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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Senior figures from Boston Consulting Group (BCG) and its tech division, BCG X, discussed the intricate dynamics of the global AI race, the dominance of superpowers like the US and China, the role of emerging “middle powers,” and the implications for multinational corporations.

AI investments expose businesses to increasingly tense geopolitics

Sylvain Duranton, Global Leader at BCG X, noted the significant geopolitical risk companies face: “For large companies, close to half of them, 44%, have teams around the world, not just in one country where their headquarters are.”

Many of these businesses operate across numerous countries, making them vulnerable to differing regulations and sovereignty issues. “They’ve built their AI teams and ecosystem far before there was such tension around the world.”

Duranton also pointed to the stark imbalance in the AI supply race, particularly in investment.

Comparing the market capitalisation of tech companies, the US dwarfs Europe by a factor of 20 and the Asia Pacific region by five. Investment figures paint a similar picture, showing a “completely disproportionate” imbalance compared to the relative sizes of the economies.

This AI race is fuelled by massive investments in compute power, frontier models, and the emergence of lighter, open-weight models changing the competitive dynamic.   

Benchmarking national AI capabilities

Nikolaus Lang, Global Leader at the BCG Henderson Institute – BCG’s think tank – detailed the extensive research undertaken to benchmark national GenAI capabilities objectively.

The team analysed the “upstream of GenAI,” focusing on large language model (LLM) development and its six key enablers: capital, computing power, intellectual property, talent, data, and energy.

Using hard data like AI researcher numbers, patents, data centre capacity, and VC investment, they created a comparative analysis. Unsurprisingly, the analysis revealed the US and China as the clear AI frontrunners and maintain leads in geopolitics.

The US boasts the largest pool of AI specialists (around half a million), immense capital power ($303bn in VC funding, $212bn in tech R&D), and leading compute power (45 GW).

Lang highlighted America’s historical dominance, noting, “the US has been the largest producer of notable AI models with 67%” since 1950, a lead reflected in today’s LLM landscape. This strength is reinforced by “outsized capital power” and strategic restrictions on advanced AI chip access through frameworks like the US AI Diffusion Framework.   

China, the second AI superpower, shows particular strength in data—ranking highly in e-governance and mobile broadband subscriptions, alongside significant data centre capacity (20 GW) and capital power. 

Despite restricted access to the latest chips, Chinese LLMs are rapidly closing the gap with US models. Lang mentioned the emergence of models like DeepSpeech as evidence of this trend, achieved with smaller teams, fewer GPU hours, and previous-generation chips.

China’s progress is also fuelled by heavy investment in AI academic institutions (hosting 45 of the world’s top 100), a leading position in AI patent applications, and significant government-backed VC funding. Lang predicts “governments will play an important role in funding AI work going forward.”

The middle powers: Europe, Middle East, and Asia

Beyond the superpowers, several “middle powers” are carving out niches.

• EU: While trailing the US and China, the EU holds the third spot with significant data centre capacity (8 GW) and the world’s second-largest AI talent pool (275,000 specialists) when capabilities are combined. Europe also leads in top AI publications. Lang stressed the need for bundled capacities, suggesting AI, defence, and renewables are key areas for future EU momentum.
• Middle East (UAE & Saudi Arabia): These nations leverage strong capital power via sovereign wealth funds and competitively low electricity prices to attract talent and build compute power, aiming to become AI drivers “from scratch”. They show positive dynamics in attracting AI specialists and are climbing the ranks in AI publications.   
• Asia (Japan & South Korea): Leveraging strong existing tech ecosystems in hardware and gaming, these countries invest heavily in R&D (around $207bn combined by top tech firms). Government support, particularly in Japan, fosters both supply and demand. Local LLMs and strategic investments by companies like Samsung and SoftBank demonstrate significant activity.   
• Singapore: Singapore is boosting its AI ecosystem by focusing on talent upskilling programmes, supporting Southeast Asia’s first LLM, ensuring data centre capacity, and fostering adoption through initiatives like establishing AI centres of excellence.   

