The real challenge is no longer adoption. It is AI governance. Where is AI being used? What data is it processing? Who remains accountable for the outcome?

These questions are becoming increasingly important as AI evolves from tools that simply generate content to systems that can take actions on behalf of users.

A recent article by CIDAI, featuring insights from Alfonso Corral, CEO of Conversia, highlights this shift. For many SMEs, the challenge is no longer gaining access to AI. It is ensuring that AI is used responsibly, transparently and effectively across the organisation.

From AI assistants to AI agents

For the past few years, most organisations have experimented with AI assistants that answer questions, generate text or support decision-making. The next wave is different.

AI agents can access applications, retrieve information, execute workflows and interact with business systems with limited human intervention. An agent may schedule appointments, manage inventories, update records or communicate with customers. This increased autonomy creates new opportunities, but it also raises new governance challenges.

The question is no longer: “Can AI help us complete this task?” It is: “How do we ensure AI performs this task safely, responsibly and in line with business objectives?”

The rules already apply

Many organisations still assume that AI governance can wait until regulation becomes stricter. In reality, several obligations are already in force.

The EU AI Act is being implemented progressively. Rules on prohibited AI practices and AI literacy obligations are already applicable, while Article 50 transparency requirements became applicable on 2 August 2026. These transparency obligations include informing users when they are interacting with certain AI systems and identifying specific categories of AI-generated content. Additional requirements for high-risk AI systems will follow according to the Act’s implementation timetable.

A key principle of the AI Act is that obligations depend on risk, not on the technology itself.

Using AI to draft an internal newsletter presents very different risks from using AI to screen job candidates, assess employee performance or support decisions that affect individuals. The latter uses may trigger substantially greater regulatory obligations.

Another common misconception is that responsibility belongs entirely to the software vendor.

The AI Act distinguishes between providers, who develop AI systems, and deployers, who use them within their organisations. A company may have obligations as a deployer even if it never develops an AI model itself. Existing requirements relating to data protection, confidentiality, intellectual property and employment law continue to apply regardless of whether AI is involved.

We explored these requirements in greater detail in our previous article, “Key Aspects of the European AI Act“, where we break down the regulation’s risk-based approach, implementation timeline and the practical implications for organisations adopting AI solutions.

What the summer of 2026 revealed about AI Governance

In July 2026, an incident involving AI cybersecurity evaluations attracted significant attention across the AI community.

According to OpenAI’s published investigation, highly capable AI models participating in internal cybersecurity evaluations circumvented intended controls, gained internet access and compromised parts of OpenAI’s research infrastructure and systems belonging to Hugging Face. OpenAI attributed the behaviour to misaligned incentives during evaluation and described it as a form of “reward hacking.”

The significance of the incident was not simply the technical breach. It highlighted a broader governance challenge: highly capable AI systems may pursue objectives in unexpected ways when success metrics, permissions and oversight mechanisms are poorly designed. OpenAI’s investigation identified issues relating not only to model behaviour, but also to monitoring, isolation and escalation processes.

The lesson is clear: AI governance is not solely about the model. It is also about the environment in which the model operates.

Understanding reward hacking

Many modern AI systems rely on reinforcement learning, where the system learns by attempting actions and receiving feedback based on predefined objectives. Reward hacking occurs when a system discovers a way to maximise its score without achieving the underlying goal intended by its designers.

A simple business example illustrates the concept.

Imagine a customer support team being rewarded only for reducing call duration. Some employees might learn that ending difficult conversations quickly improves the metric, even though customer satisfaction suffers.

The metric improves. The outcome does not. The same principle can emerge in AI systems.

As AI agents gain access to applications, workflows and business processes, poorly designed objectives can lead to unintended behaviours that technically satisfy performance targets while undermining business goals.

Importantly, this does not imply intentional deception or consciousness. Rather, it reflects the fact that optimisation systems will often exploit shortcuts when those shortcuts are rewarded.

Why this matters for SMEs

Few SMEs will ever train a frontier AI model. Many, however, will deploy AI agents connected to email, CRM platforms, ERP systems, customer portals or e-commerce environments.

At Mosaic Factor, we increasingly see organisations exploring these use cases while simultaneously asking questions about traceability, accountability and control. Examples might include:

• An AI agent tasked with reducing open support tickets closes cases prematurely instead of resolving underlying issues.
• An inventory management agent avoids stock shortages by systematically over-ordering products.
• An AI assistant with excessive permissions accesses sensitive information it never needed to perform its task.

