Despite huge and ongoing advancements in generative AI, the massive wave it was supposed to create in the world of work looks more like a ripple so far.

Researchers Anders Humlum (University of Chicago) and Emilie Vestergaard (University of Copenhagen) didn’t just rely on anecdotes. They dug deep, connecting responses from two big surveys (late 2023 and 2024) with official, detailed records about jobs and pay in Denmark.

The pair zoomed in on around 25,000 people working in 7,000 different places, covering 11 jobs thought to be right in the path of AI disruption.   

Everyone’s using AI chatbots for work, but where are the benefits?

What they found confirms what many of us see: AI chatbots are everywhere in Danish workplaces now. Most bosses are actually encouraging staff to use them, a real turnaround from the early days when companies were understandably nervous about things like data privacy.

Almost four out of ten employers have even rolled out their own in-house chatbots, and nearly a third of employees have had some formal training on these tools.   

When bosses gave the nod, the number of staff using chatbots practically doubled, jumping from 47% to 83%. It also helped level the playing field a bit. That gap between men and women using chatbots? It shrank noticeably when companies actively encouraged their use, especially when they threw in some training.

So, the tools are popular, companies are investing, people are getting trained… but the big economic shift? It seems to be missing in action.

Using statistical methods to compare people who used AI chatbots for work with those who didn’t, both before and after ChatGPT burst onto the scene, the researchers found… well, basically nothing.

“Precise zeros,” the researchers call their findings. No significant bump in pay, no change in recorded work hours, across all 11 job types they looked at. And they’re pretty confident about this – the numbers rule out any average effect bigger than just 1%.

This wasn’t just a blip, either. The lack of impact held true even for the keen beans who jumped on board early, those using chatbots daily, or folks working where the boss was actively pushing the tech.

Looking at whole workplaces didn’t change the story; places with lots of chatbot users didn’t see different trends in hiring, overall wages, or keeping staff compared to places using them less.

Productivity gains: More of a gentle nudge than a shove

Why the big disconnect? Why all the hype and investment if it’s not showing up in paychecks or job stats? The study flags two main culprits: the productivity boosts aren’t as huge as hoped in the real world, and what little gains there are aren’t really making their way into wages.

Sure, people using AI chatbots for work felt they were helpful. They mentioned better work quality and feeling more creative. But the number one benefit? Saving time.

However, when the researchers crunched the numbers, the average time saved was only about 2.8% of a user’s total work hours. That’s miles away from the huge 15%, 30%, even 50% productivity jumps seen in controlled lab-style experiments (RCTs) involving similar jobs.

Why the difference? A few things seem to be going on. Those experiments often focus on jobs or specific tasks where chatbots really shine (like coding help or basic customer service responses). This study looked at a wider range, including jobs like teaching where the benefits might be smaller.

The researchers stress the importance of what they call “complementary investments”. People whose companies encouraged chatbot use and provided training actually did report bigger benefits – saving more time, improving quality, and feeling more creative. This suggests that just having the tool isn’t enough; you need the right support and company environment to really unlock its potential.

And even those modest time savings weren’t padding wallets. The study reckons only a tiny fraction – maybe 3% to 7% – of the time saved actually showed up as higher earnings. It might be down to standard workplace inertia, or maybe it’s just harder to ask for a raise based on using a tool your boss hasn’t officially blessed, especially when many people started using them off their own bat.

Making new work, not less work

One fascinating twist is that AI chatbots aren’t just about doing old work tasks faster. They seem to be creating new tasks too. Around 17% of people using them said they had new workloads, mostly brand new types of tasks.

This phenomenon happened more often in workplaces that encouraged chatbot use. It even spilled over to people not using the tools – about 5% of non-users reported new tasks popping up because of AI, especially teachers having to adapt assignments or spot AI-written homework.   

What kind of new tasks? Things like figuring out how to weave AI into daily workflows, drafting content with AI help, and importantly, dealing with the ethical side and making sure everything’s above board. It hints that companies are still very much in the ‘figuring it out’ phase, spending time and effort adapting rather than just reaping instant rewards.

What’s the verdict on the work impact of AI chatbots?

The researchers are careful not to write off generative AI completely. They see pathways for it to become more influential over time, especially as companies get better at integrating it and maybe as those “new tasks” evolve.

But for now, their message is clear: the current reality doesn’t match the hype about a massive, immediate job market overhaul.

