Category: 3. Business

Study design

This randomized, prospective study included 90 patients, aged between 18 and 65, with ASA classification I-II-III, who were scheduled for total knee arthroplasty surgery under spinal anesthesia. Patients with a history of bleeding diathesis, receiving anticoagulant treatment, allergies or sensitivity to local anesthetics and opioid drugs, patients with infection in the area where the block will be applied, pregnancy suspicion, pregnant or breastfeeding mothers, and patients who did not accept the procedure were excluded from the study. Ethical approval was obtained from the Bursa City Hospital Ethics Committee on 07.12.2022, protocol number 2022-17/3. Our study was registered on https://clinicaltrials.gov/ with the number NCT06084403. The patient recording and disribution were illustrated by the Consolidated Standards of Reporting Trials (CONSORT) flowchart (Fig. 1).

Patients were randomized into three groups, each containing 30 patients, using a computer program (Group 20 = group receiving ACB with 20 ml, Group 30 = group receiving ACB with 30 ml, Group 40 = group receiving ACB with 40 ml). The practitioners were blind to the data collection, and patients did not know which volume was applied. Postoperative pain scores and opioid consumption were recorded by a pain technician who was blind to the study.

Anesthesia application

After the patients were taken to the operating room, standard monitoring (electrocardiography, noninvasive arterial blood pressure, and peripheral oxygen saturation) was performed, and premedication was administered with 2 mg intravenous midazolam. For spinal anesthesia, the patients were positioned sitting, and following the necessary asepsis and antisepsis rules, a 25-gauge pencil-point spinal needle (B.Braun, Melsungen, Germany) was inserted into the subarachnoid space through a median approach from L3-4 or L4-5. After observing free clear cerebrospinal flow, 15 mg of hyperbaric bupivacaine was administered. To prevent postoperative nausea and vomiting, 4 mg of ondansetron was given intravenously. Total knee arthroplasty surgery was performed by the same surgical team using the same surgical procedure.

Block interventions

The block procedure was performed after the surgery was completed. All adductor canal blocks were performed by two experienced anesthesiologists with advanced clinical expertise in regional anesthesia. After ensuring aseptic conditions, a high-frequency linear ultrasound probe (GE ML6-15-D Matrix Linear, Boston, USA) was covered with a sterile sheath, and a 100 mm block needle (Stimuplex Ultra^®, Braun, Melsungen, Germany) was used. The linear ultrasound probe was placed medial to the patella, and the probe was advanced cephalad to visualize the femoral artery, the adductor hiatus, and the apex of the femoral triangle. Then, 5 ml of saline was injected under ultrasound guidance into the midpoint of the adductor canal to confirm the block site (Fig. 2). Then, local anesthetic solution containing bupivacaine at 0.25% concentration was administered as 20 ml in Group 20, 30 ml in Group 30, and 40 ml in Group 40.

After the operation, patients were taken to the postoperative recovery room. Patients reaching a score of 9 on the Aldrete scoring system were transferred to the ward.

Postoperative analgesia management

All patients received 0.5 mg/kg of intravenous tramadol and 20 mg of tenoxicam 30 min before the end of the surgical procedure. In the postoperative period, patients received 20 mg of tenoxicam intravenously twice a day. A patient-controlled analgesia (PCA) device was connected to all patients as soon as they were taken to the recovery room. The PCA was prepared with tramadol at a concentration of 5 mg/ml, with a lockout interval of 20 min and a bolus dose of 10 mg without a basal infusion. During the ward follow-ups, hourly NRS scores were monitored. If the NRS score was 4 or above, 0.5 mg/kg of intravenous meperidine was administered as a rescue analgesic. NRS scores at designated times as well as rescue analgesic needs and hours were noted in detail.

Postoperative pain assessment was performed using the NRS scoring system (0 = no pain, 10 = worst pain imaginable). Resting and moving NRS scores were evaluated and recorded at 2, 4, 8, 16, 24, and 48 h. If the NRS score was ≥ 4, 0.5 mg/kg of intravenous meperidine was administered as a rescue analgesic.

Motor block was evaluated using the modified Bromage scale (0: can move leg, foot, and knee freely; 1: normal knee and foot movements, but cannot raise leg straight; 2: cannot flex knee; 3: cannot move foot and knee) [8]. The need for rescue analgesics, postoperative opioid consumption, and side effects such as nausea, vomiting, and itching, as well as complications that may arise due to the block, were recorded.

