The human-machine era marks a shift in how organisations think about work, productivity and capability.
AI is no longer confined to isolated tools or functions; it is becoming embedded across workflows, influencing decisions, coordination and execution at scale.
Today’s leaders face two immediate challenges. First, they must understand how their own role is changing and what they must do to remain relevant and impactful. Second, they must collaborate with boards, partners and executive teams to redesign organisations where humans and machines complement rather than compete.
Here, we examine why organisations must pause and reflect on the structural, governance and workflow redesigns needed to truly harness the power of AI without draining innovation, talent and goodwill.
The insight distils some of the key lessons from just one chapter in the latest in-depth executive briefing offered as part of membership to the AI Business Leaders Circle.
The underlying dynamics are more nuanced than alarming headline narratives about mass layoffs suggest.
In the United Kingdom, sustained AI deployment is beginning to translate into measurable organisational restructuring. A 2026 analysis by Morgan Stanley found that UK firms operating AI systems for at least 12 months reported an average 8% net reduction in roles attributable to automation, one of the highest rates observed among developed economies, including the United States, Germany, Japan and Australia.
This suggests that once AI moves beyond experimentation into embedded operational use, structural workforce effects can materialise relatively quickly.
However, the picture in the United States, which leads the world in AI adoption and innovation, indicates a more complex pattern. Broader analysis shows that only around 4% of US layoffs last year were directly connected to AI implementation. In many cases, reductions were anticipatory with organisations “getting lean” ahead of projected efficiency gains rather than responding to proven displacement.
Some companies have also been accused of “AI-washing”: using automation narratives to obscure weaker performance, cost pressures or post-pandemic over-expansion.
At the same time, forward-looking warnings are intensifying. Dario Amodei, CEO of Anthropic, has argued that AI could eliminate up to half of all entry-level white-collar roles within five years. Supporting this concern, data suggests that graduate roles, apprenticeships and junior positions without degree requirements have declined significantly since late 2022.
Entry-level roles are capability incubators. They serve as the training ground where professionals develop judgement, institutional understanding and domain expertise required for future leadership.
If AI disproportionately compresses these early-career pathways, organisations may inadvertently hollow out their own talent pipelines. The result would not be immediate productivity loss but a delayed capability crisis emerging within five to seven years.
According to the World Economic Forum (WEF), by 2030 an estimated 170 million new roles could be created globally (14% of current employment), while 92 million existing roles (8%) may be displaced, resulting in net growth of 78 million roles. However, this headline figure masks a deeper structural tension. Over the same period, global population growth of an estimated 338 million will place additional pressure on employment systems, productivity and social infrastructure.
For senior leaders, the defining issue is not whether AI creates or destroys more jobs in aggregate. It is whether organisations can manage the pace and sequencing of transition. Organisations that actively redesign work, invest in skills and support effective human-machine collaboration will be the ones better positioned to absorb disruption and realise productivity gains.
The WEF also indicates that while machine-led tasks are growing, the majority of work still requires human-led judgement or structured human-machine collaboration. Rather than whole roles disappearing, jobs are being reconfigured into portfolios of tasks, where routine activities are automated and human effort concentrates on judgement, creativity, emotional intelligence and strategic contribution.
Organisations must examine whether they are redesigning work intentionally, or allowing automation to reshape roles by default?
Evidence suggests that AI generates substantial economic value, but that value is unevenly distributed.
PwC’s 2025 Global AI Jobs Barometer found that AI-skilled workers earned a 56% wage premium in 2024, the most AI-exposed industries achieved 27% growth in revenue per employee (three times that of less exposed sectors), and productivity growth has almost quadrupled in industries most exposed to AI since generative AI’s advent in 2022, rising from 7% to 27%. These figures suggest that AI creates substantial value, but concentrates that value among workers who can effectively leverage the technology. AI does not automatically create productivity. It rewards preparedness.
The contrast between BMW and Klarna illustrates how strategic choices determine whether AI augments or erodes organisational capability.
In late 2024, BMW launched AIconic, a multi-agent AI system serving its purchasing and supplier network. The system integrates 10 specialised AI agents that streamline tender analysis, supplier data management and quality checks. With over 1,800 active users performing 10,000 searches monthly, the solution demonstrated immediate value.
