Demographic shifts, AI, and geopolitical tensions are accelerating the demand for new skills and greater workforce productivity. But many organizations are structurally unprepared — despite a plethora of process efficiency and skill development initiatives. One common cause: operational and people strategies running in isolation, preventing a successful combined impact on performance. This Viewpoint outlines how executives can align their agendas to build a future-ready workforce by unlocking productivity on a new scale.
According to Arthur D. Little’s (ADL’s) latest “CEO Insights” study of 309 global CEOs from major industries with revenues above US $1 billion, companies aim to boost productivity by an average of 8% over the next three years. Productivity is at the top of corporate agendas, yet traditional methods are losing steam: from 2019 to 2023, gains averaged only 4% (CAGR).
Beyond common barriers (e.g., siloed thinking, misaligned priorities, or an insufficient digitalization agenda), a critical bottleneck is often ignored: employees with the skills needed to transform and run productive operations simply aren't available — and mostly these missing capabilities are not developed as required by the company's transformation agenda.
The impact is global. A 2024 German Economic Institute study warns that by 2027, skilled labor shortages could cost Germany €74 billion annually in lost output unless they are addressed by measures such as AI. In the US, the Chamber of Commerce reports persistent labor gaps despite high prime-age participation, driven by demographics, reduced immigration, and skill mismatches.
In Japan, the recent “Labor Economy White Paper” highlights one of the world’s most severe shortages, affecting industries from manufacturing to healthcare, a trend that’s worsening due to an aging population and low birth rate.
Workforce productivity has become a strategic pillar of the operating model, shaping how quickly organizations adapt, scale technologies, and achieve goals (see Figure 1). Sustainable gains require tightly aligning people and business strategies, essentially treating productivity as the bridge between operational excellence and talent strategy.
The strategic imperative: integrate talent development and efficiency measures leveraging AI-driven approaches to secure success.
Companies often underestimate the link between process efficiency and workforce development. Although the need to boost productivity and secure skilled talent is widely acknowledged, integrated management is rare. HR initiatives and efficiency programs typically run in isolation, leading to optimized processes that can’t be applied due to missing skills or underused talent in misaligned structures. HR efforts focus on acquisition, development, and retention but often fail to align with the company’s evolving business model and future role requirements. Employees are not strategically prepared for the capabilities the organization will need.
Efficiency programs, led by COOs or CFOs, target process optimization, automation, and cost savings, overlooking human factors and implementation feasibility. Theoretical gains fail to translate into practice, and innovative solutions falter due to a lack of skills and workforce ownership. In our projects, we often observe several of the following challenges.
Technology levers like AI are accelerating the need for strong integration of HR and efficiency programs. At the same time, AI has the potential to kickstart productivity. What matters most is that technology, capability building, and organizational steering interact in a coordinated way.
ADL’s experience in multiple recent transformation projects shows that productive organizations are the result of an integrated workforce operating model led by the CEO, COO, and CHRO and aligned around shared goals, data, and actions. Those who break down silos and rethink governance continuously unlock efficiency reserves while strengthening their resilience. Figure 2 illustrates five levers to increase workforce productivity:
How far have companies come in integrating HR and efficiency strategies? Many organizations launched initial initiatives, but a systematic, holistic approach is often still missing. “Case study: Energy company’s SWP drives efficiency gains & savings” illustrates how companies can unlock hidden efficiency reserves through integrated management while building structural readiness. It also shows how a modern, data-driven people strategy can be linked to operational excellence and what that means for leadership, the workforce, and overall business success.
Case study: Energy company’s SWP drives efficiency gains & savings
In a project led by HUMAN, a leading European energy company used SWP as a central lever to align efficiency gains with business transformation. The goal was to build a future-ready workforce structure while generating significant savings. Based on the corporate strategy, new targets for the workforce were developed, including FTE (full-time equivalent) allocations, technological substitution potential, and future skills. In areas where digital solutions could replace tasks, re-skilling initiatives were launched. Where new competencies were needed (e.g., in renewable energy, grid modernization, or data analytics), tailored qualification programs were created.
Rather than being treated as an HR initiative, the project was viewed as the development of an enterprise-wide management tool, jointly owned by the CHRO, COO, and CFO. This integration with business and investment planning enabled workforce needs to become plannable, measurable, and shapeable for the first time. The organization gained operational efficiency, reduced costs, and built a stable foundation for the energy transition. Employees were actively transitioned into new roles instead of being replaced. This created a future vision that involved technological change, talent security, and financial manageability.
