Leading in the age of artificial intelligence means designing how people, technology, data, and capabilities work together to turn strategy into decisions, services, and real results.
The current conversation about technology leadership no longer revolves solely around incorporating new tools or accelerating projects. For years, technology leadership has been discussed in terms of technology adoption, training, and improving efficiency. Today, with the emergence of generative artificial intelligence and autonomous agents, the questions have changed: Which tasks should we rethink from the ground up? Which decisions can be delegated to technology? How do we transform the time saved into real value for people and the business? Who takes responsibility when a machine acts? And which capabilities will remain exclusively human?
The eight topics covered in this article actually point to a single, fundamental transformation: the shift from technology adoption to building organizational capabilities. Let’s dive in.
How is AI transforming work and the role of technology leadership?
The first decision for technology leadership is not which tool to buy, but which work deserves to continue to exist. When AI can draft, analyze, coordinate, and execute actions, overlaying it onto legacy processes only increases complexity. The transformation begins by reviewing, from end to end, how value is generated for the customer and what frictions can be eliminated.
Recent Data
Deloitte surveyed 501 executives responsible for strategy or the implementation of agent-based AI: 15% reported orchestrated multi-agent adoption, and 16% considered their processes ready. Cited obstacles included non-unified data, governance, trust, and integration.
McKinsey’s global survey reveals a similar gap: 44% said they were scaling AI across the entire company, while 37% attributed some positive impact on EBIT. Among top-performing organizations, a profound redesign of workflows was much more common.
What Changes for Leadership
Leaders are shifting from technology sponsors to architects of the work system. They must decide which tasks to eliminate, which to automate, which require human review, and how decision-making authority will change. This approach promotes operational simplification: fewer layers of activity and more accountability closer to the market, the process, and the customer.
The Opportunity for Europe and Spain
Europe cannot limit itself to measuring its position by the number of pilot projects. It needs to turn infrastructure, regulation, and capabilities into replicable solutions. In Spain, the AI Atlas specifically addresses the connection between capabilities, growth, public services, and digital sovereignty. The opportunity lies in industrializing high-value use cases without passing internal complexity on to the customer.
In this context, Telefónica takes on the undeniable challenge of becoming the best gateway for citizens to access digital technologies. This ambition aligns with the need to transform innovation, capabilities, and infrastructure into services that deliver real value to the customer.
Why doesn’t individual productivity with AI guarantee better business results?
AI can help a person finish a task sooner and yet still fail to improve the organization’s results. This paradox distinguishes local efficiency from business productivity. The management challenge lies in reallocating the freed-up capacity and eliminating work, controls, or meetings that no longer add value.
Recent Data
The McKinsey report puts the percentage of those who perceive an improvement in individual productivity at 80% and those who perceive better decision-making at 50%, compared to 37% who attribute a positive impact on EBIT.
One of the European Central Bank’s most recent analyses indicates that the average user reports a time savings of three hours per week. The analysis itself warns that this time only translates into productivity when the organization can repurpose the freed-up capacity.
Korn Ferry provides the human perspective: 62% report a heavier workload, 45% say they are too busy to produce meaningful results, and among workers described as fatigued by AI, 52% indicate that using it has increased their workload.
What Changes for Leadership
Measuring digital activity is not enough. Leadership must link each use case to an outcome: response time, quality, satisfaction, revenue, avoided costs, or reduced risk. Furthermore, it must close the loop: if a task is automated, what is no longer required? What oversight is eliminated? What decision is accelerated? The discipline lies in transforming savings into capacity and capacity into better service.
The Opportunity for Europe and Spain
For Europe, productivity is inseparable from competitiveness. For Spain, the useful metric is not how many people use AI, but which processes have been verifiably improved. In service-intensive sectors, technology must reduce customer effort, improve resolution, and strengthen trust; otherwise, internal savings may remain invisible outside the organization.
How should agentic AI be governed when it can execute decisions and actions?
With agentic AI, technology is no longer limited to making recommendations; it begins to take action. Leadership must govern identities, access, tools, memory, traceability, and the conditions for human intervention. Responsibility cannot be diluted among the provider, the model, the platform, and the user department.
Recent Data
Morgan Stanley notes that 68% of S&P 500 companies had at least one non-executive director with expertise in AI, although only 24% had three or more. Approximately 90% of the 200 executives surveyed had assigned or were in the process of assigning governance responsibilities.
Microsoft describes an evolution from static model review toward continuous governance of connected systems: agent identity, permissions, monitoring, and controls throughout the lifecycle.
In the European Union, transparency obligations linked to Article 50 of the AI Regulation reinforce the need to pay special attention to certain interactions and synthetic content.
What Changes for Leadership
Effective governance should not be an additional brake applied at the end. It is an architecture of trust that enables calculated risks to be taken: process ownership, limits on action, oversight proportional to impact, logging of actions, incident management, and the ability to reverse decisions. Governing bodies must help define the acceptable level of risk; the business is accountable for the outcome; technology ensures operation and security; and people retain authority at critical junctures.
