As autumn settles across Europe, culture is moving to the top of the travel agenda. According to the European Travel Commission, cooler months such as October and November are increasingly prompting travellers to build trips around history, heritage and landmark experiences.

TUI Musement’s latest data reinforces that shift. The travel company found that 94% of respondents say they are interested, or very interested, in experiences tied to history, culture and heritage on their next city break. Meanwhile, eight in 10 said they have already visited a monument or landmark near where they live.

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Against that backdrop, TUI Musement has released a new ranking of Europe’s 30 most popular castles and palaces for 2026, based on accumulated Google reviews. The analysis compares review volumes from 2023 and 2026, offering a useful snapshot of which historic sites are gaining the most traction with visitors.

Spain Stands Out In A Wide-ranging European List

The ranking reveals a broad geographic spread, but Spain emerges as the most represented country, with six sites in the top 30. Both the Alhambra in Granada and the Royal Palace of Madrid secured places in the top 10, underscoring the country’s enduring appeal as a destination for heritage tourism.

At the top of the list, Prague Castle retains first place, while Schönbrunn Palace in Vienna climbs into the top three. The only new entrant is Buda Castle in Budapest, which posted a 65% increase in accumulated Google reviews compared with 2023.

The Top 10 Castles And Palaces In Europe

Prague Castle remains the benchmark for European heritage tourism. With 199,000 reviews, a 31% increase from 2023, it is one of the largest palace complexes in the world and a concentrated showcase of centuries of history. Visitors can explore St Vitus Cathedral, the Old Royal Palace and Golden Lane with a single ticket.

In second place is Buckingham Palace, one of London’s most recognisable landmarks and one of the official residences of the British monarchy. Its daily Changing of the Guard continues to draw crowds, while summer opening periods allow visitors inside the state rooms.

Schönbrunn Palace moves up to third, marking the 30th anniversary of its designation as a World Heritage Site. In Vienna, the palace offers a window into Austria’s imperial past and the dynastic legacy that shaped the country’s history.

Versailles follows in fourth place. The former residence of the kings of France remains one of Europe’s most significant historical sites, with the Hall of Mirrors, royal apartments and formal gardens helping tell the story of absolutism, monarchy and the later Treaty of Versailles.

Wawel Castle in Kraków holds fifth place despite slipping two positions. Once the residence and coronation site of Poland’s kings, it remains one of the country’s most important cultural attractions, with the Dragon’s Den statue at its base adding another layer of local symbolism.

Spain claims sixth and seventh place. The Alhambra in Granada ranks sixth with its palaces, gardens and fortresses, including the Nasrid Palaces, Generalife, Alcazaba and Palace of Charles V. The Royal Palace of Madrid climbs to seventh after a 47% rise in accumulated Google reviews since 2023. Still used for official receptions, it also opens select highlights such as the throne room, Gasparini Room and royal chapel to the public.

London appears again in eighth place with the Tower of London, a fortress that has played a defining role in English history. Today, it is best known as the home of the Crown Jewels and for its Yeoman Warders and resident ravens, which have become part of its enduring identity.

Neuschwanstein Castle rises to ninth place after a strong increase in reviews. Set in the Bavarian Alps, the fairy-tale palace reflects the imagination of King Ludwig II of Bavaria and his fascination with art, architecture and medieval legend.

Rounding out the top 10 is Bran Castle in Romania, long associated with the Dracula myth but historically important in its own right. Beyond its fictional reputation, the fortress tells the story of Transylvania through its role as a frontier stronghold and later a royal residence.

The Top 10 Most Popular Castles In Europe

1. Prague Castle, Czechia
2. Buckingham Palace, United Kingdom
3. Schönbrunn Palace, Austria
4. Palace of Versailles, France
5. Wawel Castle, Poland
6. The Alhambra, Spain
7. The Royal Palace of Madrid, Spain
8. The Tower of London, United Kingdom
9. Neuschwanstein Castle, Germany
10. Bran Castle, Romania

For travellers looking beyond the usual city break circuit, the message is clear: Europe’s castles and palaces are not just surviving history. They remain some of the continent’s most powerful magnets for modern tourism.

Anthropic has quietly established a wet biology lab in the Bay Area, confirming a move that underscores how quickly frontier AI companies are moving from simulation to physical experimentation.

