AI to increase productivity and economic growth

AI to increase productivity and economic growth

According to Accenture, artificial intelligence (AI) is expected to help increase labour productivity by up to 40% and potentially double annual economic growth rates by 2035. But for organisations to capitalise on these and other benefits, decision-makers must ensure AI effectively integrates into their existing operations.

AI must be treated like any other hyped technology; the focus must be on addressing specific business challenges, such as productivity, that provide a return on the investment needed and can lead to economic growth. Adding impetus to the rush towards implementing AI and its related technologies is the perception that the majority of early adopters have already achieved economic benefits, as outlined in the 2017 Deloitte State of Cognitive Survey.

Even so, AI hasn’t always enjoyed positive reviews.

Some see it as threatening employment. Gartner research shows that AI is expected to eliminate 1.8 million jobs by 2020. This number pales into insignificance compared to worldwide job losses caused by poor business models, uncompetitive labour markets, and wrong leadership decisions. However, the Gartner findings also reflect that AI will create 2.3 million jobs. If a business can take advantage of the possibilities to amplify human performance with AI, it will be well underway to secure future growth and empower employees with the tools needed to improve their skills in a digitally-led environment.

Take for example US-based online personal styling service, Stitch Fix. It recommends and delivers an outfit personally chosen for each customer monthly. AI algorithms trawl through thousands of clothing SKUs and automatically produce short lists based on each customer’s unique preferences from Facebook and Pinterest. However, human stylists ultimately make the final choice for the best fit from these short lists. Currently, Stitch Fix employs 2 800 stylists – jobs that would not have otherwise been available without AI.

Understanding AI

Today, businesses are taking advantage of tools based on Artificial Narrow Intelligence (ANI). This is where specific tasks are solved with software algorithms in a way that can seem human-like. These algorithms generally learn from large amounts of historical data, and eventually find patterns and trends which help them process new input, rather than being pre-programmed for every eventuality in advance. It is this new approach to AI, known as machine learning, that has produced the massive breakthroughs seen in the last decade – powered specifically by big data, always-on connectivity and cloud-based cheap compute and storage capabilities. Capabilities such as identifying spam emails based on the historical categorisation of spam by users, flagging of customers most likely to buy from a competitor, and even self-driving vehicles, are all powered by machine learning.

On the other hand, Artificial General Intelligence (AGI), or General AI, deals with software that can manage any general human task requiring intelligence. Apple’s charismatic co-founder Steve Wozniak once famously said that he would believe AGI has arrived when a robot can enter a stranger’s house and make a cup of coffee.

Importance of AI

AI is important to help solve problems that are too expensive or large-scale to address using human brain power alone. Already in the medical field AI is used to help tag diseases such as lung cancer from CT scans and X-Rays with far higher accuracy and capacity than human doctors. On the consumer side, AI applications have been developed to act as a ‘seeing eye’ that provides aural input to blind people based on their surroundings.

In food production, AI can help farmers improve yields by deploying drones to detect weeds and plant growth issues. It can also assist in processing soil sensor data to identify what impacts high production in certain soils. In energy, AI could integrate renewable energy sources into an existing grid more optimally and better cater for demand expectations at peak rates.

Clearly, businesses across industry sectors could harness AI to optimise systems and outperform competitors.

But fundamental to its success is its judicial use inside the organisation. Despite all the trendy perspectives around AI, the technology can still deliver on the fundamental requirements of business software such as reducing costs and increasing profits. Where it differentiates from traditional solutions is in the new approaches it enables decision-makers to embrace. And these are the areas where companies can really start excelling in as they head towards a digital future.

This article was originally published on ITWeb.

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