These are not technical failures alone. They are governance failures.

How success is defined, what permissions are granted, when human approval is required and how behaviours are monitored are business decisions. They should not be left solely to whoever installs the AI tool.

A practical starting point for AI Governance

Effective AI governance does not require a large compliance team. It starts with a few practical disciplines:

1. Map AI usage across the organisation. Document every AI system, who uses it, what data it processes and which provider supplies it.
2. Assess risk and impact. Distinguish between low-risk productivity applications and systems that influence people, finances, compliance or strategic decisions.
3. Apply least-privilege access. Grant AI systems only the permissions required for their intended purpose.
4. Keep humans involved in critical decisions. Require approval for actions that are difficult to reverse, such as payments, customer communications or record deletion.
5. Monitor outcomes, not only metrics. Verify that business objectives are genuinely being achieved rather than simply measuring target indicators.
6. Maintain audit trails and logs. Monitoring is essential for identifying unexpected behaviours before they become significant issues.
7. Invest in AI literacy. Employees should understand both the opportunities and the limitations of the AI systems they use.
8. Evaluate suppliers carefully. Ask how data is handled, what transparency mechanisms exist and which controls and audit capabilities are available.
9. Be transparent with customers. When AI interacts directly with users or generates content they may rely upon, transparency builds trust and supports compliance.

Governance is what enables Trustworthy AI

Responsible AI does not mean using less AI. It means understanding what AI systems do, why they do it and where their limitations lie.

The organisations that succeed with AI over the long term will not necessarily be those that deploy it first. They will be those that deploy it responsibly, with appropriate controls, clear accountability and a strong understanding of risk.

Governance should not be viewed as a barrier to innovation. It is what allows organisations to innovate confidently, scale successful AI initiatives and maintain the trust of customers, employees and regulators.

At Mosaic Factor, we help organisations build trustworthy AI systems that are explainable, auditable and aligned with both business objectives and regulatory expectations.

Because the future belongs not simply to companies that use AI, but to companies that govern AI well.

→ Check our Trustworthy AI solution

La entrada Using AI is easy. Governing it is the real challenge for SMEs se publicó primero en Mosaic Factor.

1. Unacceptable risk is prohibited. Therefore, the following types of models should not be used:
   1. a. Subliminal, manipulative, or deceptive AI
   2. b. Systems that exploit vulnerabilities related to age, disability, or socio-economic circumstances to distort behaviour, causing significant harm.
   3. c. Biometric categorisation systems inferring sensitive attributes (race, political opinions, trade union membership, religious or philosophical beliefs, sex life, or sexual orientation), except labelling or filtering of lawfully acquired biometric datasets or when law enforcement categorises biometric data.
   4. d. Social scoring systems.
   5. e. Systems assessing risk of an individual committing criminal offenses.
   6. f. Compiling facial recognition databases from the internet or CCTV footage.
   7. g. Inferring emotions in workplaces or educational institutions.
   8. h. ‘Real-time’ remote biometric identification (RBI) in publicly accessible spaces for law enforcement.
2. High-risk AI systems: they are regulated, and the AI Act focuses mostly on these.
3. Limited risk AI systems: they are subject to lighter transparency obligations. This means, developers and deployers should make sure that end-users are aware that they are interacting with AI (thus making it clear that there is an AI model behind chatbots and deepfakes).
4. Minimal risk AI models are unregulated: these include most AI applications that were available on the EU single market at the moment of starting AI Act in 2021. For instance, AI-enabled video games and spam filters.

Clearly, this scenario is changing with generative AI, which raises the level of risk of AI models, making them mostly high-risk. Even if now AI regulations are advancing, we think AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes. This includes making sure we are able to generate trustworthy AI systems that are fair by design and are explainable and clear to decision makers. Legislation will clearly not fast-track adoption of responsible AI but organisations are the ones who need to share experiences and solutions to show what “good practice” looks like. Boards need to embrace Corporate Digital Responsibility to assess digital impacts of products/services on all stakeholders by examining societal, economic, technological & environmental impacts. Therefore, we are supporting companies in their role to ensure the technology is not deployed in “negative use cases” that could harm society and generating AI models that are transparent and accountable across industries.