Sample size calculation

The sample size calculation was performed using G*Power software version 3.1.9.2 (Kiel University, Kiel, Germany). Power analysis based on preliminary data on total opioid consumption [Group 20: 107.86 ± 42.17 (n = 7), Group 30: 88.57 ± 50.14 (n = 7), Group 40: 50.14 ± 51.2 (n = 7)] showed an effect size of 0.49, with a 95% confidence interval, an alpha error of 0.05, and a power of 0.95. Based on this effect size and confidence interval, a sample size of 22 patients per group was calculated to be sufficient. To account for potential dropouts, 30 patients were planned to be enrolled in each group.

Statistical analysis

Statistical analysis was performed with IBM SPSS v20.0 (SPSS Inc., Chicago, Illinois, USA) software package. The normality distribution of variables was checked with the Kolmogorov-Smirnov and histogram tests. Descriptive data were expressed as median [%25–75] and a number. Categorical variables were expressed as a number and analysed using the Chi-square and Fisher exact tests. Non-normally distributed continuous variables were analyzed using the Kruskal-Wallis test, followed by Dunn’s test for post-hoc analysis to assess differences among groups. For the statistical analysis, p < 0.05 was considered statistically significant.

Partnership aims to deliver faster agentic AI capabilities through IBM watsonx Orchestrate and Groq technology, enabling enterprise clients to take immediate action on complex workflows

Oct 20, 2025

ARMONK, N.Y. and MOUNTAIN VIEW, Calif., Oct. 20, 2025 /PRNewswire/ — IBM (NYSE: IBM) and Groq today announced a strategic go-to-market and technology partnership designed to give clients immediate access to Groq’s inference technology, GroqCloud, on watsonx Orchestrate – providing clients high-speed AI inference capabilities at a cost that helps accelerate agentic AI deployment. As part of the partnership, Groq and IBM plan to integrate and enhance RedHat open source vLLM technology with Groq’s LPU architecture. IBM Granite models are also planned to be supported on GroqCloud for IBM clients.

Enterprises moving AI agents from pilot to production still face challenges with speed, cost, and reliability, especially in mission-critical sectors like healthcare, finance, government, retail, and manufacturing. This partnership combines Groq’s inference speed, cost efficiency, and access to the latest open-source models with IBM’s agentic AI orchestration to deliver the infrastructure needed to help enterprises scale.

Powered by its custom LPU, GroqCloud delivers over 5X faster and more cost-efficient inference than traditional GPU systems. The result is consistently low latency and dependable performance, even as workloads scale globally. This is especially powerful for agentic AI in regulated industries.

For example, IBM’s healthcare clients receive thousands of complex patient questions simultaneously. With Groq, IBM’s AI agents can analyze information in real-time and deliver accurate answers immediately to enhance customer experiences and allow organizations to make faster, smarter decisions.

This technology is also being applied in non-regulated industries. IBM clients across retail and consumer packaged goods are using Groq for HR agents to help enhance automation of HR processes and increase employee productivity.

“Many large enterprise organizations have a range of options with AI inferencing when they’re experimenting, but when they want to go into production, they must ensure complex workflows can be deployed successfully to ensure high-quality experiences,” said Rob Thomas, SVP, Software and Chief Commercial Officer at IBM. “Our partnership with Groq underscores IBM’s commitment to providing clients with the most advanced technologies to achieve AI deployment and drive business value.”

“With Groq’s speed and IBM’s enterprise expertise, we’re making agentic AI real for business. Together, we’re enabling organizations to unlock the full potential of AI-driven responses with the performance needed to scale,” said Jonathan Ross, CEO & Founder at Groq. “Beyond speed and resilience, this partnership is about transforming how enterprises work with AI, moving from experimentation to enterprise-wide adoption with confidence, and opening the door to new patterns where AI can act instantly and learn continuously.”

IBM will offer access to GroqCloud’s capabilities starting immediately and the joint teams will focus on delivering the following capabilities to IBM clients, including:

• High speed and high-performance inference that unlocks the full potential of AI models and agentic AI, powering use cases such as customer care, employee support and productivity enhancement.
• Security and privacy-focused AI deployment designed to support the most stringent regulatory and security requirements, enabling effective execution of complex workflows.
• Seamless integration with IBM’s agentic product, watsonx Orchestrate, providing clients flexibility to adopt purpose-built agentic patterns tailored to diverse use cases.

