Navigating natural language processing for growth opportunities

AI can be a complicated topic with many sub-layers and nuances.

McKinsey defines it as: “Artificial intelligence is a machine’s ability to perform the cognitive functions we usually associate with human minds.” Simply put, AI is created by algorithms or automated rules, fed by data. The subsets of AI include natural language processing (NLP), machine learning, neural networks, deep learning, computer vision, and generative AI. With so much information and innovation in today’s business environment, this article explores how NLP opportunities are creating new ways of working and why they should be included in your technology roadmap.

Embracing human and computer-derived languages

Last year, there were 7,151 living languages reported around the world, which is a number constantly in flux. Languages are continually evolving with new words, meanings, and contexts because people and communities are changing, adapting, and innovating. Often, there are multiple definitions for the same word, syntax, grammar, sentiment, rules, and tone of voice, all adding to the complexity of languages.

Human language is the foundation for NLP, an AI-based technology where computers are coded to make sense of spoken or written words or sentences, similar to how our human brains work. NLP enables computers to understand our language. From a bird’s-eye view, the computer code searches and connects a multitude of data points to build context out of that data and develops algorithms (a set of switches). This type of approach is referred to as a “semantic search” — creating meaning out of unstructured data by making connections.

The NLP and Generative AI landscapes

Because interpreting any living or computer-based language is complicated, there is continuous room for improvement with this type of AI technology. However, for those who are using NLP, the impact can result in exponential and lucrative rewards for businesses — especially for companies without an army of resources and large budgets at their fingertips. In fact, by 2025 in the U.S., IDC predicts NLP is expected to dominate a large part of the projected $120 billion in yearly investment in AI.

In a 2022 Deloitte survey, Fueling the AI transformation, 94% of business leaders agreed that AI is critical to success over the next five years. Analysts and industry pundits agree that NLP opportunities have great potential because the technology can bring insights and context to data — much more than simple keyword searches that pull data but do not interpret its meaning.

Creating context opens the door to elevating top-level initiatives like customer experience, social and corporate responsibility, and lowering operational costs as well as day-to-day tasks like sending chatbot messages to the right department or spelling and grammar checking. One telling example is Grammarly, which is powered by NLP and has grown its user base to over 30 million.

So, while NLP has been ingrained in our daily lives for a long time, generative AI tools, which tap into NLP, like OpenAI’s ChatGPT, DALL-E, Midjourney, and Jasper, have become commonplace conversations at the dinner table and at work as well as in mainstream conversations among top executives on how to embrace and protect company data through policies, ethic committees, and other avenues. It’s taking over talk tracks at conferences in many industries with its plethora of use cases. Venture capital firms invested $1.4 billion in 2022, up 27% from the previous year, in generative AI alone, betting big on its potential.

Generative AI, using NLP, has made great strides in bringing search capabilities and context together by sifting through mountains of text to formulate intelligently written answers – but its accuracy remains remiss.

Using massive data sets to create algorithms, generative AI is capable of producing content in many forms — text, images, audio, and video — by predicting the next word or pixel. Its popularity is gaining as people are using it for everything from, social media posts, writing wedding speeches, and creating artwork and architectural designs, to debugging code, researching any topic, and creating personalized patient care plans and even pharmaceutical drug designs.

With tech companies embracing numerous NLP opportunities within their business applications, NLP is tracking for a whopping 39% compound annual growth rate (CAGR) from 2022 to 2030. It can be used in any industry, although financial services, retail, healthcare, and manufacturing sectors seem to be leading the AI and NLP race in their respective use cases.

Let’s get practical: finding growth areas

NLP is typically used to analyze colossal amounts of data quickly. So, where does your data live? Invoices, contracts, claims, medical records, lab reports, statements, forms, emails, call center logs, archives, transcripts, tax records, financial information, mortgage documents, and utility bills are common documents with large volumes of high-value data and great places to inject NLP technology into those workflows.

Most of these types of documents (80-90%) are unstructured data, meaning you can’t easily search for data or information because PDFs, images and physical documents don’t have that capability. The data is trapped. Documents and the data they contain must be transformed into structured data in order to be easily accessed and turned into insights — and this is where NLP comes in.

The technology can classify, extract, and export the data into a usable, structured format that the computer or human can then analyze. These steps eliminate manual steps and tedious work, with companies reporting tremendous efficiency, productivity, accuracy, and cost savings.

With accessible data and these kinds of results, technology using NLP has already been tapped as a solution to assist companies that have too much data and not enough employees. And recent and continuing talent shortages can be offset by automating and modernizing systems and processes.

Beyond unstructured data and document workflows, generative AI using NLP has uncovered many use cases, especially in healthcare, pharmaceuticals, manufacturing, media, energy and utilities, architecture and engineering, and automotive manufacturing, among others.

Next steps

NLP is ready for prime time. The value has already been identified and can be applied to many use cases — and it’s only going to get more advanced as data science and AI evolves. Now it’s time to expose gaps in your document processes and transform them. As you explore your options, you might consider intelligent business platforms, co-innovating with your technology partner, a proof of concept (POC), or just dive right in.

In our next innovation series topic, we’ll look at several co-innovation success stories that Ricoh customers embarked upon and how these NLP opportunities impacted their business. Read article #2 in the series.