Overview
In 2025, Laerdal Medical launched and scaled multiple AI initiatives under the Samaritan AI
framework. This is a comprehensive, responsible AI framework powering life-saving
training globally. These initiatives strategically integrate AI technologies, including
conversational AI, computer vision, and natural language processing, to address critical
gaps in for example emergency response preparedness from bystanders and in clinical
nursing education.
The Samaritan AI Framework
Samaritan is Laerdal’s responsible AI framework built on five pillars: Mission, Trust,
Fairness, Compliance, and Sustainability. It serves as the ethical and technical foundation
for all AI features, ensuring artificial intelligence enhances learning outcomes while
maintaining safety, privacy, and medical accuracy standards.
The framework powers two flagship AI initiatives in 2025:
1. vrClinicals for Nursing with Conversational AI
vrClinicals for Nursing is an immersive VR simulation solution that enables nursing
students to practice clinical judgment in realistic multi- and single-patient scenarios. In the
spring of 2025, Laerdal introduced conversational AI capabilities powered by Samaritan,
allowing students to engage in natural voice conversations with virtual patients.
Students practice communication skills, patient prioritization, delegation, and time
management where AI-powered virtual patients respond naturally, preparing nurses for
real-world clinical challenges.
The addition of Conversational AI to vrClinicals for Nursing, helped secure several new, big
adoptions, resulting in 267% growth in sales and 500% growth in number of sessions from
2024 to 2025. This growth is mirrored by the experience of learners who find that the AI-
driven conversations provide a vital, safe space to practice communication and build
rapport with patients, leaving them feeling more confident than ever, before they begin
working with real people. Some students even report that it has helped reduce social
anxiety.
Students practice communication skills, patient prioritization, delegation, and time
management where AI-powered virtual patients respond naturally, preparing nurses for
real-world clinical challenges.
2. RevivR AI – Emergency CPR Training for Everyone
RevivR is a free, interactive, 15-minute online CPR training course developed by Laerdal
and the British Heart Foundation (BHF) that teaches lifesaving cardiopulmonary
resuscitation (CPR) and defibrillator use for the general public. Accessible via mobile,
tablet, or desktop, it provides a personalized training session requiring only a cushion to
practice chest compressions, culminating in a certificate.
In 2025, Conversational AI was added (in a beta version), introducing an interactive
emergency dispatch call simulator where learners practice calling 999 and speaking with
an AI dispatcher. The RevivR’s AI dispatcher training addresses hesitation to call
emergency services, reducing time-to-call in real emergencies and thus directly correlating
with improved cardiac arrest survival rates.
Following the implementation of conversational AI, users demonstrated improved accuracy
on the final quiz, with the product share rate increasing from 4% to 9%. Additionally, there
was a 57% rise in self-efficacy for cardiac arrest recognition, indicating that participants
are now substantially more likely to apply these skills effectively in real-world scenarios.
Frende Forsikring has launched an ambitious automation initiative aimed at transforming claims handling for travel and contents insurance. The project combines generative AI with robotic process automation (RPA) to create intelligent agents that streamline complex workflows and deliver faster, more accurate outcomes for customers.
The solution automates a wide range of tasks traditionally handled manually, including estimating claim amounts with deductions for age and deductibles, processing payments, generating and sending customer letters (such as confirmations, rejections, and informational updates), and gathering critical data like claim history, coverage details, and product information. For approved claims, the process is fully automated from start to finish, while rejections are validated through a “human-in-the-loop” approach to ensure fairness and compliance.
Currently, the system supports around 20 different outcomes for each insurance type, enabling nuanced decision-making and personalized communication. By leveraging generative AI, Frende ensures that customer interactions remain clear and empathetic, while RPA agents handle repetitive tasks with speed and precision. This combination reduces operational complexity and frees employees to focus on high-value activities.
The impact is already measurable: the automation corresponds to about a quarter of a full-time equivalent (FTE) annually, and the scope is expanding rapidly. As the project scales, Frende expects significant efficiency gains, improved customer satisfaction, and reduced turnaround times for claims. Beyond time savings, the initiative strengthens risk management and compliance by standardizing processes and minimizing human error.
