The intelligent workplace (part 3): Technology’s next transformation of work

Part 3: Preparing for the Workplace of 2030

The workplace of 2030 will emerge from the convergence of AI, autonomous agents, robotics, automation, skills intelligence and connected physical and digital environments. Together, they will change how organizations define jobs and create value for their businesses, customers and commercial partners.

Parts 1 and 2 of this series consider how intelligent technologies are transforming the employee experience and changing the way organisations manage performance and workforce development. These shifts are also early signals of a much larger transformation that will reshape the structure of work over the remainder of the decade.

Part 3 looks toward the workplace of 2030. It explores the technologies likely to influence how work is organized and the workforce strategies required to remain competitive. The challenge for organizations is no longer simply adopting AI, but building the adaptability to redesign roles and develop new capabilities as technology continues to evolve.

The scale of the transition will be substantial. The World Economic Forum expects structural change to affect 22% of today’s jobs by 2030. It forecasts that 170 million roles will be created and 92 million displaced, producing a net gain of 78 million jobs. This is not a simple story of technology eliminating employment. It is a redistribution of tasks and opportunities on a scale that will test every organization’s ability to adapt.

The question is not whether the workplace will change, but whether companies can change with it. Preparing for 2030 means building an organization capable of redesigning work and moving skills to where they generate the greatest value.

Emerging technologies will reorganize work

AI will become foundational workplace infrastructure. Adoption is already accelerating. Stanford’s AI Index reports that 88% of surveyed organizations were using AI in 2025, with 70% using generative AI in at least one business function. Generative AI reached approximately 53% of the general population within three years—faster than either the personal computer or the internet. Agent deployment, however, remained in single digits across almost all business functions, showing how early the next stage remains.

Wendy Harris, VP of EMEA at Rippling, says the real shift will come from “the convergence of AI, automation, skills intelligence and autonomous agents.” Instead of organizing work around fixed jobs and rigid processes, companies will increasingly match people and machines to tasks and outcomes in real time.

“The real transformation happens when organizations move away from assigning work based solely on headcount and job titles, and instead allocate tasks to the capabilities best suited to deliver an outcome, whether those capabilities are human, machine or a combination of both,” Ciara Harrington, chief people officer at Skillsoft, tells ITPro.

The transition extends beyond office-based generative AI. Robots will work beside people in factories, hospitals, construction, and logistics. Immersive systems will support training and remote maintenance. The opportunity lies in connecting these technologies to redesigned workflows rather than accelerating yesterday’s processes.

As Part 1 of this series considered, successful workplace transformation depends on more than introducing advanced tools. These technologies must form part of an intelligent employee experience that reduces digital friction and gives people greater capacity for judgment, creativity and collaboration.

Jobs will change faster than they disappear

Predictions of mass technological unemployment obscure a more complex reality. The International Labour Organization estimates that one in four jobs worldwide has some exposure to generative AI, yet only 3.3% of global employment falls within the highest exposure category. Transformation is more likely than wholesale replacement.

Even highly exposed occupations may prove difficult to automate completely. Managers, engineers, and other professionals often depend on social interaction and contextual judgment. AI may take over parts of these roles while increasing the value of the human capabilities surrounding them.

“Every role is made up of hundreds of tasks,” Harris explained. “AI doesn’t eliminate most roles—it changes the balance of those tasks.” Repetitive, administrative, and analytical work can move to technology, creating more space for judgment, creativity, communication, and problem-solving.

That requires organizations to design work at the task and capability level rather than making workforce decisions solely through job titles. Oliver Shaw, CEO of Orgvue, warns that many companies have invested in AI without considering its effect on work or the workforce. His company’s research found that 57% of business leaders deployed AI primarily because competitors had done so, while 78% of organizations had seen AI projects fail or remain stuck in pilots.

“A clear example of this is the PR and marketing industries,” says Shaw. “Both [are] fields that have been at the centre of the conversation on the disproportionate impact of AI adoption on entry-level roles, and what this’ll mean for the future of the industry when entry points are few and far between, and the foundational skills are missing. What these industries are forgetting is to train staff on the critical thinking required to use AI, and the cognitive offloading that occurs when there is too much reliance.”

Leaders risk automating fragments of work without considering which decisions remain human, or how entry-level workers develop expertise when foundational tasks disappear. Organizations should identify where AI can execute and where collaboration produces greater value.

This division of responsibilities also has direct implications for the performance and leadership questions explored in Part 2 of this series. Organizations will need to assess how effectively employees delegate to AI and apply human judgment, while ensuring accountability for consequential decisions remains clearly defined.

Continuous learning becomes core infrastructure

Skills will be the pressure point of the 2030 workplace. Employers expect 39% of workers’ core skills to change by the end of the decade. AI and big data are forecast to be the fastest-growing skills area, followed by networks and cybersecurity and then technological literacy. However, creative thinking, resilience, agility, curiosity, and lifelong learning will also increase in importance.

Technical and human capabilities are not competing categories. According to the OECD, 72% of vacancies in occupations highly exposed to AI already require at least one management skill, while 67% require a business-process skill. The employee who can operate an AI system but cannot question its output will offer limited value.

Jen Paterno, senior behavioral scientist at CoachHub, emphasised that judgment, adaptability, critical thinking, collaboration, and self-awareness develop through experience, reflection, and feedback—not conventional training alone.

“These capabilities are difficult to build through conventional training alone because they develop through experience, reflection, and feedback,” Paterno explained to ITPro.

“Organizations will need more continuous, personalized development models that help employees practice new behaviors in the flow of work. Coaching will also be critical in ensuring people have objective, brave spaces to experiment and ideate with skills that feel more foreign to them.”

The scale of the challenge is considerable. The World Economic Forum predicts that around 59% of the global workforce will require training by 2030. For every 100 workers, 11 may not receive the upskilling or reskilling they need, leaving more than 120 million people at medium-term risk of redundancy. Although 85% of employers intend to prioritize workforce upskilling, the intention must be converted into accessible learning and credible pathways into new work.

Annual courses and static competency frameworks are too slow. Harrington says organizations need systems that identify emerging skills and adapt as requirements evolve. Skillsoft found that although 86% of employees use AI at work, fewer than one-quarter feel equipped to use it effectively. Access without capability risks creating two workforces.

Skills-based models can help companies see capability beyond formal qualifications or job titles. They can also support internal mobility. Half of employers plan to move people from declining roles into growing areas, while 29% of workers requiring training could be upskilled in their current positions and 19% retrained and redeployed elsewhere, according to the World Economic Forum.

Adaptability will define competitive advantage

Workforce strategy must become more dynamic. Leaders need shorter review cycles, real-time skills visibility, plus the ability to test new roles and workflows before scaling them.

David Shrier, Professor of Practice, AI and Innovation at Imperial Business School, argues that annual planning and conventional five-year strategies can no longer support decision-making. He recommends “nimble scenarios” that allow leaders to respond more rapidly to technological and market uncertainty. AI can support that planning, but it cannot determine the organization’s purpose or appetite for change.

Business leaders must also ensure that opportunity is distributed fairly. Generative AI exposure differs significantly between countries and demographic groups. Around 34% of employment in high-income economies has some AI exposure, compared with 11% in low-income countries. Women are also more highly represented in the most exposed occupations, making inclusive training and transition programs essential, the International Labor Organization reports.

Heather Delaney, managing director and founder of Gallium Ventures, emphasised the importance of flexible planning cycles that create room for experimentation. “AI is advancing faster than a lot of us can keep up with, so flexible planning cycles that allow room for experimentation are integral to ensuring decisions made are future-proofing you and your business to grow with emerging technologies.”

Preparing for 2030 is ultimately an organizational capability rather than a forecasting exercise. No leader can know precisely which tools or roles will dominate at the end of the decade. Companies can, however, build the capacity to sense change, move skills quickly, involve employees in redesigning work, and preserve human agency as machines assume greater responsibility.

Across this series, one conclusion is clear: the intelligent workplace is not defined by how much technology an organization deploys, but by how effectively it combines technological capability with human judgment. From improving the everyday employee experience to rethinking performance, success depends on using AI to strengthen rather than diminish people’s contribution.

As 2030 approaches, the workplace of 2030 will not be won by the organization with the most AI. Competitive advantage will belong to businesses that combine technology with judgment and turn disruption into opportunity for both the enterprise and its people. Technology will shape the next generation of work, but the quality of leadership and workforce strategy will determine who benefits from it.