As AI operating experience grows, hiring criteria shift and expertise-driven workforce restructuring gains momentum
Matthew Reuter is a senior economic correspondent at The Economy, where he covers global financial markets, emerging technologies, and cross-border trade dynamics. With over a decade of experience reporting from major financial hubs—including London, New York, and Hong Kong—Matthew has developed a reputation for breaking complex economic stories into sharp, accessible narratives. Before joining The Economy, he worked at a leading European financial daily, where his investigative reporting on post-crisis banking reforms earned him recognition from the European Press Association. A graduate of the London School of Economics, Matthew holds dual degrees in economics and international relations. He is particularly interested in how data science and AI are reshaping market analysis and policymaking, often blending quantitative insights into his articles. Outside journalism, Matthew frequently moderates panels at global finance summits and guest lectures on financial journalism at top universities.
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Workflow and organizational restructuring accelerates after AI adoption Ability to use analytical results and apply them in practice gains importance Growing efforts to identify talent that also possesses AI skills
Companies that cut staff while adopting artificial intelligence (AI) are once again seeking experienced workers for their new operating structures. The move appears to reflect greater clarity about the roles people must perform and the expertise required after companies reorganized workflows and teams in pursuit of automation. As more work involves applying AI-generated results to business operations and coordinating collaboration across departments, employees familiar with internal operations and equipped with hands-on experience have become more valuable. In rehiring, the ability to combine accumulated expertise with AI and translate it into results for restructured organizations is emerging as a key criterion.
Amazon explores bringing back experienced AI and cloud workers
According to a report on the 10th by U.S. business publication Business Insider, Amazon recently contacted former employees, including those it had laid off, to offer positions in areas such as AI and cloud computing. All dates cited are local time. In particular, the AI agent organization at Amazon Web Services (AWS) approached people with AI and machine-learning experience about returning, while the finance division offered some former employees a shortened interview process. Amazon explained that rehiring former employees is a longstanding company-wide recruiting practice. However, the combination of the people targeted and the positions offered suggests an effort to selectively bring back workers with the skills needed to expand its AI business. The aim is to reduce the time needed to deploy staff to new businesses by securing experienced workers familiar with existing operations and internal affairs.
Indeed, this workforce redeployment is underpinned by changes to workflows and organizational structures following AI adoption. In an analysis titled “The key to AI value is hiding in plain sight: Your operating model,” released on the 5th of last month, global consultancy McKinsey surveyed more than 700 executives and senior managers. It found that companies further along in reorganizing for AI use were more likely to assemble teams around products, customer services, and workflows by bringing together employees from multiple departments. Under this approach, decision-making authority and accountability for results are assigned together to the teams performing the work. An earlier survey conducted last year found that companies achieving strong results with AI had procedures specifying when and how people should verify AI outputs. This suggests that companies are refining team roles and staffing criteria as they clarify which tasks to delegate to AI and where employees must exercise judgment.
AI skills reshape rehiring terms
As the division of work becomes clearer, the roles employees will perform in restructured organizations have also emerged as an important criterion in rehiring. In February, market research firm Gartner forecast that half of the companies that reduced customer service staffing because of AI would rehire employees to perform similar functions under different job titles by 2027. It singled out the expertise, empathy, and judgment required in customer interactions as core human contributions. Gartner’s “Workforce Planning Model,” presented last month, also includes a process for breaking work into measurable units and estimating the volumes to be handled by people and AI, respectively. Applied to rehiring, this places weight on both an understanding of existing work and the ability to review AI-generated results and resolve exceptional situations. It points to a structure in which experienced workers familiar with a company’s internal operational context take charge of running new systems.
Compensation is also increasingly likely to reflect the productivity expected under new operating structures. In the “Global AI Jobs Barometer 2026,” released in June by global consultancy PwC, job postings requiring AI skills carried an average wage premium of 62%. Productivity, measured as revenue per employee, at companies in industries with high AI usage also increased by 34% between 2018 and 2025. These two indicators provide grounds for linking the higher pay offered for AI skills to expectations about the added value each employee can generate. Applied to rehiring negotiations, this logic would mean companies assess the workload and scope of responsibility a returning employee can handle using AI before offering a higher annual salary. PwC’s survey also confirmed a growing emphasis on judgment, creativity, and leadership among the skills companies seek.
AI operating experience clarifies rehiring needs
Companies’ efforts to reassess the skills they need appear to have been shaped by experience gained from actually running their restructured systems. In a survey by U.S. staffing consultancy Robert Half, 40% of respondents cited a need for internal organizational knowledge and operational context as a reason for rehiring for roles previously cut following AI adoption, while 38% said more oversight and quality control were needed than expected. This means that work requiring people remained even after automation, and its scope became clearer during operation. Meghan Slabinski, a technology talent executive at Robert Half, explained that companies are redesigning jobs as they identify areas where collaboration between AI and employees is effective. In effect, they are securing the workers needed to fill operational gaps identified after AI adoption.