The partnership also plans to integrate and enhance RedHat open source vLLM technology with Groq’s LPU architecture to offer different approaches to common AI challenges developers face during inference. The solution is expected to enable watsonx to leverage capabilities in a familiar way and let customers stay in their preferred tools while accelerating inference with GroqCloud. This integration will address key AI developer needs, including inference orchestration, load balancing, and hardware acceleration, ultimately streamlining the inference process.

Together, IBM and Groq provide enhanced access to the full potential of enterprise AI, one that is fast, intelligent, and built for real-world impact.

Statements regarding IBM’s and Groq’s future direction and intent are subject to change or withdrawal without notice, and represent goals and objectives only.

About IBM

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs, and gain a competitive edge in their industries. Thousands of governments and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently, and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM’s long-standing commitment to trust, transparency, responsibility, inclusivity, and service. Visit www.ibm.com for more information.

About Groq

Groq is the inference infrastructure powering AI with the speed and cost it requires. Founded in 2016, Groq developed the LPU and GroqCloud to make compute faster and more affordable. Today, Groq is trusted by over two million developers and teams worldwide and is a core part of the American AI Stack.

Media Contact:

Elizabeth Brophy

elizabeth.brophy@ibm.com

SOURCE IBM

Commercial insurance has long struggled to adopt new technology at the pace of other financial services. Manual workflows, outdated mainframes, and fragmented systems from years of mergers have slowed modernization efforts. Many insurers still view underwriting as an “art” rather than a process, which has historically delayed even basic digital upgrades.

Industry data underscores the substantial adoption gap across the insurance sector and beyond. In MIT Center for Information Systems Research’s global study of enterprise AI maturity, only 7% of organizations have fully embedded AI across operations, while most remain in pilot or mid-stage phases. At the same time, regulatory agendas are finally catching up. 

The EU AI Act came into effect in 2025, requiring insurers to categorize AI systems by risk level and comply with strict transparency rules. Meanwhile, the vast majority of enterprise data — more than 90% — is unstructured, stored in documents, contracts, and PDFs that are difficult to analyze without advanced tools.

This mix of legacy systems, compliance demands, and data challenges creates a critical inflection point for insurers. How can they adopt AI responsibly while ensuring ROI and minimizing risk? Drawing on insights from Sidharth Ojha, Head of Process Optimization, Data & AI at AXA XL, in a recent episode of the AI in Business podcast, this article explores how commercial insurers can modernize operations, empower teams to experiment, and lay the foundations for scaling.

This article examines three key insights from Ojha’s perspective on AI adoption in insurance:

• Empowering business users with low-code AI: Provide underwriters a compliant sandbox to experiment safely and uncover constraints early.
• Turning data into a strategic asset: Map data end to end and convert unstructured contracts into structured insights that drive growth.
• Building foundations for scalable AI: Standardize roles, processes, and data definitions to prevent pilots from stalling and unlock enterprise adoption.

Listen to the full episode below:

Guest: Sidharth Ojha, Head of Process Optimization, Data & AI, Global Chief Underwriting Office, AXA XL.

Expertise: Commercial Insurance Transformation, Process Optimization, and Applied AI

Brief Recognition: At AXA XL, Ojha leads initiatives to apply AI in underwriting and operations, balancing compliance with efficiency and cultural change. His experience spans legacy process modernization, regulatory alignment, and enabling practical AI adoption in one of the world’s largest commercial insurers.

Empowering Business Users with Low-Code AI 

Ojha sees that, among the clearest challenges for driving AI adoption in insurance, is cultural inertia. Executives often recognize AI’s potential but hesitate to let non-technical staff engage with it directly, which Ojha sees as a missed opportunity.

He describes the importance of creating “safe lanes” where underwriters and business users can test AI tools in controlled environments. By embedding low-code platforms into existing systems, insurers can enable experimentation without risking data leaks or regulatory breaches.

“Think of it like bowling with bumpers,” Ojha explains. “You want to let people take the shot, but keep them from rolling into the gutter.” His approach builds confidence and helps uncover limitations early, before a project absorbs significant budget or time.

