Serious AI deployment in your marketing: where it adds real value and where it's noise. Predictive analytics, personalisation, content automation, chatbots and generative models integrated with judgement into your current operation.
Board or leadership has asked you to 'have AI' without knowing what for. You need a map of real use cases, not a fashionable word in the plan.
You accumulate data in CRM, GA4, emails, transactions. Well-applied AI turns that data into operational decisions: churn prediction, purchase propensity, dynamic segmentation.
You produce a lot of content, manage many campaigns, attend to many leads. AI frees the team from repetitive tasks and raises the average quality.
E-commerce with thousands of SKUs, hotels with personalisation, banking with digital onboarding. AI improves experience where it shows most.
I review your current marketing processes and detect where AI can add concrete value (not where it looks nice). Map of use cases prioritised by impact and effort.
OpenAI, Anthropic, Mistral, open-source models, vertical SaaS platforms. I choose the right combination according to case, budget and level of control you need.
We connect the AI to your existing stack (CRM, GA4, automation, CMS). Controlled trials with before/after metrics. No major operational disruption.
Usage protocols, ethical criteria, human control over output, continuous model improvement. AI isn't 'installed' — it's governed.
Beyond the enthusiasm and the scepticism, the areas where AI delivers measurable return in marketing are these:
Models that predict churn, customer value, purchase propensity. Allows commercial actions to be prioritised with judgement, not blindly.
Product recommendations, adapted editorial content, individualised offers. What only the big players could do, now you can do too.
Generative AI for first versions of copy, campaign variations, translations, image alt-text. The team reviews and improves — they don't start from scratch.
Conversational chatbots that resolve 60-70% of enquiries without operator and properly route those that need a human. Real measurable savings.
Brand monitoring on social and press with AI that distinguishes irony, sarcasm, context. Early detection of crises and opportunities.
Smart bidding, dynamic segmentation, adaptive creatives on Google Ads, Meta, TikTok. What the platform offers, configured with judgement.
Implementation of proprietary AI recommendation engine + segment-based homepage personalisation. AOV +18% in four months.
El marketing del cerebro es más predictible que el marketing de la opinión. — Ángel Ortega Castro
Propensity model that prioritises the 20% of leads generating 70% of sales. Sales stopped chasing what doesn't suit them.
System generating product sheets, tasting descriptions and monthly newsletters. Editorial team reviews and enriches. Production 4× with the same headcount.
AI isn't a magic box. It's one more component in a data flow. Structured information comes in, it's processed by a well-chosen model (not always the fashionable one) and a measurable output comes out that your team can integrate into daily work.
AI delivers value when there is enough data, well-identified use cases and willingness to govern it. Four typical scenarios where it makes sense to invest:
CRM, GA4, ERP, forms, emails. Data sleeping in silos. AI is the most efficient way to convert that asset into decisions.
Extensive catalogues, active blogs, multilingual. Well-governed AI lets you maintain quality without doubling headcount.
High volume of repetitive enquiries, seasonal peaks that saturate the team. Chatbot with AI frees capacity and improves satisfaction.
The competition starts to apply AI. Waiting until it's mature usually means arriving late. Better to start small and learn than to arrive last.
Not if you govern it well. Well-deployed AI frees the team from repetitive tasks and lets them focus on the strategic (judgement, customer relationships, complex decisions). The teams left behind are those that DON'T integrate it — because their relative productivity falls compared to competitors who do.
It depends on the use case. There are AI applications with affordable monthly cost (ChatGPT Plus, Claude Pro, vertical SaaS tools) that cover 70% of an SME's needs. Custom projects with proprietary models are more expensive and only justified when volume and differentiation demand it.
It's part of the work from day one. GDPR, European AI Act, internal AI usage policy, supplier contracts with data protection clauses, anonymisation of sensitive information before going to external models. Without this, no deployment is viable.
No. I choose technology by case. OpenAI, Anthropic Claude, Google Gemini, Mistral, self-hosted open-source models. For sensitive cases where data can't go to the cloud, models on your own infrastructure. What matters is the usage logic, not the model brand.
It's for SMEs — in fact, SMEs are the ones that can take advantage of it most quickly. Without heavy structures or frozen processes, an SME can integrate AI in weeks and see return before a multinational does. What matters is choosing the first use case well and doing it properly.
First 45-minute session, free of charge and no commitment. If we fit, I send you a detailed proposal within 5 days. If we don't, you take away a useful initial diagnosis.