From Model Predictions To Real-World Testing

The company says its models are being used to support physical experiments in the lab, a step that brings AI closer to the realities of biological research. That matters because even the most sophisticated language model cannot validate a scientific hypothesis without evidence from the real world.

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“We believe that to do biology, the final test is still, and will be for a while, in real lab work,” Anthropic’s head of life sciences, Eric Kauderer-Abrams, told Reuters. “We absolutely are doing that today.”

According to Anthropic, the lab operates much like a traditional biotech facility, with some research conducted internally and other work carried out alongside external partners.

A Strategic Expansion, Not A Surprise

The move is consistent with Anthropic’s broader push into life sciences. In April, the company acquired Coefficient Bio, a stealth AI biotech startup, signaling that biological research had already become part of its longer-term strategy.

Anthropic has not disclosed what the wet lab is studying in detail, but it says the primary focus is fundamental biology rather than drug discovery. That distinction is important. The company has major relationships across the pharmaceutical sector and has made clear it does not want to position itself as a direct competitor to those customers.

That caution appears deliberate. Anthropic recently announced a partnership with Novo Nordisk on joint drug discovery, and it has already faced scrutiny over products perceived to overlap with offerings from some of its own clients.

Building For Life Sciences Without Alienating Pharma

To reinforce that balance, Anthropic this week introduced a Life Sciences Verification Program designed to give vetted bio researchers access to its most advanced models. It has also published research aimed at supporting drug development, including work on accelerating protein design and improving biomolecular modeling.

The message is clear: Anthropic wants a deeper role in the life sciences stack, but on terms that preserve trust with the pharmaceutical industry rather than threaten it.

Safety Warnings Continue To Shape The Debate

Still, the company’s biology ambitions are landing in an atmosphere of heightened anxiety about AI safety. In recent weeks, Anthropic researcher Jacob Coxon resigned after warning that “the people building AI earnestly believe that it could kill us all by the end of the decade.” Anthropic’s own alignment lead has put the odds of AI exterminating humanity within the next decade at greater than 10%.

Those concerns have become central to the company’s public posture. CEO Dario Amodei has called on the industry to slow down and adopt self-regulation, and he has repeatedly identified bioterrorism as one of AI’s most serious risks.

Why The Wet Lab Matters

That is what makes the new wet lab so striking. On one hand, it reflects the practical reality that AI systems must be tested against physical biology if they are to contribute meaningfully to medicine. On the other, it places a frontier AI company at the center of one of the most sensitive and heavily scrutinized areas of scientific research.

The juxtaposition has not been lost on the tech industry. Investor and AI coding startup founder Chamath Palihapitiya joked on X that the group behind “We’re All Going To Die” and “Regulate Me Now” is building a wet lab in San Francisco. His point, half in jest, was unmistakable: Anthropic’s expanding role in biology is as provocative as it is strategic.

If artificial intelligence were making the call, would you trust it to decide who receives a life-saving transplant?

A new study suggests that when large language models are asked to weigh scarce medical resources, they do not think like human doctors — and in some cases, they do not think like humans at all.

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AI Weighs The Wrong Things, Or At Least Different Ones

Researchers at Penn State University tested large language models using hypothetical kidney-allocation scenarios drawn from prior human research. In each case, the AI had to choose between two eligible patients competing for a single available kidney.

The patients were defined by traits such as age, health and drinking habits, allowing researchers to compare how AI systems prioritized competing factors against how people had previously made the same decisions in human studies.

The results were striking. Human participants tended to place more weight on age, often favoring younger patients. By contrast, many AI models gave greater priority to lower alcohol consumption. More importantly, the models frequently narrowed complex ethical judgments to a single attribute, while humans tended to consider the broader context.

“AI chatbots often diverge from human values in how they weigh a patient’s traits,” said Hadi Hosseini, who led the study at Penn State University. “They fixate on a single factor, like drinking habits, rather than balancing multiple considerations the way people do.”

Indecision Is A Human Feature — And An AI Weakness

Another key difference was hesitation. Human respondents often recognized that there is no single objectively correct answer in a scarcity decision such as organ allocation. Their choices reflected nuance, ambiguity and moral trade-offs.

The models, by contrast, typically committed to one answer with little sign of uncertainty.

