Scalable Yet Personalized

sasha uritsky

Aug 02, 2016

How to offer businesses and organizations a solution that personalizes and scales consumer interaction process at the same time?

Personalizing the user relationship process. Today end users and consumers demand to be targeted individually and to be approached based on their actual interests. nmodes AI (Artificial Intelligence) powered solution helps organizations accurately identify user needs in real time. Our solution delivers information on each user individually thus providing the necessary level of personalization required of the successful customer service.

Scaling the user relationship process: Once the organization identifies a user and a problem that needs to be addressed, next step is reaching out to that user individually. Currently this is a manual non-scalable procedure. nmodes AI (Artificial Intelligence) solution provides automated assistance to human personnel, including substitution when deemed appropriate, thus making the entire process scalable.

Today more than 90% of all organizations and businesses rely on solutions based on keywords, even though these solutions provide low quality results not sufficient for the new generation of personalized scalable services.

nmodes solution enables sustainable delivery of high quality results, with x5 costs reduction and up to 45% increase in conversation (engagement) capacity.

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How AI is changing the work landscape

"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.

What Is AI Engine and Do I Need It?

Chatbots and assistant programs designed to support conversations with human users rely on natural language processing (NLP). This is a field of scientific research that aims at making computers understand the meaning of sentences in natural language. The algorithms developed by NLP researchers helped power first generation of virtual assistants such as Siri or Cortana. Now the same algorithms are made available to the developer community to help companies build their own specialized virtual assistants. Industry products that offer NLP capabilities based on these algorithms are often called AI engines.

The most powerful and advanced AI engines currently available on the market are (in no particular order): IBM Watson, Google DialogFlow, Microsoft LUIS, Amazon Lex.

All these engines use intents and entities as primary pnguistic identifies to convey the meaning of incoming sentences. All of them offer conversation flow capability. In other words, intents and entities help to understand what the incoming sentence is about. Once the incoming sentence is correctly identified you can use the engine to provide a reply. You can repeat these two steps a large number of times, thus creating a conversation, or dialog.

In terms of language processing ability and simplicity of user experience IBM Watson and Google DialogFlow are currently above the pack. Microsoft LUIS is okay too; still, keeping in mind that Microsoft are aggressively territorial and like when users stay within their ecosystem, it is most efficient to use LUIS together with other Microsoft products such as MS Bot Framework.

Using AI engine conversation flow to create dialogs makes building conversations a simple, almost intuitive, task, with no coding involved. On the flip side, using AI engine conversation flow limits your natural tendency to make conversations natural. The alternative, delegating the conversation flow to the business layer of your chatbot, adds richness and flexibility to your dialog but makes the process more comppcated as it now requires coding. Cannot sell a cow and drink the milk at the same time, can you?

Amazon Lex lacks the semantic sophistication of their competitors. One can say (somewhat metaphorically) that IBM Watson was created by linguists and computer scientists while Amazon Lex was created by sales people. As a product it is well packaged and initially looks pleasing on the eye, but once you start digging deeper you notice the limitations. Also, Amazon traditionally excelled in voice recognition component (Amazon Alexa) and not necessarily in actual language processing.

The space of conversational AI is fluid and changes happen rapidly. The existing products are evolving continuously and a new generation of AI engines is in the process of being developed.