There is a question that should be on the agenda of every executive committee, regardless of the industry:
Are we developing talent at the same speed at which we are adopting new technologies?
In most organizations, the answer is still no. And that gap—more than a lack of capital, infrastructure, or technological solutions—is becoming the real bottleneck of any transformation.
The pressure continues to increase. According to the BCG AI Radar 2026, organizations expect to double their investment in AI during 2026, increasing it from approximately 0.8% to 1.7% of revenue. In addition, 72% of the CEOs surveyed say they are already the primary decision-makers for matters related to this technology. AI is no longer a project owned exclusively by the technology department; it has become a transformation of the business, its culture, risks, and talent.
The question, therefore, is no longer simply how much we will invest in technology, but whether our organizations have the people, processes, and leadership required to turn that investment into better decisions, greater productivity, and new sources of value.
In this edition, we explore how this gap is affecting industries and the capabilities, profiles, and leadership styles organizations need to adapt, manage change, and convert their investments into sustainable value.
When Technology Advances Faster Than People
The industry does not matter: banking, manufacturing, healthcare, logistics, retail, telecommunications, or energy. The same pattern is repeated.
Organizations acquire new platforms, automate processes, create innovation labs, implement artificial intelligence models, and announce ambitious digital roadmaps. Months later, they discover that the greatest obstacle was not whether the tool worked, but the organization’s ability to adopt it.
One statistic illustrates this clearly. In 2025, BCG found that only 5% of companies were generating value from AI at scale, while another 35% were beginning to scale it and the remaining 60% were obtaining little or no material value. In addition, two-thirds of major technology programs, across all industries, fail to meet their original schedules, budgets, or scope for various reasons:
- The data was not sufficiently organized.
- Processes were still designed for a previous reality.
- Responsibilities were unclear.
- Incentives continued to reward traditional behaviors.
- Teams did not understand how their roles would change, and leaders were unable to explain clearly why the transformation was necessary.
Installing a tool is relatively simple. Transforming how an organization works, makes decisions, learns, and interacts with its internal and external customers is much more difficult.
The reason is not that the technology does not work. It is that installing a tool is not the same as transforming an organization.
“True innovation does not occur when we install a new tool, but when people begin using it to make better decisions.”
That is why every technology initiative should begin with a different sequence:
Problem we want to solve → process that must change → decisions that must improve → data required → enabling technology → human capabilities → adoption and governance → measurable outcome.
This sequence prevents one of the most common mistakes in business transformation: starting with the tool and then looking for somewhere to use it.
A Revealing Case Study: The Energy Industry
To understand the scale of this challenge, we need only look at the energy industry, a sector undergoing one of the most profound transformations in its history.
This transformation is taking place at a unique moment: the energy transition is coinciding with the acceleration of the digital era. Two forces that once moved along separate paths are now converging and reinforcing each other.
- On the one hand, renewable energy, energy storage, distributed generation, electric mobility, and industrial electrification are changing the way we produce and consume energy.
- On the other, data, automation, artificial intelligence, and digital platforms are redefining asset operations, business models, risks, and decision-making.
This convergence is also transforming the labor market. Electricity is already the largest source of employment within the global energy sector.
- Since 2019, the power sector has created approximately 3.9 million jobs, representing almost three-quarters of total energy employment growth.
- In 2024, global energy employment reached 76 million people and grew by 2.2%, almost twice the growth rate of employment across the broader economy.
However, the progress of investments and projects is encountering an increasingly evident constraint: a shortage of people with the capabilities required to respond to this new reality.
Approximately 60% of the companies surveyed by the International Energy Agency indicated that labor shortages were already putting project schedules, system reliability, and cost control at risk.
Electricians, operators, power-line workers, installers, pipefitters, and engineers are needed. But organizations also require data analysts, cybersecurity professionals, energy-storage specialists, meteorologists, regulatory experts, and leaders capable of coordinating multidisciplinary projects.
This case is not an exception. It is a reflection of what is already happening—or is about to happen—in manufacturing, logistics, healthcare, finance, and technology: technological disruption is advancing faster than our ability to prepare people to navigate it.