This project reflects Frende’s commitment to being a highly data-driven organization. AI is embedded across the company’s operations, and this solution exemplifies how advanced technologies can deliver tangible benefits for both customers and employees.
In 2025, Novo Nordisk accelerated an enterprise-wide AI transformation that spans research, development, and enterprise operations.
The company’s AI platforms unify data and models, provides a copilot for advanced reasoning, enables governance of “reasoning chains,” and functions as an “AI factory” to build, reuse, and fine-tune models across use cases spanning regulatory affairs, early research, discovery, and trial design. Early outcomes include predictive AI models for advanced cardiovascular risk detection that outperform leading clinical standards, pointing to meaningful clinical impact for precision medicine. [1] Another key result is a Co-Scientist ecosystem — an agentic AI layer that surfaces clinical insights in seconds, democratizing access to data (now used by over 20% of development), automating workflows, and improving trial design. It is expected to deliver >$157M in net new value and >$14M in operational efficiency gains over the next five years. [2]
To meet compute demands, Novo Nordisk leverages the Gefion national AI supercomputer (funded by the Novo Nordisk Foundation) to scale training and simulation for drug discovery—enabling larger experiments in protein engineering, biological modeling, and generative molecule design. In parallel, a strategic collaboration with NVIDIA advances customized GenAI models and agentic workflows for early research and clinical development, including single-cell response prediction, drug-like molecule generation, and biomedical LLMs powered by NVIDIA BioNeMo for computational drug discovery. This collaboration includes several AI research programs, including using single-cell models to predict cellular responses to drug candidates and structures, as well as designing models to build molecules with drug-like properties. The companies will also collaborate on tapping Novo Nordisk’s vast global scientific literature to build biomedical large language models, enabling researchers to uncover correlations between genes, proteins and diseases. [3]
The company is executing a company-wide Agentic AI strategy. Internally, agentic frameworks such as Nautilus (assisting researchers), FounData (assisting with analysis of clinial data), MedIQ (assisting field workers) and Biological Reasoning (contributing to drug discovery) are already deployed, with targeted initiatives to replace entire specialized research workflows with agentic AI to accelerate the scientific cycle. Looking ahead to public science impact, Novo Nordisk will present models at ECO 2026 that support innovative identification of the highest-need patients for obesity treatment—work with potential to significantly reduce obesity related comorbidities, a significant challenge to society. [4] Responsible AI is a standing priority, overseen by an internal committee that guides safety, governance, and ethics across platform, agents, and use cases [5]
The company also operates a GenAI platform with 30,000+ users, access to leading foundation models from major providers, and more than 250 billion tokens consumed in 2025—on the order of 40 times the full text scale of Wikipedia—demonstrating production-grade adoption and impact at scale. [6, 7]
To expand frontier capabilities, Novo Nordisk is advancing partnerships that explore multi-agent systems and foundation models in real-world data (RWD) contexts. A collaboration with Google Health is evaluating how multi-agent systems can enhance data science productivity and uncover new RWD-informed hypotheses. On the modeling frontier, Novo Nordisk is progressing protein LLMs to predict attributes and functions from molecular sequences, informing target discovery, protein engineering, and developability assessments. In addition, they entered a partnership with QPurpose to explore potential in Quantum Compute in relation to e.g., AI modelling. [8]
Organizationally, Novo Nordisk appointed a Chief AI Officer and executed a re-organization that places Enterprise AI at the center of its operating model—aligning leadership, funding, security, and accountability behind AI-enabled value creation. Together, these moves strengthen governance and scale for agentic systems, LLMs, and scientific AI. [9]
Altogether, Novo Nordisk’s 2025 achievements demonstrate a coherent, outcomes-focused AI operating model: a governed GenAI platform reaching tens of thousands of users; Gefion supercomputing for large-scale training and simulation; NVIDIA collaborations for customized models and agentic workflows; and a responsible AI governance structure. The result is faster, safer discovery cycles; improved reproducibility and explainability; and clinically relevant advances with potential for population-scale benefit in areas like cardiovascular and obesity care.
Aura Guidance is an AI-powered onboarding companion developed by CCP Games as part of the EVE Evolved initiative. It is designed to help new players navigate EVE Online’s famously complex sandbox by providing contextual, data-informed answers directly within the game client.
EVE Online is widely recognized for its depth and freedom, but that depth can create significant onboarding friction for new players. Historically, rookies relied on volunteer player support in chat channels or external third-party resources to answer foundational questions. Over a nine-month period, players submitted more than 5.8 million messages in the in-game Rookie Help channel alone. This volume highlights both the strength of EVE’s community and the scale of the accessibility challenge.
Aura Guidance transforms this vast body of historical player support knowledge into a structured AI assistance system. Built using insights from millions of real player interactions, the tool surfaces relevant, context-aware answers during a new player’s early experience. Responses are informed by in-game context, such as location and ship type, increasing relevance and reducing confusion.
Importantly, Aura Guidance is not generative AI and does not create content, art, or gameplay systems. It is designed as a responsible, bounded assistance layer grounded in curated support data. When the system does not have sufficient confidence in a response, it redirects players to the Rookie Help channel, ensuring that community support remains central.
The feature launched as a controlled A/B test, allowing CCP to measure behavioral and retention impact between players with and without access to the system. This ensures the solution is evaluated based on measurable outcomes rather than perception.
Aura Guidance addresses a core business and design challenge: reducing early friction without simplifying the game’s depth. By making foundational knowledge more accessible, CCP strengthens player confidence, supports retention, and enhances the long-term sustainability of EVE Online’s complex ecosystem.
Ørsted and Amazon Web Services (AWS) teamed up to solve a practical engineering challenge in offshore wind: understanding the thermal conditions around undersea export cables that carry power from offshore wind farms to the onshore grid. These cables are critical, high-value assets. Their safe operating capacity depends on how efficiently heat from dissipates into the seabed. That makes accurate temperature prediction essential for reliability and long-term value.
Historically, thermal modelling relied on seabed property estimates (such as thermal resistivity) gathered early in a project’s design phase. These estimates are based on samples plus interpretation of ground modelling techniques. However, the models can never fully capture the reality, as complex effects from seasonal variations, complex material structures and disturbances from installation cannot be fully captured by modelling. The models deal with this uncertainty by making conservative assumptions, allowing a working model, but conservative, estimate to be made. That can lead to overly cautious loading limits and underused capacity.
To reduce this uncertainty, we worked with AWS to build a data-enhanced, physics-based modelling and optimisation framework. It combines real operational measurements with scalable cloud computing to improve how Ørsted estimates seabed thermal properties at a local level.
The solution brings together:
1. Operational datasets, including distributed cable temperature measurements, burial depth surveys, and environmental inputs.
2. A physics-based thermal model, TEEM (Thermal Electrical Equivalent Model), which simulates heat transfer by mapping thermal behaviour to electrical analogues.
3. Iterative optimisation methods (including gradient descent and Simultaneous Perturbation Stochastic Approximation, SPSA) that adjust uncertain parameters until model outputs align with measured temperatures.
4. Statistical techniques, such as bootstrap confidence analysis and temporal modelling, to validate results and distinguish genuine seabed variability from model artefacts.
5. Massively parallel processing on AWS infrastructure to run thousands of simulations efficiently, making high‑resolution calibration feasible in engineering timeframes.
With this approach, our engineering teams can move from broad, conservative seabed assumptions to more local, data-driven parameter estimates. That improves the accuracy of temperature predictions under different operating and environmental conditions. It also helps us understand where our assumptions can be updated with confidence, thus supporting safer operation, better utilisation, and more consistent performance across the cable network.
Cloud-based scaling on AWS made the work practical. Analyses that would previously have been difficult to run at the needed resolution could be completed quickly, supporting timely engineering decisions without disrupting operations.
The project also sets a clear direction for future development: a living, model-based capability “AI‑enabled cable digital twin” that can be recalibrated as new data arrives, strengthen anomaly detection, and inform next-generation export cable design and planning.
At its core, this project is an industrial AI system that learns from IoT‑style time‑series data to continually update a physics model of a critical asset. Overall, this initiative shows how data, AI and scalable computation can strengthen core engineering models and deliver tangible value in critical renewable energy infrastructure.
Foreign objects—such as rock bolts, drill rods, plastic poly-pipe, tree roots, vent bags, giant boulders, or other debris—pose serious risks in conveyor systems, particularly in mining and processing operations.
