No longer a nascent innovation, natural language processing (NLP) has reached a technical maturity that can be relied upon to solve complex business challenges. Today’s NLP applications go beyond simple chatbots, and are used in complex business processes like claims, email management and robotic process automation. Teams who are looking to achieve ROI from their NLP investments need to consider how and where they will apply NLP-driven applications into their business. So, what does it take to ensure the success of your NLP projects?
View All the Language in Your Enterprise as a Form of Data
Before we answer that, it’s important to focus on the core element of the challenge that NLP addresses: language.
In the enterprise, language is how we transmit data to each other, and it’s pervasive. All the ways we communicate and how we share knowledge is in the form of language: emails, chats, documents, reports, notes. As a result, digital technology has produced a massive increase in unstructured data, which includes pictures, videos and language data. This data carries a tremendous amount of potential information depending on how it is used. The trouble is, the ability to understand language is one of the most difficult challenges in AI.
Natural language processing is the key that unlocks the potential of AI to comprehend and utilize unstructured language data. It bridges the gap between humans and technology so that we can leverage existing data assets for new insights that were previously unavailable. In fact, analysts see NLP as technology at the forefront of delivering on the promise of AI. According to Forrester, “70% of data and analytics decision-makers whose firm is adopting AI say they expect their firm to use natural language technologies in 2021.”
Choose Solutions for Real-World Data Sets
Many NLP experts responsible for adopting and implementing projects in the enterprise first go to deep learning and machine learning as techniques available to develop these solutions. A major problem when implementing such techniques for NLP is that they require a significant amount of text data for training purposes. However, the data available in the enterprise for complex, domain-specific use cases is usually insufficient for these techniques. There simply aren’t enough relevant documents to effectively train a system, or the related privacy and data sharing issues make them unavailable or difficult to use.
Given the continued growth of unstructured language data in the enterprise, today’s organizations need to be able to select the AI technology or combination of approaches that fit the business case and the data sets available—not the other way around.
This need is reflected in the concept of Hybrid or Composite AI, which Gartner defines as “the combination of different AI techniques to achieve the best results.” According to Gartner, by 2024, “70% of organizations relying solely on machine learning for AI initiatives will spend more money per model than those leveraging composite AI techniques.”
Domain Knowledge Matters
When it comes to NLP, there is growing acceptance that a “best of all worlds” approach produces optimal results:
Andrew Ng, AI Visionary, Founder and CEO of Landing AI: “a departure from the ‘let’s throw more data at the problem’ approach often taken by AI today, pointing more towards approaches based on curation, metadata, and semantic reconciliation. In other words, there is a move towards the type of knowledge-based, symbolic AI that preceded machine learning in the AI pendulum motion.”
Afraz Jafri, Director Analyst, Gartner: “By 2024, companies that use graphs and semantic approaches for natural language technology projects will have 75% less artificial intelligence technical debt than those that do not.”
The latest enhancements to the expert.ai hybrid NL Platform provide even greater levels of integration with enhanced taxonomy management via third-party sources, integration of standardized libraries and GPT access.
A single approach rarely solves complex language problems. NLP issues require the full range of AI approaches – including machine learning, deep learning, knowledge-based and large language models (LLMs) – to enable the most cost-effective and accurate solutions in a single environment for a range of real-world applications. Also, NLP platforms can provide low-code orchestration capabilities, allowing users to define the entire end-to-end process from document ingestion to API deployment of their language solution.
Don’t Start from Scratch
Today, AI, and specifically, NLP projects have turned from being experimental to being a crucial step companies must take to stay competitive. Organizations must learn to use these technology advancements to be better, faster, smarter and more efficient than the rest…otherwise, they’ll miss out on opportunities that their competitors will take.
Organizations must think of these projects as any other mission-critical technology investment—which they are. In many cases, there are technical teams and individuals who are dedicated to and passionate about creating and deploying AI solutions, and they certainly don’t want to be cut out of the equation. In fact, these technical users should be empowered, as should business users, to find the best solution for the problem at hand. Talented individuals surely CAN build on their own solution. The question is whether an organization trying to solve a business problem SHOULD build on their own.
“There are a few cases where building a text analytics solution from scratch will be worth the investment of time, budget and staff… in most cases, Forrester recommends buying a commercial solution.” – Boris Evelson, VP and Principal Analyst, Forrester Research
That’s why we say that teams don’t have to start from scratch. Why not provide a platform that gives everybody a valuable head start? Why not let the technology groups focus on finding new ways to integrate NLP into business workflows, building new connectors to more systems that can utilize multiple models and deliver value to other lines of business? The flexibility of our platform will ensure businesses can continue to pick the best combination of tools as AI language technology evolves.
“Bottom line: If you’d like to have the cake and eat it too — have a future fit, adaptable NLP solution, that is accurate out of the box, only requires moderate support, and can continuously learn and self-improve — look for an NLP solution based on hybrid AI.” – Boris Evelson, VP and Principal Analyst, Forrester Research