The geopolitics of generative AI: Strategy and sovereignty

The geopolitics of generative AI is being shaped by four clear dynamics: the US retains its lead, driven by an unrivalled tech ecosystem; China is rapidly closing the gap; middle powers face a strategic choice between building supply or accelerating adoption; and government funding is set to play a pivotal role, particularly as R&D costs climb and commoditisation sets in.

As geopolitical tensions mount, businesses are likely to diversify their GenAI supply chains to spread risk. The race ahead will be defined by how nations and companies navigate the intersection of innovation, policy, and resilience.

(Photo by Markus Krisetya)

See also: OpenAI counter-sues Elon Musk for attempts to ‘take down’ AI rival

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 partnership with Tsinghua University researchers, DeepSeek has created a technique detailed in a research paper, titled “Inference-Time Scaling for Generalist Reward Modeling.” It outlines how a new approach outperforms existing methods and how the team “achieved competitive performance” compared to strong public reward models.

The innovation focuses on enhancing how AI systems learn from human preferences – a important aspect of creating more useful and aligned artificial intelligence.

What are AI reward models, and why do they matter?

AI reward models are important components in reinforcement learning for large language models. They provide feedback signals that help guide an AI’s behaviour toward preferred outcomes. In simpler terms, reward models are like digital teachers that help AI understand what humans want from their responses.

“Reward modeling is a process that guides an LLM towards human preferences,” the DeepSeek paper states. Reward modeling becomes important as AI systems get more sophisticated and are deployed in scenarios beyond simple question-answering tasks.

The innovation from DeepSeek addresses the challenge of obtaining accurate reward signals for LLMs in different domains. While current reward models work well for verifiable questions or artificial rules, they struggle in general domains where criteria are more diverse and complex.

The dual approach: How DeepSeek’s method works

DeepSeek’s approach combines two methods:

1. Generative reward modeling (GRM): This approach enables flexibility in different input types and allows for scaling during inference time. Unlike previous scalar or semi-scalar approaches, GRM provides a richer representation of rewards through language.
2. Self-principled critique tuning (SPCT): A learning method that fosters scalable reward-generation behaviours in GRMs through online reinforcement learning, one that generates principles adaptively.

One of the paper’s authors from Tsinghua University and DeepSeek-AI, Zijun Liu, explained that the combination of methods allows “principles to be generated based on the input query and responses, adaptively aligning reward generation process.”

The approach is particularly valuable for its potential for “inference-time scaling” – improving performance by increasing computational resources during inference rather than just during training.

The researchers found that their methods could achieve better results with increased sampling, letting models generate better rewards with more computing.

Implications for the AI Industry

DeepSeek’s innovation comes at an important time in AI development. The paper states “reinforcement learning (RL) has been widely adopted in post-training for large language models […] at scale,” leading to “remarkable improvements in human value alignment, long-term reasoning, and environment adaptation for LLMs.”

The new approach to reward modelling could have several implications:

1. More accurate AI feedback: By creating better reward models, AI systems can receive more precise feedback about their outputs, leading to improved responses over time.
2. Increased adaptability: The ability to scale model performance during inference means AI systems can adapt to different computational constraints and requirements.
3. Broader application: Systems can perform better in a broader range of tasks by improving reward modelling for general domains.
4. More efficient resource use: The research shows that inference-time scaling with DeepSeek’s method could outperform model size scaling in training time, potentially allowing smaller models to perform comparably to larger ones with appropriate inference-time resources.

DeepSeek’s growing influence

The latest development adds to DeepSeek’s rising profile in global AI. Founded in 2023 by entrepreneur Liang Wenfeng, the Hangzhou-based company has made waves with its V3 foundation and R1 reasoning models.

The company upgraded its V3 model (DeepSeek-V3-0324) recently, which the company said offered “enhanced reasoning capabilities, optimised front-end web development and upgraded Chinese writing proficiency.” DeepSeek has committed to open-source AI, releasing five code repositories in February that allow developers to review and contribute to development.

While speculation continues about the potential release of DeepSeek-R2 (the successor to R1) – Reuters has speculated on possible release dates – DeepSeek has not commented in its official channels.

What’s next for AI reward models?

