One of the distinct advantages of AI is the ability to ingest unstructured data, be it text, imagery, video or more.
With advances in natural language understanding and processing and progressions in machine learning as regards the recognition of objects in images and video, data can be analysed in an automated fashion with no human involvement whatsoever.
And this data comes from everywhere: sensors used to gather shopper information, posts to social media sites, digital pictures and videos, purchase transactions, and cell phone GPS signals, to name but a few.
In fact, with access to these exabytes of public and private data, and through AI engines that can process text, video, voice and images (coupled with natural language understanding and generation, sentiment and tone analysis), cognitive processing and analysis can revolutionise the way in which we consume, create, and cohabit with current and future technologies – effectively blurring the line between human and machine processing capabilities.
Used in business, any repetitive task that requires a human to consistently and mundanely repeat it multiple times is a great candidate for the use of AI and Robotic Process Automation (RPA) solutions. And this won’t just be at the factory level – we’ll also see positions within fields such as accounting, law, and even medicine changing in the future.
Human labour will be reallocated and repurposed into more complex data-modelling, scientific analysis and sales-focussed tasks, once again increasing revenue while driving down costs. We are already seeing practical examples of AI all around us. As a case in point, one of the more prevalent implementations today would be text-based automated bots.
These automated bots can also be voice-enabled, like Google Home and Amazon’s Alexa. Having said that, there are companies that have accomplished much in the field of AI, such as Snips.ai, SoundHound and Rasa. These are companies that do not merely orchestrate cloud-based services, choosing rather to develop their own platform and address a specific niche market.
Take a practical approach towards AI
However, not everybody is building AI into their business strategies, mostly because it all seems so “out there” – too many talking about AI, not enough showing businesses how to implement it. So what can you do to find a workable approach to implementing AI?
The first step should be to look at existing manual, human-based services and functions, and secure a clear understanding of which of these can be automated.
Next, follow a clear and defined process of mapping out the required automation steps in detail. Once that is done, choose an AI platform and vendor that will help you map the required automation steps to a list of AI micro-services, formalising integration points and data structures, testing it all, and moving into production.
A practical working formula, as prescribed by Cobus Greyling, technical product manager: Design & Innovation at Ocular Technologies, has been to:
The final piece of advice is that you should have fun! AI inroads can be immensely satisfying and extremely gratifying when everything goes according to plan.
Enjoy making your own mark on AI rules and solutions, and you should find it to be a richly rewarding experience.