The Yokohama platform release features teams of preconfigured AI agents designed to deliver immediate productivity gains. These agents operate on a single, unified platform, ensuring seamless integration and coordination across different business functions. The platform also includes capabilities to build, onboard, and manage the entire AI agent lifecycle, making it easier for enterprises to adopt and scale AI solutions.

Data is the lifeblood of AI, and ServiceNow recognises this by expanding its Knowledge Graph with advancements to its Common Service Data Model (CSDM). This expansion aims to break down barriers among data sources, enabling more connected and intelligent AI agents. By unifying data from various sources, ServiceNow’s platform ensures that AI agents can operate with a comprehensive view of the enterprise, driving more informed decisions and actions.

The growing need for ‘Guardian Agents’

According to Gartner, by 2028, 40% of CIOs will demand ‘Guardian Agents’ to autonomously track, oversee, or contain the results of AI agent actions. This underscores the growing need for a coordinated, enterprise-wide approach to AI deployment and management.

ServiceNow’s Yokohama release addresses this need by serving as the AI agent control tower for enterprises. The platform removes common roadblocks such as data fragmentation, governance gaps, and real-time performance challenges, ensuring seamless data connectivity with Workflow Data Fabric.

Unlike other AI providers that operate in silos or require complex integrations, ServiceNow AI Agents are built on a single, enterprise-wide platform. This ensures seamless data connectivity and provides a single view of all workflows, AI, and automation needs.

Amit Zavery, President, Chief Product Officer, and Chief Operating Officer at ServiceNow, commented: “Agentic AI is the new frontier. Enterprise leaders are no longer just experimenting with AI agents; they’re demanding AI solutions that can help them achieve productivity at scale.

“ServiceNow’s industry‑leading agentic AI framework meets this need by delivering predictability and efficiency from the start. With the combination of agentic AI, data fabric, and workflow automation all on one platform, we’re making it easier for organisations to embed connected AI where work happens and both measure and drive business outcomes faster, smarter, and at scale.”

New AI agents from ServiceNow aim to accelerate productivity

ServiceNow’s new AI Agents are now available to accelerate productivity at scale. These agents are designed to drive real outcomes for enterprise-wide use cases. For example:

• Security Operations (SecOps) expert AI agents: These agents transform security operations by streamlining the entire incident lifecycle, eliminating repetitive tasks, and empowering SecOps teams to focus on stopping real threats quickly.
• Autonomous change management AI agents: Acting like seasoned change managers, these agents generate custom implementation, test, and backout plans by analysing impact, historical data, and similar changes, ensuring seamless execution with minimal risk.
• Proactive network test & repair AI agents: These AI-powered troubleshooters automatically detect, diagnose, and resolve network issues before they impact performance.

ServiceNow AI Agent Orchestrator and AI Agent Studio are now generally available with expanded capabilities to govern the complete AI agent lifecycle.

These tools help to streamline the setup process with guided instructions, making it easier to design and configure new AI agents using natural language descriptions. Their expanded performance management capabilities include an analytics dashboard for visualising AI agent usage, quality, and value—ensuring that AI agent performance and ROI can be easily tracked.

At the core of the ServiceNow Platform is Workflow Data Fabric, enabling AI-powered workflows that integrate with an organisation’s data, regardless of the system or source. This fabric allows businesses to gain deeper insights through AI-driven contextualisation and decision intelligence while automating manual work and creating process efficiencies.

The Yokohama release continues to expand ServiceNow’s Knowledge Graph data capabilities with enhancements to its Common Service Data Model (CSDM). CSDM provides a standardised framework for managing IT and business services to accelerate quick, safe, and compliant technology deployments.

Several customers and partners have already seen the benefits of ServiceNow’s AI solutions. CANCOM, Cognizant, Davies, and Sentara have all praised the platform’s ability to drive efficiency, cost savings, and productivity. These organisations have successfully integrated ServiceNow’s AI agents into their operations.

