Improving experience via NLP, NLU, analytics-focused AI

The News: Twilio Inc. and Frame AI announced in late June a partnership to leverage AI to enhance customer engagement delivered within Twilio Flex. The use of Frame AI’s platform will help Twilio Flex, the company’s cloud-based digital engagement solution for personalized interactions across contact centers, sales, and in-app concierge, provide decision makers with key insights and recommendations to optimize their contact center operations and improve customer experiences. You can read the full press release here.

Improving Contact Center Experiences via NLP, NLU, and Analytics-Focused AI

Analyst Take: The announcement of a partnership between Twilio and Frame AI to leverage natural language processing (NLP), natural language understanding (NLU), and analytics-based AI to improve personalized interactions is a clear example of how, despite the hype surrounding generative AI, more mature machine learning (ML)-based AI will continue to power enterprise applications, personalization, insights, and recommendations.

Matching the Right Technology to Solve the Challenge

Although Twilio previously announced CustomerAI, which uses generative AI to summarize cases and share recommendations, this current announcement reinforces the relevance and importance of matching the right type of technology to the problem or challenge that needs to be addressed. Frame AI will enable Twilio Flex customers to utilize NLP and NLU to analyze customer intent and case severity, which can then be used to drive insights and recommendations that inform critical decisions.

The use of analytics-based AI, which essentially utilizes machine learning to crunch copious amounts of data, and then identify patterns within that data, is key to identifying points of friction, inflection points within interactions, and other operational or behavioral patterns that could be impacting the overall experience, from both a customer and agent perspective.

These insights and recommendations, whether positive or negative, can then be used to help leadership make the decisions that can improve operations by reassigning agents, based on their strengths or weaknesses, or through updating representative training, processes, or systems to better reflect the types of interactions they are experiencing.

Non-Generative AI Will Power Enterprise Applications for Years to Come

Perhaps most interestingly, it is refreshing to see an enterprise software vendor highlighting the value of technology beyond generative AI. Based on The Futurum Group’s forthcoming AI market model and forecast, four categories of AI use cases – process optimization, predictive analytics, visual and audio analytics, and pattern identification, analysis, and decisioning – are projected to generate more software and cloud compute revenue on an annual basis each year from 2023 through 2029.

The forecast data, which will be released by Futurum Group by the end of Q3, reinforces our belief that organizations across the spectrum will continue to invest in the AI technologies that can deliver meaningful real-world benefits at scale, with reasonable implementation timeframes.

Many of the generative AI use cases are still unproven, in terms of value, when compared against the costs of properly deploying the technology (which can include significant compute costs, training costs and time, and changes in the way human workers are asked to do their jobs). While some initial use cases (such as content summarization) may be easy to implement, utilizing generative AI for heavier lifts (such as supporting customer-facing, fully self-service applications) will require significant testing and the implementation of guardrails, which can impact ROI and slow time to market.

More mature AI technology use case categories, such as optimization, prediction, and analytics, have been in production for years, and have demonstrated the ability to drive impactful insights and recommendations, while also delivering real-world ROI. Notably, these use cases have also demonstrated the ability to drastically transform operations with less impact on staff and everyday procedures.

Disclosure: The Futurum Group is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.

Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of The Futurum Group as a whole.