These items can:
Tear or rip conveyor belts
Damage or destroy rollers
Block or obstruct chutes and screens
Drive up maintenance expenses
Trigger expensive production halts from unscheduled downtime
Lead to serious safety hazards, including rocks or materials falling (“raining down”) from elevated belts
Rapid identification and removal of these foreign objects—regardless of their size or type—prevents major damage, minimizes costly interruptions, and enhances overall operational safety.
To address these risks, Boliden—a leading Swedish mining company—has developed an innovative AI-powered computer vision system that detects foreign objects on conveyor belts in real time.
The in-house-developed solution uses advanced cameras and AI algorithms to continuously monitor the belt, instantly identifying anomalies such as oversized boulders, tools, rock bolts, or other debris that could cause damage.
Upon detection, the system automatically triggers an emergency stop of the conveyor, preventing tears, roller failures, chute blockages, and potential safety incidents.
What sets this apart as a world-first in the mining industry is its direct integration: the AI doesn’t just alert operators—it issues commands that immediately act on the control system to halt operations, representing a pioneering step in autonomous, AI-driven process intervention for enhanced safety and reliability.
This breakthrough minimizes unscheduled downtime, reduces maintenance costs, and significantly improves overall operational safety in harsh mining environments.
Summary: The “Maximus” Transformation
In 2025, Lomax moved from ad-hoc AI experimentation to a structured, governed transformation designed to scale responsibly and create measurable value. The model was scaled across departments and embedded deeper into operational workflows. The approach was built on two synchronized tracks:
Track 1 — Organizational Anchoring (governance, adoption, and responsible use)
Lomax established a clear operating model to ensure AI is deployed with leadership ownership, accountable execution, and broad adoption:
• AI Steering Group (CEO, Head of HR, Head of Strategy, Head of Digital): prioritization, governance, and strategic alignment.
• AI Group (execution and technical expertise): delivery of AI tools and projects, model training, prompt development, standards, enablement, and departmental support.
• AI Ambassadors (cross-department network): local change agents and subject matter contacts, feeding use cases and driving adoption.
To reduce risk and increase trust, Lomax implemented practical guardrails by ensuring centralized management of AI licenses, internal AI guidelines, and a default human-in-the-loop approach for AI outputs.
Mandatory training was rolled out via a training platform: short modules on AI fundamentals and Lomax AI guidelines, followed by a quiz to ensure understanding. As of 2025, ~80% of employees completed the training and all new hires are required to complete it.
Track 2 — Value Creation (Maximus: AI embedded in workflows)
In parallel, Lomax developed and deployed Maximus: a centralized suite of in-house developed AI tools integrated into day-to-day work. Maximus is depicted as a red Viking-android persona that makes it easier for employees to refer and relate to. This has been a central part of the successful AI adoption across departments. Maximus is designed as modular applications so model components can be upgraded over time, while workflows remain stable.
Key Maximus solutions and outcomes:
• Maximus Product Translator (from Danish to Swedish)
Reduced translation cycle time significantly. Where translating 250 products previously required 4 weeks, the new workflow demonstrated translation of 250 products in 1 hour, lowering time-to-market and reducing dependency on external agencies while freeing capacity for quality control and review.
• Maximus Data Lab (product text optimization at scale)
Automatically optimizes and generates product titles, punchlines, highlights, and descriptions for both Google and the website across 41,000+ products, improving consistency, discoverability, and customer experience.
• Maximus MaxInsights (voice-of-customer + experimentation engine)
Aggregates and analyzes customer feedback across sources (NPS feedback, Trustpilot, on-site surveys) and drives structured experimentation. Using AB Tasty and best-practice testing rules, Lomax executed 30 A/B split tests based on the findings in the customer feedback and the accumulated impact contributed to a 20% site conversion rate uplift (defined as the accumulated CVR effect of implemented winning tests plus avoided potential CVR losses from losing tests that were not rolled out).
• Maximus Media Library (visual quality at scale)
Upscales images and generates lifestyle backgrounds to improve visual consistency and illustrate use cases for a better customer experience.
