The AI Revolution: How Will Artificial Intelligence Impact the World of Work?


So joked Disguise's Alex Wills during a recent roundtable event on the future of technology. But the sentiment rings true with the wider picture we have seen in recent months, with an explosion of interest in AI technologies and applications, and a number of large investors, not least Meta, seemingly shifting their focus from metaverse to AI.  

Gartner's hype cycle predicts that emerging technologies will reach a peak of inflated expectation before falling into a trough of disillusionment. By this measure, in popular perception and the press, the metaverse is falling into the trough of disillusionment, whereas AI is at a peak of inflated expectation. But will AI too fall into the trough of disillusionment following its moment in the sun? Or, as some are suggesting, will AI really be as significant as the industrial revolution? What will this mean for the world of work?



There is no shortage of arresting figures that illustrate just why everyone is talking about AI.  

ChatGPT, the overnight star of AI, has seen a meteoric rise to fame to say the least. It is the fastest growing consumer application in history, reaching one million users just five days after it was launched. The next fastest growing app is Instagram, which took two and a half months, whilst Facebook took ten months to reach the same milestone.



Even starker are figures predicting the extent to which AI will disrupt the world of work. In a recent study, Goldman Sachs forecast that AI could replace the equivalent of 300 million full-time jobs worldwide. With OpenAI, the company behind ChatGPT, reporting that the jobs most at risk are better-paid, desk-based jobs, it is clear that the workplace as we have come to know it could soon look very different. How long is it before employers look to automate certain tasks through AI platforms rather than hire new staff, and existing jobs are put at risk? 



Artificial intelligence is nothing new, with academic research into the field established as early as 1956. Some readers will remember the famous series of matches in the 1990s between chess grandmaster Garry Kasparov and 'supercomputer' Deep Blue, in which the computer eventually won, much to Kasparov's chagrin. But the relatively recent resurgence of interest in AI can really be put down to one fast-moving branch of development: machine learning (ML).

Kasparov vs Deep Blue (Source: NBC News)

Deep Blue itself (Source: IEEE Spectrum). The 1997 supercomputer’s processing power was bettered by the iPhone 4 released in 2010 

Traditionally, computer programs output an action when given an input command. If you type a specific phrase into a search engine, it will comb the internet and return websites that are relevant. ML systems, on the other hand, are designed to teach themselves through experience – much like we do as humans. They are fed an enormous amount of data, which they can interpret through algorithms to identify patterns and information. So when the same phrase is asked of an ML application such as ChatGPT, for instance, it 'thinks' about the answer by analysing the data, before framing a human-like response based on its findings.  

ML drives a wide variety of technologies, ranging from driverless cars to climate modelling. But there are several current applications that are interesting to consider in the world of work.



ChatGPT is an example of many ‘generative AI’ tools based on ML. These tools are able to solve problems and create content in a way which is remarkably human in its output. Tools such as Midjourney and DALL-E produce high-quality images from a written prompt. Below you can see an image produced by DALL-E based on the prompt "A photo of Michelangelo's sculpture of David wearing headphones djing." There are emerging tools which can make high-quality music and video content based on simple input prompts. The music, film and content production industries are understandably scrambling to figure out how to approach this.

                              Source: OpenAI’s DALL-E 

Equally concerned are other sectors, ranging from finance to coding, from HR to customer service. Very soon many tasks that are currently believed to require human intervention will be accomplished in a fraction of the time, with minimal human input.  

Driverless vehicles could soon replace drivers of taxis, delivery vans and even heavy good vehicles. Chatbots will become the norm for customer service, as you will probably have come across on websites already. The service industries on which London relies will see automation in tasks like data analysis, research and strategy, creating upheaval in the financial and legal sectors. It’s difficult to think of a job that won’t be affected. This potential impact hasn’t gone unnoticed by governments. US Congress has recently questioned chief executive of OpenAI, Sam Altman, about possible routes towards regulation of the industry. 

Already we have seen AI based on ML creep into everyday technologies such as Microsoft Teams. Teams has adopted ChatGPT itself to power its transcription and translation functions available to Premium users, whilst all users benefit from echo cancellation and real-time video optimisation that is powered and constantly improved by AI.



In coming years, we expect to see increasingly widespread adoption of next-generation AI technologies in the physical workplace.

“An abstract painting of artificial intelligence” produced by OpenAI’s DALL-E 

If you have been through a UK airport in the past few years, you’ll be familiar with facial recognition systems. In the workplace, these systems can allow access to employees without the use of fobs or key cards, and in the near future we could see visitor management platforms integrating with facial recognition, meaning visitors would need to offer a picture of themselves in advance for appointments. Facial recognition remains controversial as some2 have raised concerns about how their data and privacy might be affected.

Intelligent building management systems (iBMS) use ML to analyse data sets about a building in order to automate, optimise and predict workflows for services such as HVAC, lighting and energy management. The system will continually improve and adapt as it learns from trends in the data, meaning the building will essentially run itself. 

360 Workplace is partner to leader in the intelligent building sector Metrikus, who are continually incorporating ML into their technology. Keith Jump, CPTO, says “We see structured and unstructured data and the connections and insights we can generate through this information within the platform as critical for our clients to measure success and generate true return on investment. ML will inform our Predictive Data modeling strategy and services, so we can provide our customers with data-driven insights that inform business-critical decisions in corporate real estate and other sectors that need to manage their buildings more efficiently."  

Soon, it will not be only the building itself that will be automated. In fact, the experience of each and every person in the workplace will be curated to their preferences. The iBMS system will adjust the brightness of a room to a person’s desire ahead of their meeting; it will set the sit-stand desk to the correct height for them; it will set the temperature at their specific desk to what’s just right for them. All without any manual intervention.



AI’s impact on the world of work cannot be overestimated. Although the power and ability of the tools we now have in our hands can seem daunting, there is almost immeasurable potential for a root-and-branch overhaul of work, the economy, even life itself.

Textile Mills in Yorkshire (Source: Jasper Littman) 

Although, as noted earlier, a huge number of full-time jobs may be at risk, studies by the World Economic Forum and Gartner amongst others have predicted that, on balance, AI and automation will create more jobs than are displaced. For existing jobs impacted by AI, there is potential to use the technology to streamline and automate processes that previously have taken a lot of time and money to achieve. To take an example from our recent round table event, Hywel Glynn-Jones, of content-creation firm Do Digital, is harnessing the power of AI to run tasks such as coding. Rather than burying his head in the sand, he is embracing the change and using it to his advantage.  

Shrewd workers in the textile industry whose jobs were displaced by the machines of the industrial revolution soon found opportunities in the mills as operators and mechanics. So too can the desk-based professionals of the present day explore the opportunities of the AI revolution.  


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