That may seem efficient, but in high-stakes settings, certainty is not always a virtue. Decisions about kidneys, jobs or other scarce resources often involve values that cannot be reduced to a clean formula. Humans often absorb that ambiguity through discussion, debate and institutional safeguards. AI systems, the researchers argue, tend to skip over it.

“When we allocate something scarce, whether it’s a kidney, a job or access to some other resource, there isn’t always a single objectively correct answer,” said John Dickerson, chief executive officer at Mozilla.ai, who collaborated on the study. “Humans recognize that ambiguity and codify it via open debate into the allocative process. AI models often don’t.”

Why This Matters For Healthcare

The study arrives at a moment when AI is moving rapidly into healthcare, where it is already being used to support diagnosis, clinical workflows, treatment planning and the allocation of scarce medical resources.

That growing role makes the question of alignment especially urgent. In healthcare, the issue is not simply whether a model can produce an answer, but whether that answer reflects the moral standards and professional judgment that society expects from life-altering decisions.

Kidney allocation is a particularly sensitive example because it sits at the intersection of ethics, medicine and resource scarcity. Choosing one patient over another is never just a technical decision; it is a judgment about fairness, need, prognosis and social values.

“The ethical stakes are high, and AI’s role in such life-altering decisions requires deep reflection,” Hosseini said. “Moral decisions in settings like organ allocation directly determine who lives and who dies, so getting AI’s role in them right isn’t optional.”

The Broader Debate Over AI And Moral Judgment

The researchers say their findings speak to a wider debate over whether AI can make moral decisions — or whether it can ever truly align with human values.

That debate is no longer theoretical. As organizations increasingly rely on AI systems for recommendations, rankings and triage decisions, understanding how those systems reason has become a practical governance issue.

The study does not argue that AI should replace professional judgment in medicine. If anything, it reinforces the opposite conclusion: the more consequential the decision, the more important it is to understand where AI diverges from human reasoning.

In healthcare, as in business and public policy, the danger is not only that AI may be wrong. It may also be confidently, efficiently and consistently wrong in ways that humans would immediately question.

President Donald Trump has entered the artificial intelligence debate in familiar fashion: by reframing a policy fight as a political attack. In a series of Truth Social posts on Saturday, Trump dismissed growing concerns about AI safety and data center expansion as yet another Democratic “hoax,” while also floating a new name for the technology and hinting at a future federal AI czar.

Trump Rebrands AI, Then Calls Safety Concerns A Hoax

Trump first suggested that the term “Artificial Intelligence” sounds “inaccurate” and “ineloquent,” before posting a poll asking followers to choose a replacement: Superior Intelligence, Extreme Intelligence or Supreme Intelligence. The poll remained active at the time of publication.

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Hours later, he escalated the message, claiming that efforts to “decimate” or “destroy” AI were part of a broader pattern of Democratic deception, grouping the issue with everything from Russia and Ukraine to global warming and impeachment.

He offered no evidence for the assertion. Nor did he address the fact that skepticism toward AI and large-scale data center projects has emerged across the political spectrum, including in states such as New York, where officials have moved to pause permits for major new developments.

Data Centers Have Become A Political Flashpoint

Trump’s comments also reflect the growing backlash around the infrastructure behind AI. Across the country, communities and lawmakers have raised concerns about energy demand, water use, land consumption and the industrial footprint of hyperscale data centers. Supporters argue the projects bring investment and tax revenue; critics say the benefits are often overstated and the costs are local.

That tension has made data centers an unusually broad political target. Trump framed the criticism as an attack that began with data centers before shifting to AI itself, but the reality is more complex: opposition has come from both left and right, driven as much by zoning, utility constraints and environmental concerns as by ideology.

Trump Signals Support For The Industry — On His Terms

Even as he attacked the backlash, Trump insisted he would “cherish” the AI industry, help it and “watch over it” as it grows. He then said he is creating an AI Force, drawing a parallel to the Space Force launched during his first term.

He also said he will soon announce an AI “Czar,” adding that “only high I.Q. individuals need apply.” Trump did not outline what the role would do or how it would fit into the federal government’s existing technology and national security apparatus.