Technology is increasing the industry’s potential capacity, but it is also increasing both the demand for labor and the complexity of that demand.
Talent Must Be Considered Critical Infrastructure
For decades, companies have treated talent primarily as a human resources function. In an industry undergoing transformation, that perspective is insufficient.
Talent must be managed as critical infrastructure, with the same rigor used to manage physical assets, technology, liquidity, and operational continuity.
A company may have capital, permits, equipment, and contracts. But without the people needed to design, build, operate, and maintain its projects, investments will be delayed, become more expensive, or lose value.
Demographic trends are not helping either. In advanced economies, there are 2.4 workers approaching retirement for every young person under the age of 25 entering the energy sector. It is estimated that, by 2035, two out of every three new hires in the energy industry will be needed simply to replace retirees rather than support growth.
This means the challenge is not only to attract new talent. It also involves preventing the loss of knowledge accumulated over decades.
Organizations need to identify critical positions in advance, document decisions and exceptions, create mentoring mechanisms, develop successors, and combine the operational experience of senior employees with the digital fluency of younger generations.
Knowledge transfer can no longer be left to informal conversations during the final weeks before someone retires.
New Profiles: The End of Isolated Specialization
A similar shift is occurring in almost every industry undergoing transformation: exclusively technical roles, isolated within a single discipline, are becoming less effective.
This does not mean specialization is no longer important. On the contrary, technical depth remains essential. What has changed is that this depth must be complemented by the ability to understand and collaborate with other disciplines. Organizations now need hybrid profiles: people capable of connecting several disciplines at the same time.
Organizations need professionals with T-shaped profiles: people with deep knowledge in one field and sufficient breadth to identify interdependencies, ask the right questions, and translate between technical, commercial, financial, regulatory, and human perspectives.
In the energy sector, for example, the following profiles are becoming increasingly relevant:
- The energy systems and data engineer, who understands physical assets but also masters analytical tools, forecasting, and data quality.
- The operational cybersecurity specialist, who can protect industrial networks and SCADA systems, assess risks, and explain their implications to nontechnical audiences.
- The market, regulation, and technology analyst, who connects energy scenarios, business models, regulation, and investment.
- The digital reliability engineer, who combines maintenance, sensors, the Internet of Things, digital twins, and asset management.
- The digital adoption leader, who understands technology but also knows how to redesign processes, manage resistance, facilitate conversations, and measure benefits.
The same logic appears in other industries. In banking, it emerges at the intersection of risk, finance, and data science. In healthcare, it appears between medicine, patient experience, and technology. In manufacturing, it connects operations, automation, sustainability, and cybersecurity.
The common denominator is not knowing everything. It is having the ability to connect specialized knowledge and turn it into understandable decisions.
Human Capabilities That Gain Value in the Age of AI
The World Economic Forum estimates that 39% of workers’ core skills, across all industries, will change or become obsolete between 2025 and 2030.
Out of every 100 workers, 59 will need some form of training before then. Twenty-nine could be upskilled in their current positions, 19 would require reskilling and reassignment, and 11 would be at risk of not receiving the training they need. In addition, 63% of employers consider skills gaps to be the primary obstacle to transforming their organizations.
At first glance, it might appear that the solution is simply to develop more technical capabilities. However, something apparently paradoxical is happening: the more automation advances, the more valuable genuinely human capabilities become.
- Critical thinking — to question incomplete data or biases in a model.
- Systems thinking — to understand how the different parts of a business are connected.
- Communication — to translate complex information into clear executive decisions.
- Adaptability — to modify the plan when conditions change.
- Judgment under uncertainty — to make decisions without waiting for perfect information.
- Leadership — to mobilize people, build trust, and give meaning to change, especially during periods of transformation.
- Social influence — to build relationships, align interests, and encourage others to commit to a shared vision, even without formal authority.
PwC found that new tasks added to jobs most exposed to AI are 2.5 times more likely to require empathy, judgment, and creativity. In entry-level positions with the greatest exposure to AI, demand for leadership and strategic thinking is also increasing.
