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The AI Side of Things

Azaria Canales — Tue, 23 Jun 2026 06:12:36 +0000

Dos charlas técnicas sobre cómo llevar agentes de IA del experimento a producción: qué ha cambiado, qué implica para la governance y qué hace falta para dar el salto.

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16 de Julio

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18:00 h

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Oficinas Capitole Balmes 89

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Programa

18:00h Llegada y presentaciones

Oficinas de Capitole, 7a planta.

Global AI Barcelona y Capitole.

18:30h · Tres años de LLMs: del wow inicial al modelo de trabajo

Víctor Saenger · Capitole · ES

Evolución desde ChatGPT hasta hoy: de la euforia inicial a la explosión de modelos, multimodalidad y agentes autónomos. Implicaciones en coste, despliegue y privacidad. Governance integrada con MLOps.

El reto: transformar el modelo de trabajo y cerrar la brecha entre quienes experimentan y quienes deciden.

19:05h · ¿Está tu organización lista para agentes?

Rubén Toribio · Global AI Barcelona · EN

Framework de readiness enterprise para desplegar IA en Azure con governance, separación de entornos y base operativa sostenible.

Lo que nadie te cuenta hasta que ya estás en producción.

19:40h · Q&A conjunto

Tus preguntas a Víctor y Rubén, en directo.

20:00h - 21:00h · Networking + Pizzas

Seguimos en el Bar Capitole donde cerraremos el evento con pizzas, bebidas, buen ambiente y temas pendientes del Q&A.

FAQ

¿Cómo puedo asistir?

Beyond Aesthetics: Human-Centered Design, the Key to Real Impact

Azaria Canales — Tue, 09 Jun 2026 08:18:38 +0000

Good design is hard.

Not because technology gets in the way, but because it’s tempting to build what we imagine rather than what people actually need.

This article explores that tension: the one between doing things fast, doing them beautifully, and doing them in a way that truly works.

The current problem with digital design

Products nobody asked for

Digital product design today operates under constant pressure, the push to ship fast versus the commitment to ship well. The drive to innovate and stand out often leads teams to prioritize speed over the kind of thoughtful iteration that good design actually requires.

In this environment, novelty and aesthetics frequently take precedence over real usefulness. The result: visually polished solutions that don’t necessarily align with how users think, work, or live.

Add to this the frequent disconnect between designers, stakeholders, and end users, and you introduce biases that quietly shape the final product. Without ongoing validation with real users, design risks drifting away from the problems it was meant to solve.

More often than not, what you end up with is a product nobody quite asked for, that costs more to maintain than expected, and that users abandon sooner than hoped.

The big challenges in design

Fast, beautiful, and… useful?

Digital product design faces a recurring set of challenges that can undermine both user experience and product success. Chief among them: the pressure to launch quickly, which leaves little room for research, iteration, or validation — and the tendency to prioritize aesthetics over functionality, often at the cost of usability.

Then there’s the challenge of designing for real users when the people making decisions — whether designers or clients — don’t always represent them. Lack of user testing, unnecessary complexity, and the difficulty of building inclusive experiences for diverse audiences round out the picture.

Overcoming these obstacles starts with a genuine, deep understanding of the people who will actually use the product — and keeping their perspective present throughout the entire design process.

The business impact of getting it wrong

What you don’t validate, you pay for — and greatly.

Design decisions have consequences that go well beyond the interface. When a product isn’t properly focused, or is built on flawed assumptions, the effects are tangible: low adoption, higher maintenance costs, and a weaker return on investment.

In most cases, this isn’t a technology problem. It’s the result of decisions made without enough validation during the design process.

In a competitive market, these missteps can be the difference between a product that finds its footing and one that quietly fades into irrelevance.

What is Human-Centered Design (HCD)?

Designing vs. guessing

Human-Centered Design (HCD) is an approach that places people at the heart of every stage of digital product creation. The goal isn’t just to build something that works or looks good — it’s to design products that genuinely respond to the needs, expectations, and real-world context of the people who use them.

Unlike more traditional approaches, HCD is grounded in deep user understanding, drawing on research, observation, and continuous validation. This helps reduce the biases that tend to creep in when designers or clients don’t reflect the actual end user.

One of its defining principles is the iterative nature of the design process: solutions aren’t treated as final from day one, but evolve through testing, feedback, and incremental refinement. Real user participation throughout the process is essential for validating assumptions and continuously shaping the product.

HCD also promotes cross-functional collaboration and evidence-based decision-making — moving away from individual opinions or personal preferences. It means maintaining a clear focus on what the product is actually for, always aligning with what users genuinely need.

In practice, the difference between a people-centered approach and one that isn’t often comes down to a single question: When was the last time someone on the team watched a real user interact with the product?

How HCD addresses the main challenges

Fewer assumptions, better products

Human-Centered Design offers a framework for tackling some of the most common challenges in digital product development:

Against the pressure to launch fast: it encourages early validation and continuous iteration, reducing the risk of investing in the wrong solutions.
Against an overemphasis on aesthetics: it helps balance visual appeal with usability and practical value.
Against designer and client bias: it brings in user research and real-world testing to ground decisions in evidence rather than assumptions.
Against loss of product focus: it helps identify what truly matters and aligns design efforts with the goals that actually create value.
Against unnecessary complexity: it drives simpler, more intuitive solutions built around solving concrete problems.
Against the diversity of user profiles and contexts: it fosters a broader understanding of users, leading to more inclusive and accessible experiences.

More than a methodology, HCD is a way of making decisions — one rooted in knowledge of the people you’re designing for, which reduces risk and improves the chances of building something that actually succeeds.

HCD best practices in a technology consultancy

Small changes, big results

Embracing a people-centered approach doesn’t require a complete organizational overhaul from day one. Often, small shifts in how you work lead to significant results.