→ Check our Trustworthy AI solution

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The BYOD app transforms everyday courier operations into a fully connected, data-driven workflow. From parcel validation to proof of delivery and disruption reporting, every action is securely recorded and transmitted to the central platform, ensuring logistics providers remain fully informed. 

When a courier logs in, the application automatically adapts to the configuration of the specific Living Lab deployment. The available features and workflows depend on the operational model of each environment. The BYOD app is designed to support different city deployments with tailored configurations without requiring changes to the core application. 

In the Athens Living Lab, for example, couriers can operate through either Parcels or Stops within the main menu. This flexibility allows the same application to support multiple logistics scenarios without altering the core system. 

Parcel function

In the Parcel function, couriers add parcels by scanning QR codes or by manually entering parcel IDs. For greater efficiency, multiple parcels can be selected at once by scanning code sets or entering a set ID for batch processing. 

Once validated, parcels appear in the current working list, confirming that they are correctly linked to the courier. They remain visible until delivery completion or manual removal or once the delivery is confirmed in the system. A refresh option allows the courier to retrieve the most up-to-date parcel information at any time.  

Selecting a parcel provides access to essential delivery data, including its identification number, status, delivery address, expected delivery date, weight, service type, and associated round. During the delivery process, couriers can register events and update parcel quality directly within the app. 

For proof of delivery, a single parcel is selected, and the receiver signs directly on the device. The signature is securely recorded and immediately reported, ensuring reliable confirmation of delivery and traceability. 

Stop function

Through the main menu, couriers can switch to the Stop function, which provides a structured overview of planned stops and related parcel information grouped by delivery location. Stops can be visualised on an interactive map, offering clear route visibility and improved situational awareness through real-time geolocation.  

Selecting a stop reveals the parcels assigned to that location, allowing couriers to manage grouped deliveries efficiently. If a disruption occurs, the courier can report it directly within the app by selecting the disruption type, adding comments, and automatically sharing their position. This real-time communication supports immediate operational adjustments and proactive issue management.  

BYOD impact

The Green-Log BYOD tool ensures that every action, from parcel validation to signature capture and disruption reporting, is securely transmitted to logistics operators. This continuous flow of information enhances: 

• Transparency 
• Improves coordination 
• Supports data-driven decision-making 

By combining flexibility, live operational visibility, and secure reporting, BYOD strengthens last-mile delivery efficiency while contributing to more sustainable and optimised urban logistics operations across different city environments. 

→ Check our Digital Twins solution

La entrada Bring Your Own Device overview se publicó primero en Mosaic Factor.

Within the AutoMoTIF project, this challenge is addressed through automated shunting as a service, with Mosaic Factor leading the development of the simulation framework. 

From operational bottleneck to coordinated rail operations

Shunting plays a central role in intermodal terminals, linking maritime cargo flows with inland distribution. However, traditional shunting operations are often reactive, fragmented across systems, labour-intensive, and energy-inefficient. 

Shunting as a service reimagines these operations as a digitally orchestrated service platform where autonomous locomotives, yard resources, and scheduling systems operate as an integrated ecosystem. The aim is not simply automation, but service optimisation. 

Simulation driving the transformation

Mosaic Factor’s advanced simulation environment replicates the operational complexity of rail terminals, including train movements, wagon marshalling, yard capacity constraints, container handling cycles, resource allocation, and disruption scenarios. 

Autonomous shunting locomotives are modelled as intelligent agents that dynamically respond to congestion, schedule changes, and infrastructure constraints. 

Through scenario modelling, the simulations evaluate: 

• Reduced shunting time 
• Faster wagon turnaround 
• Lower idle and waiting times 
• Optimised energy consumption 
• Increased yard throughput 
• Improved safety and lower operational costs 

This data-driven approach ensures automation concepts are validated before real-world deployment. 

Shunting as a Service Platform

Shunting as a service introduces a shift in how rail yard operations are structured. Instead of a fixed internal activity, shunting is modelled as a service-oriented platform where capacity is dynamically allocated, operations are digitally coordinated, and performance is continuously monitored. 

This approach supports greater interoperability between terminal operators, rail infrastructure managers, logistics providers, and port authorities, while enabling integration with other automated processes within AutoMoTIF. 