The Opportunity for Europe and Spain
Europe can turn its regulatory demands into an advantage if it reduces ambiguity and creates reusable mechanisms. Spain has institutions and frameworks for experimentation that can facilitate this transition. The challenge is to avoid two extremes: documentary compliance without real oversight, or paralyzing caution without operational learning.
What Role Do Middle Managers Play in AI-Driven Transformation?
Organizations are seeking flatter structures while simultaneously needing more alignment between strategy, technology, and practice. And here lies the paradox: we remove layers to simplify the organization, but we run the risk of eliminating precisely those who bridge the gap between management’s “what we want to achieve” and the teams’ “what I need to do differently tomorrow.”
Recent Data
Korn Ferry highlights three findings in one of its latest articles: 42% of the organizations surveyed had reduced the number of management positions; 55% of the remaining managers reported feeling exhausted; and 52% of employees stated that their manager encouraged experimentation.
McKinsey observed negative consequences associated with AI more frequently among middle managers and individual employees than among executives and senior leaders: 47% versus 31%. The European Central Bank, meanwhile, ranks managers among the heavy users who report saving the most time.
What Changes for Leadership
Leadership ceases to add value by controlling information and gains relevance by creating context. Its new role is to reduce noise, prioritize, resolve conflicts between metrics, facilitate safe experimentation, monitor overload, and foster autonomy. Simplification does not mean removing people without redesigning decision-making processes; it means bringing accountability closer and providing the tools to exercise it.
The Opportunity for Europe and Spain
In large European organizations, flattening the organizational structure without clarifying responsibilities can increase processing times, the need for escalation, and risk. The redesign must distinguish between bureaucratic coordination and hands-on leadership. For Spain, where many companies combine mature structures with rapid transformation, the quality of leadership can determine whether AI becomes an operational improvement or another source of pressure.
Which human skills gain value with generative artificial intelligence?
The more accessible it becomes to generate content, analysis, or code using generative artificial intelligence, the scarcer it becomes to know which question matters, which answer is sufficient, and which consequences are acceptable. Human value does not disappear—it shifts. It moves from initial production toward selection, contextualization, review, and accountability.
Recent data
The inter-agency report published by the ILO notes that AI increases the demand for higher-order cognitive skills, socio-emotional competencies, data literacy, and AI literacy. It also describes a shift toward contextualization, review, oversight, and judgment.
The European Central Bank highlights an additional barrier: approximately half of non-users indicate that better training and a clearer understanding of the benefits would encourage adoption.
What Changes for Leadership
Human leadership will no longer be valued for competing with AI in terms of speed. Leadership must treat culture and skills as productive infrastructure. This involves teaching people to verify information, acknowledge uncertainty, assess risks, communicate decisions, and collaborate with automated systems. Trust is not demanded—it is built through competence, transparency, consistency, and the real possibility of intervention.
The Opportunity for Europe and Spain
Europe needs technological talent, but it also needs business professionals capable of working with AI without sacrificing their judgment. Spain can leverage its educational, business, and service sectors if it connects training with real-world problems, mobility, and new responsibilities. Attracting talent and reskilling are not separate agendas: together, they underpin competitive capacity and cohesion.
Why Has Technological Sovereignty Become a Business Decision?
Technological sovereignty is no longer a conversation reserved for governments. Every choice regarding the cloud, model, data, network, or provider defines dependencies, jurisdictions, portability, and the ability to substitute. For business leadership, technological architecture increasingly resembles a strategic supply chain.
Recent Data
DFRLab describes a transatlantic digital trust deficit and a gap between Europe’s ambition for autonomy and its technical, financial, and administrative capabilities. It also warns that a rapid separation from U.S. providers is unrealistic.
UN ESCAP identifies digital networks as strategic infrastructure for productivity, public services, competitiveness, and resilience.
The Spanish AI Atlas connects domestic capabilities, infrastructure, training, rights, and digital sovereignty. For Telefónica, digital sovereignty is also viewed as a challenge related to competitiveness, security, regulation, talent, and infrastructure.
In this context, contributing to Europe’s technological sovereignty is part of a vision in which networks, infrastructure, innovation, and competitive capacity take on strategic value for European digital development.
What Changes for Leadership
The answer lies in decision-making autonomy: identifying critical dependencies, diversifying where reasonable, demanding portability, protecting sensitive data, ensuring continuity, and evaluating the cost of change. Leadership must combine technological ambition with economic rigor and resilience.
The Opportunity for Europe and Spain
Europe needs scale, investment, and interoperable infrastructure so that its trust framework can also be a competitive proposition. Spain can play a significant role through its connectivity, data centers, computing capabilities, talent ecosystem, and geographic location, but this opportunity depends on sustained execution and European collaboration.
How Does AI Change the Decision Between Developing or Purchasing Technology?