1. Involve users from the start Don’t wait until the product is built to seek feedback. The earlier you validate ideas and assumptions, the lower the risk of heading in the wrong direction.

2. Prototype before you build A prototype lets you explore solutions, spot opportunities for improvement, and validate concepts at a fraction of the cost of full development.

3. Let evidence do the talking The most effective decisions are backed by data, observation, and test results — not just opinions or personal preferences.

4. Work cross-functionally Design, business, and technology need to collaborate from the earliest stages to ensure a shared understanding of both the problem and the solution.

5. Measure and keep learning Launch isn’t the finish line. Analyzing user behavior and gathering ongoing feedback is what surfaces new opportunities for improvement.

6. Build a people-first culture HCD shouldn’t sit solely with the design team. Its real impact happens when the entire organization shares a commitment to understanding the people who will use the product.

The products that endure aren’t the most innovative or the most visually striking. They’re the ones that solved something that truly mattered, in a way people didn’t expect — but immediately recognized as obvious. Getting there isn’t luck: it’s the result of paying attention to people from the very beginning.

The post Beyond Aesthetics: Human-Centered Design, the Key to Real Impact appeared first on Capitole.

Capitole en ExpoQA 26

Azaria Canales — Fri, 22 May 2026 08:44:43 +0000

Completa este formulario para participar en nuestro SORTEO y para recibir futuras comunicaciones.

Asegúrate de que tus datos de contacto estén completos para que podamos comunicarnos contigo en caso de que resultes ganador/a.

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(*) Campos obligatorios

acepto la política de privacidad

He leído y acepto la política de privacidad de Capitole y autorizo el tratamiento de mis datos para gestionar mi inscripción al evento.*

Deseo recibir información sobre futuras

Deseo recibir información sobre futuros eventos, formaciones, charlas o actividades tecnológicas organizadas por Capitole.

Bases del Sorteo

1. Presencia obligatoria

Para recibir el premio es imprescindible estar presente en el stand de Capitole (o en sus inmediaciones) en el momento del sorteo. No se realizarán envíos. Si la persona ganadora no está presente, se sorteará de nuevo entre el resto de participantes.

2. Entrega del premio

Los premios se entregarán en mano en el stand de Capitole durante ExpoQA 2026. Son personales, intransferibles y no canjeables por dinero u otros productos.

3. Participación

Cada persona puede ganar un único premio durante el evento. Si participas el primer día y no resultas ganadora, tu inscripción seguirá activa automáticamente para el sorteo del segundo día.

4. Quién no puede participar

No podrá participar en el sorteo el personal de Capitole Consulting ni colaboradores y colaboradoras directas de la compañía.

5. Datos y contacto

Asegúrate de que los datos del formulario son correctos: los usaremos para avisarte si resultas la persona ganadora. Seguirnos en LinkedIn no es obligatorio, pero te lo recomendamos para no perderte nada.

6. Aceptación

Participar en el sorteo implica aceptar estas bases.

FAQ

¿Cuándo y dónde será el sorteo?

Copiloto Industrial: La IA del diseño a la planta

Azaria Canales — Tue, 19 May 2026 13:03:54 +0000

Tu competencia ya usa la IA en sus plantas industriales.

La pregunta no es si aplicarla, es cómo hacerlo sin que el proyecto muera en el piloto. Casos reales, ROI demostrado y una hoja de ruta lista para implementar.

Fecha

17 de Junio

Hora

9:00

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Presencial

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Oficinas Capitole Balmes 89

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Selecciona un país

Comunidad Autónoma

Empresa

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Área

Industria

(*) Campos obligatorios

acepto la política de privacidad

He leído y acepto la política de privacidad de Capitole y autorizo el tratamiento de mis datos para gestionar mi inscripción al evento.*

Deseo recibir información sobre futuras

Deseo recibir información sobre futuros eventos, formaciones, charlas o actividades tecnológicas organizadas por Capitole.

Programa

Presentaciones 9:30-10:30

Metalurgia sin paradas: Cómo CELSA anticipa los fallos con IA

Departamento de Innovación de Celsa

Agroalimentario: Mantenimiento predictivo en planta con IA

Giulia Baccarin, CEO de MIPU

Lo que hemos aprendido en 260 plantas industriales

Giulia Baccarin, CEO de MIPU

Casos de uso 10:30-11:30

Química especial: Optimización de proceso en tiempo real con IA

Pharma y alimentación: Del fallo detectado a la orden de trabajo en menos de 4 min

Packaging industrial: Cero defectos con inspección continua al 100%

Automoción: Cómo la IA acelera el diseño de producto antes de llegar a planta

Workshop 12:00-13:00

Del piloto a la escala: Tu hoja de ruta con IA

FAQ

¿Cómo puedo asistir?

Copiloto Industrial: La IA del diseño a la planta

Lia Vazquez — Fri, 15 May 2026 10:07:30 +0000

Programa

¿Sobre qué hablamos?

Presentaciones 9:30-10:30

Metalurgia sin paradas: Cómo CELSA anticipa los fallos con IA

Departamento de Innovación de Celsa

Agroalimentario: Mantenimiento predictivo en planta con IA

Departamento de Innovación de Cereal Docks

Lo que hemos aprendido en 260 plantas industriales

Departamento de Innovación de Cereal Docks

Casos de uso 10:30-11:30

Química especial: Optimización de proceso en tiempo real con IA

Pharma y alimentación: Del fallo detectado a la orden de trabajo en menos de 4 min

Packaging industrial: Cero defectos con inspección continua al 100%

Automoción: Cómo la IA acelera el diseño de producto antes de llegar a planta

Workshop 12:00-13:00

Del piloto a la escala: Tu hoja de ruta con IA

FAQ

¿Cómo puedo asistir?

