AI for executives: Applications, skills and training

Artificial Intelligence is already part of the decisions made by many Spanish companies. According to the latest data published by the Spanish National Statistics Institute (INE), 21.1% of Spanish companies with 10 or more employees were using Artificial Intelligence technologies in the first quarter of 2025. This figure shows the extent to which understanding these tools is becoming part of the responsibilities of professionals who manage teams, budgets and strategies. For executives, the challenge lies in understanding where AI can add value, what risks it involves and how to integrate it into business decision-making.

The impact of Artificial Intelligence for executives on business strategy

AI for executives expands the ability to analyse information before making a decision. Predictive models make it possible to work with large volumes of data, detect patterns and develop scenarios that would be difficult to identify through exclusively manual processes. This can be applied to areas as diverse as forecasting demand, reviewing costs, identifying risks or analysing customer behaviour.

Business adoption is progressing rapidly. The 2025 State of AI report by McKinsey indicates that virtually all organisations surveyed were using AI and that 62% were already experimenting, at a minimum, with Artificial Intelligence agents. Even so, around two thirds had not yet begun scaling AI across the organisation, highlighting an important difference between testing a technology and turning it into a business capability.

This scenario is also changing the responsibilities of management teams. Introducing Artificial Intelligence requires determining which problems are worth solving with AI, how to measure the return on investment, what information can be used and which decisions should remain under human supervision.

For this reason, education is beginning to incorporate a perspective that connects technology and management. The Master in AI Applied to Business, for example, addresses the application of Artificial Intelligence to strategy, decision-making and business innovation from a perspective designed for professionals.

The strategic question, therefore, should not be reduced to deciding which tool to implement. An AI strategy requires specific business objectives, clearly defined responsibilities, indicators for measuring results and a governance system capable of controlling risks throughout the solution’s entire lifecycle.

Real-world applications of AI in business management

The applications of AI in business management extend across virtually the entire value chain. Its usefulness depends on the quality of the available data, the process being improved and the organisation’s ability to incorporate its outputs into everyday work.

Some examples that illustrate how Artificial Intelligence is being applied across different areas of an organisation include:

• Finance: cash flow forecasting, anomaly detection, risk analysis and the automation of certain administrative processes.
• Marketing and sales: audience segmentation, customer behaviour analysis, personalisation and support for sales forecasting.
• Human Resources: information organisation, document management and support for certain recruitment and assessment processes, taking into account the applicable regulatory requirements.
• Operations and logistics: demand forecasting, inventory optimisation, route planning and predictive maintenance.
• Customer service: request classification, conversational assistants and automated responses to recurring enquiries.

In finance, for example, AI can facilitate anomaly detection, risk analysis, forecasting and the automation of certain administrative processes. In marketing and sales, it can segment audiences, analyse behaviours, personalise content and identify consumption patterns. Operations teams can use predictive models to estimate demand, optimise inventories or anticipate maintenance requirements.

In Human Resources, applications include information classification, document management and certain recruitment and assessment processes. This area requires particular attention. Regulation (EU) 2024/1689, known as the Artificial Intelligence Act or AI Act, establishes a risk-based approach and classifies certain uses related to employment and worker management as high-risk.

European regulation therefore requires legal and ethical considerations to be incorporated into technological decisions. Not all AI applications have the same consequences or are subject to identical requirements. An informational chatbot, an algorithm used to select candidates and a predictive maintenance system present very different scenarios.

There are also differences in the recommended degree of automation. In certain tasks, AI can generate an initial proposal that is subsequently reviewed by a professional. In others, it can perform routine operations under previously defined rules. When decisions have significant consequences for people or companies, human oversight becomes particularly important.

For professionals who need to understand both data and its strategic application, the Master in Business Analytics & AI provides the opportunity to deepen their knowledge of analytics, data governance and tools designed to transform business information into insights for decision-making.

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Benefits of AI for business management: productivity examples

One of the reasons behind the growth of Artificial Intelligence within organisations is its ability to reduce the time spent on information-intensive tasks. Preparing drafts, summarising documentation, classifying data, locating internal information or generating initial versions of certain analyses are activities in which current tools can deliver productivity improvements.

Results depend on the use case. An international Google Cloud study involving 2,500 senior executives found that 74% of the organisations analysed were achieving a return on their Generative AI investments. The same study indicated that 84% were able to take a Generative AI use case from idea to production within six months.

In practice, productivity improvements can be observed across different business tasks:

• Hours required to prepare a report.
• Average customer response time.
• Error rate in an administrative task.
• Accuracy of demand forecasts.
• Cost per automated process.
• Time spent locating information.
• Increase in revenue attributable to the use case.

These results do not mean that every investment in AI automatically generates a return. Productivity improvements arise when there is a clear relationship between the tool and a business problem. Automating a low-value task may save time without having a significant impact on the company’s overall results.