Jason Wojahn, Global Head of the ServiceNow Business Group at Cognizant, said: “At Cognizant, we are helping companies harness the next phase of AI with agentic AI workflows that could bring unparalleled efficiency. We were the first to bring ServiceNow’s Workflow Data Fabric to market and are working to help our clients to seamlessly connect their data with AI.

“With the Yokohama release and the integration of AI agents onto the Now Platform, clients can now operate their agents virtually effortlessly with connected data, driving productivity and ROI across their entire business.”

Darrell Burnell, Group Head of Technology at Davies, added: “Agility is essential for Davies, given our work with clients in heavily regulated markets. We’ve transformed our agent experience with ServiceNow’s generative AI, deploying Now Assist for ITSM in just six weeks to streamline information retrieval and accelerate resolution times.”

ServiceNow’s Yokohama platform release is a step forward in the evolution of AI for business transformation. By unleashing new AI agents and expanding data capabilities, ServiceNow aims to empower businesses to achieve faster and smarter workflows to maximise impact.

(Image by Thomas Fengler)

See also: Opera introduces browser-integrated AI agent

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According to the platform’s published strategy, the company will focus on three key principles – AI Blocks, Product Power-ups, and a Digital Workforce. Its aims are to “accelerate its vision to democratise the power of software.” By making its software more accessible and usable for its users, monday.com hopes to address the challenges encountered by businesses and work forces in teams of all sizes, streamline workflows and increase productivity.

The latest AI strategies are designed to help small and medium-sized businesses (SMBs) and mid-market companies grow and adapt quickly without the need to hire more staff. According to monday.com, AI will boost process times that are typically slowed by the scale of larger enterprises and Fortune 500 companies.

In line with monday.com’s stated goal to “democratise access to AI,” the platform’s approach is to be user-friendly, allowing any users regardless of technical knowledge to create, customise, and use AI tools in their workflows.

Daniel Lereya, Chief Product and Technology Officer at monday.com spoke about the company’s approach to making software accessible to everyone. “By embedding intelligence into the products our customers already know, use, and love, AI will accelerate our mission to democratise the power of software,” he said.

“With a majority of our customers in non-tech industries, they’re looking to us to lead them through the AI transformation. Our productisation of AI ensures that intelligence serves our customers and not the other way around.”

Of the three principles ‘AI Blocks’ are customisable AI tools that can be added to existing workflows, regardless of technical knowledge. The AI Blocks feature capabilities like “Categorise” and “Extract,’ letting users analyse data, and recognise patterns in a few clicks.

The second principle, ‘Product Power-ups,’ refers to the integration of AI features directly into monday.com’s existing suite of products. This is designed to help the company’s customers address challenges in areas like CRM data automation, resource management, predictive risk management, and real-time service ticket resolution. The features could help teams make quicker, more informed decisions, streamline workflows and increase efficiency.

Finally, ‘Digital Workforce’ is a collection of AI agents that operate to support users and customers. Digital Workforce is can handle specific tasks that are automatable, including project risk analysis, the identification of ongoing customer service issues, and helping move delayed sales deals forward.

monday.com is planning the launch of ‘monday Expert’ in March, its first AI agent that’s designed to support the onboarding of new users, and perform some tasks on behalf of users.

The company claims its AI capabilities have had a notable effect, with reports of monday.com users performing around 10 million AI-driven actions in 2024. The numbers of AI use instances nearly tripled each quarter of 2024, indicating a rapid rise in the use of AI in the CRM platform.

(Image: “Monday – Back to work!” by tinto is licensed under CC BY 2.0.)

ion Summit: Leaders call for unity and equitable development

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The report places the UK ahead of its G7 counterparts in terms of AI adoption but also underscores areas ripe for improvement, such as support for SMEs, fostering cross-sector partnerships, and investing in talent development.