AI introduces a new economic unit into management: inference capacity consumed per task. Cost, latency, energy, accuracy, security, and vendor lock-in are beginning to compete within the same decision-making process. This may reopen the debate over whether to buy a solution, build it, or combine both approaches.
Recent Data
32% of those surveyed by McKinsey had ruled out purchasing certain functionality because they could build it in-house using programming tools. About 20% indicated that AI operating costs were limiting its use.
Deloitte also points to budgetary tensions between investing in people and investing in tokens and infrastructure. For now, the evidence supports treating this shift as a possibility, not as a trend that can be implemented in the short term.
What Changes for Leadership
Leadership must avoid two oversimplifications: always buying for speed or always building for control. The decision requires calculating total cost of ownership, learning speed, differentiating data, dependency risk, internal capacity, and strategic value. The mature metric will not be cost per token, but rather cost per useful and governed outcome.
The Opportunity for Europe and Spain
For Europe, the build vs. buy debate also involves data residency, portability, and supplier availability. For Spain, it can open up opportunities for integrators, developers, and specialized SMEs, provided there is aggregate demand, talent, and access to infrastructure. Financial discipline and sovereignty converge here in a single decision.
How is AI changing the learning and development of junior professionals?
Routine tasks have historically served a dual purpose: to generate work and to develop judgment. If AI takes over drafting, research, classification, or documentation, junior professionals can move more quickly to validation and decision-making activities without having followed the same learning path.
Recent evidence
The ILO report warns that the automation of entry-level tasks may accelerate exposure to jobs that require validation, context, judgment, and responsibility. It also notes that qualifications based solely on task execution may become obsolete more quickly and weaken traditional entry pathways.
Furthermore, competition for talent is no longer limited to job portals; it begins earlier—with the organization’s customer service, reputation, and the experience it offers. While this does not in itself prove the trend toward seniorization, it helps contextualize the transformation of career paths within a broader competition to attract and develop talent.
What Changes for Leadership
The answer should not be to retain low-value tasks out of nostalgia. A new learning ladder must be designed: simulations, case studies, mentoring, job rotation, observing experts, deliberate practice, and progressive responsibility. The goal is to develop judgment more quickly without hiding risk or compromising quality.
The Opportunity for Europe and Spain
Europe and Spain need to bring in qualified talent while also expanding the pool of people capable of working with AI. If the entry point disappears, the shortage of expert profiles will increase. Companies that turn the workplace into a learning environment will have an advantage in recruitment, mobility, and retention.
What Is the New Leadership Contract in the Age of AI?
These eight points do not constitute a list of independent trends. They describe “a new contract” for leadership in the face of technology that is advancing faster than organizations’ ability to redesign work, responsibilities, decision-making, and culture. A “new contract” that establishes an intimate and immediate relationship between strategy, technology, and people:
- Strategy determines where we want to create value, what capabilities we need, and what dependencies we are willing to accept.
- Operational design simplifies work, eliminates tasks that no longer add value, and brings decisions and accountability closer to where results are produced.
- Governance enables organizations to take calculated risks and move forward quickly without sacrificing traceability, control, and trust.
- Leaders and managers are gradually shifting from being mere transmitters of instructions to becoming translators of strategy: they provide context, set priorities, develop capabilities, and foster autonomy.
- People contribute what becomes increasingly valuable as AI capabilities grow: judgment, context, accountability, creativity, and the ability to make decisions in the face of ambiguity.
- AI infrastructure and economics determine which capabilities can scale in a sustainable, resilient, and competitive manner and what technological dependencies we are building.
Ultimately, leading today means less about controlling what people do and more about creating the conditions for people and technology to work together, turn strategy into action, and generate real results. And this goes far beyond the simple adoption of generative artificial intelligence.
Frequently Asked Questions
What is technological leadership in the age of AI?
It is an approach that goes beyond simply incorporating tools and focuses on redesigning work, responsibilities, decisions, and capabilities to turn technology into real results.
Why doesn’t AI automatically increase a company’s productivity?
Because saving time on an individual task does not guarantee a better organizational outcome. The freed-up capacity must be repurposed, and activities, controls, or meetings that no longer add value must be eliminated.
What does a company need to govern AI agents?
It must define, among other elements, identities, access, permissions, traceability, operational limits, human oversight, incident management, and accountability for results.
What human skills will be most important in the age of artificial intelligence?
Judgment, context, review, accountability, the ability to recognize uncertainty, and decision-making become increasingly important as AI takes over part of the initial production process.
What is the relationship between artificial intelligence and technological sovereignty?
Decisions regarding the cloud, models, data, networks, and agents create dependencies and affect issues such as portability, jurisdiction, continuity, and the ability to replace technology.
How might AI affect entry-level roles?
The automation of initial tasks may accelerate their exposure to activities that require validation, context, and judgment, making it necessary to design new pathways for learning and accountability.