A simple example can be found in report preparation. A team may use AI to gather and organise information from different sources and then spend more time interpreting it. The value for management lies in freeing up time for activities with a greater analytical component, not simply in how quickly the document can be generated.

A similar situation applies to customer service. Conversational assistants can resolve repetitive enquiries and classify requests before forwarding them. In operations, predictive models can help anticipate changes in demand. In commercial departments, they can prioritise information and identify patterns that support professionals in their work.

The Master in Artificial Intelligence & Machine Learning for Business explores this connection between the technical capabilities of Artificial Intelligence and their application to challenges in marketing, sales, finance, operations, Supply Chain Management and Human Resources.

Measurement is essential in all these cases. Before implementing a solution, it is advisable to establish indicators such as hours saved, error reduction, revenue increases, maintenance costs or user satisfaction. Returns should be assessed against the full cost of implementing and maintaining the system, including data processing, infrastructure, training and supervision.

Skills needed to lead Artificial Intelligence projects

Leading an AI initiative does not require every member of a management committee to become a programming specialist. It does, however, require a sufficient level of AI literacy to understand the possibilities and limitations of the systems being used.

This capability has also acquired a regulatory dimension. Article 4 of Regulation (EU) 2024/1689 establishes obligations related to AI literacy for providers and deployers. These provisions began to apply on 2 February 2025.

Some of the competencies that are becoming particularly important for executive profiles include:

• AI literacy: understanding capabilities, limitations and potential errors.
• Business judgement: connecting each application with a measurable objective.
• Data governance: understanding what information the system uses and under what conditions.
• Return assessment: comparing results against total costs.
• Risk management: identifying legal, operational and reputational impacts.
• Human oversight: determining which decisions require review.
• Change Management: preparing teams and redesigning processes.

The Spanish Agency for the Supervision of Artificial Intelligence (AESIA) also provides 16 practical guides to support the implementation of and compliance with the European AI Act. These include documents covering risk management, human oversight, data governance, transparency, cybersecurity, record-keeping and incident management.

For executives, understanding these areas enables more precise conversations with technology, legal, financial and Human Resources departments. Managing an AI project is necessarily cross-functional, as its consequences can simultaneously affect processes, people, data, security and financial results.

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Why study an executive master’s degree in Artificial Intelligence?

The speed at which new tools emerge can create the impression that studying Artificial Intelligence simply means learning how to use the latest applications available. For executive profiles, education provides greater value when it develops judgement and principles that remain relevant even as technology changes.

A specialised programme can help you understand how to select use cases, evaluate suppliers, interpret data-driven business models, calculate returns and establish governance mechanisms. It also enables you to develop a common language with technical specialists without needing to take on their responsibilities. When considering this specialisation, it is also useful to understand the requirements and career opportunities of a master’s degree in Artificial Intelligence, particularly in order to identify which skills are developed and how they can be transferred to a professional environment.

This combination is particularly relevant because many organisations are still at an early stage of adoption. McKinsey’s 2025 data shows that, although business use of AI is already widespread, scaling initiatives and achieving impact across the organisation remain significant challenges.

Executive education also makes it possible to analyse projects from different perspectives. An Artificial Intelligence decision may be technically viable while simultaneously raising issues related to data quality, privacy, security, regulation or profitability. Executive judgement involves integrating all these dimensions before making a decision.

The objective should not be to accumulate technical knowledge in isolation. For professionals in positions of responsibility, it is more useful to learn how to ask the right questions: what problem are we trying to solve, what data does the system need, how will we verify its results, who will be responsible if an error occurs and what mechanisms will allow us to stop or correct it?

Prepare for the future with executive programmes

Artificial Intelligence is expanding the range of variables that executives need to manage. Strategy, finance, operations and people management now coexist with issues related to algorithms, data, automation, cybersecurity and regulation. The advantage does not lie in adopting every available technology, but in knowing which ones make sense for each organisation.

This judgement is particularly important within the European regulatory context. The Artificial Intelligence Act entered into force on 1 August 2024 and establishes a progressive implementation timeline. Provisions relating to AI literacy and certain prohibitions began to apply in February 2025, while other obligations are being introduced gradually. Organisations should therefore review the timetable applicable to each system and use case rather than assume a single compliance date.

Developing these competencies is also part of a broader trend towards continuous professional development among those in positions of responsibility. Executive education provides different alternatives for updating knowledge related to management, leadership and business transformation.

For professionals seeking a broad management perspective, the Executive MBA integrates areas such as corporate strategy, leadership, finance, marketing, operations, digital transformation and people management. AI can therefore be understood as part of the wider set of decisions that shape business management rather than as an isolated technology initiative.

The evolution of Artificial Intelligence requires processes and knowledge to be reviewed more frequently, but one core management responsibility remains unchanged: deciding how to allocate resources and assume risks in order to achieve the organisation’s objectives. Studying AI for executives means developing the judgement required to govern technology, measure its contribution and maintain human responsibility for decisions that affect both businesses and people.