Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! McKinsey's newly-released Technology Trends Outlook 2022 named applied AI and industrializing machine learning as two of 14 of the most significant technology trends unfolding today. According to McKinsey, the study builds on trend research shared in 2021, adding new data and deeper analysis and examining "such tangible, quantitative factors as investment, research activity, and news coverage to gauge the momentum of each trend." Applied AI, considered by McKinsey as based on proven and mature technologies, scored highest of all 14 trends on quantitative measures of innovation, interest and investment, with viable applications in more industries and closer to a state of mainstream adoption than other trends.

These and many other fascinating insights are from McKinsey's latest survey and report on AI, The State of AI in 2020. McKinsey's methodology relied on an online random sampling of 2,395 respondents representing a broad spectrum of regions, industries, company sizes, functional specialties and senior management tenures. One thousand one hundred fifty-one respondents say their enterprises had adopted AI in at least one function. For additional details on the methodology, please see page 13 of the report. What makes this report noteworthy are the insights into how enterprises who adopted AI early focused on creating revenue-based business cases are getting results today.

Artificial intelligence and automation are continuing to drill deeper into our society. Yet, there seems to be a disconnect between the people designing and implementing these systems, and those who will be most affected by the outcome. The reported median annual salary for an AI programmer in the UK in 2019 is currently around £60,000. Meanwhile, the reported median annual salary for all workers in the UK is reportedly around £36,611. Automating routine operations presents a lot of benefits.

Machine learning and artificial intelligence are integral components of any modern organization's IT stack but these data-harvesting tools can have a dark side if appropriate risk management and planning protocols aren't in place. There's no denying the power and possibilities created by AI and machine learning. With this astounding power to build models designed to improve the efficiency and performance of everything from marketing and supply chain to sales and human resources comes considerable responsibility. A recent McKinsey report sheds some light on how companies in every industry should be wary of assuming that these relatively new and remarkably complex tools will always deliver the desired outcome as they're integrated with other applications and processes. These tools are just like every other tool that's ever existed: they're only as good as the people designing and using them.

Robots helped build your car and pack your latest online shopping order. A chatbot might help you figure out your credit card balance. A computer program might scan and process your resume when you apply for work. What will work in America look like a decade for now? A team of economists at the McKinsey Global Institute set off to figure out in a new report out Thursday.

British companies that fail to invest in artificial intelligence soon are at risk of losing 20pc of their cash flow, McKinsey has warned. New figures claim that AI could boost the UK economy by 22pc in the next decade by making companies more productive, and fast-moving businesses could stand to grow in value by 120pc if they invest in AI tools. A new report produced by the consultancy's business and economics research arm McKinsey Global Institute has claimed the UK is "potentially more AI-ready compared with the global average", but could miss out on the opportunity if investment does not occur. "The United Kingdom has impressive pockets of innovation but is failing to scale to business more broadly," the report stated. It cited DeepMind, which was bought by Google for £306m in 2014, and Magic Pony, which was taken over by Twitter in a £102m deal in 2016, as examples of these pockets.

Anyone who uses Facebook can't have failed to notice that in recent years it has become rather good at recognising faces – it is as if the social network has been scrolling through photos of your friends for years and is now as familiar with them as you are. You may find this unsettling or remarkable, but the reality is that this is only the most visible manifestation of the rapid improvements in artificial intelligence under way across the globe – improvements that have, for example, led the US government to announce it is trialling facial recognition systems as a security measure at the White House, and sparked protests – on ethical and privacy grounds – against e-commerce giant Amazon for selling such technology. "The exciting part is that the results produced are free from bias, which improves confidence and relationships between clients and contractors" The artificial intelligence (AI) technologies behind facial recognition are machine learning, in which computers use algorithms to analyse data and learn without assistance, and deep learning – a similar but more advanced system based on "recurrent neural networks" – that replicate some of the ways the brain works, for example making decisions based on an ability to differentiate between pieces of information. This is smart stuff, but it's tech that the construction industry has been somewhat sluggish to seize on and apply to its own processes. But now it seems construction is waking up to the potential of AI.

The average worker of the future is a socially adept leader, entrepreneur, and life-long learner with transferrable technology skills, who is also happy to work in a team, suggests a new McKinsey report. Chris Middleton looks at whether organisations can really find such people. Reports about the growing IT skills gap in digitally enhanced organisations have been circulating for as long as the internet has existed as a business tool, suggesting that the supposed urgency of fixing the problem has not been an impediment to many successful organisations. However, a new report from management consultancy McKinsey suggests that the rapid introduction of automation and artificial intelligence systems within companies is changing the very nature of work itself, as technologies increasingly augment some human skills, and replace others completely. Over the next decade, this will force companies to reconsider how work is organised internally.

When Devaki Raj, the founder of the Mountain View-based deep learning startup CrowdAI, and Inc. 30 under 30 alum, learned that an artificial intelligence engineer she was trying to recruit was in the hospital, she didn't think twice about what she did next: Raj showed up at the hospital with flowers and balloons. She wanted to make sure the candidate got the message that her startup would be a very different atmosphere than what he'd get at a larger company. There's a talent war brewing between startups and big companies as both scramble to find top-notch artificial intelligence experts in a relatively small labor pool. "There's just not enough talent out there," Raj says. The number of experts in this field is not clear--Montreal startup Element A.I., which helps businesses build machine learning teams, estimates that there are some 20,000 PhD-level scientists around the world capable of building A.I. systems.

One of most significant difficulties confronting undertakings today is Data Security and Data Privacy. The rise of high information advances, for example, Big Data, Machine Learning (ML), and the Internet of Things (IoT) in the Data Management scene has now started another enthusiasm for Data Governance. With so much multi-channel information streaming into a joint association, the issues of Data Quality and Data Governance are accepting prime significance. The present undertaking information, on account of cutting-edge information advancements, is currently gathered, sorted out, and stored in multi-layered, investigation stages, which makes comprehensive details taking care of and Data Management techniques more unpredictable than any other time in recent memory. To put it plainly, endeavor information can't be viewed as reliable without a decent Data Strategy set up.