“Despite rapid adoption and substantial investments… our key finding is that AI chatbots have had minimal impact on productivity and labor market outcomes to date,” the researchers conclude.   

It brings to mind that old quote about the early computer age: seen everywhere, except in the productivity stats. Two years on from ChatGPT’s launch kicking off the fastest tech adoption we’ve ever seen, its actual mark on jobs and pay looks surprisingly light.

The revolution might still be coming, but it seems to be taking its time.   

See also: Claude Integrations: Anthropic adds AI to your favourite work tools

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The AI Disclosures Project, led by technologist Tim O’Reilly and economist Ilan Strauss, aims to address the potentially harmful societal impacts of AI’s commercialisation by advocating for improved corporate and technological transparency. The project’s working paper highlights the lack of disclosure in AI, drawing parallels with financial disclosure standards and their role in fostering robust securities markets.

The study used a legally-obtained dataset of 34 copyrighted O’Reilly Media books to investigate whether LLMs from OpenAI were trained on copyrighted data without consent. The researchers applied the DE-COP membership inference attack method to determine if the models could differentiate between human-authored O’Reilly texts and paraphrased LLM versions.

Key findings from the report include:

• GPT-4o shows “strong recognition” of paywalled O’Reilly book content, with an AUROC score of 82%. In contrast, OpenAI’s earlier model, GPT-3.5 Turbo, does not show the same level of recognition (AUROC score just above 50%)

• GPT-4o exhibits stronger recognition of non-public O’Reilly book content compared to publicly accessible samples (82% vs 64% AUROC scores respectively)

• GPT-3.5 Turbo shows greater relative recognition of publicly accessible O’Reilly book samples than non-public ones (64% vs 54% AUROC scores)

• GPT-4o Mini, a smaller model, showed no knowledge of public or non-public O’Reilly Media content when tested (AUROC approximately 50%)

The researchers suggest that access violations may have occurred via the LibGen database, as all of the O’Reilly books tested were found there. They also acknowledge that newer LLMs have an improved ability to distinguish between human-authored and machine-generated language, which does not reduce the method’s ability to classify data.

The study highlights the potential for “temporal bias” in the results, due to language changes over time. To account for this, the researchers tested two models (GPT-4o and GPT-4o Mini) trained on data from the same period.

The report notes that while the evidence is specific to OpenAI and O’Reilly Media books, it likely reflects a systemic issue around the use of copyrighted data. It argues that uncompensated training data usage could lead to a decline in the internet’s content quality and diversity, as revenue streams for professional content creation diminish.

The AI Disclosures Project emphasises the need for stronger accountability in AI companies’ model pre-training processes. They suggest that liability provisions that incentivise improved corporate transparency in disclosing data provenance may be an important step towards facilitating commercial markets for training data licensing and remuneration.

The EU AI Act’s disclosure requirements could help trigger a positive disclosure-standards cycle if properly specified and enforced. Ensuring that IP holders know when their work has been used in model training is seen as a crucial step towards establishing AI markets for content creator data.

Despite evidence that AI companies may be obtaining data illegally for model training, a market is emerging in which AI model developers pay for content through licensing deals. Companies like Defined.ai facilitate the purchasing of training data, obtaining consent from data providers and stripping out personally identifiable information.

The report concludes by stating that using 34 proprietary O’Reilly Media books, the study provides empirical evidence that OpenAI likely trained GPT-4o on non-public, copyrighted data.

(Image by Sergei Tokmakov)

See also: Anthropic provides insights into the ‘AI biology’ of Claude

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Developed in partnership with KPMG, the blueprint offers nine strategic objectives to support government leaders, organisations, and key stakeholders through every phase of the AI lifecycle – from innovation to deployment – at local, national, and international levels. These strategies aim to bridge disparities in AI access, infrastructure, advanced computing, and skill development to promote sustainable, long-term growth.

Cathy Li, Head of AI, Data, and the Metaverse at the WEF, said: “Leveraging AI for economic growth and societal progress is a shared goal, yet countries and regions have very different starting points.

“This blueprint serves as a compass, guiding decision-makers toward impact-oriented collaboration and practical solutions that can unlock AI’s full potential.”

Call for regional collaboration and local empowerment

Central to the ‘Blueprint for Intelligent Economies’ is the belief that successful AI adoption must reflect the specific needs of local communities—with strong leadership and collaboration among governments, businesses, entrepreneurs, civil society organisations, and end users.

