AI job growth, not layoffs, at leading firms

Leading corporations are increasingly rejecting the notion that AI adoption should trigger layoffs, instead embracing strategies that redeploy talent to roles emphasizing uniquely human skills. Companies like Everpure and BetterUp caution against using AI as a cover for headcount cuts, advocating for a value-driven approach where AI handles routine tasks such as interview scheduling or document processing, freeing employees to focus on higher-value, client-centric activities. This mindset shift underscores that AI integration is not a cost-cutting exercise but a strategic investment in workforce transformation and morale.

Industry leaders such as IBM, Bank of America, and Callan exemplify human-first AI strategies that augment productivity without reducing headcount. IBM’s CEO Arvind Krishna highlighted a 40% increase in software developer productivity alongside tripled entry-level hiring, reinvesting AI-driven savings into expanding talent rather than layoffs. Similarly, Bank of America filled 45% of open roles internally, emphasizing upskilling and career mobility to sustain engagement, while Callan uses AI to streamline repetitive tasks like investor letter processing, enhancing efficiency without compromising professional judgment.

Companies are adopting workflow redesign and Lean principles to integrate AI collaboratively, fostering human-AI partnerships that elevate employee roles rather than replace them. C.H. Robinson’s CEO Dave Bozeman applies continuous improvement and value-stream mapping to identify automation opportunities, shifting employees 'up the value stack' and rejecting headcount reduction as a performance metric. This approach is echoed by HLIB’s innovative ‘institutional brain’ AI platform, which pairs each employee with specialized AI agents to preserve institutional knowledge and mitigate talent scarcity without layoffs.

A comprehensive, enterprise-wide embrace of AI as an intelligent co-worker is emerging, with firms like KBank, Central Pattana, and DBS investing heavily in talent development, AI literacy, and governance to drive sustainable transformation. KBank’s deployment of AI self-service tools to over 5,000 employees and DBS’s large-scale upskilling of 40,000 workers—including 11,000 for AI-reshaped roles—illustrate this commitment. These organizations combine AI automation of mundane tasks with human career advisory services, ensuring employees are equipped to leverage AI as a cognitive enhancer that frees them to focus on critical thinking and complex problem-solving, thus sustaining long-term growth and workforce resilience.

By early 2026, IKEA had pioneered a transformative AI integration in its customer service operations, deploying the AI chatbot Billie to handle nearly half of all customer calls. This strategic move allowed the company to reassign 8,500 call center agents into remote interior design consultants, shifting employees from repetitive inquiry handling to creative, value-added roles. The repurposing not only preserved jobs but also generated an impressive €1.3 billion in additional annual revenue, demonstrating how AI can catalyze workforce evolution while boosting financial performance.

Hong Leong Investment Bank (HLIB) exemplifies AI-driven talent transformation in finance through its 2026 Project Alpha 2.0, which created an 'institutional brain'—a network of specialized AI agents designed to retain and codify institutional knowledge. Each risk management employee partners with an AI counterpart, collaborating under a central orchestrator named Mala, ensuring continuity of expertise despite staff turnover. This approach not only accelerates onboarding but also extends across the financial group, signaling a shift from isolated AI tools to integrated digital workforces that enhance decision-making and operational resilience.

Noam AI’s concierge solution showcases how AI can revolutionize customer support without triggering layoffs, instead enabling staff to focus on higher-value roles such as operations and account management. With the AI handling up to 94% of routine tickets and deployments going live in under four hours, companies like BNSN AI and Myvalk have transformed support into lead generation channels. Employees like Nicole Wobal at AMP youP Brand have experienced improved work-life balance and career advancement, illustrating that AI can elevate rather than replace human workers while driving measurable revenue gains.