The position would follow the departure earlier this year of venture capitalist David Sacks from his post as Trump’s AI and crypto czar. Sacks later moved into a leadership role on the President’s Council of Advisors on Science and Technology.

AI Safety Debate Continues To Intensify

The president’s remarks come as the AI safety debate has sharpened in recent weeks. An AI researcher recently said he was leaving Anthropic over fears that leading companies believe the technology could pose a catastrophic threat by the end of the decade. Anthropic chief executive Dario Amodei has since published a plan aimed at slowing the frontier of development, a position that appears to have earned at least partial backing from OpenAI CEO Sam Altman and Elon Musk.

At the same time, critics argue that high-level existential warnings can obscure more immediate concerns, including labor displacement, misinformation, surveillance, discrimination and the concentration of power among a handful of tech giants.

That tension captures the heart of the current policy fight: whether AI safety is a genuine governance challenge, or a rhetorical cover for broader competition over who gets to shape the next major computing platform.

Nvidia’s Jensen Huang Backed Trump’s Framing

Adding to the momentum behind the industry, Nvidia chief executive Jensen Huang recently appeared alongside Trump at the All-In Summit and agreed with the president that the backlash against AI amounts to a “hoax.” Huang also said the industry would not allow a slowdown to take hold.

The remarks underscore how closely the AI boom now intersects with politics, industrial policy and corporate influence. What was once framed as a technical debate about models, compute and guardrails has become a far larger contest over power, regulation and economic advantage.

Moody’s Ratings has raised its outlook on Greece’s sovereign credit rating to positive from stable, while affirming the country’s Baa3 investment-grade rating, in a sign that the country’s reform agenda is beginning to translate into measurable credit strength.

The agency said Greece’s economic and fiscal resilience has been improving faster than it had anticipated, with reforms helping to support a higher structural growth rate and strengthen the government’s capacity to keep cutting public debt.

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Reforms Are Strengthening The Credit Case

Moody’s said the country’s greater resilience should help preserve the multi-year decline in debt, including through further early repayments of liabilities accumulated during the financial crisis.

The positive outlook also reflects increasing, though still incomplete, confidence that recent fiscal gains and political support for continued debt reduction will hold through the economic cycle.

Greece’s Baa3 rating, the agency said, is supported by a long record of reforms, favourable debt sustainability dynamics and a marked improvement in the public finances. But the credit profile is still constrained by high public debt, large external deficits, moderate productivity and a sizable stock of distressed debt outside the banking system.

Broader Reform Gains Are Beginning To Show

Moody’s said structural reforms are gradually easing long-standing barriers to investment and resource allocation, while bringing more businesses into the formal economy.

The agency pointed to progress in tax administration, business licensing, insolvency procedures, the justice system, land management, spatial planning, labour taxation and skills policy. Those changes have been accompanied by stronger employment, firmer exports and healthier private-sector balance sheets.

“The strength of the evidence varies across reform areas and remains uncertain in several of them, but the breadth of these positive signals increases the likelihood that their cumulative effect will prove significant for Greece’s credit profile,” Moody’s said.

An Investment-Led Growth Model Is Emerging

Moody’s also said Greece’s growth model has become more investment-focused and increasingly supportive of productivity. Private investment accounted for almost two-thirds of the five percentage point increase in the investment-to-GDP ratio since 2020, suggesting the recovery is broader than a temporary lift from the Recovery and Resilience Facility.

That matters. In effect, Moody’s argued that EU-backed support through grants, subsidised loans and complementary public infrastructure has reinforced an investment cycle already underway rather than creating it from scratch.

The agency estimated Greece’s potential growth rate at about 1.5 per cent, adding that the ongoing structural transition raises the possibility that both growth and fiscal resilience could outperform current expectations.

Debt Metrics Continue To Improve

Greece’s public debt fell to 146.1 per cent of GDP in 2025, down from 154.2 per cent in 2024 and far below the 209.4 per cent peak reached in 2020. Moody’s expects that ratio to decline further to 120 per cent by 2030.

Primary budget surpluses of roughly 2.5 per cent to 3.0 per cent of GDP are expected to support the reduction.

Digitalisation of transactions and employment has also reduced the scope for under-reporting income. Greece’s estimated VAT compliance gap fell to about 9 per cent in 2024 from 24 per cent in 2019, underscoring the impact of better enforcement and a more formalised economy.