Solly Malatsi, South Africa’s Minister of Communications and Digital Technologies, commented: “The significant potential of AI remains largely untapped in many regions worldwide. Establishing an inclusive and competitive AI ecosystem will become a crucial priority for all nations.

“Collaboration among multiple stakeholders at the national, regional, and global levels will be essential in fostering growth and prosperity through AI for everyone.”

By tailoring approaches to reflect geographic and cultural nuances, the WEF report suggests nations can create AI systems that address local challenges while also providing a robust bedrock for innovation, investment, and ethical governance. Case studies from nations at varying stages of AI maturity are used throughout the report to illustrate practical, scalable solutions.

For example, cross-border cooperation on shared AI frameworks and pooled resources (such as energy or centralised databanks) is highlighted as a way to overcome resource constraints. Public-private subsidies to make AI-ready devices more affordable present another equitable way forward. These mechanisms aim to lower barriers for local businesses and innovators, enabling them to adopt AI tools and scale their operations.  

Hatem Dowidar, Chief Executive Officer of E&, said: “All nations have a unique opportunity to advance their economic and societal progress through AI. This requires a collaborative approach of intentional leadership from governments supported by active engagement with all stakeholders at all stages of the AI journey.

“Regional and global collaborations remain fundamental pathways to address shared challenges and opportunities, ensure equitable access to key AI capabilities, and responsibly maximise its transformative potential for a lasting value for all.”  

Priority focus areas

While the blueprint features nine strategic objectives, three have been singled out as priority focus areas for national AI strategies:  

1. Building sustainable AI infrastructure 

Resilient, scalable, and environmentally sustainable AI infrastructure is essential for innovation. However, achieving this vision will require substantial investment, energy, and cross-sector collaboration. Nations must coordinate efforts to ensure that intelligent economies grow in both an equitable and eco-friendly manner.  

2. Curating diverse and high-quality datasets  

AI’s potential hinges on the quality of the data it can access. This strategic objective addresses barriers such as data accessibility, imbalance, and ownership. By ensuring that datasets are inclusive, diverse, and reflective of local languages and cultures, developers can create equitable AI models that avoid bias and meet the needs of all communities.  

3. Establishing robust ethical and safety guardrails

Governance frameworks are critical for reducing risks like misuse, bias, and ethical breaches. By setting high standards at the outset, nations can cultivate trust in AI systems, laying the groundwork for responsible deployment and innovation. These safeguards are especially vital for promoting human-centred AI that benefits all of society.  

The overall framework outlined in the report has three layers:

1. Foundation layer: Focuses on sustainable energy, diverse data curation, responsible AI infrastructure, and efficient investment mechanisms.  
2. Growth layer: Embeds AI into workflows, processes, and devices to accelerate sectoral adoption and boost innovation.  
3. People layer: Prioritises workforce skills, empowerment, and ethical considerations, ensuring that AI shapes society in a beneficial and inclusive way.

A blueprint for global AI adoption  

The Forum is also championing a multi-stakeholder approach to global AI adoption, blending public and private collaboration. Policymakers are being encouraged to implement supportive legislation and incentives to spark innovation and broaden AI’s reach. Examples include lifelong learning programmes to prepare workers for the AI-powered future and financial policies that enable greater technology access in underserved regions.  

The WEF’s latest initiative reflects growing global recognition that AI will be a cornerstone of the future economy. However, it remains clear that the benefits of this transformative technology will need to be shared equitably to drive societal progress and ensure no one is left behind.  

The Blueprint for Intelligent Economies provides a roadmap for nations to harness AI while addressing the structural barriers that could otherwise deepen existing inequalities. By fostering inclusivity, adopting robust governance, and placing communities at the heart of decision-making, the WEF aims to guide governments, businesses, and innovators toward a sustainable and intelligent future.  

See also: UK Government signs off sweeping AI action plan 

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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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?

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The report reveals a stark contrast in how the C-suite and V-suite view the potential of generative AI. While the C-suite focuses on visible use cases such as customer experience, service, and sales, the V-suite sees opportunities across various functional areas, including operations, HR, and finance.

Risk perception

The divide extends to risk perception as well. Fifty-one percent of C-level respondents expressed more concern about the risk and ethics of generative AI than other emerging technologies. In contrast, only 23 percent of the V-suite shared these worries.

