Is there anything that Artificial Intelligence cannot do to tame data?

Data – dubbed as the new oil – demands tremendous attention in ways of collecting, collating, maintaining, and analyzing. Every possible digital process in this computerized world generates tons of data that needs to be structured to make sense of and ultimately analyzed with a single large purpose of servicing the customer in a way better than before.

At every stage of data from its origin to its consumption, humungous human intervention is required, but not until Artificial Intelligence (AI) strikes it.

From Alan Turing in the 1950s to researchers and scientists of today have been indispensable contributors to the evolution and sophistication of AI for the modern world. So much sophistication that today 85% of customer interactions are done through intelligent AI tools, knocking off human intervention, saving time and money.

International Data Corporation (IDC) in one of its predictions has pointed out that in this data-driven era, AI is becoming a priority for organizations globally, including India, as stressed times urge them to automate, finding a faster route to market.

IDC rightly points out that India is no different from other developed countries as today organizations are staring at a large set of unstructured data – a consequence of the digital era, and AI will be the backbone, unraveling & extracting data to derive valuable insights.

And the latest study by PwC reveals that business decision-makers feel really optimistic when it comes to using AI in the future.  The study indicates that more than 70% of US business leaders believe that AI is fundamental and absolutely necessary for the development of future business opportunities and to save operation costs. Just like Netflix, which saved $1 billion in annual revenue by deploying AI to improve its recommendations to users.

Netflix saved $1 billion in annual revenue by deploying AI to improve its recommendations to users.

The streaming platform giant Netflix recently revealed that it avoided thousands of canceled subscriptions by improving its AI-based personalized recommendation algorithm. Lauding AI and its impact on the business outcome, Netflix had said that on an average, users give up after 90 seconds when searching for a movie on the platform. But with the new changes made on the basis of AI-based data analysis, the company was able to accelerate the process and provide viewers with an offer closer to their choice. As a result, Netflix was able to save a lot of money on canceled subscriptions, which would have amounted to a massive $1 billion. Apart from personalization, Netflix deployed AI to better the streaming quality and also in its production efforts to ultimately deliver an enriched and enhanced user experience.

In the business of taming unstructured data

The multitude of digital platforms and omnichannel options to engage customers have certainly revolutionized the way businesses operate. Tremendous amount of data is generated every day in different formats. As said before, data from social media, text, images, videos all constitute a form of data today. More so, unstructured data is available in images, scanned and digital documents, voice and video formats. In the competitive landscape today, companies are looking to structure their data rather quickly using AI and Analytics tools to aid in decision making.

Dealing with unstructured data using AI is 'teX.ai', a SaaS product division of Indium Software founded by Ram Sukumar. Explaining why converting unstructured data to structured data is of paramount importance, Ram Sukumar, says, “Be it large enterprises or SMEs, structured data represents only 20% of the information available to an organization. Eighty percent of the data is in an unstructured form. If businesses are flourishing by analyzing only 20% of their data, imagine what can be done if they can make sense of the rest of the 80%. And, out of which nearly, 50% of the unstructured data is text. With an enormous amount of unstructured data being text, it should be a priority to analyze it and obtain business insights.”

If businesses are flourishing by analyzing only 20% of their data, imagine what can be done if they can make sense of the rest of the 80%.

As Sukumar rightly says, what can you do with heaps of data that cannot be analyzed and cannot contribute to decision making for an organization? However, unstructured data contains important information that can be harnessed for businesses to transform and enhance customer experience. Hence, there is an increasing need to convert unstructured data into structured data. One of the key aspects of digital transformation is the digitization of legacy documents and analytics on the text content. Text is the easiest form of data from which insights can be gleaned using text analytics tools and algorithms.

Text is the easiest form of data from which insights can be gleaned using text analytics tools and algorithms.

Sukumar says that researchers, real estate companies, investment bankers, and financial analysts have to pour over several annual reports on a quarterly basis, where summarizing the content and driving analysis can take considerable time and effort. Similarly, market research firms have a challenge establishing the veracity of the metrics and KPIs in their reports. E-commerce players have classification and optimization problems with the same products across different catalog categories. All of these kinds of critical business challenges are prevalent across several industries and need NLP and Text Analytics as a solution. This is where Sukumar found importance for his ‘teX.ai’ that provides enterprises an edge.

“Our relationship with our customers is not just a transactional engagement but we look forward to building long term alliances with them. So, when one of our customers operating in the retail sector approached us with huge amount of customer reviews in text format, we decided to solve their unstructured data problem. Going through thousands of reviews manually is an imposing task. We used our teX.ai product to convert unstructured text into a structured format,” discussed Sukumar.

