En este modulo
State of the art: what AI can do today
In 2026, generative AI is no longer a promise. It is infrastructure. 76% of professionals use it weekly. Companies that integrate it report an average savings of 2 hours per employee per day. Yet only 25% of organizations have a formal AI strategy.
What AI does well today in a business context:
Individual productivity
- Writing, document analysis, information summarization: +40% productivity in knowledge work
- Code: +55% development speed (GitHub Copilot Impact Report 2025)
- Customer service: automated resolution of 30-50% of tier-1 tickets
Business processes
- Due diligence: from 2 weeks to 2 days for document review
- Candidate screening: 80% reduction in first-stage time
- Fraud detection: models outperforming manual rules by 15-20 percentage points
- Forecasting: 20-35% improvement in demand prediction accuracy
What it CANNOT do (yet)
- Make strategic decisions (it can inform them, not make them)
- Replace human judgment in ambiguous, ethical or political contexts
- Guarantee 100% accuracy on any task (always requires oversight)
- Manage complex human relationships (negotiation, leadership, empathy)
For the executive
AI does not replace people. It replaces tasks. An employee who spent 4 hours on a report now takes 30 minutes. The 3.5 freed hours go to higher-value work: strategy, client relationships, innovation.
Impact by industry: who wins and who loses
Not all industries benefit equally from AI. Those handling the most text, data and repetitive processes are the most impacted:
High impact (already transforming)
- Professional services: consulting, legal, audit. Average AI spend per employee is the highest: $3,470/year. AI automates 40-60% of junior associate work.
- Financial services: credit scoring, fraud detection, algorithmic trading, automated compliance. Banks adopting AI report 20-30% reduction in operational costs.
- Technology: software development, technical support, QA. GitHub reports that 92% of developers use AI for code.
- Marketing and media: content generation, segmentation, personalization. 50-70% savings in content production time.
Medium impact (adopting)
- Healthcare: assisted diagnosis, medical image analysis, clinical documentation. Heavy regulation slows adoption but does not eliminate it.
- Education: personalized tutoring, material generation, automated assessment.
- Manufacturing: predictive maintenance, visual quality control, supply chain optimization.
Slower impact (but inevitable)
- Construction: planning, budgets, document management. Low baseline digitization.
- Hospitality and tourism: revenue management, personalization, booking chatbots.
- Public administration: enormous opportunities but strong regulatory and cultural inertia.
AI as a competitive advantage (not a fad)
AI is only a competitive advantage if it delivers one of these 4 outcomes:
- Do the same thing faster and cheaper: reduce operational cost. Example: automate 50% of support responses. Result: same service, half the cost.
- Do the same thing but better: improve quality. Example: contract review that catches risks humans miss. Result: fewer lawsuits, better due diligence.
- Do something new that was previously impossible: create new products/services. Example: real-time personalized recommendations for each customer. Result: new revenue stream.
- Make better decisions: more data, better analyzed, faster. Example: forecasting that predicts demand with 30% more accuracy. Result: less dead stock, better cash flow.
If AI does not deliver any of these 4 outcomes for your company, you do not need it (yet). If it delivers at least 2, it is urgent.
Quick test
Ask your department heads: "What 3 tasks consume the most time each week?" If at least 2 of those tasks involve text, data or repetitive processes, AI can solve 60-80% of the problem.
Assessing your organization's AI maturity
Before investing, you need to know where you stand. 5 maturity levels:
Level 1: Exploration
Some employees use ChatGPT on their own. No policy, no approved tools, no measurement. Shadow AI is likely.
Action: approve tools, create a basic policy, start training (IAcademy Base Program).
Level 2: Experimentation
The company has approved 2-3 tools. Some teams use them. No systematic measurement of impact.
Action: measure ROI (B11), identify champions, scale to more departments.
Level 3: Integration
AI is integrated into key processes. Impact is measured. There is a person or team responsible.
Action: create formal governance, evaluate advanced automation, consider a CAIO.
Level 4: Optimization
AI is part of daily operations. Processes are continuously optimized. There is a formal strategy.
Action: explore proprietary models (fine-tuning), monetize data, create AI products.
