What to expect in Work Management and Field Service Management by 2045
Updated: Oct 5
This article is adapted from Advancements of AI Integration in Work Management Optimisation, written by Joao Zoio, Co-founder and CEO of Acumen Software.
When could Artificial General Intelligence be achieved?
There is no agreed timeline for the development of Artificial General Intelligence (AGI), broadly understood as AI capable of performing a wide range of intellectual tasks at or beyond a human level. Forecasts vary considerably depending on how AGI is defined, which capabilities are measured and who is making the prediction.
Metaculus, an online forecasting platform, has tracked predictions about the arrival of general AI for several years. These forecasts have moved substantially as AI capabilities have developed. As of October 2026, its community prediction placed the median date for the first general AI system to be developed, tested and publicly announced at around 2031. Because this is a live forecast, it will continue to change as new evidence and technological developments emerge.
Expert surveys reflect similarly high levels of uncertainty. A 2023 survey led by Katja Grace and published in 2024 collected responses from 2,778 researchers who had published at leading AI conferences. Collectively, respondents placed a 50% probability on unaided machines outperforming humans in every possible task by 2047—13 years earlier than the equivalent estimate in the preceding survey. Predictions nevertheless varied widely, demonstrating the difficulty of establishing a reliable timeline for human-level AI.
Recent advances in generative AI, multimodal models, reasoning systems and AI agents have led some forecasters to shorten their expected timelines. These developments do not, however, provide enough evidence to predict precisely when—or whether—AGI will be achieved.
The pace of AI development will be influenced by several interconnected factors:
advances in algorithms, data availability and computing power;
investment and international interest in AI research;
access to energy, infrastructure and specialised hardware;
ethical, legal and regulatory considerations;
the reliability and safety of increasingly autonomous systems; and
scientific progress in understanding intelligence, reasoning and human cognition.
AGI forecasts should therefore be approached cautiously. Rather than depending on a particular date, organisations can consider how current and emerging AI capabilities may influence their operations over the next two decades.
The future of work management and field service management
By 2045, work management and field service management could be substantially transformed by developments in robotics, generative AI, autonomous vehicles, drones and self-diagnosing equipment.
More autonomous field operations
Work management may shift towards increasingly autonomous operations in which vehicles, drones and robotic units perform certain tasks with limited human intervention.
Autonomous vehicles could support the movement of goods, equipment and field personnel while using live operational data to adjust routes and maintenance schedules. Drones may play a greater role in inspecting power lines, wind turbines, buildings and other assets that are difficult or dangerous to access. Robotic systems could also undertake selected maintenance and repair activities in hazardous environments, reducing the exposure of human workers to safety risks.
The extent of this automation will depend on technical reliability, regulation, cost and the conditions in which field teams operate. Human approval and intervention are likely to remain necessary for safety-critical or unusual situations.
AI-assisted decisions and predictive maintenance
Work management systems are likely to make greater use of operational data to recommend or automate certain decisions. AI could continuously assess information from work orders, assets, mobile devices, sensors and connected systems to identify changing conditions and recommend appropriate actions.
Predictive maintenance may also become more advanced. AI systems could analyse Internet of Things (IoT) data, service histories and operating conditions to identify patterns associated with equipment failure. Generative and simulation-based AI may help organisations test possible scenarios and determine when preventative action should be taken.
Assets equipped with AI-enabled sensors could monitor their own condition, identify faults and initiate predefined responses. In some cases, equipment may be able to correct minor issues automatically or create a work order when human intervention is required. These capabilities could reduce unplanned downtime, improve asset availability and extend asset lifespans.
Changing roles for the human workforce
Greater adoption of AI will change the responsibilities of field workers, planners, dispatchers and operations managers. Although automation may take over certain repetitive, administrative or hazardous tasks, people will remain important for complex problem-solving, accountability, relationship management and situations requiring contextual or ethical judgement.
Workers may need to develop new skills to supervise AI systems, assess their recommendations and manage autonomous operations. Technical teams may also require stronger data, digital and systems-management capabilities.
Augmented reality (AR) could support this transition by enabling remote assistance and contextual guidance. A field technician may, for example, receive visual instructions or assistance from a remote specialist while working on an unfamiliar asset. This could reduce unnecessary travel, support faster responses and provide access to training while work is being performed.
Personalised services and connected infrastructure
Generative AI may allow services and work processes to be adapted more closely to individual customer requirements. It could assist with planning complex work, generating instructions, analysing previous service interactions and proposing responses to unusual operational problems.
Work management systems may also become more closely connected to smart city and built-environment infrastructure. Information from public assets, utility networks, buildings, traffic systems and environmental sensors could contribute to a broader operational view.
AI could use this information to adjust work plans in response to factors such as weather, traffic, asset condition, technician availability and service priorities. This would support more responsive field service operations, although organisations would still need appropriate controls over data quality, privacy, cybersecurity and automated decisions.
Will AI replace field service workers?
AI is unlikely to remove the need for human involvement in field service management entirely. It may replace or automate aspects of certain roles, particularly routine data capture, scheduling, monitoring and repetitive physical tasks. At the same time, it is likely to create responsibilities related to AI supervision, autonomous equipment, data analysis, systems integration and operational governance.
By 2045, human roles may increasingly concentrate on:
overseeing and improving AI-supported processes;
handling complex or exceptional technical work;
making safety-critical and strategic decisions;
managing customer and stakeholder relationships;
maintaining autonomous equipment and connected infrastructure; and
performing work that requires creativity, empathy or nuanced judgement.
The effect on employment will not necessarily be uniform. Some occupations may decline, while others will change or emerge. Organisations will therefore need to invest in upskilling and establish clear responsibilities for decisions made or supported by AI.
Lessons from previous industrial revolutions
Technological disruption is not new. During the first Industrial Revolution, inventions such as the Spinning Jenny, developed by James Hargreaves in the 1760s, enabled one worker to operate several spindles at once. Together with inventions such as the water frame and power loom, this increased textile-production capacity and contributed to the movement from domestic manufacturing towards factory-based production.
Mechanisation caused significant disruption and resistance, particularly among workers whose livelihoods or working conditions were threatened. It also contributed to the growth of new industries, supply chains, transport networks and occupations. The outcome was not simply the replacement or creation of jobs; technology changed where work was performed, which skills were valued and how economic activity was organised.
The current period is often described as the Fourth Industrial Revolution, in which digital technology, AI, robotics and biological systems are becoming increasingly interconnected. As in previous periods of technological change, these developments are creating both optimism and anxiety.
Over the coming decades, more cities may adopt connected infrastructure and field services may become increasingly automated, data-driven and personalised. These advances could improve the management of assets, resources and essential services. They will also introduce challenges involving employment, accountability, privacy, cybersecurity, human rights and the ethical use of AI.
The future of work management is therefore likely to depend on collaboration between people and intelligent systems. Achieving useful outcomes will require technological capability, effective governance, appropriate human oversight and a clear understanding of where automated decisions should—and should not—be used.
Read the full white paper: Advancements of AI Integration in Work Management Optimisation.