Artificial intelligence can retrieve information, summarize documents, identify patterns, and generate alternatives. But people are still responsible for deciding which problems deserve attention, assessing the reliability of the answers, understanding the context, anticipating consequences, and accepting responsibility for decisions.
Technical competencies allow people to participate in the transformation; human capabilities allow them to lead it.
Leadership Must Also Transform
This is perhaps the most important point for anyone leading teams today, regardless of the industry in which they work.
The traditional leadership model was built around experience, authority, control, and compliance with procedures. These dimensions remain necessary, particularly in industries where safety, reliability, and operational discipline are essential.
But they are no longer sufficient.
The leader of an organization undergoing transformation cannot assume that he or she has all the answers. Leaders must be able to ask better questions, integrate different perspectives, and honestly acknowledge what is not yet known.
They cannot limit themselves to communicating decisions. They must listen, explain uncertainty, confirm understanding, and create spaces where specialists can challenge assumptions.
Nor can they treat every error as a failure that must be hidden or punished. They must distinguish between negligence and responsible experimentation, using unexpected results as information from which to learn.
Transformational leadership involves:
- Creating direction without pretending to have certainty.
- Connecting areas that have traditionally worked separately.
- Developing people rather than merely supervising results.
- Building trust so teams can express doubts and raise risks.
- Facilitating responsible experimentation.
- Aligning purpose, technology, and strategy.
- Turning real projects into opportunities for learning.
Psychological safety does not mean lowering standards or avoiding difficult conversations. It means combining high expectations with a genuine ability to ask questions, disagree, acknowledge mistakes, and report promptly when a tool or process does not reflect operational reality.
Teams must feel able to say that a model is wrong, that data is unreliable, that an executive decision overlooks a field constraint, or that an automation is creating a new risk.
Leadership begins long before someone gives us a formal position. It begins with the way we listen, treat people, and choose to participate in transformations.
From Delivering Courses to Building Learning Organizations
Another necessary change is abandoning the idea that developing talent is equivalent to delivering courses.
Courses are important, but they do not produce transformation on their own. Learning must be integrated into everyday work through projects, rotations, mentoring, communities of practice, feedback, and experimentation.
The 70-20-10 model provides a useful reference for designing this process. According to this approach, approximately 70% of learning occurs through real experiences and challenges in the workplace; 20% occurs through interactions with other people, including leaders, colleagues, mentors, and coaches; and the remaining 10% occurs through courses, workshops, and structured training programs.
These percentages should not be interpreted as a rigid formula, but as a reminder that capabilities are developed primarily when people have opportunities to apply what they have learned, receive feedback, and take on progressively more complex challenges.
From this perspective, capability development can combine different mechanisms:
- Upskilling, to incorporate data, artificial intelligence, sustainability, or cybersecurity into a person’s current role.
- Reskilling, to prepare someone for a different role.
- Reverse mentoring, combining the operational experience of senior personnel with the digital capabilities of younger professionals.
- Rotations, to expose people to the interdependencies among operations, commercial functions, regulation, finance, and technology.
- Transformational projects, used as structured learning environments.
- Microcredentials, allowing people to update their knowledge through short, verifiable, and stackable modules.
- Coaching, particularly to help technical leaders develop listening, communication, delegation, and change-management skills.
The effectiveness of training should not be measured solely by the number of hours delivered or the number of participants. Metrics should demonstrate whether people achieved competence, whether internal mobility increased, whether critical vacancies decreased, whether tool adoption improved, and whether measurable results were achieved in productivity, reliability, safety, or retention.
Ultimately, a learning organization is not one that offers more courses, but one that turns work, challenges, and collaboration among its people into permanent opportunities for development.
A Concrete Agenda for Companies
Talent transformation cannot be delegated exclusively to the human resources department. It requires the direct involvement of leaders from the business, operations, technology, and strategy functions.
Every organization should begin with five decisions.
- First: accompany every technology investment with a capability-development plan. The business case should explain which tasks will change, which decisions will be different, which people will require training, and which behaviors will be necessary to achieve results.
- Second: map capabilities, not only positions. Job titles change slowly, but the tasks within those positions are changing rapidly. Understanding transferable capabilities makes it possible to identify people who can grow or transition internally.
