Working with AI: Applications and Job Opportunities in Today's Labour Market

Artificial intelligence is changing the way companies organise tasks, analyse information and make decisions. In Spain, AI adoption is progressing particularly among larger organisations, while Regulation (EU) 2024/1689, known as the European Union's AI Act, is also introducing new responsibilities related to the safe and responsible use of these systems. As a result, knowing how to work with AI is becoming increasingly valuable in the job market, not only for technology specialists but also for professionals in management, marketing, finance, human resources and consulting.

Why Working with AI Will Become Essential in Almost Every Job

Working with AI does not necessarily mean developing algorithms or knowing how to code. In many professions, it involves using tools that can analyse large volumes of information, automate certain tasks, generate content or support decision-making.

This complementary role could benefit millions of professionals. Estimates included in the reference documentation suggest that 3.24 million jobs in Spain, equivalent to 15.9% of the employed population analysed, could benefit from productivity improvements through the use of artificial intelligence.

Marketing professionals can use AI to analyse campaign performance. Finance managers can identify patterns across large volumes of transactions. Human resources teams can streamline administrative processes, while business leaders can use predictive models to assess different business scenarios.

This shift also explains the growing interest in education that combines technology with business management. Programmes such as the Master in AI Applied to Business are specifically designed to help professionals understand how to integrate these technologies into business processes and decision-making.

Regulatory requirements are another important aspect of this transformation. Article 4 of Regulation (EU) 2024/1689 establishes obligations relating to AI literacy. Providers and deployers of AI systems must take measures to ensure, to the best extent possible, that people using these systems on their behalf have a sufficient level of AI literacy.

Companies therefore need more than technical specialists. They also require professionals who understand the capabilities and limitations of AI systems, can oversee their outputs, identify errors and assess issues relating to privacy, security, bias and regulatory compliance.

This evolving landscape is creating new career opportunities in artificial intelligence for professionals from a wide range of backgrounds.

Practical Applications of AI in Everyday Business Operations

Artificial intelligence already has practical applications across many business functions. Its usefulness depends on the industry, the data available, the company's technological maturity and the specific problem it aims to solve.

In marketing and sales, for example, AI can help segment audiences, generate content drafts, analyse customer behaviour and develop predictive models. Teams can combine generative AI models with CRM platforms and analytics tools to personalise certain activities.

AI-powered automation can also streamline administrative tasks. Intelligent systems can support activities such as classifying documents, extracting information from invoices, comparing records and summarising large volumes of documentation. This AI-driven automation allows professionals to devote more time to activities that require analysis, negotiation and professional judgement.

In customer service, conversational AI assistants can handle straightforward enquiries and provide information before referring more complex cases to a human agent. The goal is not necessarily to replace human interaction entirely, but to route enquiries according to their complexity.

In finance, applications include anomaly detection, risk assessment, forecasting and transaction analysis. In human resources, AI can be used to organise information, support recruitment processes and analyse aggregated workforce data.

Business leaders can also use business intelligence tools and predictive models to compare different scenarios. In these cases, it is particularly important that decisions remain subject to human oversight, as an AI-generated output does not automatically constitute an accurate or appropriate recommendation.

This strategic dimension explains why artificial intelligence has also become a priority for business leaders. Specialised training in AI for Executives can help them better understand which processes could benefit from these technologies and which require stricter controls.

10 Emerging AI Careers

Although discussions about AI careers often focus on a handful of technical roles, the artificial intelligence job market encompasses a wide range of positions. Some are accessible to professionals with business backgrounds, while others require advanced technical expertise.

Here are ten relevant career paths, listed roughly from those most accessible to business professionals to those requiring more extensive technical training:

1. AI Consultant or AI Transformation Manager. Analyses business processes, identifies automation opportunities and helps design technology adoption projects. This can be a natural career progression for professionals in consulting, strategy or project management. According to Glassdoor, the estimated gross annual salary ranges from €40,000 to €80,000.
2. AI and Digital Marketing Specialist. Uses generative AI models, automation and analytics to develop campaigns, analyse audiences and optimise content. This role is particularly accessible to professionals with backgrounds in marketing and communications. According to Glassdoor salary estimates for digital marketing specialists working with AI, the indicative annual salary ranges from €32,000 to €55,000.
3. AI Business Analyst. Bridges the gap between business needs and technical teams. Defines requirements, interprets data and helps translate business challenges into analytics projects. This can be a career option for graduates in Business Administration, Economics or Finance with data analytics skills. According to salary references such as Glassdoor, estimated annual earnings range from €28,000 to €49,000.
4. Generative AI Specialist or Prompt Engineer. Designs prompts, workflows and interaction systems for generative AI models. As these tools evolve, this role is increasingly becoming integrated into product development, automation, content creation and strategy. The salary range indicated in the reference documentation is €30,000 to €58,000 per year.
5. AI Ethics and Compliance Specialist. Assesses risks, internal policies and regulatory obligations associated with algorithmic systems. Professionals with backgrounds in law, compliance and data protection may find opportunities to specialise in this field. The reference salary range is €45,000 to €85,000 per year.
6. AI Product Manager. Defines AI-powered products and services, coordinating business, technology and user experience teams. This position typically requires previous experience in digital product management. The estimated annual salary ranges from €45,000 to €75,000.
7. Data Scientist. Develops statistical and predictive models using large volumes of data. This role requires expertise in mathematics, statistics, programming and data processing. The figures provided range from approximately €35,000 for junior professionals to €75,000 for senior professionals.
8. Machine Learning Engineer. Transforms machine learning models into solutions that can be integrated and scaled within business products or processes. This is a highly technical role that generally requires strong programming skills. Learn more about the responsibilities involved in our article on becoming a Machine Learning Engineer. The estimated annual salary ranges from €40,000 to €85,000.
9. MLOps Engineer. Responsible for deploying, monitoring and maintaining machine learning models in production environments. This role combines expertise in machine learning, cloud infrastructure, DevOps and systems architecture. The reference salary range is €45,000 to €90,000 per year.
10. AI and Data Solutions Architect. Designs the infrastructure needed to integrate AI models, data sources and technology services at an enterprise level. This position typically requires several years of experience in software development, cloud computing or data architecture. According to the reference documentation, estimated annual earnings range from €60,000 to €110,000.