Simon James, Managing Director of Data & AI at Publicis Sapient, said: “It’s likely the C-suite is more worried about abstract, big-picture dangers – such as Hollywood-style scenarios of a rapidly-evolving superintelligence – than the V-suite.”

The report also highlights the uncertainty surrounding generative AI maturity. Organisations can be at various stages of maturity simultaneously, with many struggling to define what success looks like. More than two-thirds of respondents lack a way to measure the success of their generative AI projects.

Navigating the generative AI landscape

Despite the C-suite’s focus on high-visibility use cases, generative AI is quietly transforming back-office functions. More than half of the V-suite respondents ranked generative AI as extremely important in areas like finance and operations over the next three years, compared to a smaller percentage of the C-suite.

To harness the full potential of generative AI, the report recommends a portfolio approach to innovation projects. Leaders should focus on delivering projects, controlling shadow IT, avoiding duplication, empowering domain experts, connecting business units with the CIO’s office, and engaging the risk office early and often.

Daniel Liebermann, Managing Director at Publicis Sapient, commented: “It’s as hard for leaders to learn how individuals within their organisation are using ChatGPT or Microsoft Copilot as it is to understand how they’re using the internet.”

The path forward

The report concludes with five steps to maximise innovation: adopting a portfolio approach, improving communication between the CIO’s office and the risk office, seeking out innovators within the organisation, using generative AI to manage information, and empowering team members through company culture and upskilling.

As generative AI continues to evolve, organisations must bridge the gap between the C-suite and V-suite to unlock its full potential. The future of business transformation lies in harnessing the power of a decentralised, bottom-up approach to innovation.

See also: EU introduces draft regulatory guidance for AI models

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The research – conducted by MHP – surveyed 4,700 car drivers across China, the US, Germany, the UK, Italy, Sweden, and Poland, revealing significant geographical disparities in AI acceptance and understanding.

According to the study, while AI is becoming integral to modern vehicles, European consumers remain hesitant about its implementation and value proposition.

Regional disparities

The study found that 48 percent of Chinese respondents view in-car AI predominantly as an opportunity, while merely 23 percent of European respondents share this optimistic outlook. In Europe, 39 percent believe AI’s opportunities and risks are broadly balanced, while 24 percent take a negative stance, suggesting the risks outweigh potential benefits.

Understanding of AI technology also varies significantly by region. While over 80 percent of Chinese respondents claim to understand AI’s use in cars, this figure drops to just 54 percent among European drivers, highlighting a notable knowledge gap.

Marcus Willand, Partner at MHP and one of the study’s authors, notes: “The figures show that the prospect of greater safety and comfort due to AI can motivate purchasing decisions. However, the European respondents in particular are often hesitant and price-sensitive.”

The willingness to pay for AI features shows an equally stark divide. Just 23 percent of European drivers expressed willingness to pay for AI functions, compared to 39 percent of Chinese drivers. The study suggests that most users now expect AI features to be standard rather than optional extras.

Dr Nils Schaupensteiner, Associated Partner at MHP and study co-author, said: “Automotive companies need to create innovations with clear added value and develop both direct and indirect monetisation of their AI offerings, for example through data-based business models and improved services.”

In-vehicle AI opportunities

Despite these challenges, traditional automotive manufacturers maintain a trust advantage over tech giants. The study reveals that 64 percent of customers trust established car manufacturers with AI implementation, compared to 50 percent for technology firms like Apple, Google, and Microsoft.

The research identified several key areas where AI could provide significant value across the automotive industry’s value chain, including pattern recognition for quality management, enhanced data management capabilities, AI-driven decision-making systems, and improved customer service through AI-powered communication tools.

“It is worth OEMs and suppliers considering the opportunities offered by the new technology along their entire value chain,” explains Augustin Friedel, Senior Manager and study co-author. “However, the possible uses are diverse and implementation is quite complex.”

The study reveals that while up to 79 percent of respondents express interest in AI-powered features such as driver assistance systems, intelligent route planning, and predictive maintenance, manufacturers face significant challenges in monetising these capabilities, particularly in the European market.

See also: MIT breakthrough could transform robot training

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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In this article, we delve deeper into the key findings and implications—drawing on additional sources to enhance our understanding.

Thriving industry with significant growth

The study highlights the remarkable growth of the UK’s AI sector. With over 3,170 active AI companies, these firms have generated £10.6 billion in AI-related revenues and employed more than 50,000 people in AI-related roles. This significant contribution to GVA (Gross Value Added) underscores the sector’s transformative potential in driving the UK’s economic growth.

