Top AI technologies for customer insight professionals


Towards the end of 2016, we have seen so many technology predictions by various market watchers. Most research agencies have cited Artificial Intelligence (AI) as one of the most promising emerging technologies.

Latest news from the tech world indicates that the industry-wide application of AI is in full swing and the technology is attracting investment from different verticals.

International Data Corporation forecasts that worldwide revenues for cognitive and artificial intelligence (AI) systems will reach $12.5 billion in 2017, an increase of 59.3 percent over 2016.

Besides, global spending on cognitive and AI solutions will continue to see significant corporate investment over the next several years, achieving a compound annual growth rate (CAGR) of 54.4 percent through 2020 when revenues will be more than $46 billion.

“Cognitive/AI systems are quickly becoming a key part of IT infrastructure and all enterprises need to understand and plan for the adoption and use of these technologies in their organizations,” said David Schubmehl, research director, Cognitive Systems and Content Analytics at IDC.

Technology watchers believe that AI will redefine the way businesses function. Market research firm Forrester says that AI is poised to completely reframe how businesses operate and consumers interact.

For customer insight (CI) pros, AI will soon share both the computational and sensory workload required for customer understanding.

Four emerging AI technologies – speech analytics, natural language generation, deep learning and AI-enhanced analytics solutions  – will allow CI pros to sense, think, and act on insights to degrees never before possible.

Speech analytics

By “listening” to client interactions, speech analytics identifies business-relevant trends in records of dialogue with customers. Soon, speech analytics tools will also be able to determine customer intent and sentiment — and even power interactive agents.

Solutions like NICE and CallMiner Eureka are prebuilt to analyze and categorize speech data based on a variety of topics.

According to a recently published research report, the speech analytics market is driven by factors such as growing adoption of contact centers, increased importance of voice in the multi-channel world, and increasing importance of compliance management in organizations.

The market is estimated to grow from $589.1 million in 2015 to USD 1.60 billion by 2020, at an estimated Compound CAGR of 22 percent from 2015 to 2020.

Adobe investing in machine learning, AI, VR

Natural language generation

Natural language generation (NLG) delivers text or speech to your customers at the point of interaction, delivering personalized attention at scale. Advanced NLG systems automatically generate required data and can explain reports in human languages.

According to Gartner, by 2019, NLG will be a standard feature of 90 percent of modern BI and analytics platforms, expanding the possibilities of deep learning.  Therefore, data analytics leaders should begin to integrate NLG with existing BI/data discovery and other tools.


Deep-learning platforms classify unstructured data without the manual inputs or supervision required from traditional machine learning.

DL platforms give CI pros access to deep neural networks for image and video classification, natural language processing, and facial recognition.

Vendors like Caffe, Clarifai, Google, and IBM offer pretrained APIs for image recognition.

Technology research firm Gartner recently noted that deep learning is becoming increasingly popular for both projects and hiring.

Part of the rapid evolution is a result of big research labs such as Facebook and IBM investing in the research.

Also Read: Strong growth expected in deep learning market

In the business world, about 30 percent of data science platform vendors have the first version of deep learning in products.

Amazon DSSTNE, Deep Instinct, Google TensorFlow, and Torch offer DL algorithms that let clients train deep neural networks using their own training data.

AI-enhanced analytics solutions

AI-enhanced analytics solutions use AI to customize the user experience by changing the interface, surfacing insights, and delivering alerts based on learned user preferences.

AI-enhanced BI solutions sense user behavior and can understand natural language. Many of them go a step further and can “think” — meaning they use machine learning to identify the most interesting key performance indicators (KPIs) and analyses for their users.

Tableau and TIBCO Spotfire have strong partnerships with Automated Insights for NLG, and solutions like IBM Watson Analytics and Salesforce Einstein Data Discovery possess this functionality.

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