Architecture pattern across Maximus applications:
Input → Enrichment → Generation → Human Approval → Publishing
Approval is either full review or controlled sampling depending on risk and use case. In the Media Library, every image is approved in a side-by-side module (original vs AI-generated). In Data Lab, controlled sampling is made before approving all text outputs.
Model approach (selected examples):
• Text generation uses OpenAI fine-tuned models (GPT-4o fine-tuning) with continuous iteration based on expert-produced data (e.g., SEO team outputs for Google-optimized product text).
• Customer feedback categorization and translation are implemented modularly so the underlying model can be updated to the newest version.
• Image generation/upscaling uses a Gemini-based image model, applied within an approval workflow.
Adoption and productivity impact
By October 2025, Lomax achieved broad behavioral adoption: 71% of employees used AI tools weekly (defined as using any AI tool weekly—Maximus, ChatGPT, Claude, etc.). Average time savings reached 2 hours per employee per week, equating to 296 hours saved weekly across the organization. Time savings were measured through a survey sent to all office employees (N=176; respondents=132) using a professional survey tool that prevents duplicate responses.
Platform is leveraging best of breed LLMs from Anthropic and fit-for-purpose software products to deliver scalable solutions for all parts of Nordea.
Most organisations talk about AI readiness. Saab has built AI into a fighter aircraft.
Gripen is not a platform with AI bolted on as a future aspiration. It is an operational combat aircraft with AI running at the core of its mission systems today. That distinction matters enormously. Moving AI from controlled research environments into a safety-critical, real-time airborne system, one where the consequences of failure are absolute, requires a fundamentally different standard of rigour, architecture, and design philosophy. Saab has met that standard, and in doing so has created one of the most advanced and credible AI implementations in any safety-critical system anywhere in the world.
At the heart of the Gripen AI framework is a sensor fusion and decision-support pipeline of exceptional sophistication. The system continuously ingests data from radar, electronic warfare suites, electro-optical and infrared sensors, datalinks, and the wider battlespace network, synthesising this into a single, highly curated tactical picture in real time. Machine-learning-based classification, probabilistic reasoning, and rule-based tactical logic operate in combination to identify and prioritise threats, propose manoeuvre options, recommend weapon employment, and dynamically re-plan as situations evolve. All of this happens within the stringent timing and determinism constraints of air combat, on certified airborne computing hardware. This is not a proof of concept. It is a working system delivering measurable results.
What makes Saab’s approach genuinely distinctive is the deliberate choice to combine data-driven AI with explainable, deterministic logic and human-centred design. The Gripen AI does not attempt to replace the pilot. It acts as a cognitive partner: absorbing information overload, surfacing the most relevant insight, proposing courses of action with clear rationale, and keeping the human in authoritative control of every consequential decision. This is responsible AI in the most demanding sense of the term, not as a compliance posture, but as an operational necessity. A pilot who does not understand or trust what the AI is telling them will not act on it. Saab’s human-machine interaction design is built around that reality.
The technical architecture reflects the same clarity of thinking. A modular design separates perception, reasoning, and interaction layers, allowing different AI techniques, including machine learning, heuristic methods, and optimisation algorithms, to be composed, tested, and verified. This hybrid model is not a compromise; it is the architecture that makes real-world deployment possible. Upgrades and new AI capabilities can be introduced in service without re-architecting the underlying system, giving Gripen a genuine capacity to evolve throughout its operational lifecycle.
The impact is already visible. In evaluation and exercise scenarios, pilots report significantly reduced cognitive load and improved situational awareness in dense, multi-domain environments. Mission effectiveness improves through faster, higher-quality decisions, more efficient use of weapons and sensors, and better coordination with other assets. Crucially, the same AI building blocks that operate in the live aircraft are reused in ground-based mission support systems and high-fidelity simulation environments. This creates a continuous learning loop across training, development, and live test operations, compressing the cycle between operational insight and capability improvement.
By taking AI all the way from research into a certified, operational fighter aircraft, Saab has done something that very few organisations in the world have achieved. And because the underlying framework is modular, scalable, and platform-agnostic, Gripen represents more than a combat aircraft. It is a reference implementation for how AI can be responsibly, rigorously, and progressively embedded into safety-critical systems across air, land, and sea domains. That is the standard Saab is setting, and it is one that the broader data and AI community should know about.