In the past, insurance tech projects relied on extended handoffs: business analysts translated requirements, developers built systems, and architects ensured alignment. By the time solutions reached production, critical context was often lost. Low-code AI tools enable underwriters to interact with technology directly, bypassing translation layers and accelerating actionable feedback.

Ojha stresses that leaders should not rush to pilots or MVPs. Instead, they should allocate more time to exploration and failure in the sandbox phase.

“The more time you spend failing your hypotheses, the less time you waste scaling something that doesn’t work,” he notes. For an industry where “failure” carries negative connotations, reframing the need for failure tolerance as controlled testing can help insurers adopt AI more comfortably.

This cultural shift is essential for adoption. By giving underwriters direct but safeguarded access, organizations create buy-in and align tools with real business needs — rather than building in isolation and hoping for adoption later.

Turning Data into a Strategic Asset 

Ojha insists – as many previous podcast guests have – that technology alone cannot deliver ROI without clean, usable data. He notes that Insurance companies face a particularly steep challenge because most of their critical information is locked in unstructured formats, such as policy documents, endorsements, quotes, and schedules of values.

Ojha points out that five years ago, insurers struggled to do something as basic as reading a table in a PDF. Generative AI has solved many of these hurdles, but unstructured data remains diverse and inconsistent, making transformation into structured formats difficult:

“Most of the data insurers rely on isn’t even in their systems — it’s trapped in PDFs, Word documents, and scanned contracts. The real challenge isn’t reading it, it’s standardizing it. Each policy is unique, often written like a legal manuscript. Until we can consistently turn that unstructured data into structured information, every downstream AI use case — from risk analysis to pricing — will be operating in the dark.”

— Sidharth Ojha, Head of Process Optimization, Data & AI, AXA XL

The payoff is significant. With structured data, insurers can answer portfolio questions in seconds, such as: “Which policies exclude communicable disease?” or “How much exposure do we have across a region?”

During the COVID-19 pandemic, many organizations could not respond quickly to such queries. Today, AI tools offer the chance to avoid that blind spot.

Ojha also describes new possibilities in summarization capabilities among these systems. Beyond condensing documents, he notes that AI can compare client submissions against internal appetite and compliance rules. 

For high-volume underwriting teams, these capabilities mean touching more submissions per day, declining unsuitable risks faster, and focusing on profitable opportunities. “That’s not just efficiency,” Ojha stresses. “That’s real growth potential.”

For leaders, the mandate is clear: treat data as a first-class asset. Inventory policy wordings, target high-volume pain points, and build systems that push structured outputs back into core platforms. Done well, these steps transform AI from a cost-saving tool into a revenue driver.

Building Foundations for Scalable AI 

While pilots are familiar with insurance, scaling remains rare. Ojha estimates that “80-90%” of AI projects stall between proof of concept and deployment. The reasons are less about technology and more about organizational readiness.

He outlines the data infrastructure bottlenecks that often derail scaling AI operations in insurance:

• Unclear accountability for data fields, leading to inconsistent inputs.
• Fragmented processes, where teams record different levels of detail for the same product.
• Legacy stacks that are expensive to integrate with new AI models.
• Inconsistent definitions of key metrics across business units.

Without fixing these foundations, even promising pilots fail to expand. Ojha advises leaders to ask: If this solution went live across three countries tomorrow, what would break first? Addressing gaps in that framework upfront prevents costly surprises later.

Regulation also plays a role, and Ojha sees the EU AI Act as a turning point, providing categories that boards and regulators alike can trust. 

“If you are compliant with EU rules, you are largely compliant globally,” he notes, insisting that having such assurance can ease executive concerns and accelerate project approvals.

Ultimately, success comes from patience. Insurers are often eager to jump from idea to MVP, but Ojha emphasizes the value of deeper exploration and testing. Companies that invest in clarity of roles, process alignment, and data quality will find it easier to move AI from experimentation to enterprise-wide adoption.

The EU’s regulatory framework for data centres is quickly evolving, combining support and funding programmes with measures that pursue energy transition and climate goals. The European Commission (“EC”) will be putting forward a Data Centre Energy Efficiency Package in Q1 2026 – together with the Strategy Roadmap on Digitalisation and AI – the aim of which is to achieve carbon-neutral data centres by 2030. The implications of this package could be significant for key stakeholders, including investors and operators of data centres.

