7 AI Technologies To Power The Enterprise

by Sohini Bagchi    Aug 14, 2017

artificial intelligence

Artificial Intelligence is changing the way we think of technology. It is radically changing the various aspects of our daily life. Companies are now significantly making investments in AI to boost their future businesses. 

Ericsson, in the sixth edition of its annual trend report, ‘The 10 Hot Consumer Trends for 2017 and beyond,’ predicts that 35 percent of advanced internet users want an AI advisor at work, and one in four would like an AI as their manager in the next one year. Another study performed by Forrester Research predicted an increase of 300% in investment in AI in the next one year.

CXOToday finds out some of the most revolutionary artificial intelligence technologies that will rule in the next 1-2 years.

1. NLG and NLP

The goal of natural language generation (NLG) systems is to figure out how to best communicate what a system knows. The idea is to figure out exactly what the system is to say and how it should say it. NLG systems can take the ideas they are given and transform them into language. This is what Siri use to produce limited responses.

NLP systems look at language and figure out what ideas are being communicated. NLG systems start with a set of ideas locked in data and turn them into language that, in turn, communicates them. In other words, as Gartner’s recent Hype Cycle for BI and Analytics sums up, “While NLP is focused on deriving analytic insights from textual data, NLG is used to synthesize textual content by combining analytic output with contextualized narratives.”.

Alexa, Cortana and others are ushering in the era of intelligent personal assistants, helping to make everyday tasks easy and efficient for consumers. The enterprise is catching up, with conversational interfaces that are facilitating engagement across employees and to customers, raising the bar on how these systems communicate.

 As per a recent article in the Harvard Business Review, “Conversations with systems that have access to data about our world will allow us to understand the status of our jobs, our businesses, our health, our homes, our families, our devices, and our neighborhoods — all through the power of Advanced NLG. It will be the difference between getting a report and having a conversation. The information is the same but the interaction will be more natural.”

2. Speech recognition

High profile technology investor Mary Meeker recently dedicated a large section of her annual report on the state of the internet to lift-off voice-user interface technology.

This technology makes human interaction with computers possible through speech. While Voice-user interface (VUI) has been around for decades, technology such as Siri, Cortana and Amazon’s Echo has advanced to a point where voice recognition can be used as an authentication alternative to passwords. It has made massive strides over the years and its improving accuracy continues to raise its profile. It’s only a matter of time before accuracy is no longer an issue – for instance Google has announced that it’s working to ensure its speech recognition software will work with even the thickest of accents.

Voice recognition is also finding its way into the workplace, A number of banks are seeing the need for this and have introduced new voice related security measures. Also, consumer facing organisations are already starting to trial voice recognition as a method of authentication. 

3. Machine Learning Platforms

Machine learning is a subdiscipline of computer science and a branch of artificial intelligence. Its goal is to develop techniques that allow computers to learn. By providing algorithms, APIs (application programming interface), development and training tools, big data, applications and other machines, machine learning platforms are gaining more and more traction every day.

Companies are also investing heavily in ML/AI with hardware designs intended to greatly accelerate the next generation of applications. Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Some of the companies focused on this area includes Alluviate, Cray, Google, IBM, Intel, Nvidia.

4. Deep Learning Platforms

Deep learning is the fastest growing field and the new big trend in machine learning. A set of algorithms that use artificial neural networks to learn in multi-levels, corresponding to different levels of abstraction. Some of the applications of deep learning are automatic speech recognition, image recognition/Optical character recognition, NLP, and classification/clustering/prediction of almost any entity that can be sensed & digitized.

The combination of massive data, better algorithms and powerful GPUs led to a disruption in modern AI. In many cases, deep learning is now surpassing the capabilities of humans. Examples of this progress in the past year including Microsoft’s work on image recognition with the ImageNet database, Berkeley’s work on robotics, Baidu’s speech recognition services, and most recently Google DeepMind’s AlphaGo. And this has clearly reflected in its double digit year-over-year sales growth for three consecutive quarters. Analysts expect that trend to continue for the next two quarters, boosting its annual revenue of 22 per cent to about USD 6.1 billion. That represents a massive acceleration from its 7 per cent sales growth last year.

5. Bots and Virtual Agents

Bots are set to rule the world, believe modern scientists. While some of these concepts are overhyped and it’s still early stages, in the digital era, with businesses constantly looking at innovative technologies in the thirst to stay relevant, bot will become a game changer sooner or later.

Currently used in customer service and support and as a smart home manager, experts believe the present era is supposedly about bot apps. And this opportunity is turning out to be bigger and more user friendly than any app platform yet.

According to Microsoft CEO Satya Nadella, “AI-powered bots will become the next interface, shaping our interactions with the applications and devices - as bots are now learning in human context and the relevant thing for us is to make them intelligent as we learn from customers’ experience.”

6. Robotic Process Automation

Robotics Process Automation (RPA) is expected to gain greater acceptance in the Indian business. Sunil Aryan, Director Practice – Back office & Retail, Verint, explains how  Robotic Process Automation [RPA] or software robots are helping employees improve their productivity and other aspects of life and work.

India market has been quick to identify RPA’s potential. At present most large enterprises are either in the rollout phase or at the pilot stage. In specific industries like Shared Service Centers/ GIC the adoption is second only to America, which is currently the largest market for RPA.

RPA as a solution is capable of addressing any work segment that is Computer Systems and data driven, high volume, linear , rules driven, and repeatable. However the predominant adoption is by medium and large enterprises, because of the critical mass and scale of benefits it brings.

7. Biometrics

Biometrics as a technology has been in use for sometime but in limited ways. Its adoption in India, especially in the enterprise has not been as rapid. The advent of Digital India and especially the scenario, post-demonetization can speed up the process. BFSI and government are already deploying the technology and going forward, it will see more adoption in retail, healthcare and several other verticals. We may therefore expect that biometric devices will see a bigger demand in the enterprise.

A new report by Allied Market Research, titled, ”Global Biometric Technology Market: Opportunities and Forecasts, 2015 - 2022,” projects that Asia-Pacific is projected to expand at the highest CAGR of around 22% during the forecast period.