Mark Boost, CEO of Civo, said: “In a space that’s been dominated by US companies for too long, it’s promising to see the government now stepping up to help support the UK AI sector on the global stage.”

The study shows that AI activity is dispersed across various regions of the UK, with notable concentrations in London, the South East, and Scotland. This regional dispersion indicates a broad scope for the development of AI technology applications across different sectors and regions.

Investment and funding

Investment in the AI sector has been a key driver of growth. In 2022, £18.8 billion was secured in private investment since 2016, with investments made in 52 unique industry sectors compared to 35 sectors in 2016.

The government’s commitment to supporting AI is evident through significant investments. In 2022, the UK government unveiled a National AI Strategy and Action Plan—committing over £1.3 billion in support for the sector, complementing the £2.8 billion already invested.

However, as Boost cautions, “Major players like AWS are locking AI startups into their ecosystems with offerings like $500k cloud credits, ensuring that emerging companies start their journey reliant on their infrastructure. This not only hinders competition and promotes vendor lock-in but also risks stifling innovation across the broader UK AI ecosystem.”

Addressing bottlenecks

Despite the growth and investment, several bottlenecks must be addressed to fully harness the potential of AI:

• Infrastructure: The UK’s digital technology infrastructure is less advanced than many other countries. This bottleneck includes inadequate data centre infrastructure and a dependent supply of powerful GPU computer chips. Boost emphasises this concern, stating “It would be dangerous for the government to ignore the immense compute power that AI relies on. We need to consider where this power is coming from and the impact it’s having on both the already over-concentrated cloud market and the environment.”

• Commercial awareness: Many SMEs lack familiarity with digital technology. Almost a third (31%) of SMEs have yet to adopt the cloud, and nearly half (47%) do not currently use AI tools or applications.

• Skills shortage: Two-fifths of businesses struggle to find staff with good digital skills, including traditional digital roles like data analytics or IT. There is a rising need for workers with new AI-specific skills, such as prompt engineering, that will require retraining and upskilling opportunities.

To address these bottlenecks, the government has implemented several initiatives:

• Private sector investment: Microsoft has announced a £2.5 billion investment in AI skills, security, and data centre infrastructure, aiming to procure more than 20,000 of the most advanced GPUs by 2026.

• Government support: The government has invested £1.5 billion in computing capacity and committed to building three new supercomputers by 2025. This support aims to enhance the UK’s infrastructure to stay competitive in the AI market.

• Public sector integration: The UK Government Digital Service (GDS) is working to improve efficiency using predictive algorithms for future pension scheme behaviour. HMRC uses AI to help identify call centre priorities, demonstrating how AI solutions can address complex public sector challenges.

Future prospects and challenges

The future of the UK AI sector is both promising and challenging. While significant economic gains are predicted, including boosting GDP by £550 billion by 2035, delays in AI roll-out could cost the UK £150 billion over the same period. Ensuring a balanced approach between innovation and regulation will be crucial.

Boost emphasises the importance of data sovereignty and privacy: “Businesses have grown increasingly wary of how their data is collected, stored, and used by the likes of ChatGPT. The government has a real opportunity to enable the UK AI sector to offer viable alternatives.

“The forthcoming AI Action Plan will be another opportunity to identify how AI can drive economic growth and better support the UK tech sector.”

• AI Safety Summit: The AI Safety Summit at Bletchley Park highlighted the need for responsible AI development. The “Bletchley Declaration on AI Safety” emphasises the importance of ensuring AI tools are transparent, fair, and free from bias to maintain public trust and realise AI’s benefits in public services.

• Cybersecurity challenges: As AI systems handle sensitive or personal information, ensuring their security is paramount. This involves protecting against cyber threats, securing algorithms from manipulation, safeguarding data centres and hardware, and ensuring supply chain security.

The AI sector study underscores a thriving industry with significant growth potential. However, it also highlights several bottlenecks that must be addressed – infrastructure gaps, lack of commercial awareness, and skills shortages – to fully harness the sector’s potential.

(Photo by John Noonan)

See also: EU AI Act: Early prep could give businesses competitive edge

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Commissioned by Prove AI and conducted by Zogby Analytics, the report polled over 600 CEOs, CIOs, and CTOs from large companies across the US, UK, and Germany. The findings show that 96% of organisations are already utilising AI to support business operations, with the same percentage planning to increase their AI budgets in the coming year.