According to the researchers, DeepSeek intends to make the GRM models open-source, although no specific timeline has been provided. Open-sourcing will accelerate progress in the field by allowing broader experimentation with reward models.

As reinforcement learning continues to play an important role in AI development, advances in reward modelling like those in DeepSeek and Tsinghua University’s work will likely have an impact on the abilities and behaviour of AI systems.

Work on AI reward models demonstrates that innovations in how and when models learn can be as important increasing their size. By focusing on feedback quality and scalability, DeepSeek addresses one of the fundamental challenges to creating AI that understands and aligns with human preferences better.

See also: DeepSeek disruption: Chinese AI innovation narrows global technology divide

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 DeepSeek’s AIs: What humans really want appeared first on AI News.

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

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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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.

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

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“2025 is shaping up to be a defining year for AI, as organisations transition from experimentation to scaling their AI capabilities,” said Gabie Boko, NetApp’s Chief Marketing Officer.

“Businesses are making significant investments to drive innovation and efficiency, but these efforts will succeed only if global tech executives can address the mounting challenges of data complexity, security, and sustainability.”

The findings of NetApp’s latest Data Complexity Report paints a detailed picture of where businesses currently stand on their AI journeys and the key trends that will shape the technology’s future.

Cost of transformation

Two-thirds of businesses worldwide claim their data is “fully or mostly optimised” for AI purposes, highlighting vast improvements in making data accessible, accurate, and well-documented. Yet, the study reveals that the journey towards AI maturity requires further significant investment.

A striking 40% of global technology executives anticipate “unprecedented investment” will be necessary in 2025 just to enhance AI and data management capabilities.

While considerable progress has been made, achieving impactful breakthroughs demands an even greater commitment in financial and infrastructural resources. Catching up with AI’s potential might not come cheap, but leaders prepared to invest could reap significant rewards in innovation and efficiency.

Data silos impede AI success

One of the principal barriers identified in the report is the fragmentation of data. An overwhelming 79% of global tech executives state that unifying their data, reducing silos and ensuring smooth interconnectedness, is key to unlocking AI’s full potential.

Companies that have embraced unified data storage are better placed to overcome this hurdle. By connecting data regardless of its type or location (across hybrid multi-cloud environments,) they ensure constant accessibility and minimise fragmentation.

The report indicates that organisations prioritising data unification are significantly more likely to meet their AI goals in 2025. Nearly one-third (30%) of businesses failing to prioritise unification foresee missing their targets, compared to just 23% for those placing this at the heart of their strategy.

Executives have doubled down on data management and infrastructure as top priorities, increasingly recognising that optimising their capacity to gather, store, and process information is essential for AI maturity. Companies refusing to tackle these data challenges risk falling behind in an intensely competitive global market.

Scaling risks of AI

As businesses accelerate their AI adoption, the associated risks – particularly around security – are becoming more acute. More than two-fifths (41%) of global tech executives predict a stark rise in security threats by 2025 as AI becomes integral to more facets of their operations.

AI’s rapid rise has expanded attack surfaces, exposing data sets to new vulnerabilities and creating unique challenges such as protecting sensitive AI models. Countries leading the AI race, including India, the US, and Japan, are nearly twice as likely to encounter escalating security concerns compared to less AI-advanced nations like Germany, France, and Spain.

Increased awareness of AI-driven security challenges is reflected in business priorities. Over half (59%) of global executives name cybersecurity as one of the top stressors confronting organisations today.

However, progress is being made. Despite elevated concerns, the report suggests that effective security measures are yielding results. Since 2023, the number of executives ranking cybersecurity and ransomware protection as their top priority has fallen by 17%, signalling optimism in combating these risks effectively.

Limiting AI’s environmental costs

Beyond security risks, AI’s growth is raising urgent questions of sustainability. Over one-third of global technology executives (34%) predict that AI advancements will drive significant changes to corporate sustainability practices. Meanwhile, 33% foresee new government policies and investments targeting energy usage.

The infrastructure powering AI and transforming raw data into business value demands significant energy, counteracting organisational sustainability targets. AI-heavy nations often feel the environmental impact more acutely than their less AI-focused counterparts.