Data centres in the EU: balancing strategic investments and energy efficiency

In the push for EU digital sovereignty and global competitiveness, data centres are a critical infrastructure. The EU’s “State of the Digital Decade 2025” report emphasises the need for further private and public targeted investment in advanced connectivity infrastructure, secure and sovereign cloud and data infrastructures, and AI. While investments in data centres are poised to yield significant returns in growth and productivity, ensuring that the EU remains competitive and resilient in the digital age, such growth comes with substantial energy consumption.¹

To tackle these challenges, the EU has adopted several regulatory instruments, and recently announced that in Q1 2026 it will propose a new Data Centre Energy Efficiency Package alongside the Strategic Roadmap on Digitalisation and AI for the Energy Sector, aiming at making data centres carbon-neutral by 2030.

Evolving EU rules to address energy consumption of data centres

Over the last few years, we have seen rapidly evolving EU rules in relation to the data centers and their energy consumption. In sum:

• The (revised) Energy Efficiency Directive (“EED“) in 2023, adopts the ‘energy efficiency first’ as a core principle of EU energy policy.² Member States are mandated to prioritise this principle in all relevant policy decisions and significant investment choices across both energy and non-energy sectors. It includes requirements for monitoring and reporting, specifically mandating the assessment and disclosure of data centres’ energy performance. The information provided by data centres with an installed information technology power demand of at least 500 kW will be published in a ‘European database’. The key performance indicators that must be communicated to the European database are set out in the Delegated Regulation (EU/2024/1364) on sustainability “ratings“.³ The various Omnibus packages have so far not targeted the EED, although it remains to be seen if a specific Omnibus for the energy sector would be launched at some point in the future.
• The Taxonomy Regulation, which establishes a classification system and defines criteria for economic activities that are aligned with a net zero trajectory, as well as broader environmental goals.⁴ The EU Taxonomy Climate Delegated Act⁵, enshrines rules for classification of data centre-related activities with a view to climate change mitigation, building on the European Code of Conduct for Energy Efficiency in Data Centres⁶, which is a voluntary initiative that provides data centres with guidelines and best practices to reduce energy consumption.
• Energy efficiency requirements under the AI Act, which lays down harmonised rules on the development and use of AI in the EU.⁷ The AI Act imposes transparency requirements for General-Purpose AI Models (“GPAI Models“), which includes energy consumption reporting. The EC, together with existing EU standardisation organisations and stakeholders, will create standards focused on AI, aimed for example, at improving energy efficiency. While the AI Act does not include rules on data centres, it requires the EC and the Member States to create voluntary codes of conduct on energy efficiency of data centres.⁸
• EU funding programmes to take into account energy efficiency in data centres, for example, by supporting green projects through programmes like Connecting Europe Facility 2, Digital Europe programme, Horizon Europe, InvestEU and the Recovery and Resilience Facility.
• The EU Battery Regulation, which establishes stricter requirements on the design, production, and recycling of batteries, to promote sustainability and reduce environmental impact.⁹ Those requirements may impact energy storage in data centres, both in relation to the installation and recycling of batteries.¹⁰
• The Ecodesign Regulation for servers and data storage products, which establishes energy efficiency requirements for enterprise servers and online data storage products, typically used in data centres.¹¹ These products are subject to certain requirements, including minimum efficiency, maximum consumption in idle state and information of the operating temperature. They are also subject to circular economy requirements for the extraction of components and critical raw materials.
• The Report on EU Green Public Procurement criteria for data centres, server rooms and cloud services, which offers a set of guidelines to help public authorities procure data centres’ equipment and services in line with European policy objectives for energy, climate change and resource efficiency, as well as reducing life cycle costs.¹²
• The European High Performance Computing Joint Undertaking (EuroHPC JU)¹³, which aims to build and operate an interconnected EU supercomputing and AI infrastructure ecosystem, fostering technological sovereignty and competitiveness. The EuroHPC JU, with a budget of approximately EUR 7 billion for 2021–2027, provides financial support through open calls offering procurement as well as research and innovation grants. Some of the EuroHPC JU’s projects are particularly focused on sustainability.
• The EC regularly assesses the energy efficiency and sustainability of data centres, using various tools. The first technical report on this topic provides insights from the first year of implementation of these rules and the effectiveness of the current reporting scheme. It includes an assessment of the scheme itself, the reported data, and the user experience of the reporting entities.¹⁴
• The application of State aid rules to sustainable data centres. The call for evidence for the new Cloud and AI Development Act acknowledges the potential role of financial support in line with applicable State aid rules to data centres with a high sustainability contribution, with the aim to increase capacity. In addition, the Clean Industrial Deal State Aid Framework (CISAF) supports the development of clean energy, industrial decarbonisation and clean technology, which may also have an impact on data centres.