The primary motivations for AI investment include increasing productivity (82%), improving operational efficiency (73%), enhancing decision-making (65%), and achieving cost savings (60%). The most common AI use cases reported were customer service and support, predictive analytics, and marketing and ad optimisation.

Despite the surge in AI investments, business leaders are acutely aware of the additional risk exposure that AI brings to their organisations. Data integrity and security emerged as the biggest deterrents to implementing new AI solutions.

Executives also reported encountering various AI performance issues, including:

• Data quality issues (e.g., inconsistencies or inaccuracies): 41%
• Bias detection and mitigation challenges in AI algorithms, leading to unfair or discriminatory outcomes: 37%
• Difficulty in quantifying and measuring the return on investment (ROI) of AI initiatives: 28%

While 95% of respondents expressed confidence in their organisation’s current AI risk management practices, the report revealed a significant gap in AI governance implementation.

Only 5% of executives reported that their organisation has implemented any AI governance framework. However, 82% stated that implementing AI governance solutions is a somewhat or extremely pressing priority, with 85% planning to implement such solutions by summer 2025.

The report also found that 82% of participants support an AI governance executive order to provide stronger oversight. Additionally, 65% expressed concern about IP infringement and data security.

Mrinal Manohar, CEO of Prove AI, commented: “Executives are making themselves clear: AI’s long-term efficacy, including providing a meaningful return on the massive investments organisations are currently making, is contingent on their ability to develop and refine comprehensive AI governance strategies.

“The wave of AI-focused legislation going into effect around the world is only increasing the urgency; for the current wave of innovation to continue responsibly, we need to implement clearer guardrails to manage and monitor the data informing AI systems.”

As global regulations like the EU AI Act loom on the horizon, the report underscores the importance of de-risking AI and the work that still needs to be done. Implementing and optimising dedicated AI governance strategies has emerged as a top priority for businesses looking to harness the power of AI while mitigating associated risks.

The findings of this report serve as a wake-up call for organisations to prioritise AI governance as they continue to invest in and deploy AI technologies. Responsible implementation and robust governance frameworks will be key to unlocking the full potential of AI while maintaining trust and compliance.

(Photo by Rob Thompson)

See also: Scoring AI models: Endor Labs unveils evaluation tool

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Research from Keeper Security finds that, despite the implementation of AI-related policies, many organisations remain inadequately prepared for AI-powered threats.

84% of IT and security leaders find AI-enhanced tools have exacerbated the challenge of detecting phishing and smishing attacks, which were already significant threats. In response, 81% of organisations have enacted AI usage policies for employees. Confidence in these measures runs high, with 77% of leaders expressing familiarity with best practices for AI security.

Gap between AI cybersecurity policy and threats preparedness

More than half (51%) of security leaders view AI-driven attacks as the most severe threat to their organisations. Alarmingly, 35% of respondents feel ill-prepared to address these attacks compared to other cyber threats.

Organisations are deploying several key strategies to meet these emerging challenges:

• Data encryption: Utilised by 51% of IT leaders, encryption serves as a crucial defence against unauthorised access and is vital against AI-fuelled attacks.

• Employee training and awareness: With 45% of organisations prioritising enhanced training programmes, there is a focused effort to equip employees to recognise and counter AI-driven phishing and smishing intrusions.

• Advanced threat detection systems: 41% of organisations are investing in these systems, underscoring the need for improved detection and response to sophisticated AI threats.

The advent of AI-driven cyber threats undeniably presents new challenges. Nevertheless, fundamental cybersecurity practices – such as data encryption, employee education, and advanced threat detection – continue to be essential. Organisations must ensure these essential measures are consistently re-evaluated and adjusted to counter emerging threats.

In addition to these core practices, advanced security frameworks like zero trust and Privileged Access Management (PAM) solutions can bolster an organisation’s resilience.

Zero trust demands continuous verification of all users, devices, and applications, reducing the risk of unauthorised access and minimising potential damage during an attack. PAM offers targeted security for an organisation’s most sensitive accounts, crucial for defending against complex AI-driven threats that aim at high-level credentials.

Darren Guccione, CEO and Co-Founder of Keeper Security, commented: “AI-driven attacks are a formidable challenge, but by reinforcing our cybersecurity fundamentals and adopting advanced security measures, we can build resilient defences against these evolving threats.”

