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AI’s disruptive reputation belies its potential for positive change

AI’s disruptive reputation belies its potential for positive change

Artificial intelligenceArtificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. () and machine are already changing how we work, how we shop and how we connect with each other. But their “disruptive” potential has caused much concern. A report by McKinsey Global Institute estimates that in about 60 per cent of occupations, one-third of the tasks could be automated – globally.

SwissCognitiveThe report states that can transform some business activities and has the potential to fundamentally change others. However, the story is just beginning to unfold and we do not yet fully comprehend the potential and/or impact of these technologies.

This impact, to the extent that it materializes, is massive in terms of how work gets done and what new jobs will be created in the economy. This opportunity is driving global investments in , and governments here in Canada are funding various initiatives such as the Vector Institute and its Supercluster Initiative. The lead private-sector funders of Vector represent various sectors including the banks, retailers, manufacturers and information companies. Concurrently, businesses are racing to create their own teams.

As practitioners who build -enabled applications, like most others our objective has never been to disrupt how work is done, rather to remove customer pain-points, to analyze complex tasks and ask how to achieve more with less while delivering an intuitive user experience. With this in mind, we see three broad types of opportunities for artificial intelligence.

Content automation

is already driving scale and automation in how information providers collect and aggregate content, how it’s enhanced, organized and delivered. Most creative work (content authoring) remains a manual and time-consuming exercise, but “functional writing,” such as contracts, will likely be automated.

Digital business

Digital business is the transformation of business activities and making them data-driven. Examples of where machine is heavily utilized include business intelligence and advertising. The future where companies can personalize their marketing programs to individual shoppers through their loyalty programs is closer than you think. Once retailers “know” their customers they will be able to use dynamic pricing and personalized marketing to achieve maximum returns. The transformative potential of with regard to supply chain management and forecasting is intriguing. Imagine the efficiencies achieved when manufacturers can accurately predict demand.

Enabling knowledge work

At a high level, knowledge workers do three things: (1) they search for information. is already heavily utilized in “find” technologies such as search engines. The challenge will be to enable machines to “learn” from a series of questions and to deliver intuitive context-preserving experiences while tackling information overload. (2) As workers find information they try to analyze it. Tools already exist to analyze and process data, but the real opportunity is the evolution from statistical analysis to deeper understanding of documents and data sets. Finally, (3) knowledge workers make decisions. To date, has been limited to playing a decision-support role. While likely a good thing, there are many instances where real-time requirements do not allow a human in the loop, such as fraud detection.

The opportunities are significant. However, in the near term we should expect broad yet incremental change. Boston Consulting Group surveyed more than 3,000 companies last year and found that while 85 per cent of them believed would become a competitive advantage in the future, only a quarter were implementing it now, and only 5 per cent were implementing it extensively. […]

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