How AI is changing the work landscape

sasha uritsky

Jan 11, 2021

"For better or worse, robots are going to replace many humans in their jobs,” says analysts from BBC, and the coronavirus is speeding up the process. Consumer preferences are evolving and recently consumer behavior demonstrates that we as a society have become more tolerant accepting of using automation in our daily routines.

In the professional workspace, most if not all companies have moved towards working from home. Given the unprecedented times, recruitment, the employees management, and the corporate governance processes and communication have moved online. As a result of pandemics many companies are experiencing hiring freezes, but many others have moved their recruitment efforts online. A few companies have begun piloting recruitment with the help of artificial intelligence. They are now leveraging AI to conduct online interviews and assessments and deliver data back to the employer. Now more than ever, companies are realizing the importance of moving towards a remote-friendly workforce. Being able to scale human capital on a larger scale online has definitely been accelerated recently.

I know for myself, as a current student who recently had their internship offers rescinded due to COVID-19, I’ve put myself back into the market. I’ve seen both small businesses and corporations utilize screening questions, video pitches, and unique riddles to test students’ critical thinking and how they fit into the company culture. This experience in itself has been revealing – after so many years of in-person interviews to suddenly having to emulate the same energy online or via video. Given the adjustment, at times it definitely felt unnatural to sit in front of my computer camera and pitch myself or answer video questions. However, going forward, I can see how automation and online platforms will become more explored given the time it saves and the bias it could remove during the recruitment process.

Yet it is not just a change in the recruitment process that we are seeing. The customer service environment, as I have seen first-hand, is under large stress. One of the first calls I had made was to an online retailer, to try and put in a return order. What seemed to be an idea that everyone else had as well, I was put into a queue that lasted more than 30 minutes. After hitting that 30-minute mark, I gave up and put off the task for a later date. Now, a month later, more and more companies are adopting chatbots and artificial intelligence into their customer service processes. These companies are beginning to provide information in a more efficient manner, and with less human capital.

Moving forward, in the next few months and post-COVID-19, it would be interesting to see which companies are focusing more on their digital transformation efforts. I believe that a larger number of universities and educational institutions will partner with tech companies to help digitize their working environments. And private businesses will continue to implement some of the already existing practices and produce products that cater to the remote working lifestyle and online interactions.

Interested in reading more? Check out our other blogs:

Artificial Intelligence Chat Is Evolving Faster Than IVR

Although it doesn’t feel like all that long ago, way back in the 90s one of the most important factors to a call center’s success was the ability to route a customer to the right support agent with the IVR (Interactive Voice Response). Countless hours were spent identifying the most efficient call routing patterns and expert agent capabilities to ensure that your request reached the right person quickly. This technology is still widely used today and there are still teams in the largest companies programming IVR systems to accomplish pretty much the same goal.

As the standard for customer support evolved there have been many attempts to improve the function and the customer experience associated with IVRs to reduce hold times and provide more relevant support faster. Even today some companies will use their IVR system as a way to keep a customer on hold, rather than provide a solution, when agents are inundated with calls.

For those of us who’ve worked in the voice industry for some time, we’ve seen first-hand the attempts to accomplish a customer’s need before reaching an agent. First there was expert agent routing that delivered your call to the agent most qualified to help you. Then came advances in voice recognition, which today has evolved to be a very effective tool to increase containment rates and deflect calls from reaching a live agent. My two favorite examples of the power of voice recognition are Cox Communications and Capital One, two examples of great voice recognition and routing.

Our memory, however, is short. It wasn’t so long ago that we were all pulling our hair out punching digits into the phone or constantly repeating “agent”, “Agent”, “AGENT”, AGENT!!!!!”.

Whether it was a limit of computational power or the sheer cost of developing and implementing advanced call center technology, it took decades for phone systems to be able to front end the customer support process as efficiently as they do today. Thankfully we all survived to see it without boiling over from the hypertension usually associated with calling with a customer service department.

Bad customer experience is definitely not the case with Chat Artificial Intelligence (Chat AI). While we seem to hear about the shortcomings of Chat AI like the disconnected conversations and the robotic like responses, these experiences are usually the product of Chatbots with limited AI functionality or early stage deployments. The increases in both computational power and the massive advancements in machine learning are driving excellent customer experiences that improve over time.

When was the last time you heard of technology actually performing better, on its own, without a ton of additional development work or continuous updates? Well, that’s the case with Artificial Intelligence. Like a person, the more experience it has interacting with customers and information, the better it performs with little need to be manually improved or fine-tuned.

Today, AI Chat can be used to answer a large majority of customer requests and because Artificial Intelligence learns as it is used, customers prefer to interact through AI chat to avoid all of the frustrations commonly associated with calling a contact center agent.

Integrated Real-Time Data Boosts Content Delivery

How to make content more relevant and appealing to the content consumer?

This is a problem that has been on the mind of content creators for some time now. In our age of information abundance it is not easy to stand out and make your voice heard. The competition for the consumer’s attention is escalating, and with the number of information sources ever increasing, it will only get tougher.

Traditionally, a content delivery does not change across the target audience. A commercial, or a blog, looks and is experienced in the same way by all viewers and readers. We are entrenched in this paradigm, and can hardly imagine it being otherwise.

It turns out, the advancement of new technologies capable of capturing individual intents in real time brings up new opportunities in creating personalized experiences within the framework of content delivery.

This is how content can become more relevant - by becoming more personalized.

In a rudimentary form, we are already familiar with this approach as seen in online advertising. Some web and social resources aim at personalizing their promotional campaigns based on whatever drops of behavioural patterns and interests they can squeeze out of our web searches. The problem, of course, is that the technologies used to power these campaigns understand human behaviour poorly and results, therefore, more often than not leave a great deal to be desired. To put it mildly.

nmodes has been working on semantic processing of intent for several years. We now can capture intent from unstructured data (human conversations) with accuracy of 99%. (Interestingly, many businesses do not require this level of accuracy, being satisfied with 90%-92%, but we know how to deliver it anyway).

We recently started to experiment with personalizing content by using available consumer intent.

We used Twitter because of its real-time appeal.

We started by publishing a story, dividing it into several episodes:

And we kept the constant stream of data flowing, concentrating on intent to dine in Paris:

We then merged the content of the story with consumer intent to dine in Paris as captured by our semantic software. Like this:

This merging approach shows promising results - the engagement rate jumped above 90%.

Overall we are only at the beginning of a tremendous journey. We know that other companies are beginning to experiment, and the opportunities from introducing artificial intelligence related technologies into content delivery are plentiful.

There is a long road ahead, and we've made a one small step. But it is a step in a very exciting direction.