AI, Machine Learning Are Top CIO Priorities: Experts

by CXOtoday News Desk    Feb 08, 2018

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Machine learning and artificial intelligence is becoming an integral part of  many businesses and would soon start pervading our everyday lives, believe experts. Earlier confined to sci-fi films and large industrial segments, AI technologies will be virtually pervasive in almost every new software product and service and will be one of the top five investment priorities for more than 30% of CIOs by 2020, predicted research firm Gartner last year.

AI/ML as top CIO priorities

Gartner defined AI as systems that change behaviors without being explicitly programmed, based on data collected, usage analysis and other observations and said that in May 2017, the term artificial intelligence was the seventh most popular term on gartner.com, thus indicating the rising level of interest among the company’s partner to use AI for their digital business strategy. The term did not feature in top 100 search terms back in January 2016.

Recently, MemSQL, a provider of the fastest real-time data warehouse, conducted survey of over 1,600 respondents, 61 percent, regardless of company size, indicated ML and AI as their companies’ most significant data initiative for next year, when asked to pick from several options likely to be important concerns in today’s climate. With big data and business analytics initiatives coming in at a close second (58 percent).

The majority of respondents (88 percent) indicated that their company already has, or has plans to, implement AI and ML technologies within their organization. Of those planning to implement ML/AI, nearly two thirds or 65 percent cited that a key aspect of adopting ML and AI was to enable more informed business decision making, underscoring the importance of these technologies for analytics. 

Nearly 75 percent of all respondents consider ML and AI to be a game changer, indicating it had the potential to transform their job and industry and an equal percentage of respondents actively using ML/AI indicating that creating new models was part of their short-term goals.

Another recent PwC survey of CEOs worldwide conducted in mid-2017 reveals that a majority of top executives agree that AI will ultimately impact every facet of business, offering an unprecedented opportunity to innovate and grow companies in nearly every industry. In fact, PwC estimates that AI will drive global gross domestic product (GDP) gains of $15.7 trillion by 2030.

The bigger concerns

The rapid advancement of AI technologies has however raised fundamental questions about social values and the potential unintended consequences of AI. For example, in the PwC report, business leaders also express a range of concerns about AI, starting with its potential to disrupt the companies that these executives operate.

Interestingly, the survey found that those most concerned about their businesses’ vulnerability to disruption are the leaders of two types of companies: those that are currently using AI (24% of the total number of companies surveyed), and those that are not yet using it and do not see it as a priority for their businesses (21%).

There is a learning curve involved in AI adoption, in which barriers and responsibilities are revealed the more one gains an understanding of the way AI works. Appreciating AI’s challenges and opportunities is an important part of setting your organization’s course. As noted in the research, the optimal approach to AI will vary from one organization to the next. Even within the same enterprise, executives will not apply AI in the same way to every business problem.

The experts defined four principal ways to apply AI, listed here in order of the simplest to the most advanced application:

Automated intelligence: Improves human productivity by automating manual tasks (e.g., software that compares documents and spots inconsistencies and errors).Assisted intelligence: Helps people perform tasks faster and better (e.g., medical image classification, real-time operational efficiency improvement).

Augmented intelligence: Helps people make better decisions by analyzing past behavior (e.g., media curation, guided personal budgeting, on-the-fly decision analysis). Autonomous intelligence: Automates decision-making processes without human intervention while also putting controls into place (e.g., self-driving vehicles, full-fledged language translation, robots that mimic people).

As humans and machines collaborate more intimately in the AI applications of the future, AI’s potential will grow in the number and variation of tasks it can complete. Self-maintaining factories and buildings, algorithm-based customer service representatives that sound and act human, and personalized offerings that anticipate customers’ needs are all feasible in tomorrow’s AI-enabled applications.

According to MemSQL analysts, ML and AI technologies spread through organizations, the need for data scientists and other technical professionals is growing. To make the jobs of these professionals easier, they will need to assess the technology infrastructure stacks already in place to support the new technologies. PwC researchers noted, essential to any strategy is determining the ideal mix of AI approaches. This will vary by industry, level of risk tolerance, and how quickly companies need to roll out AI initiatives to achieve their business goals.