Level 5: Transformation
The business model has been transformed with AI. New products, new markets, new value proposition.
Action: lead the industry, publish case studies, attract AI talent.
The 5 strategic decisions you must make
As an executive, these 5 decisions define your AI strategy:
1. Ambition: optimize or transform?
Optimize: do the same thing but faster/cheaper. Lower risk, quick ROI, does not change the business model.
Transform: create new products, services or models. Higher risk, higher potential, requires significant investment.
Most companies should start by optimizing and evolve toward transformation once they reach maturity (level 3+).
2. Where to invest first?
Prioritize areas with: high volume of repetitive tasks + high cost of error + available data. Typically: support/ops, legal/compliance, marketing/content.
3. Talent: hire, train or outsource?
Train (recommended to start): faster, cheaper, builds internal culture. IAcademy specializations cover this.
Hire: necessary for level 4+ or if you want proprietary AI development.
Outsource: occasional, for specific projects. Does not build internal competence.
4. Sovereignty: cloud, hybrid or on-premise?
Depends on your industry and regulation. Financial services and healthcare: you probably need full control (on-premise/hybrid). Marketing and sales: cloud is sufficient.
5. Governance: who decides what?
Designate an AI lead (not necessarily full-time). Create an AI committee with representatives from Legal, IT, HR and Business. Define a policy and review it every 6 months.
Build vs Buy vs Partner
| Option | When | Cost | Risk |
|---|---|---|---|
| Buy (SaaS) | Standard tasks, fast, no technical team | Low (subscription) | Low (vendor lock-in) |
| Build (in-house) | Competitive advantage, proprietary data, differentiation | High (team + infra) | High (complexity, time) |
| Partner | Specific projects, validate before building | Medium (project-based) | Medium (dependency) |
Rule of thumb: Buy for 80% of use cases. Build only when AI is your core competitive advantage. Partner to validate before committing to Build.
7 mistakes executives make with AI
- Waiting for it to be perfect. AI will never be perfect. The question is not "is it perfect?" but "is it better than what we have now?". If a model is right 85% of the time, and your manual process is right 70%, AI is already generating value.
- Buying tools without training the team. Tools are abandoned within 30 days without training. Training first, tools second.
- Delegating AI strategy to IT. AI is not an IT project. It is a business project with a technology component. The sponsor should be business, not IT.
- Searching for the "perfect use case". Analysis paralysis. Start with 3 simple workflows (B03) and scale from there.
- Ignoring regulation. The AI Act is already in force. Fines go up to EUR 35M. Compliance is cheaper than non-compliance.
- Believing AI replaces people. It replaces tasks. People who use AI are more productive. Those who do not fall behind.
- Not measuring. Without metrics, AI is an experiment. With metrics, it is an investment with calculable returns.
Practical exercise
- Assess your organization's AI maturity level (1-5)
- Identify the 3 departments where AI would have the greatest impact
- For each department, list the 3 most repetitive and costly tasks
- Decide your ambition: optimize or transform? Justify your answer
- Define: build, buy or partner for each use case
- Draft a paragraph of "AI vision" for your company (what do we want to achieve with AI in 12 months)
Bonus: ask the AI: "I am the CEO of [company] in [industry], [N] employees. Assess where AI generates the most impact and propose a 3-phase plan." Compare with your own analysis.
Puntos clave
Puntos clave from CX01
- 76% of professionals use AI weekly. Only 25% of companies have a strategy. There is a massive gap.
- AI does not replace people, it replaces tasks. It frees up time for higher-value work.
- 4 ways to generate value: faster, better quality, something new, better decisions.
- Assess your maturity (1-5) before investing. Most organizations are at level 1-2.
- 5 key decisions: ambition, where to invest, talent, sovereignty, governance.
- Buy for 80%. Build only if AI is your competitive advantage.
- Most common mistake: buying tools without training the team.
