Why NLP is the Next Frontier in AI for Enterprises

NLP has exploded in popularity in recent years. Even though it’s not the sole path forward for AI, it powers applications that help businesses interact better with customers and scale up. Here, we summarize NLP, its applications, the challenges it encounters, and, most importantly, how enterprises can leverage it to gain big. Let’s dig in.

Natural language processing (NLP) is one of the most promising breakthroughs in the language-based AI arena, even defying prevalent assumptions about AI’s limitations, as perOpens a new window Harvard Business Review. Its popularity is such that the global NLP market is anticipated to touchOpens a new window $43.9 billion by 2025.

But what exactly is NLP?

NLP is a branch of artificial intelligence that focuses on helping computers understand how humans write and speak. These systems capture meaning from an input of words and produce an output that can vary depending on the application.

– Rachel Roumeliotis, vice president of AI & data content strategy, O’Reilly Media

Now let’s move beyond the definition and learn more about NLP’s use cases, potential impediments and how exactly enterprises can use this AI-based technology to scale up.

Learning More About NLP, Its Application, Challenges, And More

One of the biggest advantages of NLP is that it enables organizations to automate anything where customers, users or employees need to ask questions. Roumeliotis cites an example – one of the stakeholders can pose a question to an NLP model through some sort of interface. With training and inference, the NLP system “should be able to answer those questions,” and in turn, frees up those “tasked with handling these sorts of requests” to focus on high-level tasks.

By understanding the human language, NLP can answer very basic, lower-level questions and answer them on behalf of the team. “We see this already in customer service chatbots, in which NLP is used to answer simple questions that result in immediate answers – which allows customer service reps to use their time to provide more value and dedicate more time to harder customer inquiries or issues,” the expert adds.

Now let’s figure out more such use cases that NLP could easily deliver to its stakeholders.

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NLP’s practical use cases

Enhances customer service and helps varied industries

Rajan Nagina, head of AI practice at Newgen Software, says that NLP and AI-powered chatbots or voice bots can enhance customer service 24*7, providing contextual responses to customer queries just like humans. For instance, a chatbot listening to a customer’s complaint can automatically escalate a ticket to an on-duty human supervisor. 

Enterprises across industries, including banking, insurance, government, and shared services, can also leverage NLP in their structured and unstructured document classification while extracting information and making informed decisions, Nagina adds. “Furthermore, it can analyze sentiments and help understand the customer’s journey, offer personalized products, and improve customer experience.”

Gathering unstructured data

Another use case, as mentioned by Roumeliotis, is gathering unstructured data and analyzing it. “There is a lot of automation that can help the everyday employees with different things, whether that’s writing or coding. The data we have is either spoken or in text. As time moves on and the technology becomes cheaper and faster, NLP will be a great way to gather unstructured data and analyze it. Eventually, NLP will become a de facto technology within organizations.”

Auto-completing a line of code

When paired with knowledge graphs, NLP even has the potential to assist with healthcare, says Roumeliotis. For example, NLP can power medical health lines, in which people can dial a number and list their symptoms. “Through processing spoken data from the caller, the NLP system can match up the listed symptoms with insights from the textual data contained in knowledge graphs to provide a diagnosis to the caller and arm them with actionable next steps to treat the symptoms.” As NLP already appears in our everyday life, like when we are writing a paper in Word or an email in Gmail. NLP is always at work to autocomplete sentences. “In the same way, NLP can be used to help developers autocomplete a line of code.”

The bottlenecks affecting NLP’s growth

Dave MacLeod, co-founder and CEO, ThoughtExchange, outlines the biggest challenge to NLP right now. NLP can be extremely dangerous in elevating algorithms that contain bias, including systemically racist ones, he says. He recalled the instance where Google Photos’ image recognition algorithms mistakenly labeled some peoples’ faces as those of animals. Even two years after the blunder came to light, Wired reportedOpens a new window that Google couldn’t fix the issue fully and only patched it up with an “awkward workaround.” 

“If the world’s most powerful tech giants struggle with this challenge, one can only imagine just how pervasive this problem is,” he adds. Whether NLP or any other AI technology, MacLeod believes the challenge will continue. We will have to frequently foster a greater awareness and knowledge of these types of dangers and combat them.

Roumeliotis highlights the money challenge that NLP could bring. “When we talk about NLP, we are talking about large language models like GTP3 that are processing insane amounts of data.” These large language models take a lot of time and money to train. And we are talking millions of dollars here, she adds. “In light of this, people are looking at fine-tuning models. These are smaller than GTP3 systems and focus on a certain area. For example, you can fine-tune a model so that it’s only trained on code for Python or financial data. This narrows down what the system needs to be an expert at and is one way to fix the amount of time and money it takes to train the system.”

See More: AI and NLP Tools Hold the Key to Modern Health Data Analysis

Can enterprises leverage NLP in its true sense?

MacLeod believes that when it comes to collective intelligence, NLP does offer an interesting potential for leaders to gather critical voices for effective decision-making. 

In the realm of truly open-ended question prompts, leaders, can tap into the primary advantage of NLP, which is rapidly sorting through a ton of words or data to distinguish them into larger subsets of categories

– Dave MacLeod, co-founder and CEO, ThoughtExchange

“This allows companies to surface themes and insights rapidly, and act decisively upon that vs. waiting months for the results of an annual survey to be tabulated and summarized,” he adds. 

However, “where my earlier maybe” comes in, is that – to leverage NLP to surface bias-free and truly reliable collective intelligence, organizations must layer into the “process of actual humans prioritizing contextual data” that matters to them. Questions such as: “Who are the respondents? Is this the right context for NLP? Has the data been prioritized – or is it focused on what’s more frequent?” need to be addressed.  Context is critical. Otherwise, quickly raising the more frequent data will produce nothing but fancy word clouds still tainted with bias. 

Reiterating how NLP technology can specifically help enterprises revolutionize their operations – MacLeod says that NLP’s potential lies in the summarization of contextualized and prioritized data, which has a compelling and near-term future.

When topics of strategic importance can be prioritized alongside a numerical system and NLP is applied against that, MacLeod thinks, organizations will have the ability to access valuable insights and true collective intelligence from customers, employees, and other stakeholders, distilling a great deal of information in real-time, which offers obvious dramatic benefits to enterprises.

Further, Nagina believes that AI equips enterprises with the ability to learn and adapt as data flows through the models. “Probably true pioneers of NLP have been Alexa and Siri.” We know that it is slowly getting “adopted in transforming processes and enabling employees” to be more productive. It has the ability to comprehend large disparate content and provide a summary or respond in real-time with contextual content to a customer, he states.

In conclusion – is NLP the next big thing for enterprises?

The biggest caveat here will remain whether we are able to achieve contextualizing data and relative prioritization of phrases in relation to one another. Also, being aware of and eliminating bias from the algorithms themselves. MacLeod says that if this all does happen, we can foresee a really interesting future for NLP. So, if you look at its use cases and potential applications, NLP will undoubtedly be the next big thing for businesses, but only in a subtle way.

Do you think NLP can be the next trend-setter in AI for enterprises? Let us know on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear from you!  

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