How to recycle data from AI for employee engagement efforts
By reusing the data collected for AI algorithms and the insights they generate, enterprises can boost employee performance and improve business processes.
Once you've collected, cleansed and optimized large data sets for AI algorithms designed to help improve business processes, don't assume you're finished with all that data. Enterprises need to take another look at their data and work to recycle it in AI for employee engagement and growth initiatives, according to speakers at the 2019 MIT Sloan CIO Symposium.
Implementing AI algorithms in business operations doesn't have to be a single-use process, they said during a panel discussion. The collected data and the information you glean from running the AI applications against it can be further used to analyze things like business process flows, holes in employee knowledge and internal pain points that need to be resolved.
Using AI insights to help your human workers boosts digital transformation efforts and lessens the disruption associated with them, the panelists said. They added that business executives armed with AI-generated knowledge can turn inward to better support and address the needs of their employees, ideally creating a cycle of continuous improvement in organizations.
Humans and AI
In the age of the digital worker, AI tools cannot simply be an addition to the business, the members of the panel advised conference attendees, as they shared their experiences in using the data created for and produced by AI for employee engagement efforts.
Joshua Feast, CEO of AI software vendor Cogito Corp., identified a business process that needed improvement: communication during phone calls with customers. His company developed an AI-based SaaS application that listens to calls and provides in-call behavioral nudges to improve the communication skills of customer service agents. Cogito also recycles that data to create profiles of employees and their phone call engagement styles in order to help managers individually assess workers and take steps to improve their performance, Feast said.
Sam Kapreilian, a principal at Deloitte Consulting LLP, described a similar approach. The data his team collected for machine learning tools it built for use in the hiring process at a corporate client, along with the resulting AI output data, was used to take a holistic look at the entire organization.
The hiring algorithms spot the experience levels of job applicants and see holes in the client company's skills that need to be filled. But recycling the data can also shed a light on existing business processes and hiring practices that can be improved, Kapreilian said. He added that the client company can then analyze the multivariate components necessary for finding and acquiring talent -- from specific hiring timelines to interview styles and compensation offers.
Sam KapreilianPrincipal, Deloitte Consulting LLP
When working with clients on AI projects to help optimize business processes, Deloitte applies a sense of humanity to the data. "We think about understanding the human intuition -- emotive, financial, geographic -- take all of that, unify it and drive AI over it," Kapreilian said.
Human satisfaction builds a smart enterprise
Building a smart enterprise should focus on the human workers, not just their digital counterparts, said Manas Fuloria, CEO of application development and consulting services company Nagarro. Alongside implementing AI tools, Fuloria advised C-level management that the best way to augment and improve employee work is to step back, provide context and encourage employee collaboration and understanding.
Implementing community building programs that enable same-level workers to learn alongside and from each other ultimately boosts enterprise AI integration and employee satisfaction with the new technology, Fuloria said. This is where recycled data becomes useful, he added. Managers looking to use AI for employee engagement can gain insight into employee life, tasks and satisfaction. That data can then be analyzed and shared to create an approach to AI that stems from human employees.
Like the other panelists, Fuloria urged employee empowerment as the force behind AI integration, to help workers augment and improve their jobs. "When [AI is] done right, people are yearning for these tools," he said.
Recycled data in business processes
Embracing the digital workplace also has to go beyond automating repetitive tasks and dive into using the same AI tools for multiple improvements companywide.
Ashok Pai, vice president and global head of cognitive business operations at Tata Consultancy Services, said in an interview that the journey to a unified human and AI workforce isn't a streamlined process. Companies need to take a look at the state of their employee understanding of AI and their internal data science skills -- "an AI maturity assessment," Pai said -- and then create a clear, accessible plan for integrating AI at scale.
If you're new to AI, hiring and training data scientists to run a variety of algorithms and apply data insights to business processes might be out of reach, Pai said. Instead, start simply by collecting data about a business process and how it can be improved, he recommended. Include employee insights and raw data, and search for the right platform. From there, automate the process, and begin the journey to an augmented workforce -- one step at a time.