Looking ahead

2026 is set to bring new regulatory developments. In Q1 2026, the EC will roll out a proposal for a Data Centre Energy Efficiency Package alongside the Strategic Roadmap on Digitalisation and AI for the Energy Sector.

A public consultation on the Strategic Roadmap is already ongoing and it is open until 5 November 2025, emphasising its goal to leverage the potential of digital and AI technologies for the energy system, while mitigating associated risks and supporting the competitiveness and decarbonisation of the EU economy. This would include measures to sustainably integrate data centres’ electricity demand into the broader energy system.¹⁵

The EC is also expected to publish a Cloud and AI Development Act in Q4 2025 or Q1 2026, aimed at increasing Europe’s cloud and AI infrastructure capacity.¹⁶ The goal of the proposal will be to triple EU data centre processing capacity in the next 5-7 years and allow for simplified permitting and other public support measures, if they comply with requirements on energy efficiency, water efficiency, and circularity.

Finally, on 8 October 2025, the EC published its Apply AI Strategy, where it refers to the Strategic Roadmap and Cloud and AI Development Act as including strategies to improve energy efficiency in data centres.¹⁷

To conclude, in the context of the ongoing simplification drive of EU regulation under the various Omnibus packages, energy consumption by data centres and AI infrastructure remains high on the EC’s agenda. So much is evident from the core strategies, consultations and action plans that the EC has published over the past months. Even though stakeholders are still grappling with a quickly evolving regulatory framework, new energy efficiency measures are already on the horizon, with more details expected in Q1 2026. At the same time, investors, developers and operators of data infrastructure will also be able to benefit from the various support measures that the EU and the Member States have rolled out and are expected to further deploy in the future.

Elisabetta Zuddas (White & Case, Legal Trainee, Brussels) contributed to the development of this publication.

_{1 State of the Digital Decade 2025 report of 16 June 2025, available here.
2 Directive (EU) 2023/1791 of 13 September 2023 on energy efficiency and amending Regulation (EU) 2023/955, available here. The EED sets a (revised) EU energy efficiency target, making it binding for EU countries to collectively ensure an additional 11.7% reduction in energy consumption by 2030.
3 Commission Delegated Regulation (EU) 2024/1364 of 14 March 2024 on the first phase of the establishment of a common Union rating scheme for data centres, available here. The European Commission has published a user manual (available here) for accessing the European database, a reporter guide (available here) and frequently asked questions and guidance (available here).
4 Regulation (EU) 2020/852 of 18 June 2020 on the establishment of a framework to facilitate sustainable investment, and amending Regulation (EU) 2019/2088, available here.
5 Commission Delegated Regulation (EU) 2021/2139 of 4 June 2021 supplementing Regulation (EU) 2020/852 of the European Parliament and of the Council by establishing the technical screening criteria for determining the conditions under which an economic activity qualifies as contributing substantially to climate change mitigation or climate change adaptation and for determining whether that economic activity causes no significant harm to any of the other environmental objectives, available here. The Taxonomy is being revised as part of the Omnibus simplification process, see W&C Client Alert ‘EU Omnibus Package: 10 things you should know about the proposed changes to key sustainability legislation‘.
6 European Code of Conduct for Energy Efficiency in Data Centres of 21 March 2025, available here.
7 Regulation (EU) 2024/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act), available, here.
8 See also W&C Client Alert ‘Energy efficiency requirements under the EU AI Act | White & Case LLP‘.
9 Regulation (EU) 2023/1542 of 12 July 2023 concerning batteries and waste batteries, amending Directive 2008/98/EC and Regulation (EU) 2019/1020 and repealing Directive 2006/66/EC, available here.
10 See also W&C Client Alert ‘New EU Batteries Regulation: introducing enhanced sustainability, recycling and safety requirements | White & Case LLP‘.
11 Commission Regulation (EU) 2019/424 of 15 March 2019 laying down ecodesign requirements for servers and data storage products pursuant to Directive 2009/125/EC and amending Commission Regulation (EU) No 617/2013, available here.
12 Development of the EU green public procurement (GPP) criteria for data centres, server rooms and cloud services report of 8 June 2020, available here.
13 The European High Performance Computing Joint Undertaking (EuroHPC JU), available here.
14 Assessment of the energy performance and sustainability of data centres in EU report of July 2025, available here.
15 Strategic Roadmap for digitalisation and AI in the energy sector – consultations opened, available here.
16 AI Continent – Initiative on new cloud and AI development act, available here.
17 Apply AI Strategy, available here. A public consultation and call for evidence closed on 4 June 2025, feedback available here.}