Supporting Smarter Rail Terminals 

To ensure realistic outcomes, Mosaic Factor calibrates simulations using historical operational data, planning inputs, and stress-test scenarios that reflect peak demand and future growth. 

The resulting models provide decision-support tools for infrastructure investment, automation strategies, business models, and regulatory alignment, helping reduce risk and accelerate deployment. 

Strengthening Europe’s Rail Freight Network 

By improving rail efficiency, automated shunting supports broader logistics goals, including: 

• Modal shift from road to rail 
• Reduced terminal congestion 
• Lower emissions 
• Safer working conditions 
• More reliable logistics operations 

Through simulation-driven validation, Mosaic Factor demonstrates how automated shunting can increase throughput, reduce delays, optimise energy use, and enhance safety. 

Automated shunting as a service-oriented platform represents more than a technological upgrade. It introduces a new operational model that strengthens the role of rail in Europe’s transport system while supporting a more efficient and sustainable logistics network. 

→ Check our Digital Twins solution

La entrada Automated Shunting as a Service Platform se publicó primero en Mosaic Factor.

Representing Mosaic Factor at the event was Head of Sales Joan Sampablo, who attended on behalf of Mosaic Factor to explore the latest developments shaping the future of connectivity, artificial intelligence, and digital infrastructure.

Telecom as the backbone of AI 

MWC 2026 highlighted how the telecom industry is moving beyond connectivity to become a core enabler of the next generation of AI-powered services. Leadership from the GSMA outlined three key pillars expected to drive the next phase of digital growth:  

• The completion of 5G standalone network rollouts.
• Broader access to open and inclusive AI technologies.
• Stronger global digital safety and trust frameworks 

These priorities reflect a broader shift toward networks that are not only faster but also capable of supporting distributed intelligence and AI-driven services. 

The rise of on-device intelligence

A major theme throughout the event was the rise of AI everywhere, particularly the shift toward on-device and network-native intelligence. Chipmakers and device manufacturers showcased powerful new system-on-chip architectures designed for local AI inference, enabling capabilities such as:

• Real-time translation
• On-device summarization
• Advanced photo processing
• AI assistants capable of performing complex tasks without relying on the cloud.

For developers and enterprises, this shift signals a new design standard where applications are built with local AI processing and multimodal interaction in mind, combining camera, voice, and text interfaces to deliver richer experiences.

Future of autonomous systems

Another prominent topic was the emergence of agentic AI systems, where autonomous AI agents can coordinate tasks across networks, infrastructure, and physical environments. Telecom providers and startups demonstrated how such systems could be applied to network orchestration, automated operations, and enhanced cybersecurity.

Next generation of connected systems

One of the most talked-about concepts at this year’s congress was “kinetic compute” and “kinetic tokens.” These represent ultra-fast compute units designed to synchronise real-world actions across robotics, drones, autonomous vehicles, and other distributed systems. Telecom operators argue that their networks are uniquely positioned to deliver the millisecond-level coordination required to power these emerging technologies. 

Security, digital trust, and global inclusion

Security and global digital inclusion also featured prominently across the conference agenda. With cybercrime costs continuing to rise worldwide, industry leaders emphasised the importance of:

• Privacy-preserving AI
• Stronger digital identity frameworks
• AI models capable of supporting more global languages
• Cross-border infrastructure standards

These priorities reflect the growing recognition that technological innovation must be accompanied by strong governance and inclusive digital access.

Mosaic Factor at MWC 2026

According to Joan, attending MWC provides valuable insights into how the telecom and technology landscape is evolving at a rapid pace. The event highlighted the growing importance of AI-enabled infrastructure and edge computing, as well as the increasing role telecom networks will play in enabling intelligent applications and connected systems.

For Mosaic Factor, attending the congress offers an opportunity to stay closely aligned with emerging industry trends and innovations that will shape the next generation of digital solutions.

As the industry moves deeper into the so-called IQ-Era, the convergence of AI, advanced connectivity, and distributed computing is expected to redefine how devices, networks, and businesses interact, creating new opportunities for innovation across sectors worldwide.

La entrada Mobile World Congress 2026: Entering the IQ Era se publicó primero en Mosaic Factor.

Driving efficiency and innovation with digital twins

Persi highlighted the MyEV Digital Twin, a two-layer architecture with a full off-board version for simulations and a lighter in-vehicle version, fully connected and interoperable with AI applications to enable continuous updates and optimisation. 

