The buzz on natural language AI is only getting more intense. And there’s a reason: Organizations that need the biggest return on their digital transformation dollars are quickly realizing that natural language AI is the best bet for a higher, even transformative yield.

A recent global survey revealed that utilization of natural language processing (NLP) is on the rise across market sectors. Sixty percent of respondents said their budgets for NLP increased by at least 10% from 2020 to 2021, with a third indicating an increase of at least 30%, and 15% saying they more than doubled their spending. Those budgets are already generating measurable value through improved customer experiences, deeper customer insights and higher quality decision making.

What language AI looks like in action

Much of the data businesses collect from customers is language-based, including emails, texts, live chats, and other conversations with sales representatives and service teams. But until relatively recently, it wasn’t possible to efficiently analyze, measure or interpret this kind of unstructured, disparate data.

Language AI is now capable of not only translating customer interactions and—with machine learning—understanding different types of communications (including the meaning of words and syntax), but it can also produce intelligent responses. Depending on the application, today’s natural language AI can:

• Recognize specific topics or products a customer might be inquiring about
• Analyze the overarching sentiment of a conversation (frustrated, happy, etc.)
• Scan content for specific categories of data
• Categorize conversations and cluster similar topics together
• Recognize proper nouns as either people, places or businesses
• Identify relationships between people, businesses and other named entities
• Personalize the language customers see for increasingly engaging experiences

The benefits of these capabilities are limitless. For example, a company that’s able to cluster complaints and frustrated communications about the same issue across channels can more quickly pinpoint a problem, outage or product bug. A healthcare organization that’s looking to identify trends in patient visits could use AI to protect patient data more effectively and efficiently, while keeping it anonymized. And a marketing organization can personalize communications across millions of customers by using AI to optimize the words, phrasing, and structure of individual assets—increasing conversion by as much as 40%.

Improve customer experiences

The No. 1 area leaders are seeing benefits from natural language AI is in creating better customer experiences, according to a PwC predictions report, such as in employing conversational AI in chatbots. When these language engines are trained to handle the most common and uncomplicated customer service issues, human agents can devote their energy to more complex concerns.

McKinsey reports that telecom companies have been among the heaviest adopters of language AI, precisely because of the customer service benefits of having smart, responsive and sophisticated interactive experiences. For example, one customer tasked Persado with crafting an interactive voice response script to direct callers into self-service channels rather than the call center queue, resulting in thousands of dollars in savings.

Surface deeper customer insights

Every enterprise knows they must do more with their first-party data. Customer data represents incredible value, which can be unlocked for the first time with the right technology. This is why more organizations are integrating language AI into their analytics processes to help clean up and parse data; for example, how customer data platforms can leverage language AI for customer sentiment analysis.

Language AI can interpret the emotional context of thousands upon thousands of interactions across time, enabling businesses to track customer attitudes toward products and services, the business itself, or other circumstances, as well as engagement levels. Investment management firms, in particular, benefit from these kinds of insights in their efforts to predict investment behavior.

As valuable as analysis is for direct customer communications, it can also be leveraged to evaluate interactions over social media and in online forums, giving leaders early, even predictive insight into potential changes in customer attitudes—an essential step in creating more personalized and targeted messaging.

An example of this principle in action comes from a Fortune 50 bank that used natural language AI to personally customize messages for credit card reward customers. The bank learned that customers who chose travel rewards responded to a different emotional tone than customers who redeemed cash rewards. When the AI fine-tuned the messaging to convey the right emotion to the right group, the campaign performed up to 60% better.

Empower transformative business decisions

When leaders have rapid, continual access to more accurate information, they’re able to make better, more strategic decisions at the pace modern business demands. Because language AI can interpret massive amounts of data that was formerly uninterpretable (e.g., verbal and written interactions from customers, responses, engagement, changing sentiment), this provides a strategic advantage to today’s leaders. It’s not just about making more informed decisions, it’s about the agility and speed that an AI with machine learning can provide to the business leader. Those types of advantages can present greenfield opportunities and lead to transformative results. 

Take the healthcare industry for instance. A McKinsey survey reveals that 30% of healthcare organizations are relying on language AI to improve operations, including using AI to scan patient records en masse in efforts to predict potential outbreaks of influenza or Covid-19.

Investments in digital transformations will continue to increase. But for the enterprise that needs to activate its digital investments, the key partner will be natural language AI. The new ability to analyze previously unmeasured and unusable language-based data, will not just simply (and critically) improve customer experiences, but it will deliver deeper insights into micro and macro trends fast enough that leaders can confidently make transformative strategic decisions.