Gaurav Tewari, founder and Managing Partner of Omega Venture Partners.
In 2022, the natural language processing (NLP) market was estimated to be worth $15.7 billion, and by 2027, it is anticipated to reach a value of over $49 billion.
NLP is a field of artificial intelligence (AI) that enables computers to understand, interpret and respond to human language without relying on specialized syntax or knowledge of programming. A natural language interface (NLI) enables a person to interact with computers in an intuitive manner without requiring any technical expertise.
Futurists have long imagined a world where we interact with complex computer software by simply saying what we want to do, and the rise of generative AI is finally making NLIs a reality. Because generative AI is trained on publicly available information and learns about patterns and semantic relationships within natural human language, it is a crucial enabler of NLI. Generative AI and NLIs are revolutionizing human-computer user interfaces and the user experience (UI/UX), augmenting workflows and democratizing access.
Democratizing Usability
I see NLI as the biggest revolution in human-computer interaction since the invention of the graphical user interface (GUI). New technology and processes are allowing us to grapple with significant pain points of traditional UI/UX approaches, including:
• Complexity: Users unfamiliar with a new software application or technology often find it daunting to learn how to properly navigate interfaces.
• Inflexibility: Traditional user interfaces do not adapt to different users’ specific needs, often taking a one-size-fits-all approach.
• Lack of intuitiveness: Interfaces that are difficult to understand or follow elevate user frustration, especially if the underlying application requires knowledge of a programming language.
Moving past the steep learning curves cited above, NLIs allow users to more easily interact with computers without having to learn to code or navigate a GUI. NLIs thus democratize usability by breaking down computational barriers via transparency, familiarity and technical ease. Some examples of NLIs enabling simplified interactions include:
• Communicating with customer service chatbots.
• Asking voice-activated assistants to augment mundane tasks.
• Providing queries to natural language search engines.
• Prompting a photo editor to edit a photo based on verbal descriptions.
• Requesting metrics from a business intelligence system without requiring knowledge of SQL or data wrangling.
You should note, though, that while the capabilities of NLIs are immensely beneficial, these models still struggle with handling ambiguity, understanding context and accurately responding in all cases. The technology will have to continue to evolve in order to meet current demand.
Industries Pioneering Natural Language Interfaces
From my experience leading a technology investment firm, here are some industries to watch when it comes to the growth of NLIs.
Customer Service
Enterprises continue to deploy NLIs in the form of chatbots to enhance customer service. These bots are powered by conversational AI, a subsector of generative AI that simulates human conversation. NLIs deployed in customer service can reduce the response time to a customer’s query by an average of 77%. According to MIT research, 80% of executives have reported substantial improvement in performance and customer satisfaction due to conversational AI.
Healthcare
NLIs are also helping to improve the accuracy, speed and efficiency of healthcare administration. By leveraging NLIs, healthcare professionals can now interact with medical systems using intuitive language, resulting in valuable insights due to this tool’s assistive capabilities for identifying and diagnosing. NLIs can also improve patient experiences by enabling self-help and personalized care delivery.
Financial Services
Generative AI is enhancing solutions across financial sectors, including risk and wealth management, fraud detection and financial education. Companies such as Morgan Stanley and Bloomberg have recently announced plans to incorporate GPT-4 into their products and services.
Future Trends And Resistance
Increasing proportionately to the market size of NLPs are chip capabilities, which have enabled NLP models to access exponentially more parameters and train on significantly more data. A 180-zettabyte increase in global data production is projected from 2020 to 2025. I believe that this enhanced quantity and quality of data will enable NLIs to train more robustly and efficiently.
However, the resistance to NLIs remains significant, largely due to the unique qualities of human-to-human interactions. For example, 23% of consumers still prefer live interactions instead of communicating with a chatbot about complex issues, and 60% of U.S. adults feel chatbots are being implemented too quickly with worries that NLIs will have similar issues with complexity and ambiguity as traditional UI/UX models.
To overcome resistance to NLIs, businesses can look to developing systems that blend human-like interaction with technical proficiency. This entails investing in advanced AI training to enable NLIs to handle intricate issues. Companies can also modulate the pace of NLI implementation to match consumer comfort, providing transparency to help address any concerns about complexity and ambiguity.
An Investor’s Perspective: Harnessing The UI/UX Revolution
The potential ROI when investing in generative AI and NLIs is striking but not all that surprising. Aberdeen Research found that businesses implementing AI effectively have seen up to a 3.5 times increase in customer satisfaction rates.
A business implementing NLIs into its products should first analyze key user pain points that adversely impact the usability of its products. Then, an NLI should be designed to directly combat those issues and provide end-users with an optimized and actionable experience. Once the system is implemented, you should continuously and iteratively refine your UI/UX.
Designing and implementing NLIs requires initial user research for training and testing before wide-scale use. I recommend that you start with simple tasks, progressively handling more complexity and continuously analyzing user interactions for improvements. Select NLP technology that will fit your needs and resources while maintaining transparency and communication with users to build trust. Like many things, successful NLI implementation is an iterative process of learning and adapting from user feedback.
Final Thoughts: Embracing the Shift In UI/UX
The enormous benefits unleashed by NLIs make their growth a foregone conclusion. To take advantage, I encourage businesses that want to enhance user experience, improve consumer retention and augment inter-company workflows to invest in this revolutionary technology.
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