Greece also repaid €5.3 billion of debt early at the end of 2025 and plans to repay a further €13 billion by the end of 2026. Moody’s said the move reduces gross debt and future servicing costs while signalling a continued commitment to balance-sheet repair.

Challenges Have Not Disappeared

Despite the upgraded outlook, Moody’s warned that Greece still faces structural headwinds. A deeply negative net international investment position and long-term demographic pressures will continue to weigh on labour supply and medium-term growth.

While Greek banks no longer carry the high levels of non-performing loans seen during the crisis, a substantial amount of distressed debt remains elsewhere in the economy. Greece’s debt burden is also likely to remain among the highest of all Moody’s-rated sovereigns through the end of the decade, even if its debt structure limits exposure to global interest rate shocks.

For investors, the message is clear: Greece is no longer simply a post-crisis recovery story. It is becoming a case study in whether sustained reform, fiscal discipline and investment-led growth can permanently alter a sovereign credit trajectory.

Anthropic has taken its first concrete step toward CEO Dario Amodei’s push to slow the pace of frontier AI development, announcing a new partnership with Accenture that embeds outside evaluators inside the company to assess safety practices and model behavior.

A Trial Run For Amodei’s AI Slowdown Proposal

The arrangement is the clearest early signal that Anthropic intends to put Amodei’s recent three-step framework into practice. The company said Friday it has selected Accenture as an “embedded evaluator,” a role designed to give third-party specialists employee-level access so they can test safeguards, conduct red-team exercises and help determine whether advanced systems are acting in line with human values.

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Anthropic said both companies plan to invest at least $1 billion over the next five years to build capacity in this area. But the company added that it will directly fund Accenture’s work for now, arguing that the current funding model is a stopgap until pooled industry or government financing exists.

Why Anthropic Is Moving First

Amodei’s proposal landed with unusual force in a sector already under intense scrutiny. Anthropic and OpenAI have faced mounting concern from researchers and safety advocates who warn that increasingly capable models could cause catastrophic harm if development outpaces safeguards. In that context, Anthropic is positioning itself as both a technical leader and a policy test case.

“Long-term, we think funding should come from pooled or government sources, as we called for in our Advanced AI Framework in June,” the company said. “As neither exists today, we plan to work with different evaluators under different funding arrangements.”

Accenture’s Role Inside Anthropic

Anthropic said it will initially embed employees from Faculty, Accenture’s specialist AI business, to evaluate safeguards, red-team models and assess model alignment. The partnership is not exclusive, and Anthropic said it is also in discussions with the research nonprofit METR and other potential partners.

That matters because the company is not outsourcing responsibility. Anthropic stressed that it remains accountable for the safety of its models and that outside evaluators will supplement, not replace, internal oversight. In practical terms, the structure resembles an internal audit function with independent specialists, rather than a detached consulting review.

A Governance Model The Industry May Have To Follow

Amodei said Saturday that Anthropic had “unilaterally” committed to the first part of his proposal and urged other AI companies to do the same. His plan has drawn support from some of the industry’s most visible figures, including OpenAI CEO Sam Altman and Tesla and SpaceX CEO Elon Musk. Others, including Nvidia CEO Jensen Huang, have dismissed the call for new regulation.

The debate is sharpened by Anthropic’s own business momentum. The company is widely expected to pursue a blockbuster IPO, raising a basic question for the market: can a frontier AI company slow down development, strengthen oversight and still preserve the commercial urgency that public investors expect?

The Bigger Question: Safety At Scale

Anthropic’s announcement does not settle the policy debate. But it does translate an abstract safety proposal into an operational model that other AI developers can study. If embedded evaluators can provide meaningful oversight without weakening execution, the approach could become a template for the next stage of AI governance.

For now, Anthropic is making a clear statement: in a race defined by speed, it wants to prove that discipline can still be a competitive advantage.

The Office of the Commissioner for State Aid Control and the Research and Innovation Foundation (RIF) have taken a step toward a more structured partnership, with senior officials meeting earlier this week to deepen institutional cooperation.

A More Formal Framework For Coordination

The meeting brought together State Aid Control Commissioner Stella Michaelidou and RIF board chairman Demetris Skourides. At the centre of the discussion was the creation of a clearer framework for collaboration, including regular communication and a more systematic exchange of knowledge and expertise.