Proactivity is also key for organisations—regularly reviewing security policies, performing routine audits, and fostering a culture of cybersecurity awareness are all essential.

While organisations are advancing, cybersecurity requires perpetual vigilance. Merging traditional practices with modern approaches like zero trust and PAM will empower organisations to maintain an edge over developing AI-powered threats.

(Photo by Growtika)

See also: King’s Business School: How AI is transforming problem-solving

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The report found that nearly half (46%) of Wazoku’s 700,000-strong network of problem solvers had utilised generative AI (GenAI) to work on innovative ideas over the past year. This network – known as the Wazoku Crowd – comprises a diverse group of professionals including scientists, pharmacists, engineers, PhD students, CEOs, start-ups, and business leaders.

Perhaps more strikingly, almost a quarter (22%) of respondents reported using GenAI or LLM tools such as ChatGPT and Claude for at least half of their idea submissions, with 8% employing these technologies for every single submission. Of those using GenAI, 47% are leveraging it specifically for idea generation.

The Wazoku Crowd’s collective intelligence is harnessed to solve ‘challenges’ – requests for ideas submitted by enterprises – with an impressive success rate of over 80%.

Simon Hill, CEO of Wazoku, commented on the findings: “There’s an incredible amount of hype with GenAI, but alongside that there is enormous curiosity. Getting immersed in something and being curious is an innovator’s dream, so there is rich potential with GenAI.”

However, Hill also urged caution: “A note of caution, though – it is best used to generate interest, not solutions. Human ingenuity and creativity are still best, although using GenAI can undoubtedly make that process more effective.”

The study revealed that the most common application of GenAI was in research and learning, with 85% of respondents using it for this purpose. Additionally, around one-third of the Wazoku Crowd employed GenAI for report structuring, writing, and data analysis and insight.

The research was conducted in partnership with Oguz A. Acar, Professor of Marketing and Innovation at King’s Business School, King’s College London. Professor Acar viewed the study as a crucial first step towards understanding AI’s potential and limitations in tackling complex innovation challenges.

“Everyone’s trying to figure out what AI can and can’t do, and this survey is a step forward in understanding that,” Professor Acar stated. “It reveals that some crowd members view GenAI as a valuable ally, using it to research, create, and communicate more effectively.”

“While perhaps it’s no surprise that those open to innovation are curious about new tools, the survey also shows mixed opinions. Most people haven’t used GenAI tools yet, highlighting that we’re only beginning to uncover AI’s potential in innovative problem-solving.”

Wazoku collaborates with a range of customers, including Sanofi, A2A, Bill & Melinda Gates Foundation, and numerous global enterprise businesses, government departments, and not-for-profits, to crowdsource ideas and innovation.

Recently, Wazoku launched its own conversational AI to aid innovation. Dubbed Jen AI, this digital innovation assistant has access to Wazoku’s connected innovation management suite—aimed at accelerating decision-making around innovation and enhancing productivity to deliver consistent, scalable results.

“The solutions to the world’s problems are complex, and the support of AI brings vast benefits in terms of efficiency, creativity, and insight generation,” explained Hill.

As the adoption of AI in innovation processes continues to grow, it’s clear that – while these tools offer significant potential – they are best used to augment rather than replace human creativity and problem-solving skills.

(Photo by Ally Griffin)

See also: Ivo Everts, Databricks: Enhancing open-source AI and improving data governance

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post King’s Business School: How AI is transforming problem-solving appeared first on AI News.

The study, which polled nearly 700 IT experts, highlights security as the paramount concern. An overwhelming 72% of respondents emphasised the critical need for measures to secure infrastructure. Privacy follows closely behind, with 64% of IT professionals urging for more robust rules to protect sensitive information.

Rob Johnson, VP and Global Head of Solutions Engineering at SolarWinds, commented: “It is understandable that IT leaders are approaching AI with caution. As technology rapidly evolves, it naturally presents challenges typical of any emerging innovation.

“Security and privacy remain at the forefront, with ongoing scrutiny by regulatory bodies. However, it is incumbent upon organisations to take proactive measures by enhancing data hygiene, enforcing robust AI ethics and assembling the right teams to lead these efforts. This proactive stance not only helps with compliance with evolving regulations but also maximises the potential of AI.”

