Machine learning systems , accurate facial recognition biometric technology and artificial intelligence concept. 3D Rendering of Man face and dots connect on face with blur city background.
Machine learning systems , accurate facial recognition biometric technology and artificial intelligence concept. 3D Rendering of Man face and dots connect on face with blur city background. (Photo Credit: www.shutterstock.com)

By Keith Fenner, vice-president, Sage Enterprise Africa & Middle East

Machine learning, which used to be something only computer scientists in server rooms discussed, has become a hot topic, along with big data and artificial intelligence (AI).

Machines develop algorithms that allow them to make predictions, such as the shows you might want to watch on Showmax or Netflix, to use a simple example. Machines will also update their models as new data is received, without human intervention.

Advances in machine learning have caused many to fear that machines will replace human jobs. However, I’d like to argue that it can offer enormous benefits to business, and that now is the time to invest in it.

Machine learning is all around us

Retailers can already successfully predict the performance of retail promotions using advanced machine learning. This not only maximises ROI, but also streamlines the inventory ordering process. Machine learning algorithms also enable effortless personalised marketing.

The financial services sector uses machine learning to detect fraud and provide pre-approved loan offers, while Google uses it to run its driverless cars. Brands use it to get a sense of what’s being said about them on social media.

The age of machine learning is here. In fact, 40% of businesses surveyed by the Accenture Institute for High Performance in a 2016 study are already using it to improve sales and marketing performance.

 What machine learning offers to Enterprise

Machine learning is proficient at handling analytical tasks within defined parameters. For example, companies use machine learning to track new leads, upsells, and sales cycle times.

Elliot Yama points out that there is huge opportunity in what he terms “Quote-to-Cash” solutions:

  • These encompass all the business processes involved in selling: from compiling initial offers right through to collecting payments.
  • Quote-to-Cash solutions connect many previously manual tasks and disjointed processes, automating and optimising them seamlessly.
  • They are now also helping to drive business outcomes across all sales channels and to optimise sales reps’ performance. For example, by improving quoting speed or the time it takes to generate a contract, machine learning can substantially improve salespeople’s chances of closing a deal.

But machine learning offers advantages beyond sales and marketing. It can be used to predict customer credit risk, to recognise text or speech (goodbye, painful data-capturing processes) and even to approve insurance claims quickly, without human intervention.

AI helps make work more meaningful

People will now have time to focus on other aspects of the business like driving innovation and exploring meaning. The beauty of machine learning is that it is able to do the work where humans can’t compete. A great example of machine learning at work is in the accounting industry.

AI can streamline accounting and compliance. It’s why we recently released Pegg, the world’s first accounting chatbot. Accounting is a perfect use case for automation, because it centres on repetitive, manual tasks.

By using AI to automate it, businesses are able to focus more on core business activities and on the human elements of their business.

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