QA in the Age of AI: Impact, Challenges and Evolution of the Role

Azaria Canales — Thu, 14 May 2026 09:58:38 +0000

The integration of Artificial Intelligence into Quality Assurance is profoundly transforming both its processes and the role of QA within the software development lifecycle. This article examines the current state of AI adoption in QA — its benefits, risks, and implementation costs — as well as the emergence of new metrics designed to assess the effectiveness and reliability of these systems.

It also addresses the evolution of the QA role toward a more strategic profile, embedded within a quality model assisted by intelligent systems, where human intervention remains an essential factor for oversight, validation, and results control.

The Origins and Evolution of QA, and the Rise of AI

With the emergence of software and digital applications, quality control adopted a predominantly reactive approach focused almost exclusively on defect detection. However, the growing complexity of systems exposed the limitations of this model, driving a shift toward a more preventive and collaborative approach to quality assurance. This transition was supported by practices such as shift-left testing, test automation, and continuous testing within CI/CD environments — establishing QA as a core discipline within the software development lifecycle.

Against this backdrop, the rise of Artificial Intelligence introduced a new paradigm in how quality processes are conceived. This is not merely an incremental evolution, but a structural shift in the way validation processes are designed, prioritized, and executed.

The Impact of AI on the SDLC and QA

The impact of AI, however, has not been confined to QA alone. Its integration has unfolded progressively and transversally, affecting both development and validation phases — generating a direct impact on the final quality of software.

On one hand, development teams have incorporated generative AI tools for code generation, such as Copilot or Claude, significantly increasing delivery speed. Yet this advancement also introduces new risks related to the quality and maintainability of generated code, due to potential inconsistencies with the broader application context.

On the other hand, QA teams have integrated AI across multiple stages of the testing process, transforming the way quality assurance strategies are designed, executed, and maintained.

According to various industry reports — including QA and Software Testing in 2025 (based on over 100 development teams) and BrowserStack’s State of AI in Software Testing 2026 (based on over 250 technical leaders) — more than 60% of organizations have already incorporated AI into parts of their testing workflows, particularly in regression, smoke testing, and risk-based prioritization.

AI adoption is also extending to other areas of the SDLC, such as business analysis — where it supports requirements and feature definition — and design, facilitating the generation of interfaces and prototypes in tools like Figma. This reflects an increasingly transversal impact across the entire software development lifecycle.

As a result, the sense that AI has become a standard part of the toolstack for all stakeholders in the software development lifecycle is growing across the industry. This adoption is generating impact at both operational and strategic levels, redefining processes, roles, and quality metrics.

Benefits

Following several years of generative AI model adoption, the following key benefits can be identified within the QA domain:

Test Case Generation: Automatic generation of test cases from code, functional requirements, or user stories.
- Example: Given a user story such as “the user should be able to reset their password,” the system automatically generates cases covering valid/invalid passwords, expired sessions, multiple failed attempts, field format validations, and more.
Test Prioritization: Intelligent test prioritization based on criticality, change impact, and risk analysis.
- Example: Following a change to the checkout flow, the system automatically prioritizes tests related to tax calculations, discounts, and payment gateways.
Log Analysis & Processing: Analysis, rewriting, and summarization of logs, along with detection of duplicate test cases or incidents.
- Example: In an execution that has generated hundreds of log lines, the system groups repeated errors, summarizes the issue into a single incident, and reduces noise and manual analysis time.
Self-Healing Tests: Automatic test maintenance, adapting to changes in interfaces or system flows.
- Example: If a button changes from id="submit-btn" to id="submit-button", the system automatically updates the selector without requiring manual intervention.
Root Cause Analysis: Automated failure analysis and support in identifying root causes.
- Example: Faced with a login test failure, the system correlates backend logs, authentication changes, and database errors — suggesting a token service issue as the root cause.
LLM-based Evaluation: Automated results evaluation using LLM models capable of analyzing test outputs, system responses, and logs to determine their validity or relevance based on defined criteria.
- Example: Rather than validating only status codes, an LLM assesses whether an API error message is contextually coherent with the nature of the failure.
Agentic Testing Systems: Autonomous agent-based systems capable of planning, exploring applications, generating scenarios, executing tests, and reporting results iteratively — adapting their behavior based on outcomes.
- Example: An autonomous agent explores an application, identifies critical flows, dynamically generates tests, executes scenarios, and adjusts its strategy based on results.

Taken together, these advances accelerate the testing cycle across its various phases — analysis, design, execution, and reporting — particularly in well-structured environments with sufficient context available.

Risks

That said, AI integration also introduces significant new risks and limitations:

Incomplete Test Cases: Generation of incomplete or incorrect test cases due to biases in training data. Some reports indicate that between 20% and 40% of automatically generated tests require manual review or correction.
- Example: The system generates tests for a registration form but omits critical scenarios such as security validations, due to biases in the training data.
Scenario Complexity: Difficulty modeling complex scenarios, particularly in critical systems.
- Example: In a banking system, the model may fail to correctly represent flows that depend on multiple regulatory conditions, intermediate states, or external systems.
Contextual Understanding Gaps: Difficulty detecting defects arising from business logic, system integration, or contextual coherence.
- Example: A test passes at a technical level because the system fails to detect an incorrectly applied discount, not understanding the business logic associated with that promotion.
False Positives/Negatives: Inaccurate defect detection — either reporting non-existent errors or failing to identify real failures under certain conditions.
- Example: The system accepts an incorrect data result as valid because it is structurally and formally well-formed.
Excessive Dependency: Potential erosion of technical knowledge within teams due to over-reliance on automated tooling.
Automation Bias: A tendency to accept AI-generated results without sufficient validation. Research suggests that up to 30–40% of incorrect decisions made by AI systems go unchallenged.
ROI: Difficulty objectively measuring the return on investment.
Hallucinations: Model hallucinations — the generation of incorrect but apparently coherent results. Estimated rates range from 5% to 30% in complex tasks, depending on context.
Non-Functional Testing: Limited capacity to deliver value in performance, scalability, security, or observability testing compared to functional testing.

These risks reflect a still-significant gap between the theoretical potential of AI and its actual performance in complex or critical contexts — where human oversight remains an essential element.

The Emergence of New Metrics

In this new landscape — where the integration of Large Language Models (LLMs) enables test case generation to be automated at scale — it becomes necessary to introduce new metrics capable of evaluating these non-deterministic systems through measurement approaches that go beyond simply quantifying how much is being tested, focusing instead on the real utility of that testing.

Unlike traditional testing, where outcomes are binary (pass/fail), AI-based systems require metrics that capture degrees of adequacy, coherence, and usefulness of the generated responses.

Some of the most relevant and emerging proposals include:

Test Effectiveness Rate (TER): The proportion of tests that detect real defects relative to the total executed.
Signal-to-Noise Ratio: The relationship between relevant results (valid defects) and generated noise (false positives or redundant tests).
AI-generated Test Reliability: The degree of confidence in automatically generated test cases, assessed through cross-validation, golden datasets, or model-assisted review.
Defect Detection Efficiency (DDE): The ability to detect defects in early stages of the development cycle.
Actual Coverage vs. Generated Coverage: The difference between the theoretical coverage generated by AI and the effective coverage of critical functionalities.
Test Maintenance Overhead: The effort required to maintain, correct, or filter automatically generated tests.
LLM Evaluation Score: Assessment of the quality of generated responses using evaluator models (LLM-as-a-judge), based on criteria such as relevance, coherence, and correctness.
Hallucination Rate: The proportion of AI-generated responses containing incorrect or unverifiable information.
Task Success Rate: The percentage of tasks correctly completed by autonomous systems or AI-based assistants.
Consistency Score: The degree of stability of generated responses when faced with equivalent or slightly modified inputs.

These metrics reflect a paradigm shift in quality evaluation — moving from a deterministic model based on coverage and execution, to a probabilistic model centered on the reliability, consistency, and utility of AI-assisted systems.

Adapting the QA Role in an AI-Assisted Environment

Beyond its impact on development and QA processes and on validation metrics, AI adoption is driving a significant transformation that directly affects the competencies and responsibilities of QA professionals.

Traditionally, the QA role focused on requirements analysis, test case design, test execution, and defect reporting. In the current context, this role is evolving toward a more strategic profile — oriented toward the oversight, validation, and governance of automated systems.

This consolidates the human-in-the-loop paradigm, in which the QA professional takes on supervisory, validation, and audit functions that may vary depending on the seniority of the profile.

Differential Impact by Experience Level

Junior profiles (testers) AI acts as an accelerator for learning and productivity, enabling:

Assisted test case generation
Standardization of defect reports
Increased execution speed
Reduced technical barrier to entry

Mid-level profiles (analysts) Value is centered on:

Improved requirements analysis
Supervision and validation of AI-generated scenarios
Incorporation of business knowledge into models
Identification of edge cases and complex dependencies

Senior profiles (leads) AI facilitates:

Definition and optimization of quality strategies
Advanced metrics analysis and new KPI development
Filtering of noise generated by large-scale automation
Alignment between technical quality and business objectives

Transversal capabilities Across all levels, a new key competency is emerging: the ability to craft effective prompts and provide adequate context to AI systems.

Knowledge of DevOps practices is also gaining relevance — enabling the integration of these systems into CI/CD pipelines and supporting selective test execution, where systems themselves determine which tests to run based on code changes, dependencies, and defect history, and prioritize them according to risk.

Feedback loops allow these systems to learn continuously from results, progressively optimizing coverage, prioritization, and testing effectiveness.

However, this advanced automation demands constant oversight to prevent biases, incorrect decisions, or loss of control over the quality process. As a result, the QA professional evolves into an orchestrator of quality in AI-assisted environments.

New Role: QA for AI Systems and Agents

Yet the transformation of QA from functional tester to quality orchestrator is not the only role-level shift the industry is experiencing.

The proliferation of AI-based systems introduces a new dimension in QA: the need to validate non-deterministic systems.

Unlike traditional software — where expected behavior is fixed and verifiable through deterministic assertions — AI systems generate probabilistic and variable outputs for the same input. As a result, QA must validate not so much the accuracy of a specific response, but the adequacy of behavior within an acceptable range. This involves assessing aspects such as:

Coherence and relevance of responses
Robustness against diverse or adversarial inputs
Consistency of results when faced with equivalent inputs
Presence of biases in generated responses
Model degradation over time (model drift)

In this context, LLM evaluation frameworks become especially relevant — combining the use of golden datasets, automated evaluation through evaluator models (LLM-as-a-judge), and human validation.

In short, a new QA role is emerging — one in which the object of testing is no longer the various application types previously worked with, but rather the assurance of quality in non-deterministic models, where the validation focus shifts from expected outputs to the adequacy of behavior within a variable and acceptable range.

Costs and Challenges of AI Adoption in QA

All of this AI adoption and the transformation it drives across development and QA processes represents a significant investment — not only at the technological level, but also organizationally, operationally, and in terms of talent. This transformation, closely tied to the evolution of the QA role, introduces new demands that must be addressed from a strategic perspective.

Technical Costs

Integration of AI tools into existing pipelines
Architectural adaptation to support advanced automation
Management of more complex infrastructures (processing, storage, observability)
Need for additional tooling to monitor, audit, and validate AI systems

Operational Costs

Increased process complexity
Continuous oversight of automated systems
Management of noise generated by large-scale automation
Maintenance of models, prompts, and associated configurations

Organizational and Talent Costs

Need for upskilling in new competencies (prompt engineering, AI literacy, DevOps)
Greater demand for technically proficient profiles capable of validating AI-generated results
Risk of technological dependency and loss of internal knowledge if not properly managed

Economic Costs

Licensing fees for specialized AI-based tools
Computational costs associated with advanced model usage
Investment in team training and upskilling
Potential increase in senior profiles required for oversight and validation

Various industry studies reflect that initial implementation costs can be significantly higher than those of traditional frameworks, particularly during integration phases. Furthermore, the lack of specialized talent and the difficulty of integrating with legacy systems rank among the main barriers to adoption — which ultimately depends on model maturation, organizational adaptation, and team learning curves.

Accordingly, AI adoption in QA must be approached as a medium-to-long-term strategic investment, not as an immediate cost optimization.

Substitution or Complementarity?

With all of the above in mind, let us address one of the most recurring debates in the industry: will Artificial Intelligence replace QA professionals?

Current evidence points clearly toward a scenario of complementarity. AI acts as a co-pilot that automates repetitive, low-value tasks — allowing professionals to focus on higher-complexity activities such as exploratory testing, complex scenario validation, user experience evaluation, and contextual analysis, playing a more strategic role centered on validation, oversight, and decision-making.

In fact, academic research indicates that AI adoption in testing still lags behind its use in development — evidencing a testing gap where human capabilities remain critical to guaranteeing the final quality of software.

Ultimately, far from disappearing, the role is evolving: the greater the automation, the greater the need for oversight, technical judgment, and business understanding.

As Margarita Simonova notes in the Forbes Technology Council piece The State of Testing in 2025: AI suggests, but the decision still belongs to humans.

Conclusion

Artificial Intelligence has established itself as a transformative force in QA, redefining both the processes and the roles associated with quality assurance.

Far from representing a threat, its adoption constitutes an opportunity to evolve toward a more efficient, strategic, and contextually aligned model — one suited to the growing complexity of modern software development.

In a context characterized by the acceleration of code generation and the mass production of software, QA takes on an even more critical role as a guarantor of quality. The effective integration of AI will enable professionals not only to increase their productivity, but also to reinforce their positioning as key actors within the SDLC.

Nevertheless, a realistic perspective is essential in the current climate of heightened expectations around AI. While its capabilities are significant, its implementation is far from fully autonomous or free of limitations. Issues such as inconsistent output generation, lack of business context, the presence of biases, and the need for constant oversight demonstrate that these technologies still require substantial human intervention.

In this sense, the value of AI lies not in replacing the QA professional, but in amplifying their capabilities. The gap between expected potential and current reality stems largely from the quality of integration, the adequacy of context provided, and the critical capacity of teams to interpret and validate AI-generated results.

In this new landscape, competitive advantage will not reside merely in adopting AI, but in the ability to integrate it critically, efficiently, and in alignment with product quality objectives. Because, ultimately, quality is not a property of software — it is the result of the decisions made by those who build and validate it.

References:

BrowserStack. (2026). State of AI in Software Testing 2026. Recuperado de https://www.browserstack.com/blog/inside-the-state-of-ai-in-software-testing-2026/

CopilotQA. (2025). QA and Software Testing in 2025: Trends, Challenges, and AI Adoption. Recuperado de https://copilotqa.com/qa-and-software-testing-in-2025/

Forbes Technology Council. (2025). The State of Testing in 2025: The AI Adoption Gap. Recuperado de https://www.forbes.com/councils/forbestechcouncil/2025/12/15/the-state-of-testing-in-2025-the-ai-adoption-gap/

Forbes Technology Council. (2025). AI Is About to Reshape Millions of Software QA Jobs. Recuperado de https://www.forbes.com/councils/forbestechcouncil/2025/10/06/ai-is-about-to-reshape-millions-of-software-qa-jobs/

Wifitalents. (2025). AI in Quality Assurance Testing: Statistics and Trends. Recuperado de https://wifitalents.com/ai-quality-assurance-testing-industry-statistics/

Anthropic. (2024). Understanding AI Hallucinations and Model Behavior. Recuperado de https://www.anthropic.com/research

Financial Times. (2025). AI hallucinations become a growing concern for enterprises. Recuperado de https://www.ft.com/content/e074d3a9-7fd8-447d-ac0a-e0de756ac5c5

arXiv. (2026). An Empirical Study on AI-Assisted Software Testing in Real-World Repositories. Recuperado de https://arxiv.org/abs/2603.13724

arXiv. (2026). The Testing Gap: Adoption of AI in Software Development vs Quality Assurance. Recuperado de https://arxiv.org/abs/2601.21305

arXiv. (2025). Challenges and Limitations of AI in Software Testing: A Systematic Review. Recuperado de https://arxiv.org/abs/2504.04921

The post QA in the Age of AI: Impact, Challenges and Evolution of the Role appeared first on Capitole.

Calidad como estrategia de negocio

Marta Castellvi — Wed, 13 May 2026 16:33:42 +0000

Daniel García Vicario

Senior Software Engineer

¿Sobre qué hablaremos?

¿Sobre qué hablamos?

Calidad como ventaja competitiva

Por qué QA, bien articulada como estrategia, genera impacto directo en resultados de negocio, satisfacción del cliente y eficiencia operativa. Dejar de verla como un coste y empezar a usarla como palanca.

Del estado del arte a la práctica

BDD, TDD, tests de integración con Playwright, accesibilidad con AXE, testeo visual por imágenes, pipelines con Turbo y estrategias de cobertura. Todo explicado con ejemplos prácticos y anécdotas reales de casi 30 años en entornos corporativos.

Métricas, cultura y paz mental

Dashboards y KPIs que hacen la calidad visible ante el negocio, retros y feedback loops que de verdad mueven la aguja, y una promesa concreta: paz mental en tus proyectos.

FAQ

¿Cómo acceder al evento online?

Arquitectura Hexagonal

Marta Castellvi — Wed, 25 Mar 2026 16:29:06 +0000

Roger Porta

Software Architect

¿Sobre qué hablaremos?

¿Sobre qué hablamos?

Los ingredientes del Testable Design:

Descubriremos cómo el principio de responsabilidad única y la inyección de dependencias nos ayudan a lograr una alta cohesión y un bajo acoplamiento. Entenderemos por qué eso es clave para la testabilidad de los componentes.

La Receta: Arquitectura Hexagonal

Veremos cómo usar el patrón Ports & Adapters para separar la lógica de negocio de sistemas externos, definiendo fronteras claras que permitan aislar el dominio. El resultado: podremos cortar fácilmente las partes del sistema que queremos probar.

FAQ

¿Cómo acceder al evento online?

The Role of High-Speed Communication Networks in Modern Engineering Systems

Azaria Canales — Wed, 25 Mar 2026 13:25:50 +0000

Modern engineering systems in industrial automation, semiconductor manufacturing, large-scale computing platforms and advanced instrumentation are complex systems increasingly consisting of many distributed subsystems that must exchange data continuously and reliably.

High-speed communication interfaces have become an integral part of these architectures. They allow sensors, controllers, processing units and monitoring systems to operate as a coordinated network.

As system complexity grows, the role of communication infrastructure becomes increasingly important.

Beyond Bandwidth: The Real Requirements of High-Speed Networks

Discussions around high-speed communication often focus only on bandwidth. In practice, system architects must consider other equally important parameters like

Deterministic Latency

In many control-oriented systems, predictability of latency matters more than speed.

Distributed control loops, precision motion systems and instrumentation platforms require communication delays that remain consistent. Even small variations in latency can disrupt the system functionality leading to erroneous behaviour and catastrophic failure of systems.

Achieving deterministic latency typically requires hardware design specifically catering to routing of data and control signals and use of FPGAs and ASICs to avoids passing data through software layers. It also requires link initialization procedures to ensure that timing behaviour remains stable.

Reliability and Continuous Operation

Industrial plants, semiconductor fabrication lines and computing infrastructure cannot afford frequent interruptions and rely on high-speed communication networks which operate continuously for long periods. Communication architectures in these environments therefore incorporate redundancy, error detection and monitoring mechanisms that allow faults to be detected and isolated without disrupting system operation.

High-Speed Interfaces as System Infrastructure

Technologies such as PCI Express, high-speed Ethernet, and SERDES-based FPGA interconnects enable data transfers at tens of gigabits per second per lane. Modern systems often combine multiple such lanes to create aggregate bandwidths reaching hundreds of gigabits per second.

High-speed communication networks have become the most important entity connecting distributed subsystems that must operate in coordination.

Distributed Monitoring and Safety Interlocks

In many industrial environments, communication networks serve not only data transport but also monitoring and safety functions.

Large facilities often deploy Distributed Monitoring Systems (DMS) that continuously collect operational information from sensors and control units located throughout the infrastructure providing low latency visibility into equipment health and performance.

Interlock systems implement safety mechanisms and are designed to prevent unsafe operating conditions. It automatically triggers protective actions when specific fault conditions are detected.

High-speed communication networks allow data and safety signals to propagate rapidly across distributed systems, enabling automated control systems to respond quickly to abnormal situations.

Because these mechanisms are closely tied to operational safety, they often rely on deterministic communication paths and redundant network architectures.

Data Infrastructure and High-Performance Computing

High-speed communication is equally critical in computing infrastructure.

Modern data centres rely on high bandwidth interconnects to move data between processors, storage systems and accelerator hardware. AI training workloads, large-scale simulations, and real-time data analytics all depend on communication networks capable of handling large data flows with minimal latency.

Advances in Ethernet technology and optical interconnects have enabled data centre networks to scale to hundreds of gigabits per second, enabling entirely new categories of computational solutions.

The Next Phase of High-Speed Communication

The pace of development in communication technology is ever increasing.

Data centre networks are already evolving toward terabit-scale Ethernet links. Optical communication technology is advancing to push the limits of bandwidth and distance. In parallel, wireless systems are advancing toward next-generation networks capable of supporting ultra-high throughput and low-latency connectivity.

As digital systems become increasingly distributed and data-driven, communication infrastructure will remain a critical enabler of innovation across many industries.

Our Contribution to High-Speed Communication Systems

Developing reliable communication infrastructure requires expertise that spans hardware design, protocol implementation, FPGA and ASIC Design and system architecture.

Our teams contribute to the design and integration of high-speed wired communication systems used in distributed engineering platforms. These efforts include work on SERDES-based communication architectures, FPGA-based networking solutions, and system-level integration of high-speed interfaces.

By supporting the development of deterministic and reliable communication networks, we help enable complex platforms used in industrial automation, advanced instrumentation and high-performance computing environments.

Conclusion

High-speed communication interfaces have evolved into a critical system infrastructure. They enable distributed systems to operate as coordinated platforms capable of processing and transporting large volumes of data with minimum latency and maximum Reliability.

As industries continue to build increasingly complex and interconnected systems, the performance and reliability of communication networks will remain central to the design of next-generation engineering platforms.

The post The Role of High-Speed Communication Networks in Modern Engineering Systems appeared first on Capitole.

Automated Mobility: Why Infrastructure Is the Strategic Challenge

Azaria Canales — Fri, 20 Mar 2026 13:00:39 +0000

Mobility is undergoing a profound transformation. Vehicle automation, until recently viewed as a standalone technological advancement, is now being deployed in real-world environments, revealing a structural reality: the autonomous vehicle is just one component within a broader system, whose central pillar is infrastructure.

Road safety data clearly illustrates the scale of the challenge. Globally, approximately 1.35 million people die each year in traffic accidents, and between 20 and 50 million suffer non-fatal injuries, according to the latest estimates from the World Health Organization and other international sources. More than 90% of these accidents are directly or indirectly attributable to human error—such as distraction, excessive speed, or driving under the influence of substances. This context has been one of the primary drivers, for over two decades, behind the development of increasingly automated vehicles.

Advances in artificial intelligence, sensing technologies, and high-performance computing have enabled the emergence of vehicles that not only incorporate advanced driver assistance systems, but are also capable of operating autonomously under real-world conditions. Recent announcements by technology companies and manufacturers—introducing certified autonomous systems—signal the beginning of a global-scale deployment of thousands of vehicles with advanced autonomous driving capabilities as early as 2026.

However, the most significant barrier to large-scale adoption is not purely technological. Legislative constraints, societal challenges, and business model debates all play a role. Yet among these, the most critical and urgent challenge is the transformation of road infrastructure.

Infrastructure in the Era of Connected and Autonomous Vehicles

Today’s vehicles rely on conventional road networks designed for human drivers—who interpret signals, make decisions, and ensure safety. Autonomous vehicles, by contrast, are highly sensitive machines that generate and process vast amounts of data, and whose safety performance depends not only on onboard sensors but also on cooperative capabilities—namely communication and synchronization with other vehicles and infrastructure.

To unlock this potential at scale, infrastructure must evolve across three key dimensions:

1. Digital Infrastructure

Highly precise digital models of the road network—digital twins or high-definition (HD) maps—are required to provide richer information than what vehicle sensors alone can deliver. These models reduce uncertainty and enhance the prediction of both vehicle behavior and that of other agents in the environment.

For autonomous vehicles, navigation is no longer a simple route calculation problem; it becomes a critical function requiring centimeter-level accurate HD mapping, as well as dynamic information on lane status, roadworks, variable signage, temporary speed limits, and real-time incidents. In this sense, digital infrastructure becomes an extension of the vehicle’s perception system, enabling it to anticipate scenarios beyond its line of sight and improve decision-making.

Moreover, this digital layer does not only benefit vehicles. For infrastructure managers—public authorities and operators—digital twins enable new use cases: predictive maintenance planning, traffic scenario simulation, impact assessment of roadworks or regulatory changes, and investment optimization. The digitalization of road assets transforms infrastructure into a data-driven, actively managed system rather than one reliant solely on physical inspection.

2. Cooperative Communication Networks

Technologies such as Cooperative Intelligent Transport Systems (C-ITS) enable information exchange between vehicles (V2V), between vehicles and infrastructure (V2I), and between vehicles and other actors in the environment (V2X). This communication layer is essential for services such as early hazard warnings, dynamic speed management, and congestion notifications.

A cooperative network allows each vehicle not only to perceive its immediate surroundings but also to receive aggregated, system-wide information in real time. This includes incidents beyond sensor range, road surface conditions, temporary obstacles, the presence of emergency vehicles, and changes in variable signage. Through this connectivity, vehicles can anticipate critical situations and make optimal driving decisions before they fully materialize—significantly improving both safety and traffic efficiency.

3. Automated Traffic Management

By integrating data from sensors, vehicles, and digital platforms, it becomes possible to develop automated traffic control systems capable of optimizing traffic flow in real time—reducing congestion and enhancing safety beyond the capabilities of traditional fixed signaling systems.

However, this is not merely an evolution of existing traffic management centers. Automated traffic management represents a paradigm shift: much like autonomous vehicles themselves, control systems will operate autonomously, relying on optimization algorithms and machine learning to make real-time decisions without direct human intervention.

This has profound implications for system design. In a scenario where traffic is predominantly composed of connected autonomous vehicles, optimization is no longer limited to controlling traffic lights or variable message signs—it can directly influence vehicle routing. Infrastructure becomes an active participant in dynamic trajectory planning, redistributing traffic flows before bottlenecks emerge.

This systemic coordination capability is key to addressing the structural problem of congestion. Whereas current models react to traffic jams, the new paradigm enables anticipation and prevention through cooperative algorithms that optimize the entire system, rather than individual vehicles in isolation.

Together, these three elements form the foundation of an active, cooperative infrastructure—moving beyond the traditional paradigm of passive physical infrastructure.

Two Approaches to Infrastructure Transformation

The gradual deployment of autonomous vehicles inevitably requires infrastructure adaptation. Two strategic approaches are emerging: bottom-up and top-down.

A) Bottom-up Approach: Incremental Evolution

This is the predominant model in Europe. It involves progressively implementing specific C-ITS services and use cases on existing infrastructure, following standards defined by organizations such as ETSI and coordination platforms like C-Roads.

C-Roads brings together multiple EU Member States and infrastructure operators to harmonize the deployment of cooperative transport services, ensuring interoperability across regions and manufacturers. Within this framework, C-ITS services are developed in stages—from basic notification services (“Day 1”) to more advanced applications (“Day 3”).

A notable example is the European SCALE project (Strengthening C-ITS Adoption and Lining-up across Europe), funded by the Connecting Europe Facility (CEF) and involving entities from multiple countries. Its objective is to accelerate large-scale deployment of mature C-ITS services, validate interoperability, and assess their impact on safety and efficiency.

The strength of this approach lies in its alignment with standards and its ability to test solutions in real-world contexts before scaling. However, its main limitation is that incremental implementation can slow down deployment timelines, create regulatory fragmentation, and lead to dispersed investments that may not converge into a unified long-term architecture.

B) Top-down Approach: Designing for an Automated Future

In contrast, an alternative approach is based on a deterministic assumption: that 100% of traffic will eventually become automated in the medium term, whether this takes 10 or 20 years. Under this model, infrastructure transformation is not incremental—it is a redesign from the outset to support a fully connected and automated ecosystem.

This approach entails:

Designing road networks as integrated data platforms, with communication and sensing capabilities as native components
Embedding low-latency connectivity (5G / ITS-G5), edge computing capabilities, and management nodes along strategic corridors
Developing predictive traffic management architectures based on big data and cooperative algorithms

Some Asian countries—particularly China—are closer to this model. The coordinated deployment of 5G infrastructure, smart corridors, and autonomous driving pilot cities reflects a nationally integrated strategy aligned with broader digitalization and industrial innovation goals. Centralized planning and the ability to mobilize public investment enable rapid scaling, shortening the gap between pilot projects and mass deployment.

This approach is based on a clear strategic premise: if the end state is a predominantly autonomous system, designing infrastructure for that future from the outset avoids redundancy and prevents transitional investments from becoming obsolete.

The strategic question is therefore clear: should we adapt infrastructure originally designed for human drivers, or design a new architecture optimized for cooperative algorithms?

Conclusion: A Holistic Vision for Future Infrastructure

The transition to automated mobility is not merely a technological challenge centered on vehicles. It is fundamentally a systems challenge, where road infrastructure must evolve from a passive physical support into an active, digital, and cooperative platform designed to maximize safety, efficiency, and sustainability. Roads must incorporate a new layer of intelligence.

This transformation will not happen overnight—it will require coordination between public authorities, manufacturers, operators, and harmonized regulatory frameworks. But the direction is clear: the full potential of autonomous vehicles cannot be realized without infrastructure capable of supporting them both physically and digitally. And the strategy adopted for this transformation will ultimately determine who leads the future of automated mobility.

The post Automated Mobility: Why Infrastructure Is the Strategic Challenge appeared first on Capitole.

You searched for Data - Capitole

The AI Side of Things

Dos charlas técnicas sobre cómo llevar agentes de IA del experimento a producción: qué ha cambiado, qué implica para la governance y qué hace falta para dar el salto.

Programa

18:00h Llegada y presentaciones

18:30h · Tres años de LLMs: del wow inicial al modelo de trabajo

19:05h · ¿Está tu organización lista para agentes?

19:40h · Q&A conjunto

20:00h - 21:00h · Networking + Pizzas

FAQ

Beyond Aesthetics: Human-Centered Design, the Key to Real Impact

The current problem with digital design

Products nobody asked for

The big challenges in design

Fast, beautiful, and… useful?

The business impact of getting it wrong

What you don’t validate, you pay for — and greatly.

What is Human-Centered Design (HCD)?

Designing vs. guessing

How HCD addresses the main challenges

Fewer assumptions, better products

HCD best practices in a technology consultancy

Small changes, big results

Capitole en ExpoQA 26

Completa este formulario para participar en nuestro SORTEO y para recibir futuras comunicaciones.

Asegúrate de que tus datos de contacto estén completos para que podamos comunicarnos contigo en caso de que resultes ganador/a.

Bases del Sorteo

1. Presencia obligatoria

2. Entrega del premio

3. Participación

4. Quién no puede participar

5. Datos y contacto

6. Aceptación

FAQ

Copiloto Industrial: La IA del diseño a la planta

Tu competencia ya usa la IA en sus plantas industriales.

La pregunta no es si aplicarla, es cómo hacerlo sin que el proyecto muera en el piloto. Casos reales, ROI demostrado y una hoja de ruta lista para implementar.

Programa

Presentaciones 9:30-10:30

Metalurgia sin paradas: Cómo CELSA anticipa los fallos con IA

Agroalimentario: Mantenimiento predictivo en planta con IA

Lo que hemos aprendido en 260 plantas industriales

Casos de uso 10:30-11:30

Workshop 12:00-13:00

Del piloto a la escala: Tu hoja de ruta con IA

FAQ

Copiloto Industrial: La IA del diseño a la planta

Tu competencia ya usa la IA en sus plantas industriales.

La pregunta no es si aplicarla, es cómo hacerlo sin que el proyecto muera en el piloto. Casos reales, ROI demostrado y una hoja de ruta lista para implementar.

Copiloto Industrial: La IA del diseño a la planta

Programa

¿Sobre qué hablamos?

Presentaciones 9:30-10:30

Metalurgia sin paradas: Cómo CELSA anticipa los fallos con IA

Agroalimentario: Mantenimiento predictivo en planta con IA

Lo que hemos aprendido en 260 plantas industriales

Casos de uso 10:30-11:30

Workshop 12:00-13:00

Del piloto a la escala: Tu hoja de ruta con IA

FAQ

QA in the Age of AI: Impact, Challenges and Evolution of the Role

The Origins and Evolution of QA, and the Rise of AI

The Impact of AI on the SDLC and QA

Benefits

Risks

The Emergence of New Metrics

Adapting the QA Role in an AI-Assisted Environment

Differential Impact by Experience Level

New Role: QA for AI Systems and Agents

Costs and Challenges of AI Adoption in QA

Technical Costs

Operational Costs

Organizational and Talent Costs

Economic Costs

Substitution or Complementarity?

Conclusion

Calidad como estrategia de negocio

Calidad como estrategia de negocio

De la técnica al impacto: menos coste, más confianza

Calidad como estrategia de negocio