The survey’s findings come at a pivotal moment, coinciding with the implementation of the EU’s AI Act. In the UK, the new Labour government recently proposed its own AI legislation during the latest King’s speech, signalling a growing recognition of the need for regulatory frameworks. In the US, the California State Assembly passed a controversial AI safety bill last month.

Beyond security and privacy, the survey reveals a broader spectrum of concerns amongst IT professionals. A majority (55%) believe government intervention is crucial to stem the tide of AI-generated misinformation. Additionally, half of the respondents support regulations aimed at ensuring transparency and ethical practices in AI development.

Challenges extend beyond AI regulation

However, the challenges facing AI adoption extend beyond regulatory concerns. The survey uncovers a troubling lack of trust in data quality—a cornerstone of successful AI implementation.

Only 38% of respondents consider themselves ‘very trusting’ of the data quality and training used in AI systems. This scepticism is not unfounded, as 40% of IT leaders who have encountered issues with AI attribute these problems to algorithmic errors stemming from insufficient or biased data.

Consequently, data quality emerges as the second most significant barrier to AI adoption (16%), trailing only behind security and privacy risks. This finding underscores the critical importance of robust, unbiased datasets in driving AI success.

“High-quality data is the cornerstone of accurate and reliable AI models, which in turn drive better decision-making and outcomes,” adds Johnson. “Trustworthy data builds confidence in AI among IT professionals, accelerating the broader adoption and integration of AI technologies.”

The survey also sheds light on widespread concerns about database readiness. Less than half (43%) of IT professionals express confidence in their company’s ability to meet the increasing data demands of AI. This lack of preparedness is further exacerbated by the perception that organisations are not moving swiftly enough to implement AI, with 46% of respondents citing ongoing data quality challenges as a contributing factor.

As AI continues to reshape the technological landscape, the findings of this SolarWinds survey serve as a clarion call for both stronger regulation and improved data practices. The message from IT professionals is clear: while AI holds immense promise, its successful integration hinges on addressing critical concerns around security, privacy, and data quality.

(Photo by Kelly Sikkema)

See also: Whitepaper dispels fears of AI-induced job losses

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The whitepaper, titled “Unlocking Data, Unlocking People: Harnessing the Power of AI to Transform Your Business,” launched today at Scale Space White City in London’s White City Innovation District. It argues that AI can actually be a catalyst for growth, rather than a threat to jobs.

Despite evidence to the contrary, the study also reveals a lingering fear of AI among UK business leaders. 

A YouGov survey commissioned by Automated Analytics found that over half of senior decision-makers still believe AI will eliminate more jobs than it creates. Only 17% believe AI will create more jobs than it eliminates. 

This hesitancy, the report warns, could have serious consequences for the UK economy. As US firms increasingly embrace AI, British businesses risk being left behind. Mark Taylor, CEO of Automated Analytics, argues that the UK needs to shift its focus from regulation to innovation in order to remain competitive.

“AI is not about replacing jobs; it’s about enhancing productivity and creating new opportunities,” says Taylor. “Our whitepaper provides concrete examples of how AI can drive growth, efficiency, and competitiveness. The UK cannot afford to lag behind in this critical area.”

The whitepaper highlights several case studies demonstrating the positive impact of AI on businesses:

• British Gas’ Dyno-Rod: AI helped to halve the number of service calls by improving understanding of customer journeys, providing franchise operations with 100% visibility.
• Pizza Hut (US): Restaurant Management Group, the fourth-largest US franchisee, used AI to reduce recruitment costs by $1 million and increase hires by 42%.
• Fourth (UK): The hospitality recruitment firm saw a 220% increase in candidate flow and a drastic reduction in cost-per-application within 30 days of implementing its AI-driven TalentTrack software.

Taylor also expressed concern over the cultural differences between the US and UK in embracing new technologies. 

“The US is leading the charge in AI adoption, while the UK remains overly focused on regulation. This whitepaper demonstrates that many fears surrounding AI are unfounded and that it can play a crucial role in driving the UK’s economic growth.”

The whitepaper serves as a call to action for UK businesses to embrace AI as a vital tool for driving innovation and competitiveness in the global market. While it’s clear that businesses must adopt AI responsibly, the whitepaper should help to dispel fears of mass job losses.

A copy of the whitepaper can be found here (registration required.)

(Photo by Ruthson Zimmerman)

See also: UK signs AI safety treaty to protect human rights and democracy

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Whitepaper dispels fears of AI-induced job losses appeared first on AI News.