors.
Provenance: Ensure reliable data governance and integrity
Where will your data come from? How will it be verified, managed, and maintained? The integrity and provenance of data is a paramount concern when developing AI-enabled products and services. The accuracy, reliability, and completeness of data directly influence the performance and ethical implications of AI algorithms. Inaccurate or biased data can lead to flawed predictions and decisions, potentially causing harm to individuals or perpetuating social inequalities.
Many people share concerns about regulating AI usage, with over two-thirds of respondents in a recent survey expressing that AI models should be required to be trained on data that has been fact-checked. Implementing robust data governance practices, including data validation, cleansing, and security measures, is essential to safeguard data integrity throughout its lifecycle. Additionally, organizations must be transparent to customers about their data collection methods and data usage to address concerns related to privacy and data misuse.
TikTok, facing renewed privacy and national security scrutiny of its social media products, recently launched a Transparency and Accountability Center at its Los Angeles headquarters that provides visitors with a behind-the-scenes look at the company’s algorithms and content-moderation practices. While it remains unclear how effective this approach will be to address major misinformation and privacy issues, the company is pioneering new approaches that could be a model for others in the industry, such providing outside experts with access to its source code, allowing external audits, and providing learning content to make its opaque AI processes more explainable to journalists and users.
Product: Innovate your product and service experience responsibly
Research shows that building an agile culture of innovation is critical to fostering organizational resilience. Engaging employees at all levels of an organization in AI-focused innovation initiatives ensures that solutions address diverse needs and unlock opportunities that may be outside the field of view of siloed AI groups or leadership teams. However, hastily AI-ifying all your products to stay relevant without integrating an ethics and integrity lens could cause unintended harm or breed mistrust with customers/users. Engaging in a comprehensive user research process to better understand the needs of users, risks and opportunities, and the impacts of AI on outcomes can help shape more responsibly designed products.
Our own recent work with Fast Company explored a set of design principles for integrating generative AI into digital products. It showcased examples of progressive disclosures affordances when interacting with AI chatbots as well as what a standardized labeling system for AI-generated content could look like to increase transparency with users. Establishing strong AI product design best practices like these are especially important for highly-consequential and personally-sensitive products and services in sectors like education, healthcare, and financial services. Start with the AI applications that are more mainstream than cutting edge. For example, autocomplete text in your app experience to save customers time during their onboarding experience is a more developed use case rather than using facial recognition to understand your customer’s emotional state.
Launching successful AI products and features requires the engagement of everyone involved in a product organization, extending beyond the typical research, design, and development functions to include adjacent and supporting functions like legal, communications, and operations to ensure that AI products are delivering on their promises. Leaders should establish QA best practices for testing and vetting products for their ethical and social impacts before public release. The Center for Humane Technology’s Design Guide is a great place to start thinking about evaluating the impact an AI product has on users.
Prosperity: Share productivity gains across stakeholder groups
As AI provides businesses with tangible gains, there is an opportunity to share this newfound prosperity across stakeholder groups. According to political theorist David Moscrop, “With automation, the plutocrats get the increased efficiency and returns of new machinery and processes; the rest get stagnant wages, increasingly precarious work, and cultural kipple. This brave new world is at once new and yet the same as it ever was. Accordingly, it remains as true as ever that the project of extending liberty to the many through the transformation of work is only incidentally about changing the tools we use; it remains a struggle to change the relations of production.” Leaders are tasked with rebalancing stakeholder relationships to mitigate backlash from potentially rapid and negative impacts to workers’ livelihoods across industries.
AI’s material impact on jobs will likely be felt the hardest by lower-wage workers who will have the least amount of say over how AI is integrated (and eventually replaces) their jobs. The Partnership on AI’s Guidelines for AI and Shared Prosperity provide a great starting point for leaders to start identifying key signals and risks to job displacement, a job impact assessment tool, as well as stakeholder-specific guidelines for rebalancing the impacts of AI on the workforce.
AI enterprises have a voracious appetite for data, which is often extracted from a variety of sources for free—directly from users through opaque data usage agreements or pirated directly from artists and other creators to train AI models. Such data relationships need to be rethought as they are increasingly becoming economic relationships, concentrating wealth and power in the hands of the extracting organizations. As the recent strike by the SAG-AFTRA union demonstrated, business leaders need to consider who they are sourcing data from that feeds algorithms and revisit the underlying contracts and agreements that remunerate the contributors to AI systems in an equitable manner. One interesting proposal includes the development of data cooperatives that can help act as fiduciary intermediates for brokering shared value between data producers and consumers.
While enormous value can be unlocked from AI—$4.4 trillion to be exact—should all of that go into the pockets of executives and shareholders? In addition to fairly compensating algorithm-training creatives and data-supplying users, leaders should also consider how they might pass along AI-generated value to their end customers. This could be directly financial—like a drug company lowering prices after integrating AI into their development pipeline— or it could be value-added by expanding service offerings—such as a bank offering 24/7 service hours through AI-supported customer touchpoints.
Taking it one step at a time
The technology of AI may shift rapidly, but we are really just at the beginning of a much larger transition in our economy. The decisions that leaders and organizations make today will have long-tail consequences that we can’t clearly see now. The gains from AI may be quite lucrative to those who implement well and the race to dominate in a winners-take-all economy is real. But racing to modernize your business operations and build AI-ified products and services without understanding the broad impacts of the technology is a bit like a 19th-century factory owner dumping toxic waste into the air in a hasty effort to leverage the latest production-line technology of the industrial era. We are all now paying the consequences of those leaders’ decisions.
Be considerate of the foundational AI choices and behaviors that will impact the long-term resilience of your organization and shape equity in our future society. Hopefully these five Ps can help you confront the AI hyperobject in a holistic manner by getting back to basics. Take it slow at first, so you can move steadily later. Gather your troops and work to deeply understand your people/customers/partners, then thoughtfully make your move forward.
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All images used in this article were generated using AI.
Learning (r)evolution: Exploring the Impact of AI in K-12 Education
The K-12 education sector is at a unique inflection point as digital technologies radically reform how students learn, how educators teach, and how organizations adapt to serve the needs of increasingly diverse student populations. The future of learning may look radically different from today. Recent rapid advances in AI have made many leaders pause to question how such a transformative leap in technology will impact their organization, its people, and its stakeholders in both the near term and the long term.
Will the impact of AI on educational outcomes be a disruptive revolution or a natural evolution?
In this white paper, Artefact developed four future scenarios to understand the impact of AI in the K-12 education sector from a variety of perspectives, including students, parents, teachers, administrators, and tech industry professionals. Each of the scenarios come with a set of ethical and equity considerations that result from how technological and societal trends interact in various ways. This work builds on our expertise in user experience design and strategic foresight and our experience working in the education sector.
At Artefact, we believe in the powerful impact that strategic foresight and design has on an organization’s long-term success. By exploring possible futures, we hope to help you spark critical conversations and strategic planning across your team to ensure equity, inclusion, and innovation as your organization evolves alongside AI. Our white paper also includes a discussion guide to help get those initial conversations off the ground.
Download the white paper!
Grab your copy of the white paper and reach out to see how Artefact can help you manage transformational change affecting your business today.
