Employee Productivity with Conversational AI & Automation

Employee productivity might be summed up by the expression “The last thing I need in my day is_________.”

We’ve all got them. Things that show up during the workweek like loose shoelaces on a fast escalator threatening to trip us up and pull us under. In their best-selling book Nudge: Improving decisions about health, wealth, and happiness, economists Cass Sunstein and Nobel prize winner Richard Thaler, referred to these frictions that make life difficult to navigate as “sludge” (Thaler, R.H., Sunstein, C.R., 2008). In the following, you’ll learn how Conversational AI reduces sludge and improves employee productivity.

Conversational Artificial Intelligence (AI) is designed to make processes simpler and achieve outcomes faster. Moreover, Conversational AI at Aisera is unsupervised powered by Natural Language Processing (NLP), Natural Language Understanding (NLU), and Machine Learning. It doesn’t simply work based on what the developers most recently built into it. It learns.

Like a smart organization that is learning and evolving, Conversational AI is constantly aligning itself with company processes and information flows. In turn, employees can find things and solve issues even when things move and change – which happens constantly.

Importantly, the conversational nature of Conversational AI aligns with the natural or “conversational” communication method most people prefer – talking. Moreover, because the process of talking now occurs via multiple channels – email, text, interactive voice response (IVR) phone systems, Slack, chatbot, etc… – and sometimes simultaneously (such as chatting with a chatbot, while on the phone, and sending an email about an issue) a Conversational AI system integrates across these channels.

In this way, employees seeking a resolution to an issue have a cohesive experience. They don’t need to backtrack through a sludgy process and refill in fields or resubmit the information.

They don’t need to get frustrated.

The Conversational AI coordinates an understanding of the employee’s issue across multiple channels. The employee doesn’t get different answers from different channels. The seamlessness of the process, ensures employees feel they are being clearly heard and reduces frustration.

Additionally, Conversational AI is especially important as companies accelerate their digital footprint, and uncertainties such as COVID, force shifts away from previously in-person experiences (McKinsey survey, 2020). Notably, highlighted in the McKinsey survey are call center agents assisted by AI Chatbots or Intelligent Virtual Assistants. Conversational AI is increasingly seen as a way to help “employees grow” for example, by streamlining new employee onboarding and creating self-service for basic HR benefit queries. These capabilities significantly reduce redundant conversations between HR and employees about simple issues.

At this point, the not-so-great aspects of sludge are clear. But, what comes next?

Helpful nudges are like great maps, they anticipate frustration and confusion and head it off before it happens.

For example, citing the above example of a new employee’s onboarding, a Conversational AI will have answers and processes for the newbie on Day 1. As soon as the new employee opens their Slack instance a chat function could recognize them and send a query “Ready to get your MS365 setup, Yes/No?”

In a few short conversations, the new employee has approvals, downloads, and setup. Instead of days, they’re set up in minutes.

This is an example of one process out of thousands, where Conversational AI removes sludge and can instead anticipate and solve issues proactively. Instead of a first day that lands with a thud, the new employee’s first day rolls forward smoothly and he joins the company’s stream of productivity.

Easy peasy lemon squeezy.

Susstein and Thaler’s book remains a bestseller, in part, because it described a phenomenon most people intuitively understand and gave it a name. Nudge theory positions those small, but key actions, which help to make big differences for people and organizations. Similarly sludge and sludge audits identity points of friction in organizations and people’s lives involving time, information, and psychological burdens.

In fact, sludge matters to people, says Sustein, because ultimately the barriers it erects, the time it steals, and the obstacles it presents represent a reduction in personal freedom. If employees can’t get what they want they’re “not exactly free” (Sustein, 2020).

Employees don’t appreciate organizations where the frustration, and cumulative stress, of sludge is the norm. Perhaps it is no surprise, but employees are seeking companies with less sludge.

On the one hand, quantitative sludge audits can help organizations identify points of friction where employees feel frustrated. On the other hand, most people know sludge when they see it or experience it. For example, they can compare the employee experience of onboarding at one organization using a standard process versus another organization using Conversational AI.

Perhaps it’s even simpler. You may be able to identify a few if you consider your answer to this “The last thing I need in my day is ________” and think of the hundreds of processes during a workweek that would be better served via an automated and autonomous Conversational AI interaction.

It is likely that it will identify areas where you can look for ways at increasing employee productivity and nudge your organization forward with better workforce productivity, and improved employee engagement (even in remote work).

Experience the best AI technology for your enterprise by booking a free demo of conversational AI today!