When past generations imagined the future, they pictured humans and machines speaking naturally to one another. While voice assistants like Siri and Alexa still fall short of that vision, they have come a long way from the days when talking to a machine was like pulling teeth. Here’s how mastering natural language processing (NLP) can boost your bottom line.

As we move further into the future, far more forays into the digital world will involve simple speech.

Not to be confused with neuro-linguistic programming (a kind of therapy), NLP is a field of artificial intelligence focused on making machines better at understanding human communication.

Most voice applications today perform simple speech-to-text transcription or text-to-speech synthesis.

However, NLP goes significantly deeper to understand, manipulate, and generate human-like language. In the past, interacting with a computer meant using a mouse and keyboard. That won’t change overnight, but many interactions will start to play out more like seamless human conversations.

To experience the true benefits of NLP, businesses will need to adopt these new technologies proactively.

Consider the annoying exchange at the start of most customer service calls, for example. Instead of having to navigate an endless maze of options by pressing buttons in response to prompts, NLP allows callers to talk to automated customer service agents.

To talk to automated customer service agents is innovative technology. Tech like this makes the experience faster, easier, and more organic, which is why 80% of customer service interactions are expected to be at least partly automated this year.

Conversational computers have countless applications, but the most significant benefit of NLP is less about voice applications and more about its ability to “read” and “understand” written text.

Instead of identifying keywords and taking educated guesses, computers can now understand the substance of text.

Understanding the substance of text includes valuable data sitting invisibly inside countless documents. Some estimates suggest companies that manage to use all the information hidden in their documents could see $430 billion in productivity gains. That could never happen without NLP, though.

Projections suggest the NLP market will grow to $26.4 billion globally by 2024, and that figure isn’t surprising considering what the tech can do. But the excitement should be tempered with urgency. That’s because in five years, not having NLP as part of your business will be like not having a website — which is unthinkable.

Tracking the Course of NLP

Development moves fast in this space. NLP models from just five years ago seem antiquated, and some of the biggest advances happened over the past 12 months.

During that time, the application of deep learning and large scale unsupervised learning techniques has advanced NLP models. Previously models were less than 100 million parameters, and now these have moved to 8.3 billion parameters (think of parameters as loosely synonymous with synaptic connections in the human brain).

The result is performance benchmarks capable of surpassing humans. To put it simply, NLP has become incredibly smart and is getting even more intelligent by the day.

The next challenge involves miniaturizing the technology to fit inside of smaller devices, such as smartphones. Once that happens, NLP will be able to analyze all the speech and text in the world.

Analyzing all the speech in the world includes documents, websites, articles, research papers, and anything else you can imagine.

As NLP consumes the entirety of human knowledge, it will lead to the creation of super-intelligent machines that understand every nuance of human conversation.

Human language includes humor, sarcasm, and context. The NLP machines will be able to draw upon vast amounts of knowledge to respond with incredible precision.

For example, developers at Airbus built a “robo-astronaut” to interact with crew members on an upcoming Space X flight.

Using advanced NLP, the robot can perform routine tasks, hold conversations, understand emotional cues, and display a distinct personality.

A robo-assistant is an assistant as well as a friendly companion — indeed something out of science fiction.

It will be a while before anyone invites robot companions into their offices and homes, but we’re well on our way to that reality. Regardless, NLP is ready to transform the relationship between humans and machines while creating a seamless link between the physical and digital worlds.

With sweeping changes coming, now is the perfect time to get ready.

Elevating Your Bottom Line With NLP

Embracing NLP in your business is a lot easier once you understand the tangible benefits instead of the abstract potential. With that in mind, here are a few ways NLP can elevate your bottom line over the next few years:

Optimize customer service. NLP optimizes customer service in two ways. First, it lets companies elevate their service levels by providing answers faster (through the web, chatbots, or voice applications like smart speaker apps), working in multiple languages, and handling higher-level questions. At the same time, NLP allows companies to spend less on human service agents, office spaces, phones, and other costs. With NLP, delivering exceptional service has never been easier or more affordable.

NLP optimizes customer service in two ways. First, it lets companies elevate their service levels by providing answers faster (through the web, chatbots, or voice applications like smart speaker apps), working in multiple languages, and handling higher-level questions. At the same time, NLP allows companies to spend less on human service agents, office spaces, phones, and other costs. With NLP, delivering exceptional service has never been easier or more affordable. Improve regulatory compliance. NLP will transform administration in countless ways because machines can now complete work that previously required human eyes. For instance, this is tremendously helpful with regulatory compliance. Instead of asking compliance officers to pore over oceans of data looking for potential violations, computers can automate the initial review and escalate potential irregularities to compliance officers. Better yet, this approach takes less time and leads to fewer mistakes.

NLP will transform administration in countless ways because machines can now complete work that previously required human eyes. For instance, this is tremendously helpful with regulatory compliance. Instead of asking compliance officers to pore over oceans of data looking for potential violations, computers can automate the initial review and escalate potential irregularities to compliance officers. Better yet, this approach takes less time and leads to fewer mistakes. Learn from customer data. Customers leave data everywhere — think online reviews, Facebook posts, direct emails, and more. Thanks to NLP, companies can easily process this wealth of information and use it to evaluate customer sentiments. More broadly, NLP lets companies glean more insights from customer data regardless of its source, size, or format.

Customers leave data everywhere — think online reviews, Facebook posts, direct emails, and more. Thanks to NLP, companies can easily process this wealth of information and use it to evaluate customer sentiments. More broadly, NLP lets companies glean more insights from customer data regardless of its source, size, or format. Augment text-intensive tasks. The first pass for a number of text-intensive tasks can be automated using NLP-based applications. This has already been successfully applied to spam detection in emails, but it has numerous applications within enterprises. For example, HR professionals and recruiters could reduce their workloads by intelligently sorting and categorizing heaps of résumés via automation.

The first pass for a number of text-intensive tasks can be automated using NLP-based applications. This has already been successfully applied to spam detection in emails, but it has numerous applications within enterprises. For example, HR professionals and recruiters could reduce their workloads by intelligently sorting and categorizing heaps of résumés via automation. Search beyond keywords. Most businesses rely on search technologies that are antiquated and based on keyword matching. This produces less-than-optimal results for customers, employees, and partners. Applying NLP to search produces results based on an understanding of the meaning of the query instead of just by matching the keywords.

Embracing NLP in 2020

NLP benefits all businesses, but that doesn’t mean you should rush to implement this technology.

Start by assessing your readiness and identifying what the technology is meant to do. For example, do you want it to improve service, cut costs, reduce churn, or something else?

Next, decide whether you have the resources to develop NLP-enabled applications in-house or need the help of a partner.

Finally, identify the source of the data you’ll use. Like all AI-driven innovations, NLP is only as good as the data it’s using to learn. Pretrained models are available, but you will still need to feed in specific data relevant to your company, customers, or industry.

If you’re still on the fence about NLP, consider it in this context: Language revolutionized how humans interact with each other, and it’s now doing the same thing for machines.

Nothing will be the same, yet almost everything will be better.