While 72% of businesses still prioritise carbon footprint reduction, the report notes a decline from 84% in 2023, pointing to increasing tension between sustainability commitments and the relentless march of innovation. For organisations to scale AI without causing irreparable damage to the planet, maintaining environmental responsibility alongside technological growth will be paramount in coming years.

Krish Vitaldevara, SVP and GM at NetApp, commented: “The organisations leading in advanced analytics and AI are those that have unified and well-cataloged data, robust security and compliance for sensitive information, and a clear understanding of how data evolves.

“By tackling these challenges, they can drive innovation while ensuring resilience, responsibility, and timely insights in the new AI era.”

You can find a full copy of NetApp’s report here (PDF)

(Photo by Chunli Ju)

See also: New AI training techniques aim to overcome current challenges

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 Keys to AI success: Security, sustainability, and overcoming silos appeared first on AI News.

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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With data centres burning vast amounts of energy, the growing demand for GenAI has sparked a debate about its sustainability.

The cost of intelligence: Generative AI’s carbon footprint

Behind every AI-generated email, idea, or recommendation are data centres running thousands of energy-hungry servers. Data centres are responsible for both training the large language models that power generative AI and processing individual user queries. Unlike a simple Google search, which uses relatively little energy, a single generative AI request can consume up to ten times as much electricity.

The numbers are staggering. If all nine billion daily Google searches worldwide were replaced with generative AI tasks, the additional electricity demand would match the annual energy consumption of 1.5 million EU residents. According to consultants Morgan Stanley, the energy demands of generative AI are expected to grow by 70% annually until 2027. By that point, the energy required to support generative AI systems could rival the electricity needs of an entire country—Spain, for example, based on its 2022 usage.

UK consumers want greener AI practices

The survey also highlights growing awareness among UK consumers about the environmental implications of generative AI. Nearly one in five respondents said they don’t trust generative AI providers to manage their environmental impact responsibly. Among regular users of these tools, 10% expressed a willingness to pay a premium for products or services that prioritise energy efficiency and sustainability.

Interestingly, over a third (35%) of respondents think generative AI tools should “actively remind” users of their environmental impact. While this appears like a small step, it has the potential to encourage more mindful usage and place pressure on companies to adopt greener technologies.

Efforts to tackle the environmental challenge

Fortunately, some companies and policymakers are beginning to address these concerns. In the United States, the Artificial Intelligence Environmental Impacts Act was introduced earlier this year. The legislation aims to standardise how AI companies measure and report carbon emissions. It also provides a voluntary framework for developers to evaluate and disclose their systems’ environmental impact, pushing the industry towards greater transparency.

Major players in the tech industry are also stepping up. Companies like Salesforce have voiced support for legislation requiring standardised methods to measure and report AI’s carbon footprint. Experts point to several practical ways to reduce generative AI’s environmental impact, including adopting energy-efficient hardware, using sustainable cooling methods in data centres, and transitioning to renewable energy sources.

Despite these efforts, the urgency to address generative AI’s environmental impact remains critical. As Uday Radia, owner of CloudNine PR, puts it: “Generative AI has huge potential to make our lives better, but there is a race against time to make it more sustainable before it gets out of control.”

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See also: The AI revolution: Reshaping data centres and the digital landscape 

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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

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“Technology has a long history of profoundly reshaping the world of work,” the report begins.

From the agricultural revolution to the digital age, each wave of innovation has redefined labour markets. Today, AI presents a seismic shift, advancing rapidly and prompting policymakers to prepare for change.

Economic opportunities

The TBI report estimates that AI, when fully adopted by UK firms, could significantly increase productivity. It suggests that AI could save “almost a quarter of private-sector workforce time,” equivalent to the annual output of 6 million workers.

Most of these time savings are expected to stem from AI-enabled software performing cognitive tasks such as data analysis and routine administrative operations.

The report identifies sectors reliant on routine cognitive tasks, such as banking and finance, as those with significant exposure to AI. However, sectors like skilled trades or construction – which involve complex manual tasks – are likely to see less direct impact.

While AI can result in initial job losses, it also has the potential to create new demand by fostering economic growth and new industries. 

The report expects these job losses can be balanced by new job creation. Over the years, technology has historically spurred new employment opportunities, as innovation leads to the development of new products and services.