In Southeast Asia, KBank and Central Pattana have embraced a 'Human + AI' philosophy that embeds AI self-service tools across their enterprises, empowering over 5,000 employees to lead AI-driven initiatives that enhance operations, risk analysis, and customer experience. Central Pattana’s human-centric AI approach elevates employees from task executors to strategic decision-makers working alongside AI agents, underscoring a core belief that while AI augments capabilities, ultimate responsibility for decisions impacting people must remain human. This sector-wide shift highlights sustainable talent transformation as a key driver toward becoming frontier firms.

By mid-2026, SMEs have embraced AI not merely as a cost-cutting tool but as a catalyst for workforce growth and productivity enhancement. Companies like Globe have demonstrated the transformative power of AI by equipping over 80% of their workforce with AI productivity tools and developing more than 30 AI-powered applications that streamline operations from customer engagement to network fault detection. This comprehensive adoption has led to tangible productivity gains, with 84% of employees reporting weekly time savings of three to four hours, underscoring AI's role in augmenting human potential rather than replacing it.

South Korea’s 'workforce doctor' program exemplifies a structured approach to SME workforce enablement, providing tailored AI training and consulting that has significantly boosted operational efficiency and employee capabilities. For instance, Taehwa Industries increased per-worker output by nearly 40% through a carefully crafted AI training roadmap, while Probal shifted from hiring to upskilling existing staff, achieving a 37.5% improvement in internal performance and even introducing a four-and-a-half-day workweek by automating inefficient tasks. This program’s holistic support—from vocational training consultations to long-term capability clinics—empowers SMEs to build sustainable, AI-ready workforces.

SMEs are strategically leveraging role-specific AI training to attract and retain talent, with 72% incorporating AI training into onboarding and 35% prioritizing role-specific skill development as the most effective method. This focus on upskilling existing employees—favored by 64% of SMEs over external hiring—reflects a commitment to workforce enablement and satisfaction, which 53% of firms cite as key indicators of successful AI investment. Moreover, companies encourage continuous learning by allowing experimentation with AI during work hours and offering stipends for external training, fostering an environment where employees can confidently adapt to AI-driven workflows.

The shifting labor market dynamics have further empowered SMEs to capitalize on talent availability, as nearly half (47%) have hired employees laid off from larger firms amid widespread benefit reductions. This influx of experienced professionals, combined with proactive AI training initiatives, positions smaller companies to not only fill skills gaps but also enhance retention and employee satisfaction, turning workforce enablement into a competitive advantage in the evolving AI landscape.

Singapore’s National AI Strategy 2.0, launched in December 2023 and refreshed in early 2026, marks a strategic evolution from foundational AI capacity building to large-scale, sector-wide deployment. By focusing on National AI Missions targeting Advanced Manufacturing, Financial Services, Connectivity, and Healthcare—sectors comprising over 40% of its GDP—the government provides shared infrastructure and workforce upskilling opportunities that lower barriers to AI adoption. This approach is exemplified by initiatives like the Punggol Digital District, a physical testbed for real-world AI and robotics trials, and over 70 AI Centres of Excellence including NVIDIA’s Asia-Pacific research lab, which collectively nurture a robust AI ecosystem through public-private partnerships with global leaders such as Google and OpenAI.

Public-private collaborations are central to Singapore’s AI talent development, as demonstrated by major corporate commitments like HSBC’s 2026 launch of a Global AI Centre of Excellence and its partnership with educational and government bodies to build a responsible AI talent pipeline. Similarly, DBS’s strategic alliance with the Institute of Banking and Finance (IBF) focuses on foundational AI training, job redesign, and expanding youth talent through internships and management associate programs. DBS’s philosophy of using AI as a cognitive enhancer to 'save people, not jobs' underscores a human-centric workforce transformation, supported by AI-enabled career advisory services blending human guidance with tools like iGrow and iCoach.

Singapore’s National AI Impact Programme (NAIIP) and complementary initiatives such as SAP’s AI-Bilingual Workforce Program exemplify a concerted effort to train tens of thousands of workers in role-based AI skills across priority sectors. These programs address critical workforce readiness gaps highlighted by research showing only 37% of Singaporean companies provide AI training and just 45% have dedicated AI leadership. By combining practical, sector-focused workshops and hands-on projects like the Digital Leaders Accelerator Bootcamp, Singapore aims to foster confident AI adoption with strong governance and human-in-the-loop processes, accelerating the transition from pilot projects to economy-wide AI integration.

