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 7,170 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. This is why the venture capital community’s investments in AI have climbed to more than 90% of all corporate venture capital deal value, amounting to predictions of nearing $1 trillion by 2028 and global enterprise IT spending expected to surpass $4.7 trillion in 2026.

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. NLP is also boosting the adoption of voice-enabled transactions, virtual assistants, autonomous loops, and agentic AI.

One telling example is Superhuman, formally Grammarly, which is powered by NLP and has grown its daily active user base to over 40 million with the platform analyzing 200 billion words daily.

So, while NLP has been ingrained in our daily lives for a long time, generative AI tools, which tap into NLP (used by enterprises like OpenAI, Google DeepMind and Anthropic) have become commonplace conversations at home, work and beyond. Mainstream conversations include how to embrace and protect company data through policies, ethic committees, and other avenues. It makes the daily news, woven into conference talk tracks, and discussed in boardrooms globally.

Generative AI, using NLP, dominates the market and 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 still remains to be perfected.

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, creating personalized patient care plans and even pharmaceutical drug designs. The possibilities seem limitless.

With tech companies embracing numerous NLP opportunities within their business applications, NLP is tracking for a whopping 38.7% compound annual growth rate (CAGR) from 2025 to 2030 with a market forecast of $439.9B. It can be used in any industry, although financial services, retail, healthcare, IT & telecommunication, and education sectors are 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. It’s a great place 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.

Next steps

If you’re not already looking into incorporating NLP technology into your workflows, the time is now. 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. It’s helping businesses close gaps in 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. When you’re ready, we’re here to help.

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.