Shaping future generations

AI’s potential extends into education, where it could assist both teachers and students.

The report suggests that AI could help “raise educational attainment by around six percent” on average. By personalising and supporting learning, AI has the potential to equalise access to opportunities and improve the quality of the workforce over time.

Health and wellbeing

Beyond education, AI offers potential benefits in healthcare, supporting a healthier workforce and reducing welfare costs.

The report highlights AI’s role in speeding medical research, enabling preventive healthcare, and helping those with disabilities re-enter the workforce.

Workplace transformation

The report acknowledges potential workplace challenges, such as increased monitoring and stress from AI tools. It stresses the importance of managing these technologies thoughtfully to “deliver a more engaging, inclusive and safe working environment.”

To mitigate potential disruption, the TBI outlines recommendations. These include upgrading labour-market infrastructure and utilising AI for job matching.

The report suggests creating an “Early Awareness and Opportunity System” to help workers understand the impact of AI on their jobs and provide advice on career paths.

Preparing for an AI-powered future

In light of the uncertainties surrounding AI’s impact on the workforce, the TBI urges policy changes to maximise benefits. Recommendations include incentivising AI adoption across industries, developing AI-pathfinder programmes, and creating challenge prizes to address public-sector labour shortages.

The report concludes that while AI presents risks, the potential gains are too significant to ignore.

Policymakers are encouraged to adopt a “pro-innovation” stance while being attuned to the risks, fostering an economy that is dynamic and resilient.

(Photo by Mimi Thian)

See also: Anthropic urges AI regulation to avoid catastrophes

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In an unusual turn of events, Alaska legislators reportedly used AI-generated citations that were inaccurate to justify a proposed policy banning cellphones in schools. As reported by /The Alaska Beacon/, Alaska’s Department of Education and Early Development (DEED) presented a policy draft containing references to academic studies that simply did not exist.

The situation arose when Alaska’s Education Commissioner, Deena Bishop, used generative AI to draft the cellphone policy. The document produced by the AI included supposed scholarly references that were neither verified nor accurate, yet the document did not disclose the use of AI in its preparation. Some of the AI-generated content reached the Alaska State Board of Education and Early Development before it could be reviewed, potentially influencing board discussions.

Commissioner Bishop later claimed that AI was used only to “create citations” for an initial draft and asserted that she corrected the errors before the meeting by sending updated citations to board members. However, AI “hallucinations”—fabricated information generated when AI attempts to create plausible yet unverified content—were still present in the final document that was voted on by the board.

The final resolution, published on DEED’s website, directs the department to establish a model policy for cellphone restrictions in schools. Unfortunately, the document included six citations, four of which seemed to be from respected scientific journals. However, the references were entirely made up, with URLs that led to unrelated content. The incident shows the risks of using AI-generated data without proper human verification, especially when making policy rulings.

Alaska’s case is not one of a kind. AI hallucinations are increasingly common in a variety of professional sectors. For example, some legal professionals have faced consequences for using AI-generated, fictitious case citations in court. Similarly, academic papers created using AI have included distorted data and fake sources, presenting serious credibility concerns. When left unchecked, generative AI algorithms, which are meant to produce content based on patterns rather than factual accuracy, can easily produce misleading citations.

The reliance on AI-generated data in policymaking, particularly in education, carries significant risks. When policies are developed based on fabricated information, they may misallocate resources and potentially harm students. For instance, a policy restricting cellphone use based on fabricated data may divert attention from more effective, evidence-based interventions that could genuinely benefit students.

Furthermore, using unverified AI data can erode public trust in both the policymaking process and AI technology itself. Such incidents underscore the importance of fact-checking, transparency, and caution when using AI in sensitive decision-making areas, especially in education, where impact on students can be profound.

Alaska officials attempted to downplay the situation, referring to the fabricated citations as “placeholders” intended for later correction. However, the document with the “placeholders” was still presented to the board and used as the basis for a vote, underscoring the need for rigorous oversight when using AI.

(Photo by Hartono Creative Studio)

See also: Anthropic urges AI regulation to avoid catastrophes

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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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