Machine learning to beat the competition

Looking to give your business a competitive edge? Let the machines do the work for you…

While automation is commonplace across many industries now, from self-service checkouts to online banking, this is just the tip of the iceberg.

Paul Drake, director of operations at Teesside-based software firm Sapere, said: “Machine learning, that is, the branch of AI that gives computers the ability to gather, analyse and interpret data without human input, has huge potential, especially for more digitally mature businesses.”

Like many innovations, machine learning technologies are more prevalent in larger businesses; a worldwide survey of data professionals earlier this year found that larger companies had an adoption rate of 61 per cent, nearly twice that of their smaller counterparts (33 per cent).

What’s the big idea?

Paul explains: “Machine learning (ML) is all about probability; by applying clever algorithms to large, good quality data sets, predictions can be made on the likely outcome of a scenario that a human couldn’t feasibly achieve.

“It is precisely this large amount of data that makes ML so valuable; the technology can use such data to analyse trends and patterns, learning from them to refine itself continually with no human input – essentially, it becomes fully autonomous.”

However, to successfully analyse the data, ML is only achievable if a business has access to a lot of it – hence the increased adoption in larger companies.

One area in which ML is already proving its worth is in procurement, sifting through data to wean out potential problem tenders and reduce losses.

While a human is unable to process such vast amounts, ML allows software to sift through, spot patterns and act accordingly – saving time, effort and money.

A competitive edge

The advantages aren’t restricted to huge organisations with masses of digital data; the ability to predict potential challenges before they happen can have huge benefits on the ability to generate more profit and reduce the likelihood of a loss – something relevant to businesses of all sizes.

And it’s not just a white-collar application either, Paul says.

“In the construction industry for example, ML has huge scope to save time and cut costs. From identifying site risks from photographs to improving design quality, the potential is there – it just needs to be taken up.”

The barriers

While ML technology is making inroads in the business world, there is still a long way to go.

Paul believes that this is due, in part, to firms either not knowing about it, not knowing how it can be applied or not having a need for it.

“To make full use of ML and its true potential, it needs someone in the business to understand the potential for this data,” he added.

“This really highlights the necessity for each firm to have access to a digital expert at a high level within the organisation.

“These are the people who can spot the need and opportunities to monetise data assets within established businesses, creating new revenue streams or increasing profit from a service or product.”

What now?

One (the only?) upside of the recent pandemic and accompanying is the more rapid digitalisation of industries that had traditionally been somewhat resistant.

Paul said: “We’re seeing more and more businesses expand their digital horizons, so the adoption and application of ML will follow suit over the coming months and years.

“We have seen already that momentum is growing. Universities have started degree courses in ML to produce the data scientists the country needs to exploit these technologies.

“The more data businesses digitally collect, the more opportunity there is to do something clever with it – even in the most unexpected of industries, such as the work Google has been doing using AI and satellite data to prevent illegal fishing.”

However, Paul is also keen to stress that ML is only as good as the data it is supplied with.

“The adage of ‘rubbish in, rubbish out’ rings even truer where ML is concerned, as it can only work and learn from the data it has been supplied with.

“It goes without saying that if this data is poor, the results it predicts are likely to be at best useless and at worst, actively damaging.

“This is why companies need digital experts on board; to make sure they’re not just using data for data’s sake. A tech expert’s input can make sure any company, no matter their size or industry, makes the most of the data they have.”

Because ultimately, ML is about making efficiencies, learning from experience and cutting costs – and which industries can afford to miss out on that?

For more information about Sapere and its range of services, visit