The AI impact: three trends shaping the future of work

AI is becoming a foundational technology, comparable with the steam engine, electricity and the internet, with the potential to transform every industry and reshape workforce strategies. Historically, such innovations have led to both job creation and disruption. For example, the rise of ATMs did not eliminate bank teller positions; instead, it recalibrated our expectations about machine roles and the tasks that still need a human touch.

The World Economic Forum published the Future of Jobs Report¹ in January 2025, highlighting that 77% of surveyed employers recognize the need for reskilling and upskilling their workforce through 2030 to foster effective collaboration with AI. Companies are increasingly prioritizing skills such as technological literacy; creative thinking; and knowledge of AI, big data and cybersecurity.

AI will redefine productivity, emphasizing human-machine collaboration, wellbeing, and the creation of new knowledge and experiences. This shift will impact every level of organizations —how they work, recruit talent, define roles and manage teams.

Harnessing AI for organizational excellence: a new leadership paradigm

In our AI-powered future, productivity will undergo a radical transformation — not just in efficiency, but in how humans and AI collaborate to unlock new value. While much of the dialogue surrounding AI focuses on specific tasks, the broader implications for leaders are equally significant as they adapt to rapidly evolving work.

As organizations adapt to this seismic shift, leaders must confront critical questions: How are they addressing AI anxiety among their teams and fostering a culture of continuous learning? What strategies are in place to equip employees with the tools and training necessary to thrive in an AI-driven landscape? Should AI tools be used for skill-gap analyses or mentorship programs to enhance employee development?

Organizations will increasingly rely on a diverse array of executives for strategic insights, leading to a reimagining of C-suite roles or the creation of new positions. The emphasis will shift toward cultivating a new ecosystem of human-centric organizations and agentic AI entities that operate independently. This transformation will require leaders to rethink traditional business models and collaboration frameworks, fostering partnerships that leverage the strengths of both human ingenuity and autonomous AI capabilities.

Imagine a chief productivity officer — an orchestrator dedicated to overcoming organizational inertia and apathy toward change. This pivotal leader would optimize AI-human collaboration, drive workforce augmentation, deploy AI to increase value creation, and align AI efficiencies with business growth and employee wellbeing through initiatives such as:

• Implementing training programs that enhance both technical skills and emotional intelligence
• Identifying tasks and roles suitable for automation and redesigning workflows to amplify human capabilities while fostering a culture of innovation through AI experimentation
• Developing new key performance indicators (KPIs) to measure AI-driven output, balancing efficiency with ethical considerations
• Analyzing AI trends to uncover opportunities for transformative business model innovation

By embracing these challenges and opportunities, organizations can position themselves at the forefront of the AI revolution, cultivating sustainable growth and a thriving workforce in the new era of work.

The duality of AI: jobs that use and jobs that are AI

Today, AI technologies are increasingly integrated into work processes, primarily serving as assistants to humans. For example, analysts use AI to sift through vast data sets and uncover valuable insights. However, the future of work will not just adapt to AI; it will be fundamentally transformed by it, leading to the replacement of humans in many roles and the creation of entirely new positions designed around AI capabilities. As AI continues to advance, organizations will need to reassess their workforce structures, as some traditional roles may become obsolete while new opportunities emerge that leverage AI’s strengths.

In data engineering and analysis, organizations are likely to adopt self-service analytics tools that can be queried using natural language, democratizing access to technology. As a result, traditional analysts may evolve into “insights engineers,” taking on advanced responsibilities, such as guiding predictive modeling and prescriptive analytics. In this scenario, AI becomes the core function that defines the job.

In mergers and acquisitions (M&A), the distinction between jobs that use AI and those defined by AI capabilities is increasingly significant. While traditional analysts may leverage AI tools to enhance data analysis and streamline due diligence, new roles are emerging that center around AI technologies.

For example, AI-driven deal analysts or predictive modeling specialists will develop and refine algorithms that assess potential merger outcomes or identify strategic acquisition targets. These roles require a deep understanding of both AI and the complexities of the M&A landscape, focusing on harnessing AI’s capabilities to provide insights that drive decision-making. As organizations navigate the intricacies of M&A, they must cultivate a workforce that excels in both utilizing AI as a powerful tool and innovating with AI as a core element of their strategy.

Transforming careers: the shift from specialization to adaptability in AI

The idea of being a “jack of all trades, master of none” has long been discouraged in career advice. However, in the context of AI, specializing in a single task that AI can easily replicate can be detrimental. As we move into the future of work, the demand for lifelong learning and upskilling will significantly increase as AI reshapes job requirements.

Traditional learning and development models must evolve to incorporate AI-driven, adaptive learning systems. According to the World Economic Forum, essential future skills include technological literacy, creative and analytical thinking, resilience, curiosity and leadership. While AI continues to advance, it still struggles with judgment and ethics, lacking critical human abilities such as moral reasoning, intuition and deep contextual understanding — qualities vital for high-stakes decision-making in areas such as legal rulings and ethical medical judgments.

Today’s workers must shift their mindset from relying on familiar business practices to embracing uncertainty. The most valuable employees will be those who:

• Recognize AI’s strengths and limitations, understanding that it offers new perspectives but should not be followed blindly.
• Enhance emotional and ethical intelligence, including creativity, empathy and systems thinking, while cultivating the ability to envision possibilities and ask better questions.
• Leverage AI to create new opportunities rather than merely focusing on efficiency gains.

For example, at an AI summit in Paris in February 2025, European leaders emphasized the need for the European Union to prioritize innovation while ensuring that regulatory red tape does not hinder progress. This shift places the onus on businesses to define their own AI values and governance. We are witnessing a pro-business approach to AI safety, with governments lowering guardrails and reducing or eliminating existing regulations as companies race to capture first-mover advantages. Consequently, companies now bear the responsibility for risk management, requiring them to take greater ownership of responsible AI practices.

In this evolving landscape, cyber risk managers with only a foundational understanding of AI will become increasingly vital. They will need to understand organizational functions, adopt a broad view of controls, and navigate the interplay of risk and internal politics that shape how potential dangers are mitigated while fostering innovation. We can envision the role of chief risk officer evolving into the chief risk, trust and ethics officer, responsible for:

• Determining that AI systems are fair, unbiased and aligned with ethical and regulatory standards
• Developing frameworks to mitigate risks, such as algorithmic bias, misinformation and privacy violations
• Identifying and addressing biases in AI decision-making, particularly in hiring, while relying on third-party audits and applying red-team standards to test agentic systems
• Engaging with regulators and policymakers to verify responsible AI governance