Zahra Bahrololoumi CBE, UKI CEO at Salesforce, commented: “Agentic AI is revolutionising enterprise software by enabling humans and agents to collaborate seamlessly and drive customer success.

“The UK AI Readiness Index positively highlights that the UK has both the vision and infrastructure to be a powerhouse globally in AI, and lead the current third wave of agentic AI.”

UK AI adoption sets the stage for agentic revolution

The Index details how both the public and private sectors in the UK have embraced AI’s transformative potential. With a readiness score of 65.5, surpassing the G7 average of 61.2, the UK is establishing itself as a hub for large-scale AI projects, driven by a robust innovation culture and pragmatic regulatory approaches.

The government has played its part in maintaining a stable and secure environment for tech investment. Initiatives such as the AI Safety Summit at Bletchley Park and risk-oriented AI legislation showcase Britain’s leadership on critical AI issues like transparency and privacy.

Business readiness is equally impressive, with UK industries scoring 52, well above the G7 average of 47.8. SMEs in the UK are increasingly prioritising AI adoption, further bolstering the nation’s stance in the international AI arena.

Adam Evans, EVP & GM of Salesforce AI Platform, is optimistic about the evolution of agentic AI. Evans foresees that, by 2025, these agents will become business-aware—expertly navigating industry-specific challenges to execute meaningful tasks and decisions.

Investments fuelling AI growth

Salesforce is committing $4 billion to the UK’s AI ecosystem over the next five years. Since establishing its UK AI Centre in London, Salesforce says it has engaged over 3,000 stakeholders in AI training and workshops.

Key investment focuses include creating a regulatory bridge between the EU’s rules-based approach and the more relaxed US approach, and ensuring SMEs have the resources to integrate AI. A strong emphasis also lies on enhancing digital skills and centralising training to support the AI workforce of the future.

Feryal Clark, Minister for AI and Digital Government, said: “These findings are further proof the UK is in prime position to take advantage of AI, and highlight our strength in spurring innovation, investment, and collaboration across the public and private sector.

“There is a global race for AI and we’ll be setting out plans for how the UK can use the technology to ramp-up adoption across the economy, kickstart growth, and build an AI sector which can scale and compete on the global stage.”

Antony Walker, Deputy CEO at techUK, added: “To build this progress, government and industry must collaborate to foster innovation, support SMEs, invest in skills, and ensure flexible regulation, cementing the UK’s leadership in the global AI economy.”

Agentic AI boosting UK business productivity 

Capita, Secret Escapes, Heathrow, and Bionic are among the organisations that have adopted Salesforce’s Agentforce to boost their productivity.

Adolfo Hernandez, CEO of Capita, said: “We want to transform Capita’s recruitment process into a fast, seamless and autonomous experience that benefits candidates, our people, and our clients.

“With autonomous agents providing 24/7 support, our goal is to enable candidates to complete the entire recruitment journey within days as opposed to what has historically taken weeks.

Secret Escapes, a curator of luxury travel deals, finds autonomous agents crucial for personalising services to its 60 million European members.

Kate Donaghy, Head of Business Technology at Secret Escapes, added: “Agentforce uses our unified data to automate routine tasks like processing cancellations, updating booking information, or even answering common travel questions about luggage, flight information, and much more—freeing up our customer service agents to handle more complex and last-minute travel needs to better serve our members.”

The UK’s AI readiness is testament to the synergy between government, business, and academia. To maintain its leadership, the UK must sustain its focus on collaboration, skills development, and innovation. 

(Photo by Matthew Wiebe)

See also: Generative AI use soars among Brits, but is it sustainable?

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The report paints a picture of a country on the brink of a seismic shift in its economic landscape. With current productivity levels languishing 24% below pre-2008 projections, the promise of AI-driven efficiency gains offers a glimmer of hope for businesses and policymakers alike.

According to the study, large businesses in the UK could save a staggering 7.9 billion employee hours annually through the strategic implementation of AI technologies.