- Third: protect critical knowledge. Organizations must anticipate retirements, departures, and generational shifts through mentors, successors, documentation, and communities of practice.
- Fourth: develop leaders for adoption. Transformation requires managers who can explain the purpose, listen to concerns, manage resistance, and connect strategy with everyday work.
- Fifth: build external partnerships. No company can close all its capability gaps on its own. Closer relationships are needed with universities, technical institutes, governments, regulators, suppliers, professional associations, and multilateral organizations.
For Latin America and the Caribbean, the challenge is particularly relevant. The Inter-American Development Bank warns that the region faces a considerable gap between the capabilities currently available and those required to respond to the green and digital transformation. Its study, Skills to Advance Sustainability: Lessons Learned from Latin America and the Caribbean, proposes bringing together public policies, companies, and education systems to develop the human capital required for the transition.
The speed of technological change exceeds traditional curriculum-update cycles. Therefore, the most effective solutions will be modular, flexible, and closely connected to workplace realities: dual education, internships, laboratories, instructor training, recognized certifications, and projects developed with real teams.
Conclusion
The transformation of an organization will not depend solely on the availability of technology, capital, or infrastructure, but on its ability to develop the people who must understand, execute, and lead it.
We can acquire platforms, automate processes, incorporate artificial intelligence, and hire the best consultants. However, none of these investments transforms an organization on its own. True transformation occurs when people use these tools to make better decisions, redesign processes, collaborate in new ways, and generate measurable results.
For this reason, talent can no longer be considered exclusively a human resources responsibility. It must be managed as critical business infrastructure. Every technology investment must be accompanied by a plan for capability development, adoption, knowledge transfer, and leadership. Similarly, every business strategy must ask not only what technology needs to be adopted, but also which people and capabilities must be developed to obtain the expected value from it.
The challenge is not to choose between technical and human capabilities. Organizations will need both: technical depth to understand and take advantage of new tools, and critical thinking, communication, adaptability, judgment, and leadership to use them responsibly in increasingly complex contexts.
Therefore, the question executive committees should be asking is not only how much they will invest in technology over the next few years. They should also ask:
Are we developing talent at the same speed at which we are transforming our business?
The answer will determine which organizations merely adopt new technologies and which are capable of turning them into productivity, innovation, trust, and sustainable value.
Closing this gap is also not a task that companies can undertake in isolation. It requires the creation of genuine collaborative ecosystems among companies, universities, technical institutes, governments, and other stakeholders capable of connecting education and training with the real needs of the labor market.
Because, in the end, technology can accelerate transformation, but people are the ones who give it direction, meaning, and purpose.
References
Boston Consulting Group, AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value (survey of 1,000 CxOs in 59 countries, October 2024).
https://www.bcg.com/press/24october2024-ai-adoption-in-2024-74-of-companies-struggle-to-achieve-and-scale-value
IEA, World Energy Employment 2025 (Executive Summary, December 2025).
https://www.iea.org/reports/world-energy-employment-2025/executive-summary
World Economic Forum, Future of Jobs Report 2025 (survey of more than 1,000 employers across 55 economies, January 2025).
https://www.weforum.org/publications/the-future-of-jobs-report-2025/digest/
https://www.weforum.org/publications/the-future-of-jobs-report-2025/in-full/3-skills-outlook/
Jin, H., Peng, Y., and Limongi, R. (2024). The Impact of Team Psychological Safety on Employee Innovative Performance: A Study with Communication Behavior as a Mediator Variable. PLOS ONE.
https://pmc.ncbi.nlm.nih.gov/articles/PMC11524454/
Empowering the Workforce in the Context of a Skills-First Approach.
https://www.oecd.org/en/publications/empowering-the-workforce-in-the-context-of-a-skills-first-approach_345b6528-en.html
Skills for Work in Latin America and the Caribbean: Unlocking Talent for a Sustainable and Equitable Future. Second Edition.
https://publications.iadb.org/en/skills-work-latin-america-and-caribbean-unlocking-talent-sustainable-and-equitable-future-second