Professional services firm, Accenture, has launched an Applied Intelligence platform at Mobile World Congress, Barcelona, which helps organisations make use of artificial intelligence (AI) without the need for deep data science expertise. The platform aims to allow organisations to apply pre-configured self-learning industry solutions, as well as develop new solutions. Accenture's team of industry specialists and more than 3,000 data scientists have already developed a range of 40 intelligent industry solutions for the platform that helps bridge the gap between information technology and operational technology. These include asset and fleet management solutions expected to unlock efficiencies across manufacturing, transportation and logistics and energy. Examples from the platform's suite of intelligent industry solutions include: The Applied Intelligence Platform builds on the proven Accenture Insight Platform through which Accenture has been offering analytics applications using machine learning and deep learning to clients. It will integrate these capabilities with edge analytics and Internet of Things (IoT) services, as well as access to more than 350 data sources, all made accessible via an on-demand, low-code platform.

As artificial intelligence (AI) technologies accelerate business transformation, with more decisions shaped by machine-learning (ML) algorithms, responsible use of these powerful tools is paramount. Moreover, appropriate governance must be in place to achieve desired outcomes. To help organizations manage and evolve AI responsibly, KPMG has introduced AI In Control, a framework supported by a set of methods, tools, and assessments to help organizations realize value from AI technologies, while achieving imperative objectives like algorithm integrity, explainability, fairness and agility. Increasingly, algorithms are making vital decisions that impact our lives. Their ubiquity is pushing society to demand that AI technologies be reliable and ethically sound, according to Professor Sander Klous, PhD, global lead of KPMG's AI In Control offering, and a partner with KPMG in the Netherlands.

A new global study finds that while the use of artificial intelligence (AI) is low today, it is expected to create increased revenue, productivity, profitability and shareholder value for businesses within two years. Overall, only 16 percent of business leaders surveyed perceive they are currently garnering significant value from advanced AI. However, AI programs are being fast-tracked and that number is expected to more than triple to 52 percent within the next two years. This is according to a new report, "Competing in the Cognitive Age: How companies will transform their businesses and drive value through advanced AI," by global consulting firm Protiviti in collaboration with ESI ThoughtLab. To understand how companies will transform their businesses through applications of advanced AI technologies and the benefits they expect to gain, Protiviti and ESI ThoughtLab conducted a global survey of 300 senior executives across functions, industries and company sizes in the third quarter of 2018.

Accenture has purchased a minority stake in artificial intelligence-based data analytics platform vendor Splice Machine as part of a broader strategy aimed at bolstering its new data analytics services. Splice Machine is open source and is built upon the popular Apache Hadoop, HBase, and Spark distributed platforms. Moreover, its solutions can be deployed on-premise or as a fully managed cloud service. Accenture already has a relationship with the San Francisco-based company. Last year, Accenture integrated Splice Machine's intelligent application platform into its analytics-as-a-service solution.

Futurism has always fascinated me. When I was young, I would pore through science-fiction novels and dream about how far-flung technologies could improve our lives. As an adult now working in tech, I've been able to explore what the world's leading minds believe the coming decades will encompass. To my delight, it appears the real world is finally catching up to the sci-fi fantasy. Robots, autonomous vehicles and smart machines are ubiquitous in classic science fiction.

One of the latest advancements for business development tools is the advent of augmented analytics. As per a report from Deloitte "Augmented analytics marks the next wave of disruption in the data analytics market". It is an approach that automates insights using machine learning and natural language generation. Gartner predicts "by 2020, more than 40% of data science tasks will be automated", resulting in increased productivity and broader use by data scientists. According to Accenture, "1 out of 3 insurers globally now uses Big Data from IoT technologies, such as Fitbit, Samsung Gear or Apple watch to collect lifestyle data from insureds". Augmented Analytics will help reap business value from those data by automating Big Data insights.

Most executives know that artificial intelligence (AI) has the power to change almost everything about the way they do business--and could contribute up to $15.7 trillion to the global economy by 2030. But what many business leaders don't know is how to deploy AI, not just in a pilot here or there, but throughout the organization, where it can create maximum value. The "how" is the sticking point with any emerging technology, and AI is no exception. How do you define your AI strategy? How do you find AI-literate workers or train existing staff?

AI is certainly being used to attempt to solve many of the world's big problems, such as health treatment, societal security, and the water shortage crisis. But Everest Group research suggests that 53 percent of enterprises do not – or are not able to – differentiate between AI and intelligent automation and what they can do to help them compete and grow. This trivialization of AI is both eye opening and frustrating. While it's true that automation of back-office services is one strong case for AI adoption, there are many more that can deliver considerable value to enterprises. Examples we've researched and written about in the past year include intelligent architecture, front-to-back office transformation, talent strategies, and AI in SDLC.

Most of what's out there is either too fluffy or too mathy, either too general or too focused on specific applications, too disconnected from business outcomes and metrics, and too undirected. Jerry Hartanto leads the AI and Self-Service BI Practice at Trace3, a technology solution provider with growing consulting practices including data intelligence, cloud solutions, cyber analytics, devops, and data center solutions. Hartanto's background is in management consulting, corporate/business strategy, marketing and sales, operations and process improvement, and product development and engineering. He has a BS in Electrical Engineering from McGill University, an MS is Electrical Engineering from Johns Hopkins University, and an MBA from the University of Michigan. He can be reached at jhartanto@trace3.com.