What differentiates BMW is not the technology itself, but the organisational design accompanying it. Critically, BMW provides digital training and special AI innovation spaces for employees at all levels, enabling them to acquire digital literacy and share new skills throughout the organisation.
The financial results prove substantial: BMW’s AI stud correction laser alone saved over $1 million annually while enabling workforce optimisation and redeployment to higher-value activities. Rather than eliminating roles, BMW redesigned workflows around human-machine collaboration, with AI handling data-intensive tasks while humans focused on strategic supplier relationships and complex negotiations. The company now has hundreds of AI use cases in series production and plans to make every process AI-supported in the foreseeable future. Employees transitioned from routine data processing to relationship management and strategic decision-making, creating genuine career progression rather than displacement.
Swedish fintech Klarna pursued a dramatically different path. Between 2022 and 2024, the company eliminated approximately 700 positions (40% of its workforce), replacing most of them with AI-powered customer service systems developed with OpenAI. CEO Sebastian Siemiatkowski initially celebrated the transition, proudly announcing the workforce reduction and positioning Klarna as AI’s most aggressive adopter in fintech.
The consequences materialised rapidly. By early 2025, customer service ratings collapsed as users reported generic, repetitive responses inadequate for complex issues. The company’s Glassdoor rating plummeted from 3.8 in 2022 to 3.0, signalling severe damage to employee morale and employer brand. Siemiatkowski was forced to publicly admit: “Cost unfortunately seems to have been a too predominant evaluation factor. We went too far.”
By mid-2025, Klarna began rehiring human customer service agents, implementing what it termed an “Uber-style” flexible workforce model. The CEO acknowledged that AI systems lacked the empathy and nuanced problem-solving essential for customer support. The episode, dubbed “The Klarna Effect” by industry observers, represents a cautionary tale of AI deployment prioritising short-term cost reduction over sustainable capability development.
The differential outcomes between BMW and Klarna stemmed from strategic intent and execution discipline, not technology capability.
In the AI era, executives are increasingly responsible for leading human-machine systems rather than purely human ones. This requires fluency in AI and data capability, understanding of workflow architecture, governance literacy and organisational redesign competence. The leadership role shifts from command and control towards capability curation: setting direction, defining guardrails and ensuring alignment between strategy, systems and people.
When speaking to Rialto consultants, many leaders report limited confidence in their understanding of AI and uncertainty about where best to develop. Many report higher stress levels and say they are reassessing career sustainability in the face of accelerating technological change. This matters because leadership confidence and coherence strongly shape how change is experienced across an organisation.
AI investment that is matched by leadership capability consistently delivers stronger ROI. Where leadership understanding lags technology deployment, organisations risk destabilising workflows, eroding trust and undermining the very productivity gains AI promises. (See previous insights on AI Learning for Executives: Building Competence for Transformation and Transition and AI is Changing Everything – How can Executives Stay Ahead?)
Effective AI workforce transformation requires board-level governance that recognises AI adoption as strategic transformation, not merely operational implementation. Yet governance maturity remains uneven. A 2025 global survey by Deloitte of 700 board directors and executives across 56 countries found that 31% report AI is not on the board agenda, while 66% say their boards lack sufficient knowledge or experience in the domain.
This governance gap carries material consequences. According to MIT research, organisations with digitally and AI-savvy boards outperform peers by almost 11% in return on equity, while those without lag 3.8% below industry average. Meanwhile, analysis by McKinsey reveals only 15% of boards currently receive AI-related performance metrics, despite workforce transformation representing one of the highest-risk and highest-impact areas of AI deployment.
Strategic alignment therefore requires formal oversight mechanisms. Boards should mandate regular AI impact assessments covering ROI by business unit, the proportion of AI-enabled processes, workforce reskilling progress and regulatory alignment. Yet Deloitte reports that only 5% of organisations have fully incorporated AI into their core business plans, highlighting a material disconnect between ambition and integration.
Workforce capability oversight must also move beyond informal reporting. Human capital committees must track talent pipeline development, ensuring skills necessary for AI transformation are being built systematically. This includes monitoring reskilling participation rates, AI fluency at leadership levels and retention of AI-capable talent. Capital allocation frameworks must rigorously assess AI investment proposals, balancing short-term efficiency gains against long-term capability development and resisting the “Klarna temptation” to prioritise headcount reduction over institutional resilience.