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The proposed approach builds on an existing relationship between the two organisations. The commissioner’s office has already played a role in assessing, advising on and deciding matters linked to RIF schemes and programmes.

Early Consultation To Avoid Delays

Both sides underlined the value of involving the office at an early stage, before new RIF schemes and programmes are finalised. That, they argued, could help identify state aid issues sooner, reduce the likelihood of redesign later in the process and accelerate implementation.

For public bodies, this kind of early engagement can be the difference between a programme that moves swiftly from design to delivery and one that is slowed by compliance adjustments at a later stage.

Greater Use Of The Gber

The discussion also focused on wider use of the General Block Exemption Regulation (GBER), especially in the design of support schemes for small and medium-sized enterprises.

According to the commissioner’s office, proper application of the regulation can streamline procedures and speed up the rollout of compatible state aid measures. For policymakers, the appeal is clear: less administrative friction, faster approvals and a more efficient route to implementation.

Artificial Intelligence In Public Administration

Another major topic was the use of artificial intelligence in public administration. The two sides discussed how AI tools can improve productivity, process information more quickly and support public officials in their duties.

The conversation included RIF’s AI in Government programme, with both parties stressing that such tools must be deployed safely and responsibly. Protecting confidential and personal data, they said, remains essential, as does maintaining human oversight and final decision-making authority.

Training And A Possible Innovation Academy

The meeting also covered targeted training for RIF officials on state aid and related issues. In addition, the two sides explored the possible creation of a State Aid Innovation Academy, designed to build expertise in research, development and innovation funding, GBER implementation, assessment of innovative projects and the responsible use of AI and other digital tools.

Both parties said they intend to continue discussions and identify specific areas where cooperation can be expanded.

Artificial intelligence may be transforming enterprise operations at unprecedented speed, but a new study from SAS and IDC suggests that the organisations seeing the strongest returns are not simply those deploying more AI. They are the ones governing it better.

The second annual Data and AI Impact Report, titled The New Economics Of Trust, found that organisations applying what the researchers describe as trustworthy AI practices were 15 times more likely to report strong or high returns on investment from their AI initiatives.

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Governance Is Now A Performance Driver

According to the study, enterprises with the strongest governance, data quality and auditability practices reported at least twice the return on investment from AI deployments compared with other organisations. While SAS noted that these leaders still represent a relatively small segment of the market, their advantage is clear: trust is becoming a source of competitive differentiation.

At the other end of the spectrum, fewer than one in 20 organisations classified as laggards in trustworthy AI reported similarly strong returns.

“When AI works, it’s incredibly impactful,” said SAS chief technology officer Bryan Harris. “However, it is well documented that state-of-the-art agents can have error rates that exceed 25 per cent on complex tasks, which is unacceptable in high-stakes decision-making.”

Harris added that organisations must embed domain expertise into agentic workflows while keeping people at the centre of governance and oversight. “Organisations that do this successfully will close the trust gap and gain a competitive advantage in the market with AI,” he said.

Why Trust Is Becoming A Scaling Requirement

IDC vice president Chris Marshall said the challenge is intensifying as AI systems become more autonomous. “As AI becomes more autonomous, organisations face a new challenge: maintaining confidence in systems people don’t fully understand,” he said.

“Our findings show that stronger oversight, explainability, accountability and data foundations are becoming prerequisites for scaling AI successfully,” Marshall added.

The report found that employees frequently override AI-generated recommendations, with a lack of explanation emerging as the leading reason. Researchers said users were increasingly unwilling to rely on systems when they could not determine whether the output was correct or understand how the model reached its conclusion.

That trust deficit becomes more costly as AI systems take on greater autonomy. Manual corrections can slow workflows, reduce productivity and erode profitability, even when the underlying model is technically capable of making accurate recommendations.

According to the study, 97.2 per cent of users override AI-generated recommendations in at least some cases. The most common reason, regardless of whether employees believed the output was correct, was that the system could not explain its reasoning.

Autonomy Increases Both Opportunity And Risk

The report also found that trust declines as systems become more autonomous, falling from 76 per cent for generative AI to 66 per cent for agentic AI. That drop matters because organisations are increasingly deploying AI in settings where decisions are more consequential and less transparent.

