You’re frustrated. Two functional leaders are pulling you into a nasty turf war when you need them to collaborate. You’re writing a frustrated reply, when a friend stops you. They recommend more appropriate wording, and that you ask the functional leaders to schedule a meeting to discuss conflicting priorities and come up with a solution. You take the recommendation and cool off. You would like to reach out and thank your friend and confidante, but you can’t because they’re an AI. With the help of current artificial intelligence (AI) technologies, this—and many other social capabilities—may already be possible with the tools that many organizations have access to.
While 91% of business leaders surveyed in 2022 said they have an enterprisewide AI strategy, they are typically using AI in the workplace to generate insights, optimize processes, lower costs, improve collaboration across businesses, etc.1 Within the context of these applications, the potential for human-machine collaboration is well-established.2 However, the potential of AI to improve human-to-human relationships among the workforce or with customers and potential recruits—what we call the social side of work—can often be overlooked.
By analyzing interactions and communications and generating personalized, data-driven recommendations, AI can do much more than just promoting email diplomacy. It could be a powerful tool for the workforce to nurture uniquely human capabilities. AI can help us prepare for key presentations, expand our professional networks, understand the personalities and feelings of customers, promote diversity and inclusion in everyday work, and even drive innovation and culture change across an organization. Of course, such capabilities come with adoption challenges. Skepticism for this kind of AI can run deep. But a careful, user-centric, opt-in/-out approach can help overcome resistance, and gradually introduce employees to AI.
Beyond the tactical knowledge, expertise, and skills needed to do one’s job, there are enduring human capabilities that are universally applicable and harder to develop, such as emotional intelligence, teaming, and empathy.3 These capabilities enable workers to build meaningful relationships with customers, leaders, peers, and potential recruits. The value of these human-to-human relationships can be foundational and critical to organizational success.4
We surveyed 2,620 business leaders as part of Deloitte’s State of AI 2022 study. More than two-thirds of leaders noted that their organizations have either deployed or were developing AI applications for natural language processing (including sentiment detection and text summarization), computer vision, text chatbots, and voice agents. Additionally, less than a third were planning or exploring these technologies.5 Organizations are typically using these technologies to generate insights, optimize processes, lower costs, and improve collaboration across businesses. In addition to these applications, AI technologies can analyze human interactions during and after an event to generate personalized, confidential recommendations at an individual and organizational level to help improve human interactions at work.
There are multiple AI applications for the social side of work (figure 1).
Applications of social AI will likely face many of the same challenges as other AI applications—concerns about lack of explainability in AI decisions and risks associated with data privacy, trust, reliability, etc31.
We discuss below some of the key elements that organizations should consider integrating when developing and implementing social AI solutions.32 These elements can address some of the challenges and can help create better work for humans and better humans for work.
In Deloitte’s survey of business leaders conducted in 2022, 76% said they plan to increase or significantly increase their organizational spending on AI in the next year.38 In addition to the established uses of AI in the workplace for making internal processes more efficient and generating data insights, leaders have the untapped opportunity to leverage AI to enhance the social side of work. Here are some actions to consider to get started.
Define social AI use cases and establish value metrics. Define what constitutes a social AI use or interaction, so you know how to set metrics and measure them. Identify value capture for each social AI application (increase in contact center resolution rates, higher employee engagement, improved acceptance of new processes, etc.). Measure value both in terms of breadth and depth. Breadth can be assessed by looking at how far-reaching the impact of the social AI solution is. Is it compartmentalized to select functions within the organization or across the organization? Is the impact within the organization or outside as well with external stakeholders such as customers, potential recruits, etc.? Depth can be assessed by looking at whether the social AI application is simply improving existing processes or establishing new trustworthy processes, thereby reinventing work practices.
Make the workforce comfortable with social AI. It is a huge shift for the workforce to trust a machine socially—people have to get comfortable holding a mirror up to their development areas. Leaders and managers have the responsibility to enroll the workforce with the idea that the use of their data is mutually beneficial for them and the organization. It often starts with letting the workforce know how their data will be used, giving them a “trial period” to evaluate the application, and an opt-in/-out ability at any point in time. Also, professionals tend to prefer to take “recommendations” from AI—not instructions. As such, it’s important to make it clear in the social AI user interface that the application is playing the role of a coach or buddy and not that of a gatekeeper or enforcer.
Identify how the workforce would like to engage with social AI considering cross-cultural differences. Begin by identifying workforce needs for teaming, relationship-building, networking, etc., and assess where AI solutions can be implemented to address current problems or uncover value-creation opportunities. There may be cross-cultural differences in social AI deployments for a globally dispersed workforce. For instance, based on a survey of 1,015 respondents from 48 countries, respondents from East Asia are more likely to have a trusting attitude toward emotion AI compared to respondents from western countries. This could require leaders to develop location-specific strategies for their global teams.39
Build a custom solution suited to your organization’s social nuances. When implementing a solution, it’s important to work closely (as a partner) with the AI solution provider. Since every organization is different in terms of its processes, communication styles, work dynamics, etc., it’s important to deploy a solution that is customized to the needs of the organization and the unique needs of different functions within the organization (sales, customer support, human resources, learning and development, etc.). Also, it’s important to have the right training dataset to train AI models; some of the training datasets should come from the organization’s actual data to keep the model close to reality and ensure that the model keeps adapting to incoming data.
Pilot the social AI solution for internal conversations, incorporate feedback, then scale to external applications. Pilot the solution with conversations and interactions within the organization (among the workforce) and build feedback loops from the workforce before scaling the solution to external interactions (with potential recruits, customers, etc.). While scaling the solution, a transfer-learning approach may be helpful. For example, when a team is developing a microaggression detector algorithm, they will have to train the model on hours of audio inputs, which would be time- and cost-expensive. Instead, the development team can use pretrained models (used elsewhere in the organization) or external open-source models and adapt them to their needs. When using an external open-source dataset, make sure to check that it is diverse to train your model well.
Time is short—seize the opportunity. There is a confluence of cost and performance improvements in enabling technologies (such as cloud, network speeds, computer vision, and language recognition) that could make it opportune for organizations to implement social AI now.40 AI is a powerful tool in leaders’ arsenals. With it, they can drive efficiency by creating leaner and simpler organizations and enhance unique human capabilities for long-term organizational success. By driving greater trust and transparency in hybrid operations, AI can improve the quality of work, increase employee engagement, and reduce attrition. As such, organizations adopting a wait-and-watch approach may run the risk of losing competitive advantage in the current race for talent.