_{White & Case means the international legal practice comprising White & Case LLP, a New York State registered limited liability partnership, White & Case LLP, a limited liability partnership incorporated under English law and all other affiliated partnerships, companies and entities.}

_{This article is prepared for the general information of interested persons. It is not, and does not attempt to be, comprehensive in nature. Due to the general nature of its content, it should not be regarded as legal advice.}

_{© 2025 White & Case LLP}

GENEVA (ILO News) – Gilbert F. Houngbo, Director-General of the International Labour Organization (ILO), has urged international financial leaders to place decent work and social justice at the heart of their policy agendas, stressing that robust labour institutions are essential to confronting rising geopolitical tensions and trade disruptions.

In written statements delivered to the World Bank Group / International Monetary Fund (IMF) Annual Meetings in Washington D.C., Houngbo emphasized that decent work policies, including minimum wage systems, collective bargaining and social protection, are essential for sustainable and inclusive development.

The ILO Director-General noted that there have been meaningful gains: inequality between countries has declined since the early 2000s and over half the world’s population now has some form of social protection.

Yet he also warned that persistent structural challenges threaten these gains. “As uncertainty in the global economy persists with shifting geopolitical tensions and trade disruptions, the importance of building institutions that foster decent work for all could hardly be more critical,” the ILO Director-General stated.

The ILO forecasts global employment growth at only 1.5 per cent in 2025, with the creation of 53 million new jobs, down from 60 million previously projected. Around 84 million workers, mostly in Asia and the Pacific, face elevated risk due to trade uncertainty. For its part, informal employment continues to outpace formal employment, with 58 per cent of the global workforce remaining in informal employment in 2024.

“These trends underscore ongoing challenges in translating economic growth into formal economy and decent employment opportunities,” Houngbo noted.

The ILO Director-General highlighted that even as global output per worker grew by 17.9 per cent from 2014 to 2024, the labour income share declined from 53.0 per cent to 52.4 per cent.

“Had the labour income share remained at its 2014 level, global labour income would have been US$1 trillion higher in 2024, and each worker would have earned an additional US$290 on average that year.”

Houngbo stressed the importance of minimum wage systems and institutions for collective bargaining to address low pay and wage inequality.

On the future of work, Houngbo addressed the disruptive potential of generative AI, as nearly one in four workers could see their role significantly transformed, with women disproportionately affected, according to ILO estimates.

“Whether AI adoption ultimately leads either to job losses or to complementarity depends on how technology is integrated, management decisions, and – fundamentally – the role of social dialogue between employers and workers in shaping implementation,” he said.

In concluding remarks, Houngbo called for coordinated policy action under a renewed social contract.

“The real challenge is not an inherent conflict between economic and social objectives, but rather the need to take coordinated action that transforms this potential dilemma into a dynamic, mutually reinforcing synergy.”

He stressed that a renewed social contract, anchored in democratic governance, inclusive dialogue, and people-centred policies, provides the institutional foundation and political legitimacy required to sustain progress.
 

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The IAEA has launched a new research project to enhance computer security for artificial intelligence systems that may be used in the nuclear sector. The project aims to strengthen computer security strategies to support the adoption of artificial intelligence-enabled technologies by nuclear facilities, including those for small modular reactors and other nuclear applications.

Artificial intelligence (AI) and machine learning (ML) systems are being deployed across the nuclear industry, offering potential benefits such as improved operational efficiency and enhanced security measures, including for threat detection. However, these technologies also create new computer security concerns that require innovative solutions. Risks include manipulation of data or information being used to teach or run an AI system. Minimizing such risks will involve robust information security and ensuring it is being used correctly.