Start with concrete cases, measure results, scale gradually
A medida que la inteligencia artificial avanza hacia el corazón de las organizaciones financieras, la consultora española Excelia ofrece una hoja de ruta de siete pasos para que las empresas adopten esta tecnología sin perder el control ni la responsabilidad. En un momento en que casi nueve de cada diez grandes empleadores esperan que la IA transforme sus estructuras antes de 2030, la guía recuerda que la automatización más poderosa no es la más rápida, sino la más gobernada. La pregunta no es si las finanzas cambiarán, sino si ese cambio será ordenado o caótico.
- El 86% de los grandes empleadores prevé que la IA rediseñará sus organizaciones en menos de cuatro años, y el sector financiero se perfila como uno de los más expuestos a esa transformación.
- La tentación de escalar rápido choca con riesgos reales: brechas de seguridad, cajas negras inexplicables y equipos que construyen sus propias herramientas de forma descontrolada.
- Excelia propone empezar por tareas de bajo riesgo —reportes rutinarios, detección de desviaciones, clasificación de documentos— para aprender cómo se comporta la IA antes de confiarle decisiones que mueven dinero.
- Las decisiones críticas —aprobaciones de pagos, ajustes contables, informes regulatorios, evaluaciones de crédito— deben permanecer en manos humanas, con trazabilidad completa para satisfacer a los auditores.
- El mayor peligro silencioso es la automatización en la sombra: distintos departamentos construyendo modelos propios que generan duplicidades, vacíos y vulnerabilidades sin que nadie los supervise.
- El camino hacia una IA financiera sostenible pasa por la calidad del dato, la gobernanza centralizada y una escala gradual que mida resultados antes de ampliar el alcance.
Casi nueve de cada diez grandes empleadores en el mundo esperan que la inteligencia artificial transforme sus organizaciones antes de 2030, según el Informe sobre el Futuro del Empleo del Foro Económico Mundial. En el sector financiero, esa transformación ya está en marcha: bancos, gestoras de inversión y departamentos de tesorería experimentan con herramientas de IA para tareas que van desde la generación de informes hasta la detección de fraude. Pero pasar del experimento al despliegue a escala exige disciplina. Excelia, consultora tecnológica española, ha publicado una guía con siete pasos para hacerlo sin generar caos ni vacíos de seguridad.
El primer movimiento es comenzar por lo pequeño y lo seguro: automatizar reportes rutinarios, detectar desviaciones presupuestarias, resumir estados financieros o preparar materiales para el consejo. Son tareas que consumen tiempo humano pero cuyo impacto negativo, si algo falla, es limitado. El segundo paso es trazar una frontera clara entre lo que la IA puede decidir sola y lo que requiere criterio humano. Los borradores y las alertas pueden ser suyos; las aprobaciones, los ajustes contables y los informes regulatorios, no.
La infraestructura importa tanto como la estrategia: controles de acceso por rol, registros de auditoría completos y la eliminación de cajas negras son condiciones no negociables cuando se maneja información financiera sensible. Antes de elegir herramientas, las empresas deben entender qué problema concreto quieren resolver, y evaluar cada opción por su integración con sistemas existentes, su seguridad y su escalabilidad real.
Un obstáculo que muchas organizaciones subestiman es la calidad del dato. Si la información financiera está incompleta, duplicada o dispersa en sistemas desconectados, los resultados de la IA serán poco fiables. Y finalmente, hay que prevenir la automatización en la sombra: equipos que construyen sus propios modelos de forma independiente, creando duplicidades y vulnerabilidades sin supervisión central. Antonio Cerdán, responsable de hiperautomatización en Excelia, lo resume con claridad: el objetivo no es automatizar por automatizar, sino identificar dónde la IA puede mejorar previsiones, acelerar cierres mensuales, reducir errores y liberar al equipo financiero para el trabajo de mayor valor. Siempre con seguridad, trazabilidad y gobernanza del dato como cimientos.
Nearly nine in ten large employers worldwide expect artificial intelligence to reshape how their organizations work within the next four years. That's the finding from the World Economic Forum's Future of Jobs Report for 2025, and the prediction carries particular weight in one sector: finance. Banks, investment firms, and corporate treasury departments are already experimenting with AI-driven tools to handle everything from routine reporting to fraud detection. But moving from experiment to enterprise-wide deployment requires discipline—and according to Excelia, a Spanish consulting and technology firm, it requires a specific sequence of steps.