Guia de estudio — Conceptos clave de CX01
Estado del arte: que puede hacer la IA hoy
- Redaccion, analisis de documentos, resumen de informacion: +40% productividad en tareas de conocimiento
- Codigo: +55% velocidad de desarrollo (GitHub Copilot Impact Report 2025)
- Atencion al cliente: resolucion automatica del 30-50% de tickets de nivel 1
- Due diligence: de 2 semanas a 2 dias para revision documental
- Screening de candidatos: 80% reduccion en tiempo de primera fase
- Deteccion de fraude: modelos que superan a reglas manuales en 15-20 puntos porcentuales
Impacto por sector: quien gana y quien pierde
- Servicios profesionales:consultoria, legal, auditoria. El gasto medio en IA por empleado es el mas alto: 3.470 USD/ano. La IA automatiza el 40-60% del trabajo de asociados junior.
- Servicios financieros:credit scoring, deteccion de fraude, trading algoritmico, compliance automatizado. Los bancos que adoptan IA reportan 20-30% reduccion de costes operativos.
- Tecnologia:desarrollo de software, soporte tecnico, QA. GitHub reporta que el 92% de developers usan IA para codigo.
- Marketing y media:generacion de contenido, segmentacion, personalizacion. Ahorro del 50-70% en tiempo de produccion de contenido.
- Salud:diagnostico asistido, analisis de imagenes medicas, documentacion clinica. Fuerte regulacion frena la adopcion pero no la elimina.
- Educacion:tutoria personalizada, generacion de material, evaluacion automatica.
IA como ventaja competitiva (no como moda)
- Hacer lo mismo mas rapido y barato:reducir coste operativo. Ejemplo: automatizar el 50% de respuestas de soporte. Resultado: mismo servicio, mitad de coste.
- Hacer lo mismo pero mejor:mejorar calidad. Ejemplo: revision de contratos que detecta riesgos que humanos pasan por alto. Resultado: menos litigios, mejor due diligence.
- Hacer algo nuevo que antes era imposible:crear productos/servicios nuevos. Ejemplo: recomendaciones personalizadas en tiempo real para cada cliente. Resultado: nuevo revenue stream.
- Tomar mejores decisiones:mas datos, mejor analizados, mas rapido. Ejemplo: forecasting que predice demanda con 30% mas precision. Resultado: menos stock muerto, mejor cash flow.
- Test rapido: Pregunta a tus directores de area: "que 3 tareas os quitan mas tiempo cada semana?" Si al menos 2 de esas tareas son de texto, datos o procesos repetitivos, la IA puede resolver el 60-80% del problema.
Evaluar la madurez IA de tu organizacion
- Accion: aprobar herramientas, crear politica basica, iniciar formacion (Programa Base IAcademy).
- Accion: medir ROI (B11), identificar champions, escalar a mas departamentos.
- Accion: crear governance formal, evaluar automatizacion avanzada, considerar CAIO.
- Accion: explorar modelos propios (fine-tuning), monetizar datos, crear productos IA.
- Accion: liderar el sector, publicar caso de exito, atraer talento IA.
Las 5 decisiones estrategicas que debes tomar
- Optimizar: hacer lo mismo pero mas rapido/barato. Menor riesgo, ROI rapido, no cambia el modelo de negocio.
- Transformar: crear nuevos productos, servicios o modelos. Mayor riesgo, mayor potencial, requiere inversion significativa.
- Formar (recomendado para empezar): mas rapido, mas barato, genera cultura interna. Las especializaciones IAcademy cubren esto.
- Contratar: necesario para nivel 4+ o si quieres desarrollo propio de IA.
- Externalizar: puntual, para proyectos especificos. No construye competencia interna.
Build vs Buy vs Partner
- Buy (SaaS)Tareas estandar, rapido, sin equipo tecnicoBajo (suscripcion)Bajo (vendor lock-in) Build (interno)Ventaja competitiva, datos propietarios, diferenciacionAlto (equipo + infra)Alto (complejidad, tiempo) Partner Proyectos especificos, validar antes de buildMedio (proyecto)Medio (dependencia) Regla practica: Buy para el 80% de los casos. Build solo cuando la IA es tu ventaja competitiva principal. Partner para validar antes de hacer Build.
Siguiente: CX02 - AI Governance for Executives
You have the vision. Now you need the structure: AI committee, CAIO, policies, risk framework.
Ir al modulo CX02