As offices worldwide shift to remote work, our interactions with customers and colleagues have evolved in tandem. Professionals who once relied on face-to-face communication and firm handshakes must now close deals in a world where both are rare. Coworkers we once sat beside every day are now only available over Slack and Zoom, changing the nature of internal communication as well.
While this new reality presents a challenge, the advancement of key technologies allows us to not just adapt, but thrive. We are now on the precipice of the biggest revolution in workplace communication since the invention of the telephone.
It’s not enough to simply accept the new status quo, particularly as the overall economic climate remains tenuous. Artificial intelligence has much to offer in improving the way we speak to one another in the social distance era, and has already seen wide adoption in certain areas. Much of this algorithmic work has gone on behind the scenes of our most-used apps, such as Google Meet’s noise-canceling technology, which uses an AI to mute certain extraneous sounds on video calls. Other advances work in real-time right before our eyes — like Zoom’s myriad virtual backgrounds, or the automatic transcription and translation technology built into most video conferencing apps.
This kind of technology has helped employees realize that, despite the unprecedented shift to remote work, digital conversations do not just strive to recreate the in-person experience — rather, they can improve upon the way we communicate entirely.
It’s estimated that 65% of the workforce will be working remotely within the next five years. With a more hands-on approach to AI — that is, using the technology to actually augment everyday communications — workers can gain insight into concepts, workflows and ideas that would otherwise go unnoticed.
Your customer service experience
Roughly 55% of the data companies collect falls into the category of “dark data”: information that goes completely unused, kept on an internal server until it’s eventually wiped. Any company with a customer service department is invariably growing their stock of dark data with every chat log, email exchange and recorded call.
When a customer phones in with a query or complaint, they’re told early on that their call “may be recorded for quality assurance purposes.” Given how cheap data storage has become, there’s no “maybe” about it. The question is what to do with this data.
Short of replaying a contentious call for managerial review or using an exemplum for training purposes, most customer service (CS) calls fade into obscurity once the issue has been handled. A CS agent may add a few notes about the ticket, but their main duty is in rectifying issues, not labeling data. Specific features, issues, competitors or key phrases may be mentioned regularly on phone calls, but go unnoticed by customer service agents who focus on the problem at hand.
Manually reviewing these calls to gain insights is a costly affair well outside the budget of most companies. Artificial intelligence is now at the point where it can identify words, phrases and entire sentences that will be of interest to decision-makers within the company. This could include any mentions of a competitor’s product, a key feature that doesn’t work as well as it should, desired improvements and changes, or anything that would otherwise sneak past human ears. The thousands of emails, chat messages and other communication traded by employees each day becomes a key source of data, allowing the AI to improve iteratively with each use.
It’s not just the ability to collect and analyze data that makes modern AI essential. By developing the connections between what a customer says and how an agent should respond, an AI can offer potential responses in real-time. For example, the technology can learn which products and features to highlight when a customer mentions a competitor’s offerings, identifying which specific responses work best to create positive outcomes. Better yet, AI is now capable of sentiment analysis, identifying a person’s emotions from their tone of voice and cadence. Responses that create negative sentiment can then be avoided, while useful answers that create a positive emotional tone become future recommendations.
Customer communication improves even more dramatically when AI is combined with human insights. For example, virtually every business knows which of their salespeople perform the best — it’s simply a matter of identifying who’s closing the most or the largest deals. These high-performing employees should be the first to have their recorded communications analyzed by an AI. The salesperson knows which techniques they use to establish rapport and close deals; the AI, meanwhile, delves deeper for undiscovered insights.
Again, the only limit to the potential benefits here lies in the data. When an AI identifies a potential opportunity to upsell a customer, it’s up to the salesperson to follow through on the suggestion and see how the customer reacts. This information then informs the next opportunity, increasing the likelihood of success. By having employees and technology work in tandem, companies can overcome their reticence to rely on remote work, knowing the advantages are a boon to their profits.
Employee insights, analyzed
Anyone who’s worked in a customer-facing role would be eager to leverage artificial intelligence to improve CS outcomes. Moreover, since 89% of companies compete on customer service, the adoption of AI tech in CS departments will be a key differentiator for a competitive advantage.
The use of this technology in conversations between employees, however, is more subtle. Coworker interactions are about sharing information and building rapport, rather than fielding questions and complaints when speaking with customers.
Business leaders have worked tirelessly over the past few months to recreate the in-office experience as best they can using tools like Zoom, Slack and Microsoft Teams. The kinds of scheduled office meetings, typically led by a single department head or manager talking at length, are fairly easy to reproduce on a video call. Unfortunately, they’re also the type of meetings that can most easily be reduced to an email.
The in-person water cooler conversations that plant the seed of inspiration for future innovations now happen over Slack calls, group chats and direct messages. When employees share ideas and insights, both employee performance and overall company culture improve as a result. These Interactions are based on tribal knowledge — the know-how employees learn on their own as they navigate their roles. Tribal knowledge can include shortcuts, workarounds, trade secrets or any other type of knowledge that isn’t preeminent in written documentation. The more an employee learns about their role, the more tribal knowledge they create.
Sharing this knowledge is essential to building a cohesive team. Take employee onboarding, for example — most experienced teammates won’t simply go through the employee handbook word-for-word. They’ll add their own insights and experiences, answering the new hire’s questions along the way. These sessions can create a font of knowledge, provided the information revealed is analyzed by an AI.
When employees allow an AI to analyze their conversations, they’re made privy to new findings most team members were previously unaware of. By comparing the conversation of team members against written onboarding documentation, an AI can highlight which spoken words don’t appear in the text, and vice versa. In this way, companies can improve their onboarding process and overall workplace productivity by focusing on what’s proven to be effective rather than basing decisions on assumptions.
The applications for AI analysis in internal communications are as varied as the companies that could benefit from this type of digital transformation. Some teams that work together closely may be unaware of how their feelings inform their decision-making; sentiment analysis bridges the gap between emotion and reason in communication. The way employees talk to those outside of their direct teams may be vastly different from the way they communicate with more trusted colleagues. In these cases, AI algorithms can track these differences, allowing employees a chance to see how best to adapt their communication style for stronger collaboration.
The interplay between AI and human insight in corporate interactions is a virtuous cycle. As the saying goes, “Man is a slow, sloppy and brilliant thinker; computers are fast, accurate and stupid.” When workers see the real value in collaboration — or, better yet, are incentivized and rewarded for creating and sharing knowledge — the AI systems they utilize become powerful tools for better communication and accelerated growth. The best way to go about this is by taking an augmented approach — that is, focusing on human-led interactions, while allowing AI to add suggestions and highlight areas where employees can extract knowledge. What we’ll end up learning about our companies, our colleagues and ourselves will not only change how we communicate, but also help us craft more meaningful experiences for everyone.