In brief:
- Advances in AI and shifts in the global economy are redefining productivity, shifting focus from output volume to outcomes, adaptability, and effectiveness.
- Human judgment and collaboration with AI are becoming essential, requiring organizations to rethink workflows, roles, and measures of value creation.
- Real-world examples highlight that productivity gains increasingly stem from improved decision quality rather than increased output, raising new measurement and equity challenges.
“Productivity is not about simply doing more with less, but about doing better with new combinations of human and technological resources."
Productivity has long been a cornerstone of economic and business thinking, typically defined as the output produced per unit of input. However, the rise of artificial intelligence (AI) and the sweeping changes in the global economy are forcing a reevaluation of what productivity means and how it is measured in the 21st century.
C-suite leaders are increasingly operating in what can be described as a NAVI risk environment — non-linear, accelerated, volatile, and interconnected — where multiple disruptive forces are shaping the global operating environment, including climate change, technological innovation, demographic shifts, and the rising influence of non-state actors. These intersections between primary forces create megatrends, identified as global, cross-sector scenarios that shape how organizations operate, compete and create value.
The EY Megatrends 2026 report explores eight megatrends at the global macro level and highlights how each one can evolve in different sectors. Previous megatrends articles discussed how the superfluid enterprise eliminates operational friction, and how the human-machine hybrid expands capabilities.
This third article on the productivity reset explores how to measure value when traditional metrics no longer apply, suggesting that productivity is moving away from a narrow focus on volume and toward a more nuanced understanding centered on outcomes, judgment, and adaptability.
From efficiency to effectiveness
For much of the 20th century, productivity gains were closely tied to industrialization. Advances in machinery, process standardization, and labor specialization allowed companies to increase output while reducing resource use. Measurement was straightforward: output could be counted, and inputs could be quantified in terms of labor hours or capital invested.
Today, the economy is dominated by services and digital platforms, where outputs can be intangible and difficult to quantify. AI accelerates this trend by generating content, analysis, and decisions at minimal cost. In this context, high output volume does not always translate to meaningful value. The focus is shifting from sheer output to effectiveness and how well outputs achieved intended objectives. Metrics now include accuracy, relevance, and the ability to drive action, not just quantity.
The human factor in an AI world
AI’s growing capabilities do not diminish the importance of human input. Instead, they change its nature. While machines handle routine and repetitive tasks, humans are increasingly responsible for interpretation, oversight, and strategic decision-making. The emerging human-machine hybrid economy places a premium on judgment, context, and ethical considerations. At the same time, task redistribution between humans and machines isn’t neutral. Roles centered on routine, repetitive, or even some creative functions are being reshaped or displaced, often faster than workers can reskill or transition.
In this case, productivity is not about simply doing more with less, but about doing better with new combinations of human and technological resources. Organizations that embrace this shift, viewing AI as a way to enhance and not merely automate, are more likely to unlock the technology’s full potential. However, this also requires acknowledging that not all workers will experience these changes in the same way, and that productivity gains may come with real human costs if transitions are not actively managed.
Organization transformation
These shifts have significant implications for how organizations are structured and managed. Traditional hierarchies and rigid roles may be less effective in environments where information flows rapidly, and decisions must be made in real time. The concept of the superfluid enterprise, that is, an organization that adapts quickly and integrates AI into its core operations, reflects this new reality.
Achieving this requires more than investing heavily in technology because it demands rethinking workflows, redefining roles, and establishing new forms of coordination between people and machines. It is important to emphasize that high-quality data is also essential. Without it, AI systems cannot deliver their promised productivity gains. Equally important is investing in people — not only in upskilling and reskilling programs, but also in transition pathways. Without deliberate effort, organizational transformation risks creating a divide between those who can work alongside AI and those who are left behind.
The Philippine experience
Recent developments in the Philippines illustrate how these trends are beginning to take shape through emerging human-machine partnerships. In the public sector, the Department of Science and Technology has used data analytics and AI to improve disaster risk management, through initiatives such as Project NOAH and the Cordillera DREAM (Disaster Risk Evaluation, Analytics, and Management) Project, moving the focus from activity volume to decision quality and reducing the costs of natural hazards.
As AI systems increasingly complement human judgment, their impact is becoming particularly visible in the creative economy and among small and medium enterprises (SMEs). By lowering barriers to content creation, design, and digital production, AI-enabled tools allow smaller players to scale output, personalize offerings, and participate more competitively in global markets. At the same time, SMEs can leverage AI to improve decision-making, optimize operations, and access new digital platforms, helping shift productivity gains from volume to value. These developments align with broader regional efforts, including initiatives led by the ASEAN Business Advisory Council (ASEAN BAC) to advance digitalization and AI adoption, particularly among MSMEs and creative industries.
On the national scale, the Philippine Development Plan 2023-2028 highlights the importance of digital transformation and data-driven decision-making, particularly in infrastructure, transport, and logistics. These initiatives show that AI-driven productivity gains often come from better outcomes, beyond increased input, and that institutional capacity is crucial[MT2.1] to realizing these benefits.
For example, AI-enabled systems can enhance transport planning through better traffic flow analysis and real-time coordination, improving commuter experience and system reliability. In healthcare, the integration of data and AI can support earlier diagnosis, more efficient service delivery, and improved patient outcomes. More broadly, intelligent systems can help shift infrastructure management from reactive to predictive, enabling faster, better-informed decisions across planning, delivery, and operations. While adoption currently remains at a very early stage, these developments underscore how human-machine collaboration can unlock more inclusive and outcome-driven productivity gains across sectors.
Nevertheless, these gains will need to be matched with inclusive policies and workforce support systems to ensure that improvements in efficiency do not come at the expense of job quality or long-term employment stability.
Measuring productivity in a digital era
As productivity evolves, so do the challenges of measuring it. Traditional metrics like GDP and labor productivity were designed for economies where tangible goods and market transactions dominated. Today, valuable digital services fall outside these frameworks, and AI can decouple output from labor input.
According to a 2024 OECD report, AI advances enable individuals to accomplish tasks that previously required a team, posing new challenges for measuring economic performance and productivity across different sectors as well as countries. Although new ways to measure the impact of AI and related technologies are being developed, there is no widely accepted approach to capturing their full value. The World Economic Forum stated that these challenges are particularly relevant in Southeast Asia, where a youthful workforce, rapid digital adoption, and growing economic diversity present significant opportunities and risks. Yet disparities in infrastructure, education, and institutional readiness risk leaving some behind. Without targeted investment, productivity gains from AI could deepen existing inequalities.
The digital road ahead
Productivity is being redefined by AI and changing economic structures. The emphasis is shifting from output to outcomes, effectiveness, and the interplay between human and machine capabilities. For organizations and policymakers, this means rethinking how work is organized, how performance is assessed, and where investments are directed.
Ultimately, the “productivity reset” is not a one-time event but a continuous process, depending on how societies balance technology with human judgment and innovation with inclusion.
Marie Stephanie C. Tan-Hamed is the Strategy, Economic Research, and Government and Infrastructure Leader of SGV & Co.
This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinions expressed above are those of the author and do not necessarily represent the views of SGV & Co.
