Putting the ‘S’ in ESG: How AI and automation can drive social impact
Environmental, social and governance (ESG) initiatives will shape the way brands and customers behave in 2022 and beyond. Forrester predicts that more than half of U.S. adults will regularly make purchases from brands that align with their personal values and this trend will drive brands to focus on driving their ESG commitments more than ever. While each component of ESG has its own framework, the social of ESG is often regarded as the hardest to define and act on.
Many pathways can be used to define and orient towards social impact, including how unconscious and systemic biases can influence experiences for employees at work and when consumers engage with brands. Many organizations want to address these biases, but they’re not clear on which approaches would be most effective.
Social impact requires consistent experiences and inclusive processes
Providing consistent, inclusive employee and customer experiences undoubtedly enhances a brand’s reputation and increases its competitive edge. However, even when intentions are good, human behavior can be inconsistent and unconsciously biased, resulting in a disconnect between a company’s social goals and the associated experiences they provide. For example, an audit of customer service emails from 6,000 U.S.-based hotels by assistant professors at Harvard and the University of Virginia found that frontline employees were less responsive and helpful to minority customers.
While training might be the typical first step to remediate a bias issue in customer service, the whole premise of implicit and unconscious bias is defined by the assumption that the bias is not known. Thus, even with training, this response is difficult to redirect and customers will likely continue having negative experiences.
Rather than taking the traditional training approach, we believe that AI and automation technologies have the potential to help companies reduce biases in processes at scale. Organizations can collaborate with business, behavioral economics and psychology experts to help identify how biases manifest and impact customer experiences. Using these insights, enterprises can develop automation- or AI-based solutions, like automated email responses or programs that score text for implicit bias, to help employees better interact with customers and reduce the potential for unconscious bias and inconsistency.
From an internal perspective, AI and automation capabilities can also help companies enhance employee experiences and drive diversity, which in turn improves organizations’ ability to innovate and even increases year-over-year market share by 45%. For example, automation can help companies screen resumes to help level the playing field for all candidates and review employee performances to enhance consistency of evaluations.
With an arsenal of automation and AI tools available, how can organizations find the best solutions to begin addressing social inequities and help drive their ESG programs? Here are the key steps.
Build a business case for change
Some brands may start the process because of their commitment to social equity, while others may begin because they want to attract talent or retain customers, but ESG also has positive impacts on the top and bottom line, proving that profitability and social good can go hand in hand. Staying consistent to inherent corporate values can improve performance in the long run by fostering innovation, trust and loyalty.
Gain visibility into and measure existing outcomes
To set the future course, organizations will need to look at their past and gain visibility into ESG outcomes for accurate measurement. Net promoter scores, employee retention rates, customer churn and patient outcomes are just a few of many results that companies can use as benchmarks for predicting future social impact initiatives.
Understand process inputs that lead to current outcomes
Interview managers to understand the criteria they use to arrive at the outputs, to determine what inputs need to be measured, and to decide how to measure them. Consider bringing in behavioral and industry experts to conduct interviews and observe processes.
Observation is a key part of the data-collection process, to ensure that the starting point is consistent and to identify where variations from the planned process are occurring. Often, there are gaps between what people are supposed to do according to policy, what they say they do in theory and what they actually do in practice. Comprehensive understanding requires looking at theory and intention, as well as practice.
With outcomes as a benchmark and an understanding of inputs, the organization can then start setting criteria for each step in their processes where human behavior introduces inconsistent responses to customers or candidates. At this point, the organization can start to automate those steps to deliver a better and more consistent experience across diverse groups of customers and employees — an experience that can drive loyalty, reduce churn and enhance organizational performance.
By tracking KPIs such as NPS score, customer churn, candidate interviews and employee retention rate, organizations can track the effectiveness of their automation for social goals, and refine the technology as needed. Over time, companies that work to deliver more consistent experiences can strengthen their brand reputation, become more innovative and earn more revenue.
Ying Liu is ServiceNow partner executive at Capgemini Group, and Sheila Patel is VP of sustainability at Capgemini Invent.
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