There are many tools that can automate the process of entering data into an invoice application. However, the key to solving this problem is to use a tool like teX.ai which automates the extraction of invoice data fields thereby improving processing times, reducing human talent costs, and achieving improved accuracy and quality of results, says Sukumar.

Sharing some relevant info, Sukumar, says, “The global text analytics market was valued at $5.46 billion in 2019 and is expected to reach a value of $14.84 billion by 2025. The ratio of unstructured to structured data is overwhelming and firms across domains are employing text analytics solutions to make sense of the data they possess. A solution such as teX.ai is primed to take the market by storm now more than ever.”

Making sense of voice through AI

Data, obviously, at its source is always messy to deal with especially from enterprises, financial, retail, and healthcare industries. The unstructured data can be in any form – voice, text, images, etc. And where most of this data emerges is at call centers.

A Gartner analysis revealed that 71% of even the most digitally advanced companies are not helping customers with their most pressing questions and problems.

A Gartner analysis revealed that 71% of even the most digitally advanced companies are not helping customers with their most pressing questions and problems. With contact centers worldwide now managing a 100% remote workforce due to Coronavirus, a lack of quality training and call monitoring is even more problematic. The contact center floor once served as a way to monitor, train, and coach agents directly. Now, there’s a huge gap between what’s happening on calls, and what supervisors and trainers can really listen to. This is where Voice AI helps in a big way!

Envisioning a sea of opportunities in the space of Voice AI, Swapnil Jain co-founded Observe.AI, which is an AI-powered agent enablement platform for voice customer service that leverages the latest speech and natural language processing technologies.

“Observe.AI enables organizations to quickly analyze 100% of calls. With Observe.AI, support teams can improve call quality, monitor compliance, and coach agents into top performers. And my mission through Observe.AI’s is to transform the voice customer service industry by turning every agent into a best brand representative through AI-based insights and coaching,” says Swapnil Jain.

Voice AI is a technology that helps contact centers enable agents on 100% of calls with the coaching they need and insights on customer sentiment.

Voice AI is a technology that helps contact centers enable agents on 100% of calls with the coaching they need and insights on customer sentiment. It empowers organizations to transcribe and analyze 100% of voice calls for call quality and compliance and evaluate agent performance and offer targeted, data-driven coaching to agents, believes Jain. 

While knowing more about this from Jain, he indicated that his AI product has found tremendous relevance in today’s world that is facing the COVID-19 pandemic. “With this sudden shift to all-remote work, contact centers are in need of better ways to understand what's really happening on their calls while helping agents be empathetic and efficient from home. With Voice AI, quality analysts and supervisors can evaluate 10x more calls in half the time. This provides a much more accurate picture of an agent’s performance and leaves more time to provide in-depth coaching that drives behavior change."

Adding further Jain says, "Observe.AI currently leads the contact industry in its speech-to-text accuracy of 86%, which is more accurate than both Google and Amazon for support calls. This enables Observe.AI to transcribe every single support call with the highest precision, as well as understand customer sentiment from calls."

Citing an example from deploying his product for one of his clients, Jain, says, “Voice AI helps teams gain insights on not only what is being said on calls, but also how it’s being said, providing context that enables a deeper understanding of customer conversations. Getting insights on important keywords and tonality relating to Coronavirus was very relevant for today's scenario. For example, one of my clients, EmployBridge, the largest industrial staffing firm in the U.S., needed to ensure that interview candidates were not infected when they placed them in warehouses and on construction sites for jobs. When the word ‘coronavirus’ or ‘COVID’ was mentioned on calls, EmployBridge teams were able to get deeper context with Voice AI. Were callers sick, exposed, or just asking questions? What was the tone or emotion being displayed on calls? Employbridge was also able to up-skill phone coordinators to do more in-depth interviewing remotely, which was previously only handled by coordinators in the office.”

Drawing a conclusion

It is an obvious conclusion that both AI tools, Observe.ai as well as teX.ai, reduced time and manual effort required to process data, be it in the form of text or voice using AI and ML algorithms. Several such tools are needed to tackle complicated data emerging from other sources like videos, images and manufacturing processes.

Understanding the pulse of the requirement, Indian organizations and startups have geared up to prepare and procure AI. It is certain that Indian organizations are increasingly tapping into AI as they know meaningful products can be built and in turn deployed to make life-altering implications for the society.

While, today, AI is being used even at common households for day-to-day operations and by companies and academic institutions worldwide, the most promising opportunities are yet to be explored, and AI is inevitably needed to bring efficient processes in place.

And that is why India, recently, became one of the founding members of the Global Partnership on Artificial Intelligence (GPAI). India as one among the founding members will support the responsible and human-centric development and use of AI bridging the gap between theory and practice on AI by supporting cutting-edge research and applied activities on AI-related priorities – thereby setting the ground right for AtmaNirbharIndia.