Two Major Challenges of AI Adoption for Enterprises

By Konstantin Perederiy - April 7, 2021

The latest advancements in the AI space, particularly in natural language processing (NLP), are actively making a major leap in practical applications of AI. This includes activities such as using bots to guide customer experience, and there is no doubt that this trend will only accelerate. 

In fact, some agencies predict that by 2025, 95% of customer interactions be powered by chatbots in some way. A 2019 McKinsey study notes that 80% of respondents have already adopted AI and state that applications like chatbots play a key role in their customer strategy.  

However, when the term “chatbot” comes up, many of us immediately think “dumb robot.” This response is partly justified because most bots are not able to handle questions, which requires some level of intelligence or ability to process multi-step conversations. Those abilities have been hard to program for two reasons.  

First, it requires a true integration with back-end systems. This is an enormously complex task, due in large part to the challenges of navigating legacy landscapes, which has made it prohibitively expensive for many organizations. Second, to provide truly intelligent automated agents, companies need a powerful NLP engine behind them. Fortunately, the solutions to both challenges have already arrived.

#1 Integration Challenges 

The word integration is one of the most used in the IT community, as every project hits some bump in the road due to integration issues with backend systems. Solutions powered by machine learning are no exception. The integration challenges for this kind of project are even greater since algorithms feed off data, which resides in the legacy applications. Experts need to know specific ways to get relevant, rich datasets so that ML can yield impressive results.  

The solution to solving these integration challenges is obvious: organizations need the right combination of talent and the most applicable integration technologies to connect NLP features with their enterprise landscape to provide exceptional customer/chatbot interactions. 

#2 A Powerful NLP Engine 

Let us now look at the second issue, which is the use of a powerful NLP engine. Over the last few years, we have seen quite a few major improvements in the AI space, particularly in NLP. One of the most significant achievements in NLP was the introduction of OpenAI’s GPT-3 model in the summer of 2020. GPT-3 manages 175 billion parameters. By comparison, the previous version, GPT-2, managed 1.5 billion.  

Even those impressive numbers do not communicate the full scale of this improvement, so to help, Guardian published an essay explaining why humans should not fear AI. Impressively, the essay was fully written by the GPT-3 machine model. To take it one step further, a Berkley PhD student created a fully automated blog generated by GPT-3. The blog was ranked #1 by Hacker News and fooled tens of thousands of people. The only problem with GPT-3 is that its API is not currently available for public usage, but OpenAI promises to release a paid version soon.  

Leading the Way with AI Technology 

The idea of “human vs. machine” may seem inevitable when it comes to understanding the latest advancements in AI technology, but in the modern world, it is much better to think like human/machine collaboration.  

The future will not be one versus the other. It will be both, working together, and the global organizations and institutions that master AI technology will lead the way and quickly establish themselves as growth leaders.  

Many organizations have already embraced AI technologies at the enterprise level. At Customertimes, we don’t consider only the technology side of the problem. We consider the business implications as well, and we have mastered multiple ways to leverage the latest tech via the CT AI platform, which allows us to gain maximum business value from AI projects. Our team relies on a combination of existing landscape architecture and innovative AI solutions to unlock the full potential of machine augmented processes.   

We have extensive experience with chatbots and video recognition technology, and we have the deep technical expertise needed to fully integrate chatbots with your back-end systems effectively and cost-efficiently. This integration, when combined with the NLP model, allows you to realize the benefits of AI while protecting the customer experience.  

If you would like to learn more about Customertimes’ AI solutions, reach out here and let us discuss the latest in these powerful AI innovations.