How would you describe the Machine Learning (ML) landscape?

 The ML landscape growth will continue at an exponentially accelerated rate. Globally, companies are expected to invest over $265 billion in new intelligence technologies by 2023. Whether you’re ordering a book online, deciding what movie to watch or receiving a bank warning about suspected fraudulent activity on your credit card, ML is all around us. Machines now have the capability to not only execute set instructions based on algorithmic programming, but improve automatically and autonomously, through experience and the continuous injection of data. This has led to machines having the ability to make decisions without being specifically programmed to do so. This has huge implications for the world of business and society.

What are some of the standout developments in ML right now and how is this affecting business?

One of the biggest trends we’re seeing now is hyper-automation, where repetitive tasks are being performed by machines at a greater scale, timing, and accuracy than any human could ever hope to achieve. Consider the example of forecasting stock levels and ordering new stock in a warehouse or understanding documents using Optical Character Recognition (OCR). These capabilities are providing differentiated business value and competitive advantage for early adopters.  All businesses strive for improved efficiencies, greater levels of cost reduction, and greater profit margin, particularly in the wake of COVID-19 and the disruption it has wrought. Hyper-automation allows companies to focus on these strategic businesses while remaining adaptable and agile.

Is ML increasing efficiencies?

Absolutely, although there are teething problems; humans and machines are still learning how to work together. But already we are seeing ML driving efficiency in business in multiple ways.

As business processes become more digitised, companies have access to an ever-increasing stream of data which, with the help of ML, can be harnessed to automate different tasks. As this trend progresses, spending will decrease in the following areas: maintenance; payroll costs, which are now largely automated; raw material and quality control costs; equipment and machinery; and operating costs, which includes sales and marketing.

Although some have been resistant to embrace this new technology, slowly the light is dawning for SA businesses as to the extent of the savings they can achieve through AI and ML. Soon there will be an ecosystem of companies offering solutions that drive real business value and competitive advantage, based on these exponential technologies and giving support to those implementing them.

 How would you describe the evolving nature of man’s relationship with machine?


In the past, man was pitted against machine, but the new normal is not either/or, its AND. Gartner researchers posit that ML cannot match the human brain’s breadth of intelligence and dynamic ability to learn, yet. Instead, ML should be used to scope specific functions in business, particularly automating routine human activities. This is based on the current maturity of the technology and the ability of the technology to drive the best types of business value in todays complex multifaceted business environments.

The future will depend on a symbiotic relationship between man and machine, as Forrester analysts have picked up on. It won’t be a case of humans leading and machines doing the work. Instead, machines will match humans in terms of leadership, decision-making and even executive tasks.

The marketing intelligence firm IDC predicts that AI will be inescapable by 2025 and that about 90% of key enterprise applications will be driven by AI and ML. These applications will deliver incremental improvements to automate processes and replace rule-based techniques to enable applications to be operate more intelligently and dynamically.

How is ML developing based on real-time customer experience?

As far as customer experience (CX) is concerned, near real-time experiences are fast becoming the minimum requirement for most industries as companies are pressurised to build matching real-time customer experience signals. Forrester researchers predict that CX leaders will manage a portfolio of automation experiences, from the building and testing of data to the delivery and perceived value (or lack of value) of those experiences.

If a machine can memorise customers’ preferences and understand speech and text the more it’s used, we will be a step closer to achieving hyper-personalisation, streamlined processes and a memorable and ultra-convenient uniquely tailored customer experiences that truly differentiate brands.

What impact will ML have on employment? Will humans be replaced by machines?

This is a difficult topic to explore with any certainty. In a country like South Africa which is in desperate need of higher employment, anything that is seen to replace jobs is seen as a threat, which is understandable. But AI and ML won’t necessarily ONLY take people’s jobs away; they might just make them easier. If the boring part of your job could be done by a machine, it would free you up to concentrate on more important, higher-level work. Distinction must be drawn between tasks, jobs and work as a whole.

In a recent Gartner survey, 75% of respondents said they wanted AI and ML to help with tasks rather than completely take over tasks. That is probably because the same respondents said their top reasons for using AI and ML was automating repetitive or manual tasks, improving customer experience, and reducing costs.

As far as people are concerned, Forrester analysts have advised workers to learn core skills, adapt to new working models and understand what it means to be ready and fit for the future. This involves maximising your “Robotics Quotient”. In other words, it’s time to make friends with machines.