The assumption that “AI is just for young workers” may be costing UK organisations billions in unrealised productivity. Groundbreaking research from the London School of Economics challenges this narrative, revealing that a 60-year-old employee with proper AI training outperforms an untrained 25-year-old—and the productivity gap isn’t generational, it’s organisational.

Strategic Reality: The average enterprise employee now spends one-third of their time (34%) working on AI initiatives, yet 68% have received zero AI skills training in the past 12 months. This training gap, not generational differences, explains why only 70% of employees actively use AI—and why those who do achieve dramatically different results.

Beyond the “Digital Native” Myth: What Training Actually Delivers

Whilst 82% of Generation Z employees use AI tools compared to 52% of Baby Boomers, this adoption gap disappears almost entirely when training is present. The research surveyed 2,794 professional employees and 240 executives across 30 countries, uncovering a pattern that contradicts conventional wisdom: trained employees of any generation achieve double the productivity benefits of untrained workers.

The numbers are stark:

Training Status	Time Saved per Week	Productivity Gain	AI Adoption Rate
With recent training	11 hours (28% of time)	£24,810/year (senior roles)	93%
Without training	5 hours (14% of time)	£8,970/year (senior roles)	57%
Generation Z (trained)	11.5 hours (33%)	£20,730/year average	96%
Generation X (trained)	9.5 hours (25%)	£16,540/year average	90%

Strategic Insight: A Generation X employee who has received AI skills training in the past 12 months achieves greater productivity benefits from AI than a younger Generation Z employee who hasn’t been trained. This finding fundamentally challenges age-based AI deployment strategies and suggests training investment delivers higher returns than youth-focused recruitment.

The Real Story Behind the Headlines: Organisational Enablement, Not Generational Capability

The research reveals three interconnected challenges that organisations must address:

First, training distribution is uneven across generations. Only 25% of Baby Boomers receive AI skills training compared to 45% of Generation Z employees. Yet older workers are nearly twice as likely to adopt AI when training is provided—suggesting training investment may deliver higher marginal returns for senior employees who already possess deep domain expertise.

Second, younger employees dominate AI initiative teams despite evidence that generational diversity drives better outcomes. Nearly half (47%) of Generation Z employees work on AI development compared to just 30% of Generation X workers. However, AI teams with high generational diversity report 77% productivity rates versus 66% for homogeneous teams—an 11-percentage-point performance gap.

Third, motivation and belief in AI’s decision-making capabilities matter more than technical skill alone. Employees who are both personally motivated to use AI and believe it improves their decision-making save 11 hours per week (31% of working time), compared to 3.6 hours (10%) for unmotivated users. Recent training correlates strongly with both motivation and belief—suggesting training’s impact extends beyond technical capability to psychological engagement.

Critical Context: The research methodology involved structured surveys capturing self-reported time savings, productivity perceptions, and team diversity metrics. Whilst these findings align with established organisational psychology research on training effectiveness, they reflect employee perceptions rather than objectively measured output. Leaders should treat these findings as directional guidance requiring validation within their specific organisational context.

Success Factors Often Overlooked

The research identifies non-obvious drivers of AI adoption that standard change management programmes frequently miss:

• Workflow integration trumps standalone tools: 28% of non-adopters say they’d use AI if it were automatically included in existing software, compared to just 14% motivated by formal training programmes
• Peer success visibility drives adoption more than executive mandates: 31% cite “seeing colleagues successfully use AI” as the primary adoption trigger
• Role-specific workshops outperform generic training: 38% of non-adopters prefer hands-on workshops tailored to their job role versus 18% who favour online courses with certifications
• Financial incentives resonate across generations: 35% of non-adopters cite bonuses or salary increases tied to AI adoption as their top motivator, particularly among younger employees (44% of Generation Z)

Hidden Cost: The research reveals that 49% of employees not currently working on AI initiatives want to be involved and would dedicate approximately one-third of their time if given the opportunity. Meanwhile, those already working on AI want to increase their involvement from 34% (current) to 44% (desired). This represents significant unrealised capacity—organisations may be underutilising existing workforce appetite for AI transformation by an estimated 10-15 percentage points of employee time.

