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We hear a lot about how emerging technologies such as artificial intelligence (AI) and machine learning (ML) are disrupting the future of work, but very often this is a high-level narrative that doesn’t dive into specifics in terms of the enterprise business functions being most impacted.

In this article, we’ll look specifically at the procurement function to examine how and where business processes are being transformed – and even disrupted – and some of the most impactful technologies involved. These include AI/ML, predictive analytics, blockchain and IoT, as well as AR/VR and intelligent agents. While procurement might be one of the last places you’d expect to find digital transformation, the fact is that it’s well underway and a sizable organizational opportunity for unlocking cost savings and operational efficiencies…

The demand for employees in the agricultural sector is a prime example of how sociopolitical climates can influence technological adoption. Californian berry growing company Driscoll’s, for example, has installed a strawberry picking machine in direct response to the need for labour. Across the globe, small to large scale farms are…

In the UK, traditional rail services have hit a crisis point. Passenger numbers are no longer rising, and it’s not difficult to work out why. Congestion, delays, and high ticket prices are just a few of the potential factors. Those who are not eligible for discounts, or don’t buy their tickets weeks in advance, can pay extortionate prices to get from A to B. In fact, it can be cheaper to fly from one city to another. Traditional train travel is in…

Andrew Burgess – one of DISRUPTIONHUB’s expert contributors on Artificial Intelligence – provided evidence to the All Party Parliamentary Group on AI

This article is adapted version of his talk.

AI is getting emotional. In fact, in an experiment conducted at The Ohio State University, a basic AI was able to recognise different emotions more accurately than human participants. The study of systems that can recognise, interpret and even recreate human emotion is known as affective computing. By 2022, the market is expected to be worth $41bn. But can technology really be emotionally intelligent, and what are…

The rush to adopt AI has certainly improved process efficiency, but the enthusiasm for the technology has meant that businesses have implemented intelligent systems that they don’t fully understand. Thankfully, there is now a distinct push to make AI more accessible and accountable. Rainbird, a rising AI powered automation platform, aims to…

By its very nature, insurance always comes with an element of uncertainty… But technology is taking the guesswork out of policy making – not just for the policy makers, but for policy holders too. In the face of digitalisation, consumer preferences and connected products, insurance has…

Despite the scope for possible points of contention, is there a chance that AI development could be united under one ethical banner? Andrew Burgess, AI strategist and consultant, and Louis Rosenberg, CEO of Unanimous AI, think…

New technologies can improve transaction monitoring in ways unimaginable in the past…

But banks can be very critical of transaction monitoring systems. With continuous cost, operational and regulatory pressures, they question their worth.

Transaction monitoring tools are supposed to find patterns that indicate possible criminal behaviours. But, for financial crime compliance managers, these systems produce too much noise to create any meaningful intelligence that helps financial crime investigations. As many as 80-90 per cent of reports have no operational value to criminal investigations. This is commonly known as generating ‘false positive’ alerts.

For engineers, some of the best inspiration can come from the natural world. From flexible soft robots which swim like jellyfish, to four legged robots which move like dogs, evolution certainly has the upper hand when it comes to mechanical design. Rolls-Royce has teamed up with robotics experts at Harvard University and the University of Nottingham in the UK to develop miniature bug like robots capable of inspecting and fixing aeroplane…

The availability of quality data in mass quantities has encouraged machine intelligence capabilities to grow at an exponential rate. According to research conducted by McKinsey, technologies that employ machine intelligence (including advanced robotics, automation, and the Internet of Things) will create $50 trillion in global value. Businesses are…

For almost three hundred years, we have lived in a largely industrial society. But today, thanks to the rise of disruptive technology and new ways of working, societies and economies across the globe are undergoing a process of change. Instead of becoming more industrial, a reduction in traditional labour roles is challenging established socio-economic models. This process is known as deindustrialisation, and is likely to…

3D printing with metal is a difficult business. The temperatures required to melt metals are in the range of 1500°C, providing understandable limitations to the spread of this technology. As metal 3D printing technology becomes more suitable for the mass market, DISRUPTIONHUB takes a look at five companies leading…

This January, the World Economic Forum (WEF) published its Internet of Things Guidelines for Sustainability. After exploring 643 applications of IoT technology, the WEF found that 84 per cent were addressing, or could potentially address, the UN’s Sustainable Development Goals. You might not initially think of the Internet of Things as CleanTech, but bringing greater connectivity to the environments we live and work in will…

The use of machine learning in finance can do wonders, even though there is no magic involved. Successful machine learning projects often depend on choosing the right datasets and applying the right algorithms. Let’s take a closer look at why this technology is a great fit for finance, what implementations it has in that domain, and how financial services companies can utilise it.

Machine learning is a subset of Data Science. While Data Science covers the whole data processing pipeline, Machine Learning is about using specific algorithms and chosen datasets to train mathematical models to find patterns, make predictions, segmentation, and more.

As soon as the model is trained, it can process data and produce results automatically, without interference from data scientists. You can retrain models to keep them up to date and efficient.

In simple words, machine learning solutions can learn from both experience and new data, thus improving results without being explicitly programmed.

This powerful tool has numerous applications in finance, as it fits perfectly with the quantitative nature of the financial services industry.

Four reasons why financial services mustn’t ignore machine learning…

Planning and running a city is a complicated business. Maintaining infrastructure, providing public services, and managing traffic are just some of the things city authorities have to deal with successfully, or else face the wrath of angry businesses and residents. Increasingly, city leaders are turning to technology to…

The automation of retail processes is an industry shaping trend, enabling retailers to offer improved products and services based on relevant insights. Automated commerce (acommerce) involves outsourcing retail experiences to algorithms and smart devices. It is geared towards…

Is technology the problem, the cure – or both?

The potential for entrepreneurs to disrupt industries and extinguish corporate laggards has never been greater. For entrepreneurial companies, disruption is inevitable, either outwardly from technology or internally from growth. But for larger corporates, most employees crave stability and rhythm and constant change creates stress and anxiety. Disruption may mean laying off redundant staff and/or re-skilling them for new jobs as firms deploy AI and automation to increase productivity, for example, leading to ongoing employee uncertainty.

According to the World Resources Institute, the global affordable housing gap is estimated at 330 million households… And that’s just in urban environments. By 2025, this number is expected to grow by 30 per cent, leaving 1.6 billion people without secure, affordable housing. Thankfully, the housing sector is undergoing a…

One way in which organisations have responded to the limited availability of data scientists is by empowering existing analysts with data focused technology. Enter the citizen data scientist. Citizen data scientists are employees within a business that…