Breaking this down to an individual level, the numbers are equally impressive. Business leaders stand to save 1,117 hours per year – equivalent to 140 working days – while individual employees could reclaim 737 hours, or 92 working days.

“Sizeable productivity growth has eluded UK workplaces for over 15 years – but responsible AI has the potential to shift the paradigm,” explained Daniel Pell, VP and country manager for UK&I at Workday. 

The report’s findings come at a time when political figures are also weighing in on the role of technology in governance.

Former Labour Prime Minister Tony Blair recently commented that while Britain faces economic challenges, advances in technologies like AI mean there has “never been a better or more exciting time to be governing.”

Despite the optimistic outlook, the path to AI adoption is not without obstacles. The report highlights that 93% of both employees and business leaders harbour concerns related to trust in AI. This underscores the need for responsible AI strategies, comprehensive education, and transparent communication initiatives.

Other barriers to AI adoption include fears over safety, privacy, and bias (38%), the need for more time to educate teams (34%), and lack of investment (32%). Additionally, the report identified unengaged employees (41%), lack of incentives (41%), and inadequate technology (35%) as key factors hampering organisational productivity.

The potential economic impact of AI is staggering. Based on the study’s findings, an additional 2.9 hours of work per day translates to £11,058 a year of added value for each average employee. With over 10 million employees in large businesses across the UK, the cumulative effect could reach £119 billion worth of productive work annually.

However, the report also reveals a productivity paradox in the current workplace. In an 8-hour workday, employees and business leaders are genuinely productive for only 5.8 and 5.9 hours respectively—leaving over a quarter of the day unproductive.

The promise of AI extends beyond mere time savings. By taking on mundane and repetitive tasks, AI has the potential to empower workers to focus on more meaningful and impactful work. This shift could address one of the biggest barriers to productivity identified in the report: unengaged employees.

As UK businesses stand at the crossroads of this AI revolution, the report serves as both a wake-up call and a roadmap. It suggests a two-pronged approach to AI deployment: a concrete analysis of potential efficiencies coupled with a transparent strategy to tackle adoption barriers.

Realising the full potential of AI in the UK economy will require a concerted effort from businesses, policymakers, and employees alike. The successful integration of AI technologies could well determine the UK’s economic trajectory for years to come.

A full copy of Workday’s report can be found here (registration required)

(Photo by Belinda Fewings)

See also: Tech executives confident in AI skills, but adoption barriers persist

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This is according to a study conducted in early 2024 by Coleman Parkes Research and sponsored by data analytics firm SAS, which surveyed 300 US GenAI strategy or data analytics decision makers to pulse check major areas of investment and the hurdles organisations are facing.

Marinela Profi, strategic AI advisor at SAS, said: “Organisations are realising that large language models (LLMs) alone don’t solve business challenges. 

“GenAI should be treated as an ideal contributor to hyper automation and the acceleration of existing processes and systems rather than the new shiny toy that will help organisations realise all their business aspirations. Time spent developing a progressive strategy and investing in technology that offers integration, governance and explainability of LLMs are crucial steps all organisations should take before jumping in with both feet and getting ‘locked in.’”

Organisations are hitting stumbling blocks in four key areas of implementation:

• Increasing trust in data usage and achieving compliance. Only one in 10 organisations has a reliable system in place to measure bias and privacy risk in LLMs. Moreover, 93% of U.S. businesses lack a comprehensive governance framework for GenAI, and the majority are at risk of noncompliance when it comes to regulation.

• Integrating GenAI into existing systems and processes. Organisations reveal they’re experiencing compatibility issues when trying to combine GenAI with their current systems.

• Talent and skills. In-house GenAI is lacking. As HR departments encounter a scarcity of suitable hires, organisational leaders worry they don’t have access to the necessary skills to make the most of their GenAI investment.

• Predicting costs. Leaders cite prohibitive direct and indirect costs associated with using LLMs. Model creators provide a token cost estimate (which organisations now realise is prohibitive). But the costs for private knowledge preparation, training and ModelOps management are lengthy and complex.