The Implementation Reality: From Research Findings to Organisational Practice

Translating these findings into operational reality requires addressing three implementation challenges that the research hints at but doesn’t fully explore:

Challenge 1: Measuring Productivity Beyond Self-Reported Time Savings

The research relies on employees’ perceptions of time saved rather than objective performance metrics. A Generation X employee reporting 9.5 hours saved per week may be achieving dramatically different business outcomes than a Generation Z employee reporting 11.5 hours saved, depending on task complexity, decision quality, and output value.

Mitigation Strategy: Organisations should supplement training programmes with outcome-based metrics aligned to strategic priorities. For customer service teams, this might mean resolution rates and satisfaction scores; for content teams, publication velocity and engagement metrics; for analytical teams, decision cycle time and forecast accuracy. Self-reported time savings provide directional guidance, but business impact requires independent validation.

Implementation Note: The research finds that employees using AI for their job roles report leveraging AI for 31% of day-to-day tasks on average (39% for Generation Z, 25% for Generation X). However, “proportion of tasks using AI” is an imperfect productivity proxy—automating five low-value administrative tasks differs fundamentally from augmenting one strategic decision. Training programmes should therefore emphasise task prioritisation alongside technical capability.

Challenge 2: Building Trust in AI-Generated Outputs Across Diverse Experience Levels

Less than half (49%) of AI users express confidence in the accuracy and reliability of AI-driven decisions, dropping to just 20% among non-users. The research identifies demand for human oversight (47% of non-users), clear explanations of AI decision-making (39%), and transparency around error rates (37%) as top trust-building interventions.

However, implementing these safeguards whilst maintaining the productivity benefits that make AI adoption attractive creates operational tension. Human-in-the-loop review processes for “critical decisions” can introduce bottlenecks that negate time savings if poorly designed.

Mitigation Strategy: Organisations should implement tiered governance frameworks that match oversight intensity to decision criticality and employee experience level. Junior employees working on high-stakes outputs might require senior review of AI-generated work, whilst experienced staff handling routine tasks operate with audit-based sampling. This approach maintains psychological safety without creating review bottlenecks.

Resource Reality: Designing and implementing a tiered AI governance framework requires approximately 12-16 hours initial setup (policy design, stakeholder alignment, communication planning) plus 4-6 hours monthly maintenance (metrics review, policy updates, training delivery). For SMEs without dedicated governance teams, this workload should be distributed across IT/operations (technical controls), legal/compliance (policy frameworks), and line management (frontline implementation).

Challenge 3: Maintaining Generational Diversity on AI Teams Despite Uneven Participation Rates

The research demonstrates that generationally diverse AI teams outperform homogeneous teams (77% versus 66% reported productivity), yet younger workers dominate AI initiatives (47% of Generation Z versus 30% of Generation X). This creates a self-reinforcing cycle: AI expertise concentrates among younger employees, making them more attractive for future AI projects, further marginalising older workers.

Mitigation Strategy: Organisations should implement quota-based or point-based diversity requirements for AI project team composition, similar to approaches used successfully for gender diversity initiatives. A minimum representation threshold (e.g., “AI project teams must include at least two employees over age 45” or “teams must score above 0.6 on the Simpson Diversity Index”) creates structural inclusion whilst allowing flexibility in team formation.

⚠️ Warning: Diversity requirements without corresponding training investment create compliance theatre rather than genuine inclusion. The research shows that older employees involved in AI initiatives report higher organisational commitment (58% of Millennials and 51% of Generation X on AI teams versus 36% not on teams), suggesting that meaningful participation drives engagement. Token inclusion without capability-building will fail to capture these benefits.

Strategic Takeaway: Reframing AI Adoption from Generational Replacement to Workforce Enablement

The core value proposition emerging from this research challenges a pervasive industry narrative: AI productivity gains stem from organisational enablement, not workforce demographics. The implicit assumption that AI adoption requires hiring younger employees or waiting for older workers to retire misses the central insight—training investment drives adoption and productivity regardless of generation.

Three Critical Success Factors

1. Training Universality with Role Specificity: Organisations achieve maximum impact by providing AI skills training to all employees (addressing the 68% training gap) whilst tailoring content to specific job functions. The research shows that 38% of non-adopters prefer hands-on workshops tailored to their role, suggesting one-size-fits-all training programmes underperform targeted interventions.