Excelia has published a practical guide outlining seven critical moves for companies looking to embed AI into their financial operations without creating chaos, security gaps, or systems that nobody can explain. The first move is almost deceptively simple: start small, with low-stakes internal work. A finance team might begin by using AI to automate routine reporting, flag budget deviations, summarize financial statements, sort documents, or prepare materials for board meetings. These are real tasks that consume human time, but they carry limited downside risk if something goes wrong. The goal is to learn how the technology behaves in your actual environment before trusting it with decisions that move money.
The second step is to draw clear lines around what the AI is allowed to decide on its own and what still requires human judgment. An AI system might draft a financial report or alert an analyst to a suspicious variance, but the final sign-off on official forecasts, payment approvals, accounting adjustments, credit decisions, and regulatory filings must remain with people. This distinction matters because it preserves accountability and keeps the organization from accidentally delegating decisions it shouldn't.
Then comes the infrastructure work: role-based access controls, complete audit trails, and the elimination of black boxes. Finance departments handle sensitive information—customer data, transaction records, strategic plans. Any AI system touching that information must respect permission levels and record every query, recommendation, and action it takes. This creates the paper trail auditors need and prevents AI from becoming an unexplainable force inside the organization.
The fourth principle is to maintain human oversight on anything that carries real financial weight. AI can analyze, prioritize, summarize, spot errors, and recommend next steps. But approvals, adjustments to the books, official forecasts, external reporting, financing decisions, credit risk assessments, and compliance determinations all need a person in the loop. The technology amplifies human capability; it doesn't replace human responsibility.
Before selecting tools, companies need to understand what problem they're actually trying to solve. Are they looking for simple automation, predictive analytics, generative AI, intelligent assistants, or autonomous agents? The answer determines which technology makes sense. The evaluation should weigh integration with existing systems, security features, traceability, scalability, and whether the tool genuinely solves a concrete problem rather than just being the latest thing.
The sixth step addresses a reality that many organizations overlook: data quality and organizational readiness. AI in finance is only as good as the data feeding it. If financial information is incomplete, duplicated, miscategorized, or scattered across disconnected systems, the AI's output will be unreliable. Before scaling up, finance teams need to audit their data sources, check integrations, review access permissions, establish governance rules, and train staff to interpret and use the technology correctly.
Finally, companies must prevent what Excelia calls shadow automation—the risk that different teams start building their own AI tools, macros, and models independently, creating duplicates, gaps, and security vulnerabilities. Finance needs clear ownership, shared validation standards, security policies, and tracking of what's actually working. Antonio Cerdán, who leads hyperautomation work at Excelia, frames it this way: the goal isn't automation for its own sake, but identifying where AI can genuinely improve forecasting, speed up month-end closes, catch deviations, reduce human error, strengthen controls, and free the finance team to focus on higher-value work. The path forward requires starting with concrete cases, measuring results, and scaling gradually—always with security, traceability, human judgment, and data governance as the foundation.
Citas Notables
Applying AI in Finance is not about automating for automation's sake, but identifying where it can generate real impact: improving forecasts, accelerating closes, detecting deviations, avoiding human error, strengthening controls, and freeing the finance team for higher-value work.— Antonio Cerdán, Hyperautomation Managing Director at Excelia
La Conversación del Hearth Otra perspectiva de la historia
Why does finance need a special playbook for AI? Doesn't automation work the same way everywhere?
Finance is different because the stakes are immediate and measurable. A mistake in a marketing algorithm might annoy customers. A mistake in financial automation moves money the wrong way or breaks a regulatory rule. The sensitivity of the data—customer accounts, transaction records, strategic plans—means you can't afford to treat it like any other process.
So the first step is to start small. But how do you know which small process to pick?
You want something that's genuinely painful to do by hand, but where failure doesn't cascade. Automating routine reporting checks that box. It saves time, the AI can show its work, and if it gets something wrong, a human catches it before it goes anywhere.
The guide mentions "clear limits on autonomy." What does that actually mean in practice?
It means deciding in advance: this AI can draft, but not approve. This AI can flag, but not execute. This AI can recommend, but not decide. You're drawing a line between what the machine can do and what stays with the person who's accountable.
What's the biggest risk you see when companies skip these steps?
Shadow automation. Different teams build their own tools in isolation. Suddenly you have five different AI systems doing similar work, nobody knows what they're doing, there's no audit trail, and you've created more problems than you solved. That's why governance matters from day one.
How does data quality fit into this?
It's foundational. If your financial data is messy—duplicated, incomplete, scattered across systems—the AI will inherit all those problems and amplify them. You can't automate your way out of bad data. You have to fix the data first.
What does success look like?
A finance team that's faster at routine work, more confident in their forecasts, better at catching errors, and freed up to think about strategy instead of data entry. And an organization that can explain every decision the AI made.