South Korea’s government-backed 'workforce doctor' program, operational since 2023, offers a compelling model of sustained, customized AI training and vocational consulting to over 12,000 SMEs, significantly boosting productivity and enabling cultural shifts such as the adoption of a four-and-a-half-day workweek. This long-term commitment includes up to three years of close support through capability development clinics, illustrating how strategic, hands-on government intervention can foster sustainable AI talent ecosystems and transform workforce capabilities at scale.

By mid-2026, IBM's experience illustrates the dual-edged nature of AI workforce integration: while AI has boosted software developer productivity by 40%, prompting a tripling of entry-level hires, it has simultaneously rendered about 30% of operational roles redundant. This dynamic underscores the leadership challenge of balancing automation-driven efficiency with compassionate workforce management, as IBM avoids abrupt layoffs and invests in upskilling, though employee willingness to retrain remains split roughly 50/50, leaving a societal responsibility for those resistant to change.

Singaporean enterprises exemplify the critical gap between AI adoption and true workforce readiness, with only 2% fully prepared for agentic AI despite 89% recognizing its transformative potential. This disconnect is compounded by insufficient governance—27% lack human-in-the-loop processes and 30% have no permission controls—highlighting the urgent need for comprehensive AI training that extends beyond tool usage to include prompt engineering, responsible AI, and data literacy, especially as data readiness declines from 62% to 55% amid widespread data quality issues.

Although Singapore leads globally in AI maturity dimensions such as talent readiness, a striking 17-point gap persists between foundational AI capabilities and embedding AI into daily workflows, with only 10% of enterprises redesigning processes for end-to-end AI automation. This lag in workflow integration limits productivity gains, as data shows companies that reengineer workflows around AI are nearly four times more likely to achieve significant efficiency improvements, underscoring the opportunity for leadership to drive cultural and operational transformation rather than merely layering AI onto existing tasks.

Leadership and culture emerge as pivotal forces in sustaining AI-driven workforce transformation, exemplified by Standard Chartered’s COO Melinda McKinley, who advocates for mapping workflows to harmonize human judgment with AI automation. This approach reflects a broader imperative for leaders to not only champion AI adoption but also to cultivate a culture that embraces change, ensuring AI integration genuinely enhances work experiences rather than compounding existing challenges—a critical factor given that less than half of companies have dedicated AI leaders to steward this evolution.

By mid-2026, industry leaders like IBM CEO Arvind Krishna highlight that AI-driven productivity gains have paradoxically fueled hiring growth in value-creating roles, even as automation renders about 30% of back-office positions redundant. Krishna envisions a future where a 5-10% productivity boost across enterprises globally benefits all sectors, underscoring that while displacement is inevitable, the broader economic uplift can be widely shared if managed thoughtfully.

Sustaining a human-centric AI workforce hinges on continuous upskilling and fostering AI literacy, as emphasized by both IBM and Booking.com leadership. Krishna stresses that businesses must provide opportunities for reskilling but cannot compel adaptation, placing a shared onus on individuals, society, and governments to support those struggling to transition. Meanwhile, Booking.com's CEO critiques traditional government retraining programs as largely ineffective over the past five decades, signaling the urgent need for innovative, collaborative approaches that blend corporate investment with public policy to future-proof careers.

Investing in employee training for AI readiness emerges as a strategic imperative not just for enhancing company productivity but for ensuring long-term career sustainability. Booking.com's CEO cautions against short-term cost-cutting mindsets, arguing that such investments pay dividends by equipping workers with transferable skills that safeguard their employability amid evolving job landscapes, thereby reinforcing the business case for proactive talent transformation in an AI-enabled future.