The Human Edge in an AI-Accelerated World.
"AI will not fail because organisations lack technology access. It fails because they lack the people capability to make it work."
This report is the result of an extensive research initiative conducted by the Simitri team throughout 2025 and 2026. Drawing on our access to leading international research organisations, HR and L&D thought leaders, functional experts, and senior business practitioners, we reviewed and analysed a wide range of global studies, industry reports, and executive insights.
The research base includes primary studies published by Gallup, Deloitte, McKinsey, Gartner, the World Economic Forum, Grant Thornton, HiBob, TechClass, Resume.org, and other respected institutions, representing the views of tens of thousands of business, HR, and learning leaders across more than 50 countries. These insights were complemented by original findings from the Simitri Learning Trends Survey 2026, completed by HR and L&D leaders across Asia Pacific, the Middle East, Europe, and the Americas.
Rather than simply reporting the findings, the Simitri team critically analysed the research, identified common themes, assessed emerging patterns, and evaluated the implications for organisations, leaders, and learning functions. The trends presented in this report are therefore not a ranking of research findings, but Simitri's assessment of the developments most likely to shape workforce capability, leadership effectiveness, and organisational performance in the years ahead.
Named organisations, case studies, and examples are drawn from publicly available research and documented programmes. Where we offer our own interpretation, recommendations, or perspective, these are informed by more than two decades of partnering with organisations across industries and regions to develop leaders, build capability, and drive organisational transformation.
AI tools were used during the preparation of this report solely as a supporting aid to help organise, structure, format, and refine the presentation of ideas developed and reviewed by the Simitri team. AI was not used as a substitute for the research, analysis, professional judgement, or conclusions presented in this report.
The research provides the evidence. The insights and conclusions are Simitri's.
For the Simitri Learning Trends Survey 2027, we invited more than 2,500 professionals across our global network to share their perspectives on the forces shaping leadership, learning and organisational capability.
Spanning HR and L&D professionals, senior leaders, people managers and business practitioners, these perspectives, considered alongside wider market research, helped inform the three trends explored in this report.
A cross-section of HR, L&D, and business leaders, weighted toward the people closest to capability decisions.
If the headlines are to be believed, artificial intelligence is the defining challenge facing organisations today. Every week brings new predictions about disrupted industries, transformed jobs, and unprecedented productivity gains.
Perhaps. But history suggests that leaders should view such claims with a healthy degree of scepticism.
Every major technological shift has been accompanied by a wave of excitement, urgency, and fear of being left behind. From personal computers to the internet, to Y2K, organisations have repeatedly been told that they must move immediately or risk becoming irrelevant. These predictions did not prove accurate, many were overstated. The winners were rarely the organisations that adopted technology the fastest. They were the organisations that developed the capabilities needed to adapt, learn, and evolve as technology matured.
Predicted mass job loss.
Outcome: new roles outpaced losses.
Predicted overnight transformation.
Outcome: a decade-long adaptation curve.
Predicted global systems collapse.
Outcome: largely overstated.
Predicted total workforce disruption.
Outcome: still being written.
The question is not whether AI will reshape work; it already is. The defining challenge is whether organisations are investing as aggressively in human capability as they are in technology, because AI's value will ultimately be determined by the people leading, managing, and using it.
The same principle applies today.
The research reviewed in this report points to a striking conclusion. While much attention is focused on technology, the most significant challenges facing organisations are increasingly human. Leadership effectiveness, critical thinking, judgement, adaptability, collaboration, learning agility, and organisational capability have become the true differentiators of performance.
This report explores three interconnected trends shaping the future of workforce development:
1. The Leadership Deficit: Why AI Success Now Depends on Human Capability, Not Technology
As AI accelerates change, the quality of leadership becomes an even greater determinant of organisational success.
2. AI is Becoming a Thinking Partner at the Exact Moment Humans Most Need to Learn How to Think for Themselves
The same activities that have traditionally developed professional judgment, researching, analysing, synthesising information, and formulating recommendations are increasingly being performed by AI. While this creates significant productivity gains, it also raises a critical question: if technology performs the work through which people historically learned to think, where will the next generation of judgement, decision-making, and leadership capability come from? The challenge for organisations is no longer simply adopting AI. It is ensuring that employees continue to develop the critical thinking and judgement needed to use it wisely.
3. Building the Enterprise Capability Ecosystem
Sustainable performance will not come from isolated training initiatives but from creating organisation-wide systems that continuously develop capability at scale.
Taken together, these trends point to a simple but powerful conclusion: the organisations that succeed in the age of AI will not be those with access to the best technology. They will be those that build the strongest human capability systems around it.
"AI will not fail because organisations lack technology access. It fails because they lack the people capability to make it work."
Since AI exploded 4 years ago, most organisations are now well past understanding the benefits of AI adoption. The real differentiator heading into 2027 is no longer access to tools, platforms, or investment, it is whether organisations have built the human capability to use AI responsibly, effectively, and at scale.