This design enables continuous updates and interoperability with advanced AI applications, ensuring that software-defined EV functions can be optimised for energy efficiency, safety, and driver experience throughout the vehicle’s lifecycle. 

TwinOps Methodology: Continuous Optimisation

At the heart of Twin-Loop is the TwinOps concept, which merges: 

• Model-based engineering (MBE) principles 
• DevOps practices 
• Digital twin continuous integration 

This methodology allows digital twins to remain synchronised with evolving vehicle designs, supporting efficient design, validation, and operational decision-making. TwinOps also leverages vehicle and fleet data to enhance energy optimisation, safety, and system integrity without interfering with core EV functions. 

Energy, safety and sustainability

Energy efficiency was a key focus of the presentation. Persi outlined how optimised in-vehicle algorithms and compressed data transmission can reduce energy usage while maintaining high performance. He also highlighted how TwinOps integrates environmental modeling, supporting lifecycle emission reduction and the transition toward software-defined vehicles (SDVs). 

Safety and cybersecurity remain central to the framework: digital twins and apps are designed to operate behind core systems, with tools such as CyberCar, OFSCE, ModVV, and ZTBCar providing protection against individual vehicle attacks, fleet-level intrusions, and in-vehicle system violations. 

Fostering collaboration and European competitiveness

The Twin-Loop consortium brings together 12 partners from 8 countries, spanning academia, automotive manufacturers, technology providers, and consulting firms. Advisory board members include ZF Friedrichshafen AG, Volvo Group, Zunder, and Traton, and the project is supported by a €5 million budget. 

By developing an open framework for MyEV Digital Twins and a suite of connected applications, Twin-Loop aims to accelerate innovation, reduce development time, and strengthen European automotive competitiveness. The framework provides reusable modules, development guidelines, and shared services, simplifying complex digital system development. 

Looking ahead

The presentation highlighted how TwinOps and MyEV Digital Twins are driving a new era of smarter, safer, and more sustainable electric vehicles. By leveraging continuous improvement and fleet-level insights, these tools enhance energy efficiency, driver experience, and overall system resilience. Through Twin-Loop, the project is helping shape the future of software-defined vehicles and driving the European automotive industry toward the next generation of mobility.

La entrada TwinOps and MyEV Digital Twins: advancing SDV at RTR Conference se publicó primero en Mosaic Factor.

Moving beyond Traditional RAG

Traditional RAG pipelines embed a query, retrieve context, and generate a response.

Agentic RAG introduces intelligence into the process. By classifying intent before deciding whether to retrieve, call tools, or answer directly, companies report cost reductions of up to 40% and latency improvements of 35%.

Core patterns driving adoption

Industry experts point to three architectural patterns that define Agentic RAG:

• Intent-Based Query Routing: determines whether retrieval is necessary or if a direct answer suffices.
• Tool Orchestration with Error Handling: coordinates APIs, calculators, and databases while managing failures gracefully.
• Continuous Cost & Latency Evaluation: tracks token usage and performance metrics in real time.

These patterns allow systems to decide, adapt, and optimise, a critical requirement for enterprise-scale AI.

Architecture in practice

Agentic RAG systems are typically built on three layers:

• Orchestration Layer: the “decision brain” that routes queries intelligently.
• Execution Layer: handles retrieval, tool calls, and LLM inference.
• Infrastructure Layer: provides vector databases, deployment management, and observability.

Unlike traditional RAG, which always retrieves, Agentic RAG evaluates whether retrieval is even necessary, orchestrating the optimal combination of retrieval, tools, and generation.

Provider flexibility through gateway layers

Another key trend is the rise of gateway abstractions that allow developers to switch seamlessly between providers such as OpenAI, Anthropic, Google, and Bedrock. This approach enables:

• Failover routing when providers experience downtime.
• A/B testing without code changes.
• Cost optimization by directing queries to the most efficient model.
• Freedom from vendor lock-in.

Companies are increasingly adopting unified gateways to balance speed, cost, and reliability across providers.

Conclusion

Agentic RAG is no longer a niche experiment but the blueprint for production AI systems. By combining retrieval with decision-making, orchestration, and observability, the technique is setting new standards for efficiency and adaptability in enterprise AI.