Swedish fintech company Klarna offers an example of how operating experience has helped clarify the role of customer service staff. In a May interview with U.S. technology publication Big Technology, CEO Sebastian Siemiatkowski said the company was hiring staff to handle complex customer inquiries that had previously been outsourced. According to the company, AI handled two-thirds of customer service chats, and its capabilities were improved to explain why customers had been denied access to services—something it could not answer when first introduced. The work assigned to people was also defined more clearly as handling complex matters and inquiries requiring an in-depth understanding of the company. Efforts to expand the scope of AI’s work proceeded alongside efforts to secure specialist customer service staff.
Table 1. Staffing needs reaffirmed after AI adoption and examples of use by company
| Category | Required skills and roles | Key survey findings and examples |
|---|---|---|
| Robert Half survey | Internal organizational knowledge and understanding of operational context | Cited by 40% as a reason for rehiring for roles cut following AI adoption |
| Oversight and quality control | 38% said they rehired because more oversight and quality control were needed than expected | |
| Klarna | Complex customer inquiries and understanding of the company | AI handles two-thirds of customer service chats Hiring staff to handle complex customer inquiries previously outsourced |
| Greenhouse | Understanding of frontline operations and automation design | Introducing innovation leads in each department Selecting tasks and tools for automation, and building systems with a central IT engineering team |
| Workera | Documenting specialized knowledge | Hiring subject-matter experts AI agents read documented knowledge and use it across the organization |
| Reserve | Sharing and reusing operational know-how | Sharing know-how accumulated as employees build AI agents for their own work Establishing a system that allows other employees to reuse it |
Introducing innovation leads to drive automation
As companies work to embed redesigned workflows in day-to-day operations, the need for people who can connect operating departments with technology teams has also grown. According to an analysis of corporate case studies released by Boston Consulting Group (BCG) in September, recruiting platform Greenhouse is introducing “innovation leads” who understand operational work in each department. They are tasked with selecting work to automate and appropriate tools, and building systems with the central information technology (IT) engineering team. In particular, identifying automation targets and communicating development requirements requires an understanding of frontline work sequences and handoffs between departments. That is why candidates for newly created positions are assessed both on their experience within the relevant department and on their ability to apply it to automation.
Another task has been added: organizing experienced workers’ expertise into knowledge that AI can use. Workera, a skills assessment and education technology company featured in the same BCG analysis, hired subject-matter experts to document specialized knowledge so AI agents could read and use it across the organization. Insurance claims management company Reserve also established a system that allows employees to reuse the know-how others accumulated while building AI agents for their own work. These systems are designed to incorporate experienced workers’ knowledge into the work of multiple employees and AI. In a structure where the entire organization can use the judgment criteria developed by skilled workers, the economic value of rehiring also increases. Because one returning employee’s expertise can inform work across multiple departments through AI, companies can expect to recover their talent acquisition costs across a broader range of activities.
Hiring requirements become clearer as AI-driven restructuring gains momentum
Efforts to use skilled workers’ knowledge across organizations are also linked to companies’ redefinition of employees’ roles to fit new operating structures. The “Frontier Playbook,” an AI transformation guide released by Microsoft (MS) in September, includes a case in which supply chain experts and engineers jointly redesigned workflows before deploying more than 100 AI agents in areas including planning, procurement, and logistics. They established a foundation for the agents to work from the same data and assigned them tasks such as analyzing changes in demand and reviewing transportation alternatives. As the time needed to identify the causes of demand fluctuations decreased, staff were able to focus on comparing response scenarios and developing contingency plans. System restructuring clarified the work to be assigned to people, while increasing the importance of expertise in applying analytical results to actual supply chain operations when deploying staff.
As operating structures change, companies’ requirements for talent in the job market have become more specific. According to a survey released on the 29th of last month by global talent services company Randstad, 61% of 1,100 business and technology decision-makers in 11 countries said it was harder to find talent with communication and stakeholder coordination skills than to find technical staff. In other words, after putting AI to work, companies are struggling to secure people who can reconcile the needs of multiple departments and lead day-to-day operations. Demand for staff responsible for operations was also evident in a separate analysis of job postings and candidate data, which showed demand growth of 108% in AI risk management. Randstad CEO Sander van’t Noordende identified securing talent with the technical skills, judgment, and communication abilities to apply AI directly in business operations as a key hiring challenge for companies. As restructuring defines the roles needed, corporate hiring demand appears to be concentrating on experienced workers who can put their expertise to use in new systems.
Matthew Reuter is a senior economic correspondent at The Economy, where he covers global financial markets, emerging technologies, and cross-border trade dynamics. With over a decade of experience reporting from major financial hubs—including London, New York, and Hong Kong—Matthew has developed a reputation for breaking complex economic stories into sharp, accessible narratives. Before joining The Economy, he worked at a leading European financial daily, where his investigative reporting on post-crisis banking reforms earned him recognition from the European Press Association. A graduate of the London School of Economics, Matthew holds dual degrees in economics and international relations. He is particularly interested in how data science and AI are reshaping market analysis and policymaking, often blending quantitative insights into his articles. Outside journalism, Matthew frequently moderates panels at global finance summits and guest lectures on financial journalism at top universities.
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