Profi added: “It’s going to come down to identifying real-world use cases that deliver the highest value and solve human needs in a sustainable and scalable manner. 

“Through this study, we’re continuing our commitment to helping organisations stay relevant, invest their money wisely and remain resilient. In an era where AI technology evolves almost daily, competitive advantage is highly dependent on the ability to embrace the resiliency rules.”

Details of the study were unveiled today at SAS Innovate in Las Vegas, SAS Software’s AI and analytics conference for business leaders, technical users and SAS partners.

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In 1997, IBM’s Deep Blue supercomputer was used to beat World Chess Champion Garry Kasparov. At the time, all too familiar headlines suggested that computers would soon replace humans. Over two decades later, AI has proven to be an assistive tool that benefits us every day.

IBM Watson’s first commercial application was announced a little over a decade ago in February 2013 for utilisation management decisions in lung cancer treatment. In the years since, we’ve seen it used to deliver game-changing advancements in healthcare, weather forecasting, education, science, and much more.

AI News caught up with Jay Migliaccio, Senior Product Manager for Watson Orchestrate, to learn how IBM is now using its vast experience to help businesses with their digital transformations.

AI News: So, Jay, can you tell me how IBM is helping businesses to improve the productivity of their workforces?

Jay Migliaccio: Yes, Ryan. Thanks so much for the invite and for asking me here.

IBM is expanding its suite of offerings in the area of digital labour. Digital labour leverages AI and automation to help workers become more productive. And, much like human labour, digital labour performs work on business systems through “skills”.

Digital labour skills enable digital labour to interact with business applications, much like you and I would interact with a system of record or system of engagement. We can do this now through digital labour. And, what’s new and unique, is that digital labour leverages the human-centric interaction style.

So, we’ve introduced natural language and we’ve also introduced intelligent orchestration to be able to execute not just single skills, but actually multiple skills to be able to achieve higher-level tasks.

AN: Generative AI is a hot topic in the market at the moment. Do you see that being used practically in the workplace and what risks should businesses be aware of?

JM: Yeah, great question. I actually do use it myself in the workplace, I occasionally have to develop software tools and simple scripts and I have had it generate a number of scripts for me successfully. So I’m impressed not just with its ability to generate verbal and written content, but also code content. I for sure believe it will become increasingly useful in the workplace.

Current generative AI platforms have been trained on the internet, so remember your results may vary. I know anytime I Google or search for things on the internet I take the results with a grain of salt.

I believe that enterprises, as they go to look and adopt generative AI systems, they’ll lean more towards AI that they can trust. Therefore, we need to work on being able to create that trust element in generative AI solutions.

AN: What is the value of Watson Orchestrate for developers?

JM: When we talk about developers, I’m talking about automation developers. And that’s by and large developers that are integrating apps and business apps and business systems to work together.

For the most part, those developers have been integrating business systems to business systems. Now what we can do with Watson Orchestrate is we can introduce the human into the loop.

These automation developers now have a platform where they can build and integrate their automation workflows and they can bring a human experience into these automation workflows for everyday human workers.

Watson Orchestrate provides a platform for creating human-centric workflow automation, designed to interact with humans in our native communication style which is spoken or written word.

AN: How does Watson Orchestrate learn from user interactions?

JM: There are a couple of ways Watson Orchestrate is monitoring the behaviour of humans and learning from them. 

Perhaps most important is its ability to interpret the natural language through which humans are communicating. Today it’s the written word, but in the future spoken word. Watson Orchestrate can not just do a pattern match based on existing known sentences, but it can actually understand the intent of those utterances. 

Additionally, it can extract entities from those utterances. So, when you use proper nouns in a sentence, it can understand that’s an entity that it would use as part of an automation. It can match the intent of the utterance to existing skills that it has and can react accordingly. It can understand the intent of the utterance and then take action on those skills. Increasingly, it can sequence multiple skills together.