AI is no longer merely an efficiency tool; it is redefining tasks, role definitions, and business processes. However, its full productivity potential can only be realized in conjunction with skill transformation and organizational adaptation. Companies that implement AI without a coherent workforce strategy are investing in half measures, risking friction and inefficiencies during execution.
We define a human-AI workforce as teams in which human employees and digital assistants (AI systems) work together and complement one another. A future-oriented skill strategy actively promotes this collaboration, including training programs that enable employees to use AI tools effectively and the development of new role profiles such as AI trainers or automation specialists.
ADL predicts productivity gains of 15%-40% in many industries from adopting human-AI workforces — provided AI is embedded within an integrated workforce operating model.
Companies in this sector are under enormous pressure from demand for fast deliveries, a shortage of skilled workers (drivers and warehouse staff), and the need for digital transformation. Workforce productivity suffers from inefficient shift planning, high turnover rates, and the need to continuously train employees in new technologies.
AI-supported route planning, automated warehouse processes, and digital-assistance systems will take over repetitive tasks, leaving humans to handle higher-level coordination, exception management, and operational quality assurance. The key is to establish a hybrid deployment logic in which humans and machines operate as a team. This includes new shift models, cross-functional job profiles, and continuous training for roles such as AI-enabled dispatchers and digital warehouse coordinators.
Successful companies skillfully combine efficiency initiatives (e.g., AI-supported route planning and automated warehouse processes) with targeted HR measures (e.g., training programs for digital fleet management and driver assistance systems). New technologies accompany group-specific change processes and qualification offerings to increase both acceptance and productivity. In this way, operational excellence is strategically linked with employee retention and capability development.
The insurance industry faces the challenge of aligning the increasing degree of automation with the right personnel strategies. Outdated IT systems and labor-intensive manual processes burden insurers’ productivity. While digital self-service offerings and AI-supported claims processing bring efficiency gains, new competency requirements are emerging. While digital self-service and automation continue to expand across claims and other areas, customer service employees still handle very complex issues. Targeted knowledge building and training are necessary to process more complex matters quickly with fewer staff. The workforce must increasingly develop analytical, technological, and customer-oriented skills. Insurers increasingly rely on internal training programs to facilitate employees’ transition into digitalized areas of work. At the same time, more flexible working models are helping to cushion the shortage of skilled labor.
AI systems will automate pre-analyses, plausibility checks, and standard decisions, while humans handle complex cases and personal customer interactions. Insurers are specifically creating digital learning platforms to equip employees with skills in data interpretation, empathetic communication, and AI system management, establishing an adaptive workforce that operates in a scalable manner while staying close to the customer.
The energy sector is undergoing a profound transformation. The accelerated expansion of renewable energy and increasing digital demands are fueling the need for qualified personnel, but demographic change makes it harder to fill positions. Companies are responding with strategic partnerships with schools and universities to promote practical training and dual study programs. At the same time, the preservation of experiential knowledge is being supported through mentoring programs and flexible retirement models. To alleviate pressure on scarce resources, AI-supported solutions such as automated documentation, virtual training, and intelligent grid analysis are being deployed. Successful companies combine digitalization strategies (e.g., smart grids and predictive maintenance) with targeted upskilling in interdisciplinary teams to ensure the long-term development of future-proof capabilities.
AI applications such as predictive maintenance and smart grid control can enhance the responsiveness of grid infrastructure, provided operational staff are empowered to use these systems strategically. Technical specialists must evolve into AI system architects and data analysts, supported by targeted re-skilling and new career paths. The decisive factor is a structural shift in role logic, in which humans and AI jointly become the intelligent control center of the energy system.
These examples show that productivity gains through AI are not automatic. They occur where technology is introduced as part of an integrated COO-CHRO concept. Companies that pursue targeted capability development and succession planning in parallel with process digitalization can achieve rapid impact with low implementation effort and high employee acceptance levels.
AI, demographic shifts, and geopolitical uncertainties will fundamentally change the business world by 2030. Automation, a skilled labor shortage, AI, and volatile markets are not temporary phenomena — they represent a new normal. Companies that embrace workforce productivity as a strategic leadership principle, rather than an isolated optimization goal, will secure competitiveness for the long term. CXOs should prioritize five actions:
By Marius Romanescu, Carola Stryja, Benedikt von Kettler
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