SAS and IDC said the findings point to a widening performance gap between organisations that prioritise trustworthy AI and those that do not. The difference, they argued, is not necessarily the technology itself, but how it is managed and governed.

Organisations investing in trustworthy AI measures were 15 times more likely to report strong or high returns on investment, with 62 per cent doing so compared with 4 per cent among organisations without such practices. Those with the strongest trustworthy AI capabilities also reported 1.85 times greater gains across 13 business outcomes, including revenue growth, cost savings and customer experience.

The study further found that 85 per cent of AI leaders with strong trustworthy AI practices were increasing investment in the area by more than 10 per cent in 2026, a signal that the gap may widen further.

Data Foundations Remain A Bottleneck

Beyond governance, the research highlighted weak or outdated data foundations as another major barrier to adoption. SAS and IDC said many organisations are still deploying AI on underdeveloped data infrastructure, limiting their ability to deliver the transparency and explainability required for effective oversight.

Only 17.5 per cent of enterprises were found to have a fully optimised data infrastructure mature enough to meet the demands of agentic AI. Organisations with such a foundation were four times more likely to expect strong returns from AI projects and six times more likely to require data quality and explainability controls designed to build trust.

The study was based on a global survey of 2,699 decision-makers with knowledge of or influence over their organisations’ data and AI programmes across 28 countries and four industries: banking, insurance, life sciences and the public sector.

Industry Leaders Are Treating Governance Differently

In banking, 85 per cent of AI leaders had established AI governance frameworks, compared with just 29 per cent of organisations classified as laggards. The report said leading banks are increasingly viewing AI governance as both a competitive and operational issue, not merely a compliance obligation.

In the public sector, 41 per cent of leaders said they were increasing investment in trustworthy AI by more than 20 per cent in the coming year, one of the highest rates across the survey.

Life sciences showed another important pattern: 23 per cent of organisations had scaled AI across their entire companies, the highest proportion among the four sectors studied.

What The Report Means By Trustworthy AI

SAS and IDC define trustworthy AI as artificial intelligence designed to be reliable, fair, secure and compliant with regulatory requirements, while also being able to explain how it reached a decision.

The report argues that users and decision-makers across an organisation should be able to hold an AI system to a predetermined chain of accountability when its output is incorrect or absent. In practice, that means AI cannot be treated as a black box if it is expected to support high-stakes decisions at scale.

The companies assessed organisations across five dimensions of trustworthy AI and scored them out of 100. Those with an average score of at least 80 were classified as trustworthy AI leaders.

The five dimensions were data quality and governance, model governance and oversight, explainability and fairness, responsible AI policy, and audit and accountability.

The message from the report is unmistakable: in the next phase of AI adoption, the winners may not be those experimenting the fastest, but those building the strongest foundations of trust.

Disney is making a clear statement about where it wants to compete next: at the intersection of storytelling, data and artificial intelligence.

The company said Friday that it has hired Karandeep Anand, most recently chief executive officer of Character.AI, as senior executive vice president and chief technology officer, effective Oct. 2. The newly created role will report directly to Disney CEO Josh D’Amaro and is designed to sharpen the company’s technology strategy across the enterprise.

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A New Technology Mandate At Disney

Anand will oversee enterprise technology, infrastructure, data and artificial intelligence platforms, as well as product and engineering. Disney said he will work across technical teams to modernize how the company builds and delivers technology across the business.

“Karandeep brings a rare mix of experience across infrastructure, consumer technology and AI, and will be a vital addition to Disney’s senior leadership team as we further our three priorities: great storytelling as our North Star, technology in service of creativity, and operating as One Disney,” D’Amaro said in the company’s announcement.

The hire reflects a broader strategic shift under D’Amaro, who has emphasized that Disney must use technology not as a support function, but as a growth engine. Since taking the top job, he has focused on reinforcing the company’s strongest businesses — especially streaming and parks — while building new digital capabilities that can extend Disney’s intellectual property across more consumer touchpoints.

Streaming Sits At The Center

Disney+ remains central to that plan. D’Amaro has said the company is considering a free, ad-supported tier for the service as a potential “front porch” to bring more consumers into the ecosystem. The idea underscores a broader push to turn Disney+ into more than a subscription platform.