Also, we are working on systems for empowering Watson Orchestrate to monitor the user’s behaviour. And, just like any modern SaaS application today that has recommendations based on your behaviour, we’re working on recommendation engines to recommend to employees how they can use Watson Orchestrate to be more productive in the future.

AN: Talking about AI more generally, what new ways of working are today’s advancements enabling?

JM: As I just alluded to, we’re increasingly empowering systems to understand human natural language to a much more complex and sophisticated extent. Natural language interpretation has grown way beyond the basic pre-programmed bot experience.

I’m sure everybody has had an experience on a website where there’s a bot responding to your basic questions. What we’re trying to do is make that bot much more intelligent. The current generation of digital labour can understand your intent, extract entities from your utterances, and, most importantly, take action on your behalf.

AN: On the flip side, what are some of the main dangers of automation tools and how do we overcome those?

JM: I’m not sure if this is a specific category of danger, but I suppose I would put it under the law of unintended consequences. Anytime you work with technology, there can be outcomes that you don’t expect.

For example, if we think about the automobile as an automation tool for moving humans around – the intent, of course, was to move a human from A to B faster, and maybe more reliably. But the net result is occasionally we have accidents.

Much like the way we build transportation systems to constrain and reduce the potential for accidents, we have to do the same thing in our business systems with digital labour. Certainly, we’re going to want to start small and just do very selective, very specific tasks that are well-curated and well-defined.

We’ll need to create guardrails that guard against unintended and unexpected behaviour. One of the ways we’re doing this in Watson Orchestrate is to empower the digital labour to act on the user’s behalf and therefore leverage the user’s credentials when interacting with business systems.

As an employee, I’m given certain access to a business system based on my role. Therefore, we know when the digital employee performs actions on my behalf it also has those existing restrictions and permissions for those business systems.

Another option is monitoring behaviour and monitoring for unintended consequences. And, lastly, integrating governance and creating policies that permit or restrict the behaviour of digital employees.

AN: Are you a believer in the metaverse? If so, how much work do you think we will be doing in it?

JM: Yeah, great question. Metaverse for me is a very loose term. Here we are digitally speaking – we’ve never met before, you could be an avatar for all I know. So, in that sense, I’m a believer in the metaverse.

For me, like most innovative technologies, it will start on the fringe and work its way into the mainstream. You can look at entertainment and gaming and see very metaverse-like experiences being used there.

I’ve seen examples of the metaverse being used for deep meditative experiences. If you want to go into deep meditation, you can put on your virtual headsets and enter a metaverse world that is very different from the physical world we live in.

And I also can see the metaverse being initially used for education purposes. I think it’s a great way – a sort of low-risk way – to introduce people to new environments and new ideas at scale.

I don’t think we’re gonna go to the metaverse to go to work. I don’t see that as something coming in the near term.

AN: I can only promise I’m not an avatar for the time being. I might bump into you at Digital Transformation Week, next week. You’ll obviously be in attendance, what will you be sharing with the audience at the event?

JM: Yeah, Digital Transformation Week I will be talking about our view on the digital labour market and some of our solutions. I will also be sharing some of the stories about the early adopters of IBM’s digital labour solutions.

You can watch our full interview with Jay below:

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Within three months of investment in automation technologies, UK companies saw an average increase of five percent – or £14 billion – per month.

The impact on US businesses was even higher. Over the same three-month period, US companies witnessed an average year-on-year increase in revenue of seven percent—equivalent to an extra $195 billion per month.

Unsurprisingly, businesses that invested more heavily in automation displayed more resilience during the COVID-19 pandemic.

If the US entered the pandemic with similar levels of automation as Singapore, the report suggests the country could have reduced its GDP contraction by $105-212 billion.

The UK, meanwhile, could have prevented its 2020 GDP contraction by around £10-14 billion if it matched the automation levels of the US.

