Automated Intelligence (AI) is here to stay, of that we’re sure. But to begin thinking about AI in relation to your business, you must first understand the different types of AI doing the rounds out there, says Pommie Lutchman, CEO at Ocular Technologies.
An article titled `Distinguishing between Narrow AI, General AI and Super AI’ provides readers with a neat breakdown of where AI lives right now.
To summarise, we have Artificial Narrow Intelligence (ANI), also known as ‘weak’ AI, which is programmed to perform a single task. Then we have Artificial General Intelligence (AGI), which is referred to as ‘strong’ AI and talks to machines that exhibit human intelligence. Lastly, we have Artificial Super Intelligence, which is the coolest AI of them all and will ultimately surpass human intelligence in all aspects, from creativity, to general wisdom and problem-solving.
In business, AI will be able to fill many roles, offering numerous and varied benefits, but, at its most simplified, business AI is about three things: analysing data for better predictions, automating tasks that people don’t want to do, and optimising your business.
In the final analysis, the aim of any business is profit, and profit is commonly accepted as the difference between revenue and cost. AI can play a significant role in both of these elements, driving down operating costs of any business while increasing revenue.
Specifically, enterprise AI solutions and platforms can streamline operations, reducing the time, cost and effort for all but the most complex of tasks, while at the same time providing rich data and analysis around efficiencies, bottlenecks, processes and resources.
In addition, simple, mundane functions can be automated, intelligently and efficiently, using AI and Robotic Process Automation (RPA), so the more expensive, more resource-intensive warm bodies (humans) can be reallocated and repurposed into more complex data-modelling, scientific analysis and complex sales-focussed tasks, once again increasing revenue while driving down costs.
In one of its own case studies, Ocular developed and implemented an RPA solution, taking over a simple, high-volume, but mission-critical task through automation and data-modelling, and allowing the client to redeploy 95% of the human knowledge workers who were responsible for the same function.
At the close of the implementation, tasks that had historically taken anywhere between four and 72 hours to complete were now taking only three minutes. At the same time, workers who were repurposed into sales positions generated enough revenue to pay for the entire RPA solution within four months.
Costs dramatically decreased and revenue sky-rocketed for many consecutive months thereafter. What business owner isn’t working towards exactly these types of results?
The possibilities inherent in AI are endless, with futurists envisioning a world where humans and machines co-exist, with machines reinforcing human abilities. This would apply in business environments as well, but there’s no AI without IA (Intelligence Augmentation).
Rob Thomas, GM of IBM Data and AI, says in his blog titled `Scaling the AI ladder’: “AI requires machine learning, machine learning requires analytics, and analytics requires the right data and information architecture. In other words, there is no AI without IA. These capabilities form the solid rungs of what we call the AI Ladder – the increasing levels of analytic sophistication that lead to, and buttress, a thriving AI environment.”
So, companies looking to incorporate AI into their businesses in the future must begin considering their infrastructure today. Through the adoption of hybrid data management, unified governance and integration, and data science and business analytics, organisations of all sizes and all levels of understanding can begin to unleash the power of AI in the enterprise.
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