These salary figures should be treated as indicative ranges, as actual earnings vary depending on experience, location, industry, company size and the responsibilities associated with each position.

For professionals with backgrounds in business administration, marketing, consulting or management, roles such as AI Consultant, AI Business Analyst or AI Product Manager may offer a more direct career transition than highly technical positions.

Which Industries Are Hiring the Most AI Professionals?

AI-related recruitment is particularly strong in industries that handle large volumes of data or rely heavily on digital processes.

Software development and technology consulting account for a significant share of these opportunities. Companies need professionals who can develop software, connect AI models through APIs, build data architectures and implement solutions for organisations across different industries.

Telecommunications is another important sector. Network management and optimisation, cloud infrastructure and data processing create opportunities for engineers, architects and MLOps specialists.

In marketing, advertising and media, AI is used to analyse audiences, automate certain production processes, personalise communications and develop predictive models. This is creating demand for professionals who combine marketing expertise with analytics and generative AI tools.

Banking, finance and insurance use AI for fraud prevention, risk management, customer analysis and document automation. These activities also require professionals who can interpret and apply the regulatory requirements governing AI systems.

Opportunities also exist in customer service, professional services, manufacturing, public administration and healthcare, although use cases and regulatory obligations vary considerably between sectors.

Madrid, Catalonia, the Valencian Community and Aragon are identified in the reference documentation as Spanish regions with a significant presence of companies using these technologies. Public initiatives linked to Spain's National Artificial Intelligence Strategy are also helping drive activity in areas such as research, supercomputing, language technologies, digital transformation in public administration and other applications.

Skills and Tools Employers Look for in AI Professionals Today

Learning to use a specific tool can help professionals qualify for certain roles, but working in artificial intelligence requires a combination of technical expertise and human skills.

Among the most commonly sought-after technical skills is Python, widely used in data science and machine learning, alongside SQL for querying and managing databases.

Specialised positions may also require experience with frameworks and ecosystems such as PyTorch, TensorFlow, Hugging Face, LangChain and LlamaIndex. In infrastructure and business analytics, commonly used technologies include AWS, Microsoft Azure, Google Cloud, Snowflake, Databricks, Power BI and Tableau.

The importance of these tools varies depending on the role. An AI Consultant does not need the same level of proficiency in PyTorch as a Machine Learning Engineer. Similarly, a compliance specialist needs to understand how an AI system works and the risks it poses, even if they are not responsible for programming the model.

Critical thinking is particularly important. Generative AI models can produce inaccurate, incomplete or biased responses, making the ability to verify results and cross-check sources a valuable skill in virtually any role that involves using AI.

Communication, creativity, problem-solving and the ability to bridge the needs of different departments are also becoming increasingly valuable. An organisation may have access to advanced technology but still gain little value from it if its professionals cannot connect its capabilities to specific business challenges.

AI governance is another essential area of expertise. Understanding the EU AI Act, data protection requirements and risk management principles will be particularly important for professionals involved in selecting, implementing or overseeing AI systems.

What to Study to Work in Artificial Intelligence

The right educational pathway depends on the role you want to pursue and your existing qualifications. Not every career in AI requires starting from scratch with a technology-related degree.

Professionals with backgrounds in Business Administration, Economics, Marketing or Law can build on their experience through postgraduate programmes focused on business applications. The Master in AI Applied to Business, for example, explores how these technologies can be applied from a business management perspective.

Those interested in combining technical and business expertise may consider the Master in Artificial Intelligence & Machine Learning for Business. For professionals looking to develop a deeper understanding of the data lifecycle and how data can support decision-making, another option is the Master in Business Analytics & AI.

The Global Master in Business Analytics and Data Strategy may appeal to professionals interested in data strategy and business intelligence, while an Executive MBA can provide a broader business perspective for executives who need to integrate technology-related decisions into their organisation's overall strategy.

Academic education can also be complemented by hands-on projects. Developing a customer analytics model, automating a document management process or building a simple decision-support system can demonstrate the ability to apply acquired knowledge to real-world business challenges.

For professionals with corporate backgrounds, a gradual transition can also be beneficial. They can begin by familiarising themselves with generative AI models and analytics tools, then develop a deeper understanding of data and automation fundamentals before specialising in their chosen career path.

Programming skills are not essential for every AI-related job. A Machine Learning Engineer needs advanced programming expertise, whereas AI Consultants, compliance specialists and certain Product Managers primarily focus on strategy, processes, regulation and project management.

The development of artificial intelligence is therefore creating two complementary career paths: specialists who can build the technology and professionals who can apply it effectively within organisations. For those who already have business experience, learning to combine industry knowledge, data and AI tools can open up opportunities for professional specialisation across a wide range of sectors.