“Our new research confirms a significant positive relationship between automation and economic resilience,” said Josie Dent, Managing Economist at CEBR.

“The adoption of automation, spurred on by the recent pandemic, has helped organisations shield themselves from disruption and quickly position themselves for accelerated growth.”

Rather than destroy jobs, as some fear, automation is boosting employment.

US companies saw an average annual increase in employment of seven percent – equivalent to 7.2 million jobs – within three months of adopting automation technologies.

Over the same period, UK counterparts created an average increase of four percent in jobs—equivalent to around 676,000 roles.

The report suggests that automation has the potential to boost productivity in the UK by 15 percent in the long-term. The healthcare, social work, and transportation industries were noted as particularly benefiting from automation.

“Automation has also led to job creation and greater worker productivity, a significant contrast to the economic picture seen in the period following the global financial crisis,” explained Dent.

The pandemic and clear benefits of automation have driven more businesses than ever to invest in relevant technologies.

In the US, companies spent an average of 13 percent of their annual revenue (amounting to $4.4 trillion) on automation-related technologies. In the UK, companies spent an average of 8 percent of their revenue, or £268 billion in total.

“This first of its kind report from Cebr demonstrates the power of automation to help businesses navigate widespread disruption, and shows how it can be used as a tool to accelerate growth in a post-pandemic age,” said Gaurav Dhillon, CEO at SnapLogic. 

“Businesses today need to equip themselves with enterprise automation technologies that will allow them to quickly adapt and execute business strategies in a rapidly-changing world.”

(Photo by Konstantin Evdokimov on Unsplash)

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Faethm’s mission is to prepare the world “for the future of work” by using an AI engine – trained on billions of workforce data points – to predict what jobs need reskilling versus upskilling.

James McLeod, VP of the EMEA region at Faethm, said

“Employers and employees alike need to change their perspective. The future of work is already here and the introduction of technology does not affect work in a uniform way.

We must acknowledge where it supplements existing work and invest in a targeted reskilling approach that recognises the new roles technology is creating and ensures human and machine labour complement one another.

Doing so will not only help businesses add capacity and increase productivity, but also ensure they are looking after employees—making financially beneficial and morally responsible decisions and creating a digitally-adept workforce for the future.”

In its latest UK Workforce Forecast report (registration required), Faethm expects 4.8 percent of work – equivalent to 1.4 million full-time roles – to be automated over the next 12 months. A further 2.9 percent is predicted to be augmented by AI.

Adjusted for population, Wales (5.3%), Northern Ireland (5.2%), and the North of England (5.1%) have the highest proportions of jobs at risk of automation.

Nan Craig, Data Analyst at Faethm, commented:

“Even ordinary downturns tend to increase automation, as companies are tempted to replace expensive labour with cheaper automated systems. However, the new conditions created by COVID-19 – and the need to reduce human interaction in public places – are making automation more attractive than in an ordinary recession. 

In-person human labour is becoming more expensive, due to safety considerations around space, PPE, and the ability to take time off to self-isolate, whereas machines and automated systems, in comparison, can be added without increasing infection risks, at a comparatively lower cost.

Longer-term changes in consumer behaviour could make a difference too, as more interactions shift online, meaning businesses are more likely to be considering automation than without the COVID-19 crisis.”

Jobs in the wholesale, retail, and financial sectors are most at-risk. These sectors represent nine percent of the workforce – around five million people – and Faethm estimates that the equivalent of 932,000 full-time roles in these sectors could be automated.

Faethm’s research finds that many staple UK jobs are at risk from automation and may cease to exist in the future. However, around 382,000 jobs could be created by the introduction of new technologies.

As the country rebuilds from the pandemic, Faethm wants to see a focus on where AI can augment human jobs rather than replace them. Automation can handle more of the routine, mundane, and less enjoyable daily tasks while humans can focus on areas machines struggle with such as creativity, compassion, and interpersonal skills.

(Photo by Lyman Gerona on Unsplash)

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