“Production AI isn’t about retrieval alone. It’s about intelligence: knowing when to retrieve, when to call tools, and when to answer directly. Agentic RAG delivers that intelligence”.

La entrada Agentic RAG for AI se publicó primero en Mosaic Factor.

Key sessions featuring AI

Agentic AI in the Car: From Orchestration to Agentic Personalisation

• Speaker: Dogukan Sonmez, Project Lead Machine Learning Platform, BMW AG
• Focus: generative AI applications beyond text, handling 3D models, sensor streams, and domain-specific data.
• Highlight: explored multi-agent system architectures and orchestration techniques, showing how agentic AI can improve efficiency, adaptability, and decision-making in SDVs.

This session revealed groundbreaking approaches to embedding AI into the very core of vehicle intelligence, making it one of the most forward-looking talks of the event.

Scalable Function Deployment for SDV

• Speaker: Michael Niklas-Höret, AUMOVIO

• Focus: the ongoing (R)evolution of E/E architectures in vehicles, from decentralised domain-centric to Server Zone and Central Compute/Zero Edge, and the resulting challenge in deploying functions across these diverse architectures.
• Highlight: introduction of a new function development pattern designed to enable the re-use of functions across the three main architectures (and their hybrids), aiming to ease OEM migration to new E/E platforms and increase re-use across vehicle lines.

From Code to Car – Accelerating SDV Integration with Shift-Left and CI/CT

• Speaker: Felix Pretscheck, Bosch
• Focus: how the introduction of next-generation Compute ECUs, combined with a shift-left approach, virtualisation, and a modular CI/CT (Continuous Integration/Continuous Testing) framework, is transforming and accelerating the software and system integration process for Software-Defined Vehicles (SDV).
• Highlight: showcasing key methodologies and architectural enablers that streamline integration and validation, leading to improved software quality, faster integration cycles, and more agile, scalable, and production-ready complex automotive systems.

Coding for Cars: AI in the Driver’s Seat

• Speaker: Mikhail Vink, VP of Business Development, JetBrains
• Focus: the role of AI-driven development in regulated industries, ensuring the quality and security of AI-generated code, and integrating AI into DevOps.
• Highlight: demonstrated how AI can streamline coding processes while maintaining compliance and safety standards.

Between Research and Current Development in ADAS

• Speaker: Khaled Alomari, Manager Connected Vehicles, MHP (A Porsche Company)
• Focus: bridging cutting-edge research with real-world Advanced Driver Assistance Systems (ADAS) development.
• Highlight: emphasized collaboration between academia, industry, and regulators to accelerate AI-powered ADAS adoption.

Khaled’s insights underscored the critical role of AI in enhancing safety and efficiency, making this session a must-attend for anyone interested in the future of connected vehicles.

Mastering Complexity with Digital Twins

• Speaker: Ignacio Izaguirre, VP of Product, Concentrio AG
• Focus: leveraging digital twins at the signal level for software management.
• Highlight: showed how applying AI to dependency nets can identify anomalies and vulnerabilities, improving OTA updates and system reliability.

AI-Driven Autonomy in SDVs

• Speaker: Shashank Pathak, Product Management, ZF Friedrichshafen AG
• Focus: how AI enables perception, planning, and control in autonomous SDVs.
• Highlight: discussed challenges of deploying AI on automotive hardware and strategies for safe, scalable autonomy.

Workshops and world café sessions

The workshop sessions “Challenge Your Peers” brought attendees together in small, moderated roundtables where we debated pressing industry questions using collective intelligence and mind maps. Topics ranged from how software testing evolves under SDV architectures, the role of AI in automotive development, and the future of ASPICE compliance, to redefining in-car experiences through advanced HMI and exploring AI-driven autonomy.

Each workshop encouraged participants to share experiences, challenge assumptions, and co-create solution concepts. Similarly, the World Café format provided a dynamic environment for peer learning, cross-industry networking, and the generation of actionable insights that complemented the more formal presentations and case studies.

Our takeouts

The conference highlighted that AI is no longer a peripheral tool but a central pillar in the evolution of software-defined vehicles. From agentic personalisation at BMW to AI-powered ADAS at MHP, the sessions demonstrated how intelligence is being woven into every layer of automotive innovation.

These talks not only showcased current applications but also pointed toward a future where vehicles are adaptive, intelligent, and deeply connected.