2. Generational Diversity as Performance Multiplier: Building AI teams with representation across age groups delivers an 11-percentage-point productivity advantage. This finding aligns with established cognitive diversity research—teams that combine different knowledge bases, problem-solving approaches, and professional networks generate more innovative solutions than homogeneous groups.

3. Motivation Through Visibility and Incentives: The research reveals that peer success visibility (31% adoption trigger) combined with financial incentives (35% motivator) creates stronger adoption momentum than top-down mandates. Organisations should therefore implement “AI champion” programmes that showcase internal success stories alongside compensation structures that reward productive AI use.

Reframing Success: From Technical Metrics to Human-Centred Outcomes

Traditional AI success metrics—adoption rates, time savings, cost reduction—miss the strategic transformation this research documents. Employees working on AI initiatives report higher organisational commitment (51% versus 36%) and belonging (61% versus 43%), suggesting that AI engagement strengthens the psychological contract between employees and employers.

For UK organisations navigating talent shortages and retention challenges, this finding reframes AI from a cost-reduction tool to a workforce engagement mechanism. The question shifts from “How do we replace expensive senior employees with AI?” to “How do we use AI to retain experienced workers whilst improving their productivity and satisfaction?”

Strategic Insight: The research documents that employees already working on AI initiatives want to increase their time allocation from 34% (current) to 44% (desired), whilst 49% of non-participants express interest in contributing one-third of their time. This represents approximately 10-15 percentage points of unrealised employee capacity—organisations may be constrained more by opportunity access than workforce capability or appetite.

Your Next Steps: Building an Age-Inclusive AI Transformation Programme

The research provides a roadmap for organisations seeking to maximise AI productivity gains whilst maintaining workforce diversity and psychological safety. Implementation requires coordinated action across three time horizons:

Immediate Actions (This Week)

• Audit current training distribution by generation: Calculate the percentage of employees in each age bracket who received AI skills training in the past 12 months. If the gap between youngest and oldest cohorts exceeds 15 percentage points, training investment is likely suboptimal
• Review AI project team composition: Analyse the generational makeup of current AI initiatives. If Generation Z and Millennials comprise more than 70% of team membership, diversity-based performance gains are being left unrealised
• Identify “AI champions” for peer learning: Select 3-5 employees across different generations who have achieved measurable productivity gains from AI use. Schedule internal showcases where they demonstrate specific use cases and time savings achieved

Strategic Priorities (This Quarter)

• Design role-specific AI training programmes: Replace generic “Introduction to AI” training with job function workshops (e.g., “AI for Financial Analysis,” “AI for Customer Service,” “AI for Project Management”). The research shows 38% of non-adopters prefer this approach versus 18% for standard courses
• Implement diversity requirements for AI teams: Establish minimum generational representation thresholds for AI projects. A practical starting point: “AI teams must include at least one employee from each of three different generational cohorts”
• Create tiered governance framework: Develop oversight policies that match review intensity to decision criticality, addressing the trust gap (49% of AI users confident in AI decisions versus 20% of non-users) without creating bottlenecks

Long-term Considerations (This Year)

• Link AI adoption to compensation structures: Design performance incentives that reward productive AI use rather than simply measuring adoption rates. The research shows 35% of non-adopters cite financial rewards as top motivator
• Build measurement infrastructure beyond self-reported time savings: Implement outcome-based metrics (decision quality, customer satisfaction, revenue impact) to validate whether reported productivity gains translate to business value
• Establish “reverse mentoring” programmes: Pair younger employees with strong AI technical skills with senior employees who possess deep domain expertise, creating bidirectional knowledge transfer that leverages generational diversity

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Source: Bridging the Generational AI Gap: Unlocking Productivity for All Generations (Protiviti & London School of Economics, 2025)

This strategic analysis was developed by Resultsense, providing AI expertise by real people. We help UK organisations implement Prompt and Context Engineering and AI Strategy & Governance solutions that maximise workforce productivity across all generations. If you’re navigating AI adoption challenges or seeking to build age-inclusive transformation programmes, we can help you translate research insights into operational reality whilst maintaining human oversight and psychological safety.