Risk oversight requires structured approaches to monitoring algorithmic bias, data privacy breaches, compliance failures and workforce displacement risks. The AI Incident Database tracked a 26% increase in AI incidents from 2022 to 2023, with a further 32% increase in 2024.
Finally, boards must recognise cultural stewardship as a governance responsibility. AI strategy affects organisational reputation, employee trust and psychological safety, all of which materially influence adoption success. In the human–AI era, culture is strategic infrastructure.
Redesigning work is now a strategic leadership decision that determines whether AI amplifies human capability or erodes trust and engagement. The BMW example illustrates this principle: rather than automating entire procurement processes, BMW decomposed workflows into component tasks, assigned appropriate tasks to AI agents while elevating human roles to focus on strategic supplier relationship management, negotiation strategy and risk assessment requiring contextual judgement.
Process orchestration becomes a distinct capability requiring new roles and skills. Someone must design workflows determining when tasks move from human to machine and back, establish quality control mechanisms and identify failure modes.
Quality assurance mechanisms must evolve substantially, as AI systems produce outputs that appear authoritative but may contain subtle errors or contextually inappropriate recommendations.
Organisations that succeed treat human-machine redesign as core strategy, rather than a side-effect of technology adoption. They invest deliberately in workforce capability, embed AI into workflows with intent and prioritise organisational resilience over narrow cost reduction.
Not all roles can be redesigned or augmented indefinitely. Evidence suggests that up to 40% of current roles could be affected by AI, making some degree of workforce restructuring unavoidable. Responsible leadership requires early modelling of which functions are likely to consolidate within two years. Transparent communication and structured transition planning mitigate long-term cultural damage.
Where exit is inevitable, early honest communication and genuine transition support including career coaching and skills assessment, often serves employees better than extended uncertainty. The organisations managing this transition most effectively also provide reskilling for viable internal alternatives, clear timelines and meaningful severance and outplacement support that enable affected workers to plan their next moves while still employed.
As AI reshapes work and skills simultaneously, AI transformation depends on cultural readiness. Organisations that treat culture as a soft issue or delegate it entirely to HR typically struggle to scale AI beyond pilots.
CIOs and CDOs are increasingly required to work in close partnership with CHROs, CFOs and CPOs to align technology adoption with workforce design and capability development.
Leaders must ask, does the organisation reward learning, judgement and responsible experimentation, or does it default to risk aversion, silence and short-term cost control? The answer increasingly determines whether AI investment translates into sustainable growth. Klarna’s Glassdoor ratings fall demonstrates how aggressive AI deployment without cultural preparation can destroy the trust and psychological safety required for sustainable transformation.
The WEF projections suggest net job growth, but the maths reveal the deeper challenge: 78 million net new roles against 338 million population growth means transition management becomes the defining leadership competence of the next decade. Technology deployment is the simple part. Workforce transformation is the challenge that will differentiate successful organisations.
The executives who navigate this transition successfully will treat workforce capability as strategically foundational to successful technology deployment. They will invest in learning infrastructure as deliberately as they invest in computing infrastructure. They will redesign workflows around human-machine collaboration rather than automating legacy processes. They will communicate honestly about displacement risks while providing genuine transition pathways. They will choose augmentation over a displacement trajectory that hollows out.
The alternative is the worst-case scenario where short-term efficiency gains hollow out organisational capability, workforce displacement outpaces transition support and the benefits of AI accrue narrowly while the costs distribute broadly. This outcome is not inevitable, but as Klarna demonstrates, it is entirely possible when AI is treated primarily as a cost-reduction tool rather than as a strategic transformation requiring deliberate workforce design.
The human-machine era will not be defined by the speed of automation, but by the quality of organisational judgement guiding it. AI will reward those who design deliberately and penalise those who optimise prematurely. The question is no longer whether work will change but whether leaders will change fast enough to shape it.
Rialto partners with executives to navigate strategic workforce transitions in the AI era. We work alongside leadership teams to assess organisational capability, design human-machine workflows, and develop transition strategies that balance productivity gains with capability development. With deep expertise in executive capability development, transition and organisational transformation, Rialto provides trusted strategic counsel during periods of structural change and transition.
Contact Rialto on +44 (0) 20 3746 2960 to discuss your workforce transformation strategy or find out more about the AI Business Leaders Circle.
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