At the top of the organisation, leaders are being asked to govern AI with confidence. Yet Grant Thornton's 2026 research shows 78% of senior leaders lack confidence they could pass an independent AI governance audit within 90 days. At the same time, Deloitte reports that organisations taking a technology-first approach to AI are 1.6 times more likely to miss expected returns than those building human capability alongside it.
But the bigger issue is not at the top……….It is in the middle.
We know that the success or failure of any technology adoption is determined by people managers. The layer responsible for transferring technology into daily business process and employee behaviour, trust, and execution.
Gallup's 2026 global research shows that employees whose managers actively champion AI are 8.7 times more likely to say it has transformed how they work. The technology is identical. The difference is a prepared and innovative management layer.
Organisations design AI strategies at board level, approve investments at executive level, and deploy tools at enterprise level. But adoption, trust, and performance are determined in teams and most managers have not been equipped to lead that shift.
The result is a compounding risk.
01
are underprepared to govern AI with confidence
02
are under-equipped to translate AI into practice
03
are left to interpret change without support
We see this compounding into something closer to an execution deficit: change accumulating faster than people can absorb it, quietly building until it surfaces as disengagement, stalled adoption, or outright resistance. The scale is stark. The average employee absorbed more than 15 major organisational changes in the past year alone, and fewer than three in ten leaders believe their organisation manages change well.
This deficit ultimately erodes team performance rather than technical systems, leaving managers to either mitigate or amplify the fallout.
Our research definitively reinforces this dynamic:
The pattern is consistent: when managers are developed, AI adoption and change in general accelerates. When they are not, it stalls.
High-performing organisations treat leadership capability, especially people managers, as critical infrastructure, not a discretionary line item.
They embed AI governance and ethical decision-making into leadership development (not policy documents alone).
They equip managers to actively drive adoption, trust, and learning transfer inside teams.
They equip teams with a growth mindset ready to take chances with new business process supporting technology advancements.
They embed AI governance and ethical decision-making into leadership development (not policy documents alone).
They equip managers to actively drive adoption, trust, and learning transfer inside teams.
They equip teams with a growth mindset ready to take chances with new business process supporting technology advancements.
This creates alignment between strategy (executives), translation (managers), and execution (teams).
Deutsche Bank's First Time Manager Programme demonstrates what treating leadership capability as infrastructure looks like in practice. Launched in 2022 and anchored to the bank's own Leadership Kompass framework, participation is mandatory for every employee stepping into a people-management role for the first time. A separate, distinct track exists for senior leaders: the bank treats becoming a manager and becoming a senior leader as two different capability transitions, each requiring its own design, rather than one generic leadership curriculum stretched across both.
More than 4,650 managers across 36 countries have completed it to date, evidence the model holds up under the test most programmes fail: scaling across geography and business unit without collapsing into a checkbox exercise. Simitri partnered with Deutsche Bank on the programme's design and delivery, work later recognised with Gold for Best Leadership Development Programme at the 2025 Brandon Hall Group HCM Excellence Awards.
The biggest risk to AI transformation is not technology failure, it is team adoption, it is employee underdevelopment.
Organisations that invest only in systems will continue to see disappointing returns. Organisations that invest in team capability, especially the manager layer, will turn AI from deployment into positive business performance.
Because in the AI era, technology creates possibility, but managers determine outcomes.
For L&D leaders, this trend is not just an observation. It is a positioning shift.
To gain a seat at the executive table, the conversation must move away from training demand and toward enterprise performance risk and value creation.
Instead of asking for approval of programmes, L&D leaders should be framing the issue in terms of business outcomes:
This reframes L&D from a support function into a critical enabler of transformation performance.
To secure a consistent seat in executive strategy discussions, L&D leaders must position themselves as owners of the "execution layer of strategy" the system that determines whether investment translates into impact. That means bringing data on manager capability, adoption readiness, and behavioural risk into the same conversations where AI strategy, investment, and governance are decided.
The organisations that will lead in 2027 are already making this shift: they are no longer asking whether people are "trained," but whether employee capability is strong enough to deliver the strategy.
That is the conversation L&D must now own.
For decades, early career roles served a dual purpose. They delivered value to the organisation and they built the judgment, instinct, and professional capability of the next generation of leaders. That pipeline is changing faster than most organisations have prepared for and the implications extend well beyond this generation.
Resume.org's 2026 survey of nearly 1,000 US business leaders found that 21% of companies have already stopped hiring entry-level employees due to AI. Half will do so by 2027. One in three expect entry-level roles to be eliminated at their organisations by the end of 2026. The World Economic Forum's Future of Jobs Report 2025 confirms the structural shift: the tasks that once defined junior roles are among those most rapidly being reshaped by AI tools, fundamentally changing what entry-level work exists and what it requires.
Historically, junior employees developed judgment through repetition.
AI can now perform many of those tasks in seconