Physical AI is artificial intelligence acting right where production happens: on the assembly line, inside the electrical panel, and on the machinery, not just in back offices. For a manufacturing company, this means deploying AI close to factory data (at the edge), enabling it to interface with a digital twin of the product or process, and overcoming friction between OT and IT systems. Let’s dive into the details.

What Is Physical AI (and why is everyone talking about It now?)

Physical AI represents the new frontier of artificial intelligence: no longer just systems that respond to text prompts in a chat, but systems that perceive, interpret, and act within the physical world through cameras, sensors, PLCs, and industrial machinery. Recent years have seen significant buzz around generative AI and agentic AI applied to text, documents, and back-office workflows. Physical AI applies this exact principle, giving AI the capacity to act, rather than merely answer, to the factory floor, where decisions confront physical realities: mechanical tolerances, vibrations, lighting conditions, and component wear.

For a manufacturer, Physical AI is far from an abstract concept. It is the distinction between a model that identifies a defect in a manually uploaded photo and a system that detects that same defect in real time on the line as the part passes under a camera. This shift, from manually loaded data to field-captured data, is the core of Physical AI.

From the cloud to the factory floor: why AI needs to be near the machine

Until now, most enterprise AI applications have resided in the cloud: language models, dashboards, and analytical tools accessible via a web browser. Physical AI changes the rules of the game because it must respond within the timeframes of the physical process it observes, milliseconds for a robotic arm, seconds for a visual inspection. It cannot afford the latency of round-trip communications with a remote data center.

Added to this are practical constraints unique to industrial environments: factory floor connectivity is rarely as reliable as office Wi-Fi, production data is often sensitive or bound by customer non-disclosure agreements, and streaming continuous high-definition video feeds from dozens of cameras to the cloud is cost-prohibitive. Consequently, industrial Physical AI relies heavily on edge computing: processing occurs as close to the data source as possible, while the cloud remains reserved for heavy model training, historical data aggregation, and multi-facility analytics.

The digital twin: The “common language” between design and reality

A critical second component of Physical AI is the digital twin: a dynamic digital representation of a product, facility, or process that is continuously updated to mirror physical reality. It is not a static simulation, but a live baseline against which Physical AI measures deviations: comparing what should be happening according to design specifications with what is actually taking place inside the machine, panel, or plant.

Revelis has long developed solutions in this domain. With Ei-TWIN, for example, we applied the digital twin paradigm to the energy sector, integrating real-time IoT data to optimize consumption and energy exchange within a pharmaceutical plant and an energy community. Physical AI generalizes this principle to broader industrial contexts: wherever a design exists (a CAD file, a schematic, a specification sheet) alongside a physical build, automated comparison between the two yields actionable value.

OT/IT integration: where friction arises (and gets solved)

Implementing Physical AI on the factory floor means tackling a long-standing industrial challenge: the gap between the OT world (Operational Technology, PLCs, SCADA systems, sensors, line cameras) and the IT world (ERP systems, data warehouses, cloud applications). These two domains operate with different protocols, lifecycles, and cultures, historically communicating poorly.

To function, Physical AI must act as a bridge between these layers: capturing and interpreting real-time OT data, normalizing it, and serving it in a format that IT systems (and personnel) can use for decision-making. We addressed this challenge through a different angle in the InCoP project, where environmental and biometric field data collected at a bakery (for Target S.p.A.) were processed using machine learning and reasoning to optimize shift scheduling. There too, value stemmed from enabling field devices to communicate with management systems. In Physical AI applied to quality and production, that same principle connects cameras, PLCs, and CAD files.

MHS: an emerging standard for connecting AI to physical hardware

The challenge of machine protocol heterogeneity is significant enough that on August 27, 2026, Anthropic released a research preview of the Model Hardware Standard (MHS): a specification designed to connect AI agents to physical laboratory and production instruments, aiming to reduce integration times from weeks or months down to hours or minutes. 

Technically, MHS introduces a standardized driver layer between the operating system and the hardware device, built on simple read/write primitives (e.g., “read temperature”, “set temperature”), automatic device discovery, and natural language tags documenting capabilities and safety bounds. It is designed to be model-agnostic and accessible via standard protocols like the Model Context Protocol (MCP), command line interfaces, or APIs.

Initial results shared by Anthropic alongside research partners demonstrate its potential:

• QuEra Computing (quantum computing): An AI agent reduced laser recalibration times from 150 seconds to 6 seconds while boosting the success rate from 58% to 99.3%.
• Carnegie Mellon University: Integration of dilution series for dose-response curve tracking went from weeks to roughly 8 hours, executing three times faster.
• Genentech: An agent automated a BCA assay for protease analysis, autonomously optimizing liquid flow parameters and identifying errors caused by air bubbles.
• University of Washington: The system remotely coordinated qPCR and instrument monitoring from a unified dashboard, featuring real-time tracking of amplification curves.
• HHMI Janelia Research Campus: Unified seven proprietary software programs for two-photon zebrafish microscopy, enabling real-time data analysis during acquisition.
• Tetsuwan Scientific: Managed a qPCR workflow for water pollution monitoring at San Pedro Creek, achieving 12% higher predictive accuracy than the manufacturer’s published specifications.

These milestones confirm that Physical AI, when backed by a unified hardware standard, can drastically reduce integration times across diverse domains, from pharmaceutical research to environmental monitoring.

However, existing limitations remain clearly defined: Anthropic notes that Claude lacks reliable “physical intuition”—struggling to predict collisions, inertia, or structural instability. High-risk decisions still require human approval, and safety roadmaps (hard stops, emergency shutoffs, error handling) are actively being refined prior to any open-source release. This aligns directly with the core philosophy behind Physical AI at Revelis: AI accelerates execution, but human oversight remains an essential layer. MHS serves as a clear indicator of industry direction, representing a potential abstraction layer for the OT gateways Revelis builds when integrating new machinery into PlugAIn.

PlugAIn: how Revelis operationalizes physical AI on the shop floor

To make Physical AI practical for manufacturing companies, Revelis relies on PlugAIn, our proprietary platform that models production processes using BPM (Business Process Management) and Big Data Analytics components. The core approach involves collecting and processing data generated by machinery, sensors, and line cameras to deliver actionable intelligence to decision-makers—whether an algorithm or a human operator—integrating edge processing and a comparison engine between design (the digital twin) and field reality.

PlugAIn combines three complementary layers of Physical AI:

1. Computer Vision to perceive the physical state of components.
2. Reasoning Engine to compare that state against the expected design model.
3. Orchestration Layer to decide, based on the output, whether to proceed automatically or involve a human operator.

This structural principle, AI proposes, humans decide on high-value tasks,  mirrors Revelis’s agentic AI solutions on the IT side, here extended to the physical environment of the factory floor.

A real-world case study: Aquila, physical AI for quality control (AI-REGIO Project)

The best way to understand the impact of Physical AI is through a real-world application. Within the European project AI-REGIO (Horizon 2020, grant agreement No. 952003), Revelis developed Aquila, an Automatic Quality Inspection system validated at Elettrocablaggi, an Italian manufacturer of electrical panels. It serves as a direct example of Physical AI: cameras observing physical products, a CAD model representing design specifications, and an automated engine comparing the two worlds.

Before Aquila, quality control for electrical panels was entirely manual. An operator visually cross-referenced each manufactured panel against engineering schematics, taking an average of 340 seconds per panel with an error rate of roughly 1 in every 250 inspected panels, typical metrics for a repetitive, labor-intensive process prone to human fatigue. This is precisely where thoughtfully applied Physical AI creates impact.

The Aquila solution combines two complementary AI techniques:

• Computer Vision: A Mask R-CNN object detection neural network identifies installed components and their precise spatial positioning from photos of physical panels.
• Symbolic Reasoning: The CAD design file is transformed into a logical representation, effectively the electrical panel’s digital twin. An automated reasoning engine based on Answer Set Programming (the DLV engine) compares the two graph representations (the vision-captured state vs. the target design) and generates a compliance report flagging missing, extraneous, or misaligned components.

Results recorded during testing highlight the power of quality-focused Physical AI:

• The object detection model achieved an Area Under the Precision-Recall Curve (AUC) of 0.947, indicating high reliability.
• Average inspection time dropped by 60%.
• Error detection accuracy improved by 30% compared to a standalone computer vision approach.
• The system advanced from Technology Readiness Level TRL 4 to TRL 6, marking a major step toward full industrial deployment.

Why physical AI matters for manufacturing SMEs

The Aquila project demonstrates that Physical AI is not an academic exercise, but a tangible competitive lever for small and medium-sized manufacturing enterprises (SMEs):

• Less time spent on repetitive inspection yields higher throughput using existing headcounts.
• Fewer errors translate directly to fewer returns, claims, and rework cycles.
• A digitized process generates an audit trail of data, imagery, and results, transforming daily operations into an intelligence asset over time.

Once validated on quality control for electrical panels, this framework extends naturally to any operational context involving design-versus-reality verification: predictive maintenance, assembly verification, or surface and weld inspection. Once introduced to a single line item, Physical AI scales value across the enterprise.

This goes beyond theory: Revelis actively applies Physical AI to live machinery monitoring. At Target, an IoT sensor network connected to PlugAIn monitors every production phase in an industrial bakery, from ingredient dosing to baking, identifying machine idle times to inform preventive maintenance schedules.

In another predictive maintenance project, sensors mounted on textile loom feeders stream data via MQTT to a recurrent neural network (GRU). The network predicts warp yarn breaks with 70% accuracy (AUC 0.67), providing advance warnings that allow operators to intervene before machine stoppage occurs. These implementations confirm that Physical AI delivers value equally well in direct machinery monitoring and quality control.

The Revelis vision in brief

Physical AI is not simply another cloud platform promise; it is the strategic choice to bridge artificial intelligence with the physical reality of production using edge computing, digital twins, and robust OT/IT integration. Through PlugAIn, Revelis consolidates insights gained across projects like Aquila (AI-REGIO), Target, Ei-TWIN, and InCoP to make Physical AI a practical, measurable pathway for small and mid-sized manufacturers. It is our hardware-adjacent expression of the core principle guiding Revelis: making AI actionable.

Mini-Glossary for Non-Technical Readers

Ready to Evaluate Physical AI for Your Facility?

Revelis guides manufacturing enterprises through every step of implementing Physical AI on the factory floor, from edge computing and digital twins to full OT/IT integration.

Learn more at www.revelis.eu · Contact us at info@revelis.eu

Authors: Massimiliano Ruffolo and Achille Abritta

L'articolo Physical AI and digital twins: AI enters the factory proviene da Revelis.

What is Moneying™

An anomaly indicator, no matter how well formulated, does not exist in isolation: it must run on real data, produce prioritized alerts, and return the necessary context to the analyst to make a decision. Moneying™ is the environment where this happens. The platform ties together customer risk profiling, transaction monitoring, and relationship analysis, leveraging the Rialto™ framework for large-scale data processing.

Moneying™ leverages both core approaches of Artificial Intelligence: deductive and inductive models. Deductive models are structured using regulatory rules produced by investigative authorities and remain explicitly expressed: readable, versionable, and individually verifiable.

Inductive models learn from data and step in where rules fall short: in risk grading, recognizing relational patterns, and reducing the noise that stifles compliance teams under false positives. Deduction and induction do not replace each other; they complement each other.

As a result, the foundational requirement holding the entire architecture together is that every output produced by the platform is accompanied by the underlying evidence that determined it, in a format that is readable by the analyst and usable in audit/inspection settings.

AML control automation: a constant challenge

Anti-money laundering resembles cybersecurity: a perpetual game between attackers and defenders. On one side, bad actors seek new vulnerabilities to exploit; on the other, defenders reinforce systems to close them. The Bank of Italy and investigative authorities oversee this front by regularly updating their guidelines, describing emerging schemes to intercept in natural language; financial intermediaries are tasked with translating those indications into active controls over transaction flows.

This is where the mechanism slows down. Between the regulatory description of an anomalous behavior and the executable rule capable of recognizing it lies a manual translation process: slow, subject to interpretation, and required to be repeated at every update. The result is a safeguard relying on static, cumbersome architectures to modify, leaving defenders chasing behind, trying to seal the breach after it has already been crossed.

To narrow that lag and achieve true AML control automation, it is essential to make the dynamic phase of “translation”—namely, the implementation of inductive and deductive processes for detecting anomalies in financial transactions. Generative AI, and specifically Large Language Models (LLMs), intervenes precisely at this stage: given a sufficiently precise description of the behavior to intercept, the model produces the sequence of chained operations that recognize it in data, and a downstream system deploys it into production. Thanks to this AML control automation, the new control becomes available as a guardrail in a matter of days, rather than months.

Moneying-GenAML™

The capabilities described above are implemented in the GenAML module of Moneying™, a Moneying plugin developed under the Moneying-Plus project. It is a new plugin engineered to shorten the gap between the publication of a new recommendation by authorities and its deployment into production, revolutionizing AML control automation. The starting point is the document as-is: a Bank of Italy circular, an anomaly template, or an investigative report. The framework extracts its control logic: which data to observe, with which filters, on what thresholds, and over what time frame, and translates it into an executable indicator for the institution’s transaction flows.

In practice, GenAML is a plugin that operationalizes AML control automation starting directly from natural language descriptions.

The output is neither plain text nor a code snippet to be integrated manually: it is a series of concatenated operations called a workflow inspectable step-by-step, which the analyst can review, refine, and validate before production deployment. The model does not replace the compliance officer; rather, it provides an operational draft for effective AML control automation on which officers exercise their human judgment, shifting human labor from transcription to verification.

Furthermore, the cycle does not end with the initial generation. When guidance is updated, or when false positives indicate a threshold is poorly tuned, the same framework acts on the existing indicator and produces a new version. It is this incremental revision capability that makes enforcement dynamic: adapting AML control automation becomes a matter of minutes, not weeks.

Large Language Models and Model Context Protocol

Entrusting a language model with free-form code writing in a regulated domain is a difficult proposition to justify: the risk of hallucination is concrete, and the outcome is difficult to audit. GenAML’s approach to AML control automation is different. The LLM is not asked to generate arbitrary software, but rather to plan: given the regulatory text and relevant information on the underlying data structure, it composes a sequence of operations selected from a closed, pre-validated catalog of primitives data acquisition, SQL filter, transformation, export and defines how they chain together. Naturally, these primitives are customizable.

This is where the Model Context Protocol (MCP) comes into play the standard used to expose available tools and their function signatures to the model. The protocol acts as a contract: it defines what the model can invoke and with which parameters; anything outside that scope is simply non-expressible. Far from being a constraint, this limitation is the guarantee: every plan generated for AML control automation is syntactically valid by construction, fully traceable in its individual components, and reproducible.

This gives rise to an architecture where model creativity is confined strictly where needed (understanding regulatory intent and breaking it down into steps), while execution remains in the hands of deterministic and certified components. A balance that renders such AML control automation fully compliant with audit requirements typical of the financial sector.

AML Control Automation: How the Plugin Works

Figure 1 illustrates the pipeline used to achieve AML control automation through workflow generation. The user uploads reference documents (PDF or text files, typically official circulars and data schemas) and describes the control to be implemented in natural language. From there, the process is automated. The model, instructed as a planner, outputs the pipeline in JSON format by composing available tools. The execution plan is displayed step-by-step to the user, allowing every decision to be verified before execution.

The final step is conversion: the pipeline for AML control automation is translated into a native workflow that can be downloaded and imported directly into the Moneying™ platform, which relies on Rialto™ as its execution engine. The outcome is a native asset within the existing data management environment, rather than a parallel system requiring separate maintenance.

Alongside generation, the system provides an editing mode: starting from an existing workflow, it can be evolved using a new natural language instruction an essential feature for dynamic AML control automation that, in daily anti-money laundering practice, occurs even more frequently than creation from scratch.

The effectiveness of GenAML in AML control automation

As with any system automating a previously manual step, the effectiveness of GenAML in AML control automation depends on key baseline conditions worth highlighting. The first concerns data rather than algorithms: the quality of generated indicators mirrors the quality of the underlying sources. Knowing in advance which data streams are queryable, how frequently they update, and the reliability of customer profile fields is the single most critical factor determining project timelines.

Furthermore, human oversight remains central to the platform: AML control automation focused on generation does not eliminate human governance; it repositions it, shifting analyst workload from writing to verification. The return on investment is highest when the compliance team is involved early in defining the presentation format of indicators and accompanying evidence. It is the legibility and transparency of the output that makes the workflow not only executable, but fully governed which, in a regulated domain, is the most critical property.

Conclusions

The gap between the regulatory description of an anomalous behavior and the executable rule that detects it has always existed; what has changed is the speed at which money laundering techniques evolve, and consequently, the cost of compliance lag. As long as translation remains a manual task, defenders will continue to play catch-up, hindering true AML control automation.

GenAML does not promise to replace human oversight; on the contrary, it presupposes it. It redefines boundaries: assigning planning for AML control automation to the language model (constrained to a validated catalog of operations), deterministic execution to the infrastructure, and final judgment on recommendations to the analyst. GenAML completes this chain by outputting native workflows for Rialto™, while Moneying™ provides the operational environment where indicators run on real-time data alongside statistical models and risk profiling.

The primary value is not merely saving development effort significant as that is. It is the capability that this form of AML control automation provides to adapt to regulatory updates within days rather than months.

Would you like more information about Moneying?
Contact us at: info@revelis.eu

Authors

Simone Mungari – Enterprise Solution Engineer
Francesco Cupello – Enterprise Solution Engineer

L'articolo AML Control Automation: How Anti-Money Laundering is Changing with the New Moneying™ proviene da Revelis.

Generative Artificial Intelligence has now firmly entered business processes, but the real leap in value for companies is not asking for answers: it is delegating tasks. This is the promise of Agentic Artificial Intelligence, where autonomous software systems plan and execute tasks from start to finish by coordinating tools, data, and models. For an organization, however, autonomy also means responsibility: reliability, security, traceability, and cost control become indispensable requirements. 

At Revelis, we support companies through this transition with a guiding principle: making AI actionable in a reliable, repeatable, and governable way. Below, we describe our vision for building agentic solutions including the use of on-premise Small Language Models the architecture that makes it possible, and a directly applicable use case example.

From Generative AI to Agentic Artificial Intelligence

With generative AI, the user formulates a request and gets an answer: text, code, a summary. With Agentic Artificial Intelligence, the user assigns a goal: the system breaks down the problem, decides which tools to use, executes the necessary steps, and returns an operational result. It is helpful to think of it as a virtual collaborator to whom you can delegate repetitive tasks that carry a time cost but do not require high-value decision-making.

This paradigm shift unlocks high-value use cases from back-office automation to software development assistance, from document management to the preparation of complex bids but it introduces a technical challenge that becomes decisive in regulated contexts: the unpredictability of language models.

The critical friction point of enterprise adoption: unpredictability

By their very nature, language models can give different answers to the same question. In a creative chat, this is an asset; in a business process, it is a risk. On a practical level, the impact is twofold:

• Compliance and control: Difficulty in faithfully reproducing an action taken in the past, logs that are not always complete, and obstacles in meeting regulatory and audit requirements (GDPR, ISO, SOC 2).
• Operational risks: Varying behavior for the exact same request, decisions that are not always traceable, and unpredictable costs.

Revelis’ vision stems from this premise: the autonomy of agents must be channeled into an architecture that separates decision-making (what to do) from execution (how to do it), making the latter reliable and repeatable.

Revelis’ Vision: Reliable Agent Orchestration

With Agentic Artificial Intelligence, Revelis’ agentic framework is structured into layers with clear-cut responsibilities. The core concept is simple: AI proposes, but it does not improvise. The sequence of actions that produces concrete results is defined by pre-established, logged, and verifiable procedures. In this way, for the same request, the system always executes the same steps in the same order a necessary prerequisite for a company to trust an agent.

The architecture layers, in simple terms

• Guardrail (security): A filter that blocks irrelevant or non-compliant requests at the source, before they consume time or resources.
• Process Catalog (process memory): The list of tasks the system knows how to perform: for each one, it defines what data is needed and the correct procedure to follow.
• Semantic Router (routing): Understands the request expressed in natural language and routes it to the right task.
• Data Extraction: Extracts necessary information in an orderly fashion; if anything is missing, it asks for clarification instead of “inventing” answers.
• Execution Engine: Executes the procedure step-by-step, with the ability to resume where it left off and reconstruct every action for auditing purposes.
• Security Chain (fail-safe mechanism): When the system is not confident enough, it escalates through subsequent levels up to human intervention, never acting blindly.
• Memory/cache: Reuses decisions already made to reduce time and costs, without ever putting data at risk or repeating sensitive operations.

Two adoption scenarios, one architecture

The strength of this approach lies in its adaptability: the same architecture fits two very different starting points, without forcing the company to choose between reliability and pragmatism.

A. Already structured contexts: leveraging existing assets

Organizations that have already adopted copilot suites and cloud models possess a valuable technological heritage, as well as established governance and security processes worth capitalizing on. In these cases, Revelis reuses the existing infrastructure and adds the orchestration layer on top: agents become reliable and traceable directly on the infrastructure the company already owns, enabling a fast rollout without requiring new hardware investments.

B. On-premise LLMs: Small Language Models (SLMs) for data sovereignty and cost control

SMEs and organizations that prefer to keep data within their own perimeter for privacy, compliance, or vendor independence can adopt Small Language Models run on-premise: compact models that, thanks to optimization techniques, operate on limited hardware. Integrated into the architecture, they power reliable and repeatable execution of tasks, with predictable costs and full data control. This is where Revelis’ model evaluation work becomes a tangible advantage.

On-premise Small Language Models (SLMs): models evaluated by Revelis

Not all compact models are created equal. Revelis expanded its testing to eleven coding-oriented Small Language Models, measuring the correctness of the generated code against a sample set of test problems. The minimum benchmark set by Revelis was to solve at least 30% of the sample problems: four out of eleven models passed this threshold.

The highest score was achieved by Qwen3-Coder-Next (60% of problems solved), which is currently being tested within a coding agent using a context window of up to 200K tokens. This is followed by Gemma4 26B-A4B (48%), top of the list for upcoming agent testing. Qwen3-Coder-30B-A3B-Instruct (40%) has already been integrated into a coding agent, but in practice proved to be slow, heavy, and penalized by a necessarily restricted context window (32K tokens).

A concrete use case: automating the tender process with Agentic Artificial Intelligence

To understand where agents generate a real competitive advantage, it is worth looking at a process that many companies know well: participating in public tenders. It is the perfect example of a complex, high-value process involving multiple stakeholders, where most of the work today is performed manually.

Why it is a difficult (and expensive) process without Agentic Artificial Intelligence

A structured organization may participate in hundreds or thousands of tenders every year. Each tender brings an impressive volume of documentation: technical specifications, tender rules, and annexes that often exceed 200 pages, sometimes featuring thousands of individual “lots” to evaluate one by one. All of this must be read, interpreted, and turned into a bid within tight deadlines and under rules that vary from entity to entity.

The work touches multiple departments: the tenders office, sales, technical experts, finance, management control, all the way to executive sign-off. Today, coordination happens largely via email and spreadsheets, triggering a domino effect: the same specification document is read independently by multiple people, multiplying the hours spent. Above all, the margin for error is minimal: a single misread requirement or a non-compliant bid means immediate disqualification and a lost business opportunity.

How Agentic Artificial Intelligence intervenes

Revelis addresses this process using Agentic Artificial Intelligence. Thanks to a multi-agent system, an orchestrator coordinates multiple specialized agents, each responsible for a specific phase. The output of one agent feeds into the next, culminating in the draft proposal. No agent “decides” on its own: each one prepares, verifies, and proposes, leaving the decisions that matter to human experts.

• Scouting & Triage: Monitors portals, identifies relevant tenders, and assigns a feasibility/suitability score, allowing attention to focus where participation is truly worthwhile.
• Call Analysis (Tender Document Analysis): Reads the technical specifications and tender rules, extracting lots, requirements, criteria, deadlines, and grounds for exclusion—always citing the source document and page number so every piece of information is verifiable.
• Checklist & Envelopes: Generates the list of required documents for the administrative, technical, and financial envelopes, flags what is already prepared, and assigns tasks to the right team members.
• Pricing Support: Combines the tender formula, cost structures, and competitor estimations to propose a reasoned price alongside various scenario simulations. It proposes and explains, but the final decision remains human.
• Quality Simulation: Steps into the shoes of the evaluation committee: it applies the scoring rubric, estimates the proposal’s score, and highlights how to bridge any gaps to maximize points.
• Drafts & Memory: Prepares initial proposal drafts using company templates—eliminating the need to start from scratch—and builds a historical market memory (prices, outcomes, similar tenders) valuable for future bids.

The competitive advantage for companies using Agentic AI

Employing Agentic Artificial Intelligence means that the combined effect of these agents isn’t just about “doing things faster” it’s about competing better. By drastically reducing preparation time, companies can participate in more tenders including those currently discarded due to lack of time and focus on the ones with the highest chance of winning. Reducing compliance errors protects against disqualifications. Structured preparation helps avoid leaving money on the table: a more accurate bid raises the average winning price. And historical memory transforms even lost tenders into reusable market intelligence.

Governance and controlled evolution with Agentic Artificial Intelligence

Deploying Agentic Artificial Intelligence means utilizing an enterprise agentic system that is not static: it must grow without losing control. In the Revelis framework, every new capability starts as an activity in the process catalog and follows a governed path: drafting, overlap check, “shadow” testing (running in parallel without execution, to measure reliability on real-world cases), approval, and production deployment. Every human intervention thus becomes a learning opportunity, enriching the system in a fully traceable manner.

The result is an AI that observes, logs, and improves: every step is documented for the benefit of control, security, and continuous optimization.

The Revelis vision, in summary

Introducing Generative AI and Agentic Artificial Intelligence into a company does not mean giving up control. It means designing an architecture where reliability stems from the separation between decision and execution, security precedes all processing, and execution follows defined procedures rather than improvised choices.

Whether starting from an already structured copilot ecosystem or an on-premise setup based on Small Language Models, and whether the goal is automating back-office processes or winning more tenders, the objective remains the same: reliable, secure, and governable agents capable of generating measurable value.

It is our interpretation of the principle that has always guided Revelis: making AI actionable.

Mini-glossary for non-experts

• Generative AI — Systems that produce content (text, images, code) in response to a prompt.
• Agentic AI — Systems that go beyond simply answering questions to execute delegated tasks by coordinating multiple steps.
• Agent — A specialized software “virtual collaborator” assigned to a specific task; multiple agents can cooperate with each other.
• Orchestrator — The coordinator that assigns tasks to individual agents and synthesizes their results.
• LLM / Small-LLM — The language model that “reasons” over text; Small-LLMs are compact versions capable of running on limited hardware.
• On-premise — Technology installed and managed directly within the company’s premises rather than in the cloud, keeping data in-house.
• Human-in-the-loop — An approach where humans validate and approve what the AI proposes, ensuring full control is maintained.

Want to evaluate Agentic AI in your company? Revelis supports enterprises and SMEs in designing agentic solutions from model analysis to orchestration architecture, in the cloud or on-premise. Write to us at info@revelis.eu

Authors

Achille Abritta – Enterprise Solution Engineer at Revelis

Francesco Cupello – Enterprise Solution Engineer at Revelis

Luigi Granata – Enterprise Solution Engineer at Revelis

L'articolo Agentic Artificial Intelligence, SLMs, and Possible Applications proviene da Revelis.

At Revelis we work daily with AI and Big Data Analytics technologies and we observe this transformation from the inside. In this article we explain what vibe coding tools are, why they are changing the rules of software development and which platforms are most effective in 2026. To explore the conceptual foundations, you can start from our article “Vibe Coding AI”.

What vibe coding tools are and why they are reshaping software development

Vibe coding tools are not just an evolution of traditional code editors: they represent a new software development paradigm in which the programmer no longer writes instructions sequentially, but interacts with an AI agent to translate ideas into working applications. The developer becomes a sort of orchestra conductor: they define the vision, architecture and user experience, while artificial intelligence handles technical implementation and repetitive tasks, from boilerplate to syntactic debugging.

This approach redefines iteration speed. Where in the past creating a working prototype required days of environment setup, dependency management and service code, today vibe coding tools take you from idea to deployment in minutes. The real game-changer is the ability to maintain context: AI doesn’t just suggest the next line; it understands the entire codebase and proposes structural changes consistent with the project’s style and architecture.

The outcome is a dual leverage: on one hand, access to software creation is democratised, opening the door to designers, founders and product roles with less vertical technical skills; on the other, professionals gain an unprecedented cognitive amplifier that multiplies productivity and frees mental space for solving high-level logical problems.

Why vibe coding tools are changing the rules of the game

The gap between a traditional IDE with autocompletion and a true vibe coding tool is substantial: in classic IDEs, AI is a guest; in modern vibe coding tools, artificial intelligence is the core. The interaction is bidirectional and deeply integrated. Asking the agent to “switch the whole app to dark mode and optimise API calls” means seeing it work autonomously across dozens of files, handling consistency and dependencies.

According to the latest industry data, these AI coding assistants can cut front-end development time by up to 80%, freeing teams from repetitive coding and allowing them to focus on product validation and user experience.

The rise of vibe coding tools is enabled by the maturation of Large Language Models (LLMs), which now feature context windows wide enough to read and understand entire repositories. When a runtime error appears, modern vibe coding tools act in three steps:

• they analyse the error log and identify the likely cause;
• they formulate a diagnosis based on the project context;
• they propose and apply an immediate patch, verifying its effect.

This fast and intuitive feedback loop is what truly defines the coding by vibes experience: not just aesthetics, but operational efficiency that allows hypotheses to be tested in real time. For companies, integrating vibe coding tools into their processes means producing more software, faster and with greater flexibility. The risk of generating low-quality code is real but is mitigated by integrated static analysis tools that enforce security and maintainability best practices.

Vibe coding tools: concrete benefits for businesses and product teams

For those running a company or a product team, adopting vibe coding tools is not a purely technical matter: it has a direct impact on time-to-market, on the ability to experiment and on development costs. The main observable benefits are:

• Drastic reduction in prototyping time: ideas, MVPs and proof-of-concepts can be built in hours rather than weeks.
• Stronger alignment between business and development: product managers and stakeholders can actively shape the product, reducing misunderstandings.
• Optimised innovation costs: ideas can be validated on the market before committing resources to a structured development.
• Freeing up time for high-value work: developers can focus on architecture, security and domain logic rather than repetitive tasks.

At Revelis we support companies and organisations in adopting these technologies in a structured way, embedding vibe coding tools into solid development processes, with strong oversight of code quality, security and data governance. This is the approach that turns AI speed into sustainable business value.

Comparative analysis: the best vibe coding tools of 2026

Not all vibe coding tools serve the same needs. The right choice depends on the use case, the desired level of control and the team’s skills. Below we present an overview of the best vibe coding tools of 2026, with their respective strengths.

Cursor: the vibe coding tool for AI-driven code editing

Cursor is a fork of Visual Studio Code that has quickly become the reference IDE for professional developers. Its distinctive feature is the deep integration of AI into the workflow: Cursor indexes the entire local codebase, allowing users to ask questions about any part of the project and to obtain coherent changes across multiple files in a single action. It’s the ideal choice for those seeking surgical precision without giving up the benefits of vibe coding.

Lovable: the ideal solution for full-stack web applications

Lovable is one of the most effective vibe coding tools for building full-stack applications from scratch. The platform turns text descriptions into complete React interfaces, also handling database logic and authentication. The principle is the purest expression of vibe coding: the user describes what they want to see and the tool “conjures” the application, making the line between design and development increasingly thin.

Lovable’s value emerges most clearly in cutting down the time needed to turn an idea into a concrete digital product. Thanks to a fully conversational approach, advanced prototypes, dynamic landing pages or working web apps can be generated even with limited technical skills: a decisive advantage for startups, innovation teams and founders who want to quickly validate a product hypothesis.

Replit Agent: the all-in-one development platform

Replit Agent is another undisputed leader among vibe coding tools. The platform has removed every barrier to entry: simply describe an application, even a complex one such as a marketplace or a data management system, and watch it be built, configured and deployed to the cloud in a single flow. Its ability to understand semantic intent is remarkable: in response to a prompt like “make the design more professional”, the agent doesn’t just change a colour but adjusts spacing, typography and visual hierarchy.

Replit Agent’s key strength is automating all the activities that traditionally require complex configuration: deployment, development environments and cloud integrations are managed inside the same workflow. This makes vibe coding accessible to designers, marketers and founders, while at the same time allowing product teams to speed up prototyping and focus on user experience.

Further reading: AI and vibe coding: building applications with LLM prompts

Bolt: the vibe coding tool for building web apps from a prompt

Bolt is one of the most innovative platforms for turning text prompts into working web applications in just a few minutes. Thanks to integration with advanced AI models, it allows users to generate interfaces, logic and full-stack components by describing in natural language what they want to build, with modern code immediately runnable in the browser. It is particularly popular with startups, designers and developers who want to quickly prototype MVPs and modern applications.

Bolt lets you modify and iterate the project conversationally, making the entire development cycle smoother. It’s an effective choice when the goal is to test an idea quickly, directly in the browser, without setting up local environments.

v0 by Vercel: generating modern interfaces with natural language

v0 by Vercel is one of the most interesting vibe coding tools for front-end work. It lets you create UI components and web applications simply by describing what you want, turning an idea into ready-to-use React code based on modern technologies like Tailwind CSS and shadcn/ui.

v0’s stated goal is to accelerate frontend development: thanks to its conversational approach, designers, developers and product teams can quickly iterate on layouts, dashboards and landing pages, modifying components in real time and obtaining highly realistic prototypes without having to write every single line of code.

For a global view, the table below offers a comparative overview of the main vibe coding tools of 2026, based on the analysis of market data (sources: CoderFile and Index.dev).

These data clearly show how vibe coding tools now cover every niche, from the rapid prototyper to the experienced software engineer, establishing themselves as a new family of professional tools rather than a passing trend.

Conclusions: the future of software development with vibe coding tools

Adopting vibe coding tools marks the shift from the era of mechanical code writing to the era of creative software management. This isn’t about losing technical skills, but about elevating them: using these tools wisely will become a fundamental skill, just as mastery of development frameworks has been over the past decade.

Whether the goal is launching a startup, accelerating product innovation or optimising internal processes, vibe coding tools are now essential companions for anyone aiming to innovate in the digital world. At Revelis we help companies and organisations integrate them strategically into their processes, combining AI speed with engineering reliability.

Want to understand how to introduce vibe coding tools and AI into your development processes? Get in touch with the Revelis team for a dedicated consultation: www.revelis.eu

L'articolo The best vibe coding tools for programming with intent proviene da Revelis.

The point is that AI regulation is uncharted territory. With the entry into force of the European Regulation , the AI Act, companies are required to meet precise standards on transparency, risk management, traceability and human oversight. Translated: it is no longer enough to innovate fast, you need to do it by following verifiable, demonstrable rules.

The AI Compliance Officer is the organisational answer to this transformation. We are not talking about a purely legal or purely technical role, but a hybrid one that brings together IT, legal, risk management and business. Its goal is to build an AI governance system that is sustainable over time, auditable and aligned with regulations.

For companies that want to grow in a solid, reliable and competitive way, having this role is no longer optional: it is a strategic lever.

What an AI Compliance Officer does in a company

Operationally, the AI Compliance Officer is the professional who ensures that AI systems comply with regulations, ethical standards and corporate policies. But what does this mean, in practice?

The first step is the mapping of AI systems already in use: identifying where and how algorithms, predictive models or automated systems are deployed, and classifying them according to the risk level set out by the AI Act. It is a crucial step, because it determines the regulatory obligations that apply.

From there comes risk assessment. The AI Compliance Officer defines processes to analyse bias, impact on fundamental rights, security and reliability of the systems. For high-risk applications in particular, strict procedures for control, documentation and continuous monitoring are required.

Another key area is technical documentation. The company must be able to demonstrate how its AI systems work, what data they use and which decisions they support: all of this requires a robust and constantly updated documentation infrastructure.

The work happens in close cooperation with the IT and data teams, to implement logging and auditing systems that guarantee transparency and accountability. At the same time, there is constant exchange with the legal department, to ensure alignment with regulations such as GDPR, NIS2 and other connected frameworks.

Finally, there is a front that is often underestimated: the cultural one. The AI Compliance Officer promotes internal awareness of the responsible use of AI, trains the teams and helps spread a culture of compliance and digital ethics.

In short, this is not a controller: it is an enabler of responsible innovation.

Business impact and competitive advantage

Thinking of the AI Compliance Officer only as a regulatory watchdog is reductive. To all intents and purposes, this is a value enabler for the business. In a context where AI is used to automate decisions, optimise processes and generate revenue, this role allows companies to scale AI adoption in a safe and structured way, reducing the risks that could slow innovation down.

The most interesting effect is precisely this: compliance stops being perceived as a brake and becomes a strategic lever. When AI systems are governed, documented and monitored, the company can speed up the rollout of new use cases without fear of regulatory breaches or reputational issues. This is especially true in regulated sectors such as finance, healthcare, insurance and public administration, where the balance between innovation and control is particularly delicate.

There is also a direct impact on stakeholder trust. Customers, partners and investors are increasingly attentive to how companies use AI, especially in terms of transparency and data management. A solid AI governance framework, led by an AI Compliance Officer, becomes a distinctive element that strengthens corporate reputation and improves perceived reliability in the market.

Last but not least, there is the reduction of indirect costs linked to non-compliance: fines, operational blocks, project rework, brand damage. From this perspective, the role should not be read as a cost centre, but as an investment that protects and enables long-term growth.

AI Compliance Officer and the AI Act: why this role has become essential

With the introduction of the AI Act, the first comprehensive European regulation on artificial intelligence, the AI Compliance Officer has gone from a nice-to-have to a real necessity. The regulation takes a risk-based approach, imposing different obligations depending on the impact of AI systems.

The complexity is significant. Companies must classify their systems, implement a risk management system, ensure human oversight, guarantee transparency towards users and be ready to demonstrate compliance through audits and inspections.

What makes the topic even more sensitive are the penalties: fines of up to EUR 35 million or 7% of global annual turnover, whichever is higher. Non-compliance, then, is not only a reputational risk: it is also a significant economic risk.

In this scenario, the AI Compliance Officer becomes the guarantor of compliance, but also a strategic player to avoid operational blocks, project delays or legal issues. And, not least, helps turn compliance into a competitive advantage: companies that can demonstrate reliability and transparency in their use of AI strengthen the trust of customers and partners.

Skills, profile and opportunities for companies

The AI Compliance Officer is a hybrid role and, precisely for this reason, hard to find in a single traditional profile. Many companies are rethinking their organisational structure or relying on specialised external partners.

The starting point is solid regulatory knowledge, especially of the European framework: AI Act, GDPR, cybersecurity regulations. But that is not enough: a technical understanding of AI systems, machine learning models and development processes is just as important.

On top of that come skills in risk management, auditing and governance. The AI Compliance Officer must be able to design processes, define policies and monitor compliance over time.

Equally important is the ability to communicate: translating complex concepts into operational guidelines that the business can actually use is a core part of the job, as is facilitating the dialogue between functions that often speak very different languages.

The demand for these skills is growing fast: AI compliance is no longer a niche topic, but a priority for organisations that want to use artificial intelligence at scale.

For businesses, all of this translates into an opportunity. Bringing in an AI Compliance Officer not only means reducing risk, but also accelerating AI adoption in a structured and secure way. In an increasingly regulated market, the ability to demonstrate compliance can become a real competitive differentiator.

Want to find out whether your company is ready to face an AI audit? Discover the AI Governance & Compliance services by Revelis and assess your organisation’s maturity level.

L'articolo AI Compliance Officer: who they are and what they do in a company proviene da Revelis.

For this reason, the results achieved year after year are a source of pride for Revelis, confirming that the company is moving in the right direction, with the ongoing goal of continuously improving working conditions and enhancing employee well-being.

Thanks to the consistency of its actions over time, Revelis having already reached an important milestone in 2025 with the HappyIndex® AtWork certification now celebrates a further leap forward: the achievement of the Platinum Medal, with an outstanding score of 479 out of 500, based on an anonymous survey in which the Revelis team shared their perceptions of their work experience.

This recognition rewards not a single result, but a structured and continuous journey toward excellence one that Revelis is proud to pursue.

Key performance areas

• Professional growth
• Work environment
• Management
• Recognition
• Purpose
• Sustainable development

Crescita professionale: percorsi chiari e sviluppo continuo

Vediamo nello specifico i risultati ottenuti nell’ambito della dimensione “crescita professionale”.

La crescita professionale in Revelis si conferma come uno degli elementi centrali dell’esperienza lavorativa, con un punteggio complessivo dell’85.2% che riflette un sistema strutturato, accessibile e orientato allo sviluppo delle persone. Il 90.9% del team dichiara di avere concrete opportunità per imparare e sviluppare nuove competenze, attraverso progetti sfidanti, attività di coaching, percorsi formativi, certificazioni e momenti di condivisione delle conoscenze. Questo dato evidenzia una cultura in cui l’apprendimento è continuo e integrato nelle attività quotidiane, non relegato a momenti isolati.

A rafforzare questa percezione contribuisce la chiarezza nei criteri di valutazione delle performance: l’86.4% delle persone afferma di comprendere come viene valutato il proprio rendimento, segnale di trasparenza e coerenza nei processi interni. Allo stesso modo, un altro 86.4% dichiara di avere visibilità su come sviluppare la propria carriera all’interno dell’organizzazione, in termini di responsabilità, progetti e opportunità di crescita. Questo aspetto è fondamentale perché consente alle persone di orientarsi, pianificare e sentirsi protagoniste del proprio percorso professionale.

Il dato relativo alla valorizzazione dei punti di forza (77.3%) introduce invece uno spunto di evoluzione: pur rimanendo positivo, suggerisce un margine di miglioramento nel riuscire a sfruttare pienamente talenti e inclinazioni individuali. Nel complesso, emerge un ambiente che investe in modo concreto sullo sviluppo delle competenze e sulla costruzione di percorsi chiari, ponendo le basi per una crescita sostenibile e condivisa nel tempo.

Ambiente di lavoro: benessere, sicurezza e collaborazione come fondamenta

Vediamo nello specifico i risultati ottenuti nell’ambito della dimensione “’ambiente di lavoro”.

L’ambiente di lavoro in Revelis si distingue per un livello di qualità particolarmente elevato, come dimostra il punteggio complessivo del 92%. Un risultato che riflette un equilibrio concreto tra benessere individuale, sicurezza e capacità organizzativa, elementi sempre più centrali nella costruzione di contesti lavorativi sostenibili. L’86.4% delle persone si dichiara soddisfatto del proprio equilibrio tra vita professionale e personale, segno di un’organizzazione che, pur operando in un contesto dinamico e orientato ai risultati, riesce a mantenere un’attenzione reale verso la qualità della vita delle persone. Questo dato si integra con un altro indicatore chiave: l’86.4% del team riconosce una collaborazione efficace tra i diversi reparti, confermando una buona integrazione interna e una capacità diffusa di lavorare in modo trasversale.

Particolarmente rilevanti sono i risultati legati alla sicurezza e al benessere complessivo. Il 95.5% delle persone afferma di lavorare in un ambiente sicuro e sano, che include non solo il rispetto delle normative, ma anche aspetti fondamentali come la sicurezza psicologica, la qualità delle relazioni, la presenza di spazi di dialogo e una gestione equilibrata dello stress e dei carichi di lavoro. Ancora più significativo è il dato del 100% relativo alla sicurezza fisica: tutte le persone dichiarano di sentirsi al sicuro sul luogo di lavoro, in un contesto che garantisce prevenzione, rispetto e assenza di comportamenti inappropriati.

Nel complesso, questi dati delineano un ambiente solido e affidabile, in cui le persone possono lavorare con serenità, sentirsi protette e costruire relazioni professionali basate sulla fiducia. Un elemento fondamentale che contribuisce in modo diretto alla qualità dell’esperienza lavorativa e, di conseguenza, alla performance complessiva dell’organizzazione.

Management: leadership presente, feedback continuo e chiarezza decisionale

Vediamo nello specifico i risultati ottenuti nell’ambito della dimensione “’management”.

Il modello di management in Revelis si distingue per solidità e capacità di supportare concretamente le persone, come evidenziato dal punteggio complessivo dell’86.4%. I dati mostrano un equilibrio efficace tra guida strategica, accompagnamento operativo e valorizzazione del contributo individuale. L’81.8% delle persone riconosce l’utilità e l’importanza del proprio lavoro per il successo dell’organizzazione, un segnale che il management riesce a trasmettere visione e impatto, collegando le attività quotidiane agli obiettivi più ampi. Questa chiarezza si riflette anche nella comprensione dei processi decisionali: l’81.8% del team dichiara di capire come vengono prese le decisioni, indicando un buon livello di trasparenza e accessibilità delle dinamiche interne.

Particolarmente rilevanti sono i dati legati alla relazione diretta con il proprio responsabile. Il 90.9% delle persone afferma di ricevere feedback utili, sia in termini di frequenza che di qualità, elemento fondamentale per favorire crescita, allineamento e miglioramento continuo. Allo stesso tempo, un altro 90.9% si sente supportato dal proprio manager, evidenziando uno stile di leadership basato su ascolto, disponibilità e presenza. Questo tipo di relazione contribuisce a creare un clima di fiducia, in cui le persone si sentono accompagnate nelle sfide quotidiane e sostenute nel proprio percorso professionale.

Nel complesso, emerge un modello manageriale coerente e orientato alle persone, capace di coniugare chiarezza, supporto e responsabilizzazione. Un elemento chiave per costruire engagement duraturo e favorire una cultura organizzativa solida e condivisa.

Riconoscimento: valorizzare il talento e l’impegno

Vediamo nello specifico i risultati ottenuti nell’ambito della dimensione “’riconoscimento”.

Con un eccellente 90,9% di soddisfazione totale, l’area del riconoscimento conferma come Revelis metta al centro il valore delle proprie persone. La quasi totalità dei collaboratori (95,5%) si dice estremamente soddisfatta dei benefit aziendali, mentre il 90,9% ritiene che la propria retribuzione sia adeguata alle responsabilità ricoperte. Oltre l’aspetto economico, emerge un forte senso di ascolto: il 90,9% del team sente che le proprie opinioni vengono prese in seria considerazione e l’86,4% si sente costantemente incoraggiato e valorizzato negli sforzi quotidiani.

Ragion d’essere: orgoglio e spirito di squadra

Vediamo nello specifico i risultati ottenuti nell’ambito della dimensione “’ragion d’essere”.

L’identità di Revelis e il suo scopo profondo convincono l’83% della popolazione aziendale. Un dato che spicca riguarda la qualità del clima interno: il 90,9% dei dipendenti è soddisfatto delle relazioni interpersonali, descrivendole come improntate alla sincerità, alla disponibilità e alla collaborazione. La fiducia nel team di gestione (leadership e gestione del cambiamento) è solida (81,8%), così come la capacità dell’azienda di evolversi a ritmi sostenibili. Infine, l’orgoglio verso i prodotti e servizi offerti (77,3%) rappresenta il motore che spinge l’azienda verso l’innovazione continua.

Sviluppo sostenibile: etica e responsabilità verso il futuro

Vediamo nello specifico i risultati ottenuti nell’ambito della dimensione “sviluppo sostenibile”.

La sostenibilità in Revelis è un valore concreto che raggiunge un punteggio totale del 90,9%. L’impegno verso l’ambiente è particolarmente sentito, con il 95,5% dei collaboratori che ritiene le attuali problematiche ecologiche adeguatamente integrate nella strategia aziendale. L’etica è un valore cardine anche nelle relazioni esterne: il 90,9% conferma l’assenza di conflitti di interesse o corruzione nei rapporti con clienti e fornitori. Internamente, la cultura dell’equità è predominante, con l’86,4% che riconosce pari opportunità per tutti, mentre il 90,9% si dichiara in perfetta sintonia con i valori, la cultura e il progetto strategico dell’organizzazione.

Un impegno verso un futuro ancora migliore

In conclusione, i risultati dell’indagine delineano il profilo di un’azienda dove il benessere e l’etica non sono semplici dichiarazioni d’intenti, ma realtà vissute quotidianamente. Il dato straordinario del 100% sulla sicurezza fisica e prevenzione dei rischi, unito al 95,5% di soddisfazione per l’ambiente sano e i benefit aziendali, conferma che in Revelis la cura della persona è la priorità assoluta. 

Con oltre il 90% dei collaboratori che si sente supportato dal management, valorizzato nelle proprie opinioni e in piena sintonia con i valori e la sostenibilità ambientale dell’organizzazione, la medaglia di platino HappyIndex® AtWork non è solo un traguardo, ma la conferma di un ecosistema vincente. Nonostante gli ottimi punteggi sulla crescita professionale (86,4%) e l’orgoglio verso i servizi offerti (77,3%), accogliamo questi dati come uno stimolo a migliorare ancora, continuando a investire nel talento e nelle relazioni per costruire, insieme, il futuro di Revelis.

L'articolo Revelis awarded the HappyIndex® AtWork Platinum Medal by ChooseMyCompany proviene da Revelis.

On the other, there is the burden of decades-old technological infrastructures layered and consolidated that represent both the beating heart and the operational “cage” of many institutions.

Within the context of digital transformation in banking, these legacy systems serve as the operational core but also as a major barrier to innovation.

At Revelis, driven by the mindset captured in our tagline #artificialintelligenceinaction, we recently managed the migration of a critical banking procedure shaped by 30 years of continuous development. Originally written in legacy languages such as PL/I and COBOL , it was transformed into a modern microservices architecture based on Java/Spring Boot and Angular.

Below, we analyze a concrete case of digital transformation in banking, where we converted a monolithic system into a modern, scalable solution ensuring quality through SonarQube  and achieving accurate data migration with AI-assisted ETL processes.

What is digital transformation in banking?

Let’s start by clarifying what digital transformation in banking actually means: it is the process of technological and organizational modernization through which financial institutions update systems, processes, and business models by integrating technologies such as artificial intelligence, cloud computing, and microservices.

This process enables banks to:

• improve operational efficiency
• reduce costs associated with legacy systems
• deliver faster, more personalized digital services
• ensure regulatory compliance (RegTech and AI Act)
• enhance data security and management

Digital transformation in banking is therefore a strategic step for competing in an increasingly digital and regulated market.

Examples of digital transformation in banking

Examples of digital transformation in banking include the adoption of new technologies to improve processes, services, and IT infrastructure.

Key examples include:

• migrating from legacy systems to microservices
• using AI for analytics and automation
• implementing chatbots and virtual assistants
• digitizing customer onboarding processes
• leveraging cloud computing for scalability and security
• integrating RegTech solutions for compliance

These initiatives enable banks to become more agile, efficient, and competitive in the digital marketplace.

The migration process in digital transformation

To better understand the approach taken, it is useful to look at how automation and human intervention were integrated at every stage of the transformation process.

Legacy systems: the weight of the past

Legacy banking software is not just “old” it is one of the main obstacles to digital transformation in banking. Over 30 years, multiple development teams maintained procedures written in PL/I and COBOL. While these languages were once pillars of enterprise computing, today they represent significant operational risks and are ill-suited for modern transformation needs because they:

• increase operational risk
• slow down innovation
• complicate integration with new technologies

The problem of technical debt

Technical debt is one of the biggest challenges in digital transformation. Thirty years of changes result in thousands of lines of code where business logic is often intertwined with outdated technical constraints. In many cases, original documentation has been lost or no longer reflects the reality of the code.

This creates a dangerous dependency on a small number of domain experts who understand these “black boxes.”

Migrating such a system does not simply mean “changing programming language” it means decoding three decades of business decisions, compliance rules, and complex calculation logic. This is where traditional approaches fail due to excessive costs and risks. This is also where Revelis’ vision comes into play.

Vibe coding and AI in banking transformation

As part of the transformation process, we adopted the Vibe Coding AI paradigm.

This approach enables:

• faster development
• improved code quality
• reduced dependency on legacy languages

What is vibe coding?

Vibe Coding is not just a trend it represents a fundamental shift in how developers interact with code. Instead of writing instructions line by line, developers engage with AI at a higher conceptual level. They communicate the “vibe” the intent, logic, and desired structure while the Large Language Model (LLM) handles the syntactic generation.

Prompt engineering as a programming language

Prompt engineering has become a key component in digital transformation. To extract logic from PL/I and COBOL, we used advanced prompt engineering techniques.

Rather than simply asking the AI to “translate,” we built structured prompting systems to guide deep reverse engineering:

• Contextualization: Providing the LLM with legacy language manuals to ensure accurate understanding
• Decomposition: Breaking large PL/I files into manageable logical blocks while preserving global context
• Iteration: Refining results through feedback loops, testing, and co-creation

Spec-driven methodology: the role of human validation

Human validation remains essential. The Spec-Driven approach allowed us to create an intermediate layer of functional “truth” before rewriting the system.

Generative technical-functional analysis

We used LLMs to analyze PL/I and COBOL source code and generate complete technical and functional documentation describing what the procedure does independent of the original language.

This analysis was not taken at face value. At Revelis, we see AI as an enhancer, not a replacement for human intelligence. Domain experts rigorously validated the output. Senior developers and business analysts ensured that the AI correctly interpreted even the most subtle business rules.

AI-Generated development plan

Once validated, we asked the AI to generate a detailed development plan, including:

• API definitions
• database structures
• integration logic between microservices

The AI effectively acted as a senior software architect, proposing the most efficient decomposition of the original monolith.

Data migration: AI-Assisted ETL processes

Data migration is one of the most critical aspects of digital transformation in banking. A banking system is nothing without its data. Thirty years of operations result in massive volumes of information stored in outdated formats or hierarchical/flat databases typical of mainframe environments.

As part of the transformation, this data must be migrated to modern relational databases.

We used LLMs to generate dedicated ETL (Extract, Transform, Load) procedures:

• Extraction: Understanding COBOL and PL/I record structures
• Transformation: Applying cleaning and normalization rules to ensure referential integrity
• Loading: Automating SQL script generation to populate new databases

This approach reduced database migration time by 80% and eliminated typical human errors in critical phases.

The new technology stack: microservices and modernization

Digital transformation requires modern architectures designed for scalability:

• Backend: Developed in Java with Spring Boot, breaking logic into independent microservices (e.g., customer data, accounts) to allow targeted updates
• Frontend: Angular replaced legacy character-based interfaces, significantly improving user experience and operational efficiency

Quality assurance with SonarQube

Code quality is crucial. AI-generated code introduces a key challenge: ensuring it is clean, secure, and free of vulnerabilities.

We integrated SonarQube into the development process to analyze every line of code for:

• Code Smells: ensuring readability and maintainability
• Vulnerabilities: critical for protecting sensitive banking data
• Technical Debt: avoiding long-term issues

This allowed us to objectively demonstrate that the new system is not only more modern but also superior in quality.

Efficiency and savings: the role of RegTech

Digital transformation is not just about technology it also involves regulatory compliance.

This approach ensured compliance with modern standards and the AI Act, while improving efficiency through automation of critical processes.

The AI Act introduces new requirements for transparency and governance. By documenting every step from reverse engineering to development planning we ensured full traceability and alignment with emerging regulatory expectations.

Conclusion: the future of digital transformation in banking

Compared to traditional manual migration, the benefits were clear:

• 50% reduction in overall development time
• 40% reduction in project costs

These results demonstrate that digital transformation in banking delivers tangible, measurable value.

Digital transformation is no longer optional, it is a necessity. The migration of PL/I and COBOL systems was not just a technical project; it proved that with the right methodology and a conscious use of AI, it is possible to unlock the hidden value of legacy systems.

Thanks to Vibe Coding, we turned IT archaeology into future-ready architecture. We demonstrated that:

• 30-year-old code can be understood and modernized rapidly
• AI, guided by domain experts, can produce reliable analyses and development plans
• modern technologies like Spring Boot, Angular, and microservices enable agile banking
• quality must always be measured and certified (SonarQube)

At Revelis, we continue to push the boundaries of innovation, bringing AI solutions across industries from healthcare to manufacturing and into the very core of banking systems.

If your organization is struggling with outdated systems that hinder growth, it’s time to change the vibe.

Want to start your digital transformation journey in banking? Get in touch with us.

Author: Massimiliano Ruffolo

L'articolo Digital transformation in banking: how to evolve with AI proviene da Revelis.

It is not simply a matter of installing a firewall or antivirus software, but of adopting a structured and systemic approach to protection. The ISO/IEC 27001:2022 standard represents the international benchmark for Information Security Management Systems (ISMS), providing a robust framework that enables organizations to identify, manage, and significantly reduce risks related to the confidentiality, integrity, and availability of corporate data.

Part of the ongoing digital transformation that companies must embrace also involves understanding the importance of ISO 27001 security. Today, this step means realistically assessing the numbers and the evolution of cybercrime. According to the recent Clusit 2024 report, global cyberattacks increased by 12% compared to the previous year, with particularly devastating impacts on critical infrastructures and small and medium-sized enterprises lacking adequate defenses. In such a constantly evolving threat landscape, certification should not be seen as a simple “badge” to display in a website footer, but as a competitive necessity and a demonstration of resilience. Implementing a strategy based on ISO 27001 security allows companies to demonstrate to partners, customers, and stakeholders that they have adopted rigorous measures verified by third-party bodies, raising the overall quality standards of the entire production chain.

What ISO 27001 security actually is and its core domains

Looking more closely at the standard, ISO 27001 security is based on a rigorous risk-based approach. But what does this mean in practical terms for a company? The standard defines international requirements for establishing, implementing, maintaining, and continuously improving an effective ISMS.

The structure of the standard is divided into two main sections:

• The main body clauses, which define organizational and governance requirements
• Annex A, which lists a set of specific controls designed to mitigate risks

With the 2022 update, ISO 27001 security controls were reorganized into four macro-categories: organizational controls, people controls, physical controls, and technological controls. This new architecture reflects the evolution of hybrid work and the widespread adoption of cloud computing, offering organizations a flexible tool to protect data wherever it resides.

Competitive advantages and ROI of ISO 27001 Security

Companies often perceive regulatory compliance as a passive cost, while the correct perspective is to view it as a high-return investment and a way to protect corporate value.

Adopting ISO 27001 security provides tangible benefits that directly impact financial performance. First, it significantly reduces the likelihood of a data breach, which according to authoritative studies by IBM can cost an average of $4.45 million per incident, including penalties and reputational damage.

Second, compliance with ISO 27001 security greatly simplifies alignment with GDPR, as many requirements of the European regulation are operationally addressed by the standard’s controls. Finally, it enhances the company’s reputation and opens the door to new markets, facilitating participation in both public and private tenders.

How to comply with ISO 27001 security

The journey toward full ISO 27001 security compliance is neither instantaneous nor purely technical. It is a methodological process that requires strong commitment from top management.

Risk analysis

The first essential step is Risk Assessment. During this delicate phase, the company must accurately map its information assets—hardware, software, data, and human expertise—and identify the threats and vulnerabilities that could compromise them.

Without a clear and quantified understanding of risks, it is impossible to implement ISO 27001 security measures that are truly effective and proportionate. Once risks are identified, the next step is the Risk Treatment Plan, where organizations strategically decide which risks to accept, reduce through technology, transfer via insurance, or avoid by modifying internal processes.

Creating the ISMS and ISO 27001 documentation

A crucial and indispensable component of ISO 27001 security is the proper management of system documentation. This is not about producing paperwork for its own sake or creating unnecessary bureaucracy, but about formalizing procedures and policies that must become an integral part of everyday corporate culture.

To achieve full ISO 27001 security compliance, organizations must prepare essential documents such as:

• The Information Security Policy
• The ISMS Manual
• The Statement of Applicability (SoA)

The SoA is the “master document” that lists which Annex A controls have been implemented and the technical reasons for their selection. Transparency and traceability of actions are fundamental: every employee must understand their responsibilities and know exactly how to act to maintain the organization’s ISO 27001 security posture.

The role of human training in ISO 27001 security

In cybersecurity, it is often said that the weakest link in the chain is not software, but the human being. For this reason, ISO 27001 security places strong emphasis on awareness and continuous training for staff at all levels.

Complying seriously with ISO 27001 security means educating every employee from the CEO to entry level operators about real risks such as phishing, the importance of secure password management, and the correct use of both corporate and personal devices.

A well-informed workforce represents the first and most effective line of defense against cyber intrusions. Implementing regular training sessions and “social engineering” tests is not merely a formal requirement of the standard, but a practice that transforms employees from potential vulnerabilities into active guardians of the organization’s information assets.

Monitoring and continuous improvement: the ISO 27001 cycle

Achieving ISO 27001 security compliance should not be considered a static milestone, but rather the starting point of continuous evolution. The standard is based on the well-known PDCA model (Plan–Do–Check–Act), which promotes iterative performance improvement.

Once technical and organizational controls are implemented, it is essential to monitor their effectiveness over time through regular internal audits and periodic management reviews. If these checks reveal nonconformities or new areas of weakness, immediate and documented corrective actions must be taken.

This dynamic approach ensures that the ISO 27001 security management system evolves alongside technological innovation and the emergence of new cyber threats, preventing corporate defenses from becoming outdated or ineffective.

Toward certification: the final audit

The final step in the ISO 27001 security compliance journey is the certification audit, conducted by an independent third-party certification body. This inspection process typically consists of two stages:

• Stage 1, mainly focused on documentation review and verification of system readiness
• Stage 2, which involves an in-depth on-site audit of the actual implementation of the declared procedures

During these sessions, the auditor will look for objective evidence that ISO 27001 security is being properly managed. Obtaining the official certificate is the ultimate proof of an organization’s operational maturity and professional reliability.

Conclusion

Investing in ISO 27001 security is now an essential choice for companies aiming for long-term sustainability and operational excellence. Protecting information does not simply mean defending servers; it means safeguarding customer trust and the stability of the company’s entire business vision. A crucial step toward sustainable, long-term growth.

L'articolo ISO 27001 security: what it is and how to comply to protect your company’s value proviene da Revelis.

AML obligations cut across multiple business functions: from Customer Due Diligence to transaction monitoring, from data collection and retention to reporting to the competent authorities. According to the most recent data released by Unità di Informazione Finanziaria (UIF), in the first half of 2025 more than 312 million transactions were monitored in Italy, with a total value of €15.538 trillion reported in anti-money laundering filings.

On the technology front, the global AML solutions market is also experiencing strong growth. It is expected to exceed $3.3 billion in 2025 and continue expanding in the coming years, driven by increasing regulatory complexity and the proliferation of digital channels.

Key regulatory obligations for banks and financial institutions

Supervised institutions are required to comply with a strict set of regulatory obligations. The main ones include:

1. Customer Due Diligence (CDD) and Know Your Customer (KYC)

Banks must verify the identity of customers and beneficial owners, collect up-to-date documentation, and assess each client’s risk profile. This is not a one-off process: it requires ongoing monitoring and periodic updates based on a risk-based approach, meaning controls are calibrated according to the level of risk.

Current AML regulations, including those issued by the European Union and transposed at national level, require documentation and risk assessments to be transparent, traceable, and continuously updated.

2. Transaction monitoring and suspicious activity reporting

Banks must implement monitoring systems capable of analyzing financial transactions in real time or near real time to detect anomalous or suspicious patterns. Prompt reporting to Financial Intelligence Units and other competent authorities is a binding regulatory obligation, subject to strict deadlines for filing Suspicious Activity Reports (SARs).

3. Data retention and audit trail

Regulation requires banks to retain a significant amount of information for several years, including customer data, due diligence documentation, transaction records, and reporting activities. This enables supervisory inspections and internal audits, ensuring transparency and traceability.

4. Risk-based approach and control governance

The risk-based approach requires that controls, resources, and technologies be proportionate to the institution’s exposure to money laundering and terrorist financing risks. This implies not only the adoption of advanced technologies, but also strong governance frameworks and internal procedures that are continuously reviewed and updated.

To effectively meet these obligations, banks increasingly rely on advanced tools such as Moneying, the platform developed by Revelis, which helps financial institutions comply with regulatory requirements while minimizing operational risks and improving overall AML process efficiency.

Emerging challenges in meeting AML obligations

Today, banks face numerous emerging challenges in complying with AML/CFT requirements. These stem both from the evolving criminal landscape and from constantly changing regulatory and technological frameworks. Such challenges can significantly affect operational effectiveness, costs, reputational risk, and the ability to respond to global threats.

The adoption of digital technologies and the growth of online transactions have made money laundering risks more sophisticated. Banks must manage ever-increasing data volumes, complex digital channels, and constantly evolving criminal models. As a result:

• Over 70% of AML platforms now integrate machine learning technologies, improving anomaly detection accuracy.
  
• NLP (Natural Language Processing) tools accelerate document analysis and the identification of risk signals in unstructured communications.
  

Key challenges in fulfilling AML obligations include:

Evolving criminal threats

Money laundering methods are becoming increasingly sophisticated and dynamic, including cryptocurrency-based techniques, synthetic identities, automated fraud schemes, and trade-based money laundering (TBML), which often evade traditional controls.

Legacy technology and data integration

Many banks still rely on outdated AML systems that lack real-time data integration and generate excessive false positives, leading to high operational costs and inefficiencies for AML analysts.

Data governance and digital onboarding

The digitalization of customer relationships (remote onboarding, mobile banking) increases the volume of data to be monitored and complicates the management of high-quality, standardized data for due diligence and risk assessment.

Regulatory evolution and complexity

The regulatory landscape is becoming increasingly complex, with new rules requiring continuous updates to AML programs and potential overlaps across jurisdictions.

Human resources and skills gap

A shortage of professionals specialized in AML, analytics, and emerging technologies represents a major obstacle to effective compliance.

Compliance costs and operational sustainability

Maintaining robust AML programs is costly. Investments in RegTech, AI, real-time monitoring, and specialized staff significantly impact banks’ financial performance.

Reputational risks and sanctions

Failures in AML compliance can result in substantial fines, reputational damage, and loss of customer trust.

Technology and solutions for AML compliance

Emerging technologies are transforming the way banks and financial institutions address AML obligations. Artificial intelligence, machine learning, and advanced behavioral analytics make it possible to:

• Improve transaction monitoring accuracy by identifying complex patterns and reducing false positives;
  
• Automate routine tasks such as risk classification, alert generation, and case management;
  
• Optimize Customer Due Diligence (CDD) and Enhanced Due Diligence (EDD) timelines;
  
• Integrate heterogeneous data sources, including open-source intelligence, to gain a more comprehensive risk view.
  

This digital transformation not only strengthens regulatory compliance, but also enhances operational resilience and reduces costs associated with manual controls.

Revelis has developed Moneying, a platform that combines Machine Learning and Deep Learning techniques to optimize compliance activities in AML controls and risk management. Through advanced Text Analytics, the platform also enables automated processing of written customer communications, streamlining the management of complaints, claims, and interactions with investigative authorities.

Revelis solutions fit into this ecosystem as advanced, modular compliance technologies designed to address AML obligations in an effective, scalable, and fully integrable way within existing banking systems.

Key benefits include:

• Intelligent automation of AML processes: Automated AML workflows reduce manual errors, accelerate response times, and allow compliance teams to focus on higher value-added activities.
  
• Governance and risk management dashboards: Comprehensive dashboards support strategic decision-making and help meet supervisory authorities’ transparency requirements.
  
• Flexible compliance with continuous regulatory adaptation: AML regulations evolve rapidly. Revelis solutions are designed for modular updates, enabling institutions to remain compliant with minimal IT intervention and operational disruption.
  

Conclusion

Banks operate in a rapidly evolving AML environment characterized by:

• Increasingly sophisticated and digital financial crime;
  
• Legacy technologies that are difficult to modernize;
  
• Complex, multi-layered regulations;
  
• A shortage of specialized expertise;
  
• Rising compliance costs;
  
• Significant reputational and sanction risks.
  

Responding to AML obligations is not merely a regulatory requirement it is also a strategic lever to safeguard financial integrity, mitigate reputational risks, and demonstrate a tangible commitment to combating financial crime.

As digital transactions continue to grow and risk scenarios become more sophisticated, advanced technology solutions such as those offered by Revelis are becoming essential enablers for banks and financial institutions. Investing in intelligent monitoring systems, process automation, and advanced data analytics not only facilitates AML compliance, but also strengthens internal governance, reduces operational costs, and enhances an institution’s ability to navigate regulatory and market challenges.

L'articolo AML: what are banks’ obligations and how to meet them proviene da Revelis.

In this context, the AI Act represents a major opportunity for organizations that want to adopt AI responsibly, at scale, and in alignment with European values. The regulation’s goal is not to slow innovation but to create a trusted ecosystem that encourages adoption by citizens, businesses, and institutions.

The PwC Voice of the Consumer Survey 2024 involving over 20,000 consumers across 31 countries shows that trust in digital technologies, including AI, strongly depends on perceived control, safety, and accountability in the use of data and algorithms. In particular, consumers are significantly more willing to use AI in low-risk contexts (such as recommendations or informational support) while showing resistance to high-impact applications (like financial or healthcare decisions) when governance safeguards, human oversight, and rights protection are missing.

This means compliance with the AI Act becomes a competitive asset not just a legal obligation.

Operationally, the AI Act directly impacts key processes such as HR, marketing, customer service, credit scoring, healthcare, cybersecurity, and supply chain. Any system supporting automated or semi-automated decisions must be assessed according to regulatory risk criteria, documented in a structured way, and governed through proper controls.

What the AI Act is and why it matters for your company

The AI Act is a European regulation establishing binding rules for the development and use of AI systems, classifying them according to the level of risk they may pose to individuals, organizations, and society.

The framework is risk-based and distinguishes four categories:

• Unacceptable risk (prohibited)
• High risk (strictly regulated)
• Limited risk (transparency obligations)
• Minimal risk (free use)

This model makes the AI Act flexible yet structured, adaptable across contexts while maintaining high safety standards. For companies, the AI Act introduces concrete obligations in governance, accountability, data quality, human oversight, cybersecurity, and transparency. Another strategic aspect is its extraterritorial scope: the regulation also applies to non-EU companies offering AI systems to European users. In other words, complying today means aligning with tomorrow’s global standards.

From a competitive perspective, the AI Act reinforces a key concept: trustworthy AI is a business advantage. Companies embedding compliance into their digital strategy reduce legal risks while strengthening positioning, reputation, and long-term sustainability.

Who must comply?

The AI Act applies to all organizations that develop, distribute, or use AI systems in the European Union market, regardless of legal headquarters.

This includes:

• Software vendors and AI providers
• Technology startups
• Enterprises integrating AI into core processes
• Public administrations
• SaaS and cloud platform providers
• Organizations using AI for recruiting, marketing, fraud prevention, risk management, and customer experience

The impact is particularly relevant for SMEs. While compliance may initially appear complex, it actually helps structure more mature, reliable, and scalable innovation processes avoiding improvised solutions that generate technical, legal, and reputational risks.

Importantly, the AI Act assigns responsibilities not only to developers but also to “deployers” companies using third-party AI systems. Every organization therefore needs internal competencies to evaluate, monitor, and govern AI solutions, even when purchased externally.

Timeline, penalties, and non-compliance risks

The AI Act formally entered into force in 2024 but will be applied progressively through a multi-year roadmap extending to 2027, allowing companies to adapt gradually while still requiring structural governance transformation.

Key deadlines

• February 2025 — Ban on unacceptable-risk AI systems (e.g., cognitive manipulation, generalized social scoring)
• August 2025 — Rules for general-purpose AI models, including transparency, technical documentation, and systemic risk management
• August 2026 — Full requirements for high-risk AI systems in HR, healthcare, credit, education, and critical infrastructure

The regulation therefore represents a continuous transformation journey rather than a single compliance deadline.

Penalties

The AI Act introduces a strict sanction regime comparable and in some cases stricter than GDPR:

• Up to €35 million or 7% of global annual turnover for the most serious violations
• Up to 3% or 1% for lesser infringements

Sanctions apply to:

• Use of prohibited systems
• Non-compliant high-risk systems
• Transparency violations
• False or incomplete information to authorities

However, the risk is not only financial. Companies often focus solely on performance, time-to-market, and ROI, underestimating regulatory exposure. This can lead to indirect costs such as:

• Service suspension
• Customer loss
• Litigation
• Reputational damage

Authorities may also force the withdrawal of non-compliant AI systems from the market even if embedded in mission-critical processes. In a context where trust and digital sustainability are competitive factors, non-compliance becomes a growth barrier, not just a legal issue.

Key statistics on AI adoption and compliance

Recent data shows AI adoption is growing faster than governance maturity. According to Eurostat, in 2023 about 13.5% of European companies already used AI (over 40% among large enterprises). However, fewer than one third had structured policies on explainability, algorithmic auditing, and risk management.

Image source: Use of artificial intelligence in enterprises

An IBM–Ponemon Institute global study (Cost of a Data Breach 2025) found that 63% of organizations lacked governance policies to manage AI or prevent shadow AI proliferation, increasing breach risks and economic impact.

PwC Global Compliance Survey 2025 confirms compliance evolution is essential to unlock growth:

• 77% say regulatory complexity negatively impacts growth drivers
• Coordinated compliance improves decision-making, transparency, and company culture

These findings confirm the AI Act is a competitive driver influencing innovation and market positioning.

How to comply: 5 best practices

Adapting to the AI Act means rethinking the entire AI lifecycle around governance, accountability, and business value.

1. AI System Mapping and Classification: Create a complete inventory of all AI systems (internal, vendor-provided, SaaS-embedded). Classify each according to regulatory risk and impact on rights, decisions, and outcomes.

2. Governance, Accountability, and Documentation: Define roles, responsibilities, and structured technical documentation:

• Ethical development policies
• Dataset registers
• Algorithmic audit trails
• Human oversight procedures

3. Risk Management, Cybersecurity, and Data Quality: Bias, model drift, vulnerabilities, and poor data quality undermine reliability and compliance. Invest in data governance, continuous monitoring, and secure infrastructure.

4. Training and Change Management: Compliance requires cultural change. Train management, technical teams, legal teams, and decision-makers on responsible AI principles and regulatory obligations.

5. Harmonized Standards and Certifications: EU harmonized technical standards (CEN, CENELEC) support compliance demonstration, covering safety, data quality, robustness, transparency, and explainability.

Conclusion

The AI Act is not simply another tech regulation it is a structural shift in how AI is designed, governed, and used globally. For companies, it represents a call to move from opportunistic AI adoption to responsible and sustainable innovation.

Complying allows organizations to:

• Reduce legal and operational risk
• Increase stakeholder trust
• Improve automated decision quality
• Strengthen long-term digital business sustainability

Trust is also a decisive factor for AI acceptance ( 2025 Edelman Trust Barometer Flash Poll ): higher trust in transparency and value correlates with greater adoption willingness. In a trust-driven market, AI Act compliance becomes a brand positioning and competitive differentiation tool.

Ultimately, the AI Act should be viewed as a digital maturity accelerator. Companies starting today will be better prepared for future regulatory and technological evolution, positioning themselves as reliable leaders in the AI economy.

Organizations can rely on specialized consulting partners such as Revelis, where dedicated AI specialists provide the expertise and guidance needed to achieve compliance safely while enabling innovation.

L'articolo AI Act: how to prepare your company for the new regulation proviene da Revelis.