La entrada Software-Defined Vehicles Conference Berlin 2025 se publicó primero en Mosaic Factor.

Here are the key trends and developments that stood out.

Digital Twins take center stage

Digital Twin (DT) technology was one of the most prominent themes across exhibitor stands. Cities are increasingly adopting DTs to simulate and manage complex urban systems. The most common applications showcased included:

• Emergency and disaster management
• Human behaviour modelling
• Traffic and parking optimisation
• Energy demand forecasting
• Urban planning, such as identifying areas where new childcare facilities are needed

Several companies also presented the evolution of the Citiverse concept, part of a European initiative that integrates Digital Twins with cybersecurity, IoT, and other advanced technologies (European Commission Citiverse Project). Another highlight was the introduction of 4D Digital Twins, which incorporate the time dimension to enable predictive urban simulations (Nfold ROI).

Smart Cities and visual Language Models

AI innovation was another major focus. NVIDIA unveiled its Visual Language Model (VLM) platform for cities, designed to transform sensor-captured image data into an intelligent “city brain” capable of interpreting current and potential urban scenarios.

Practical applications were demonstrated in Leipzig, where AI-driven DTs are being used to optimise parking spaces and bicycle infrastructure. Meanwhile, the University of Hamburg showcased collaborative, open-source AI projects, emphasizing their interest in joining European-funded initiatives (DCS Intro 2024).

Cybersecurity and Global Engagement

Cybersecurity was a recurring theme throughout the expo, underscoring its critical role in safeguarding smart city infrastructures. Notably, the World Bank was actively involved, reflecting the global importance of secure digital ecosystems.

Our Contribution: Open Innovation Challenges

As part of our participation, we engaged in open innovation challenges, presenting proposals that leverage Large Language Models (LLMs) and Digital Twins for corporate applications. These initiatives demonstrate our commitment to pushing the boundaries of AI and urban technology, ensuring that cities of the future are not only smarter but also more resilient and inclusive.

Our presence at Smart City Expo Barcelona reaffirmed our role as a forward-looking technology provider. By contributing to discussions on Digital Twins, AI, cybersecurity, and open innovation, we continue to shape the future of urban living, driving solutions that make cities more adaptive, efficient, and human-centered.

La entrada Smart City Expo Barcelona se publicó primero en Mosaic Factor.

Let’s break down the key types of AI and how we help you apply them.

AGI: the long-term horizon

General AI, or General Artificial Intelligence (AGI), refers to machines that can perform any intellectual task a human can. It’s flexible, autonomous, and capable of reasoning across domains.

Current Status: AGI is still theoretical. No existing system has achieved true general intelligence.

We think this is a highly compelling topic, and we are monitoring developments with great interest and anticipation.

Generative AI: real-world creativity at scale

Generative AI (or GenAI) is already transforming industries. These models create new content -text, images, code, audio, amongst others- based on learned data patterns.

Use Cases we can deliver for GenAI:

• Automated content generation for specific industries (like healthcare).
• Document summarisation and contract analysis for legal or compliance teams.
• Intelligent chatbots for internal company queries or customer support.
• Code generation and debugging tools for industry-specific developers (like automotive).

Our Solutions: we are capable of building and fine-tuning generative AI models using your proprietary data, ensuring outputs are accurate, brand-aligned, and compliant. Whether you need a custom GPT-style assistant or an image generator for product design, we can make it happen.

Specific AI techniques

Our core focus is enabling businesses to solve precise challenges through advanced AI techniques. It is what we have always done -what we call ‘traditional AI’ – and it has been central to our journey since our founding.

Our Approach: We design, train, and deploy these models with full lifecycle support: from data strategy and infrastructure to governance and performance monitoring.

Why partner with Mosaic Factor?

AI is powerful, but only when applied with precision. We don’t just deliver tools, we deliver transformation.

• Strategic AI consulting and roadmap development
• Custom model design and integration
• Scalable cloud and edge deployment
• Ongoing support, compliance, and optimisation

Whether you are exploring generative AI for creative automation or machine learning for operational efficiency, we help you turn potential into performance.

Ready to explore what AI can do for your business? Contact us to build something extraordinary, together.

La entrada General vs. Generative AI: what they mean for your business se publicó primero en Mosaic Factor.