On Thursday, Disney also named Adam Smith chairman of direct-to-consumer for Disney Entertainment, further signaling the importance of streaming in the company’s next phase of growth.

Executives have also suggested that shopping and streaming will become more tightly connected on Disney+, with more details expected in the spring. At an investor conference earlier this month, CFO Hugh Johnston described the effort as an “integrated ecosystem” under the Disney+ banner — one that could bring together films, television, consumer products, parks, cruises and gaming through Disney’s vast library of intellectual property.

Why Character.AI Matters

Anand joins Disney from Character.AI, a platform that lets users create and interact with character-based chatbots. Disney said he led the company through a period of rapid growth while keeping user trust and safety at the forefront. In addition to Anand, Disney is also bringing in members of Character.AI’s technical team.

His background spans consumer technology, infrastructure and financial technology, with prior roles at Brex and Meta’s Facebook. That combination appears to fit Disney’s ambitions as it seeks to modernize internal systems while also building more personalized, scalable consumer experiences.

The move is also notable given Disney’s complicated history with Character.AI. Last year, Disney sent the startup a cease-and-desist letter over the use of copyrighted characters without authorization. Character.AI later said it removed the characters referenced in Disney’s complaint.

Still, the appointment suggests Disney is prepared to lean into the very technologies it once challenged — provided they can be used to reinforce its core assets and extend the value of its intellectual property. In an entertainment market increasingly shaped by AI, platform design and consumer data, that may prove to be a competitive necessity rather than an option.

Company websites: The Walt Disney Company, Character.AI

Cyprus cannot rely on diesel or natural gas as a long-term solution to its energy needs, according to Cyprus Institute president Stavros Malas, who argued that the country must pair renewable generation with durable energy storage if it is serious about lowering costs and strengthening energy security.

A Testbed For Cyprus’ Energy Future

Malas made the remarks on Friday during a visit by Deputy Minister of Research, Innovation and Digital Policy Nicodemos Damianou to the Cyprus Institute’s PROTEAS research facility in Pentakomo. He described PROTEAS as “a miniature version of Cyprus as it should be,” pointing to its role as a real-world platform for testing technologies in renewable energy production and storage.

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For an island economy with no interconnection to larger power systems, the challenge is not only how electricity is generated but also how it is stored and delivered when the sun is not shining. Malas said the transition must begin now “if we do not want to remain a country producing very expensive electricity.”

“Neither the fuel we have now, diesel, nor natural gas are long-term solutions,” he said.

Why Storage Matters As Much As Generation

The message reflects a broader reality facing energy markets worldwide: renewable generation alone is not enough. Without storage, solar and wind power can struggle to provide a stable, around-the-clock supply. For Cyprus, that issue is magnified by geography, limited grid flexibility and the costs of imported fuels.

Malas’ argument is that the country should treat storage as a core part of energy strategy, not an afterthought. In practical terms, that means investing in technologies that can capture excess power during periods of high production and release it when demand rises.

A Living Example Of Innovation

Damianou described PROTEAS as “a living example of what Cyprus wants to become,” framing the facility as a place where advanced technologies can be tested and translated into broader commercial and industrial use.

He said the site shows that Cyprus can evolve beyond the role of a passive energy consumer and instead become a destination for innovative firms developing solutions for a changing energy landscape.

“The reason I am here is to see and examine, as a government, these innovative solutions being implemented at PROTEAS,” Damianou said.

Research With Commercial And Climate Relevance

During the visit, the deputy minister toured the facility and received a briefing on its work, including research tied to climate change mitigation. He was accompanied by the deputy ministry’s director of research and innovation, Constantinos Kleovoulou.

PROTEAS is the Cyprus Institute’s experimental platform for the research and demonstration of renewable energy technologies, with a particular focus on concentrated solar thermal energy and energy storage. The Pentakomo site allows researchers to test solar technologies under Cyprus’ specific climatic conditions, with applications that could extend to other regions with similarly high solar potential.

The institute says its solar thermal research has also attracted interest from private companies, some of which have installed heliostats at the facility to support research and innovation efforts. That commercial interest suggests a wider opportunity for Cyprus: to turn its natural advantages into a platform for clean-tech development, investment and exportable expertise.