Over the final decade, we have invested in over 20 unicorns. The machines will take thousands and thousands of jobs—however they’re going to by no means lead like a human can

Yes, we will see disruption and job displacement — that’s inevitable. We’ve lived through the tech boom of the ’90s, the birth of the internet, cloud computing, and waves of automation over the past 35 years. Has any of this led to the predicted dystopia? Consider this: in 1991, the global unemployment rate was 5.1%. After three decades of technological revolution and exponential AI growth, the global unemployment rate in 2024 was 4.89%. If you believed only the headlines that followed every technological breakthrough of the past 35 years, you’d assume half the world would be unemployed by now. 

The truth? Technology always creates more than it destroys. 

Increased AI adoption across sectors

That same report from the WEF shows that adoption of AI is growing rapidly, albeit unevenly, across sectors. This isn’t adoption for adoption’s sake. The labor market is being driven in this direction by four powerful forces. 

● AI automation: Almost 60% of firms (nearly 85% of large firms) implemented automation over the last 12 months. 

● Economic pressures: For companies to stay competitive, they are looking for efficiency in every aspect of their operation. The use of AI is the surest and fastest way to achieve measurable increases in efficiency. 

● Green transitions: The combination of changes in climate and energy demand is causing enterprises to lean more into green technologies to slow the amount of overhead they must commit to energy. 

● Demographics: Demographic shifts are driving the need for increased roles in the caregiving industry. Aging populations need humans to help them in ways no machine can. Plus, these new and increased roles require entirely new management approaches.

These four forces are already affecting hiring pipelines, budgets, and boardroom strategy. 

Where jobs are emerging

Apart from the aforementioned care-giving sector, a historic employment boom is coming to IT and engineering. Unlike earlier tech booms, this surge is not about speculation and hype, but structural reinvention. The IDC projects AI spending will increase to $632 billion by 2028, signaling not a bubble but the emergence of sustainable growth. 

AI-native product development will come more to the forefront as we see the growth of products being enabled by AI andcompletely designed around it. AI product managers, AI UX designers, and prompt engineers are already becoming fixtures, supported by platforms like Microsoft Copilot, Salesforce Einstein, and Google Duet AI. These roles speak to the coming era of intelligent software. These are tools that learn, adapt, and anticipate. They will in turn, require builders who can manage and adapt to human needs with machine learning in real time.

The infrastructure aspect of this new age is just as transformative. AI-driven Cloud and DevOps (collectively called AIOps) will change how enterprises manage scale. New categories such as MLOps engineers, AI Cloud architects, observability engineers, and incident prediction analysts are emerging and growing in demand. The humans in these positions must be able to design systems that can anticipate failures, self-optimize, and operate with resilience at levels far beyond human monitoring. This moves the cloud from being elastic to being predictive.

There will be an increased risk associated with this growth. Cybersecurity and AI trust will be as integral to competitive advantage as innovation. As governments roll out the EU AI Act, National Institute of Standards and Technology standards, and similar regulations, companies will need AI cyber analysts, LLM red teamers, and AI risk officers to safeguard not only networks but the algorithms that drive them. Leaders whoexperience the most success now will be those who build trust into their products with as much thought and strategy as they build in features. They will understand that explainability and compliance are strategic assets.

As the growth of AI infrastructure increases, data engineers and knowledge designers will become as central as application developers once were. Enterprise knowledge ecosystems from retrieval-augmented generation (RAG) pipelines to vector databases and knowledge graphs are poised to create new categories of work. Plus, in nearly every vertical (finance, healthcare, legal, HR), AI specializations will generate hybrid roles where you not only need to master the functions of that role, but you’ll also need to be an expert in how to leverage AI to augment your duties and increase your output and efficiency. These types of positions will be drivers of industry-specific disruption.

Adaptation is non-negotiable. Software engineers must evolve into AI-assisted developers, DevOps professionals into AIOps specialists, and product managers into AI-native strategists. UX designers will focus on explainability and trust design, reshaping how people interact with intelligent systems. Those who move fastest will define the rules of the AI economy itself.

Humans have to lead

Hybrid Intelligence Operations demand executives who can create synergies between human creativity and machine execution that neither could achieve alone. AI cannot replace leadership, judgment, ethical decision-making, or vision. AI is a tool, perhaps the most powerful ever created, but it is useless without proper human oversight and leadership. 

In the arena of AI Ethics and Governance, leaders will need to serve as directors of societal responsibility. They must decide what constitutes ethical AI deployment and have the courageand backbone to stop when profit optimization crosses the line into human cost. These decisions cannot be algorithmic. They demand judgment, empathy, and ethics.

Cross-Functional Integration is becoming critical as we see traditional org charts becoming less and less relevant. Leaders have to be able to speak to and negotiate between technical, financial, regulatory, and human teams to foster solutions across age gaps, personality differences, and functional silos. 

AI can forecast trends, but only leaders can paint compelling pictures of the future that inspire teams to embrace change rather than resist it. Creating a strategic vision and being able to emotionally sell it to the team via storytelling is something no AI will ever be able to do as well as a human. Machines can execute, but they’ll never lead; humans must combine AI scale with human leadership.

How to win the future

The age of a leader delegating tasks and managing workflows no longer exists in successful businesses, as AI can handle most operational tasks. Leaders must evolve or risk becoming as automated as the roles they once managed. To do this, focus on uniquely human capabilities in your employees and hone those skills. These will be the core assets of an AI-driven world.

Begin redesigning your organization now around human skills and phase out traditional hierarchies. Drill down and find out what your people bring that is uniquely human. Double down on developing those attributes to their maximum potential. 

Then, teach and show teams that AI is a human multiplier, not a human replacement. Prove to them that technology is a competitive advantage that helps them become the most powerful version of themselves at work. Your teams need to understand not just how AI works, but how it helps them while also helping the company. The more they understand, the less they fear, and the more they buy in. 

The winning leaders of this decade will be those who recognize and show their teams that AI isn’t a threat to human jobs, it’s an augmentor of human capability. The leaders and companies that accomplish this will remember 2025-2030 not for jobs lost, but for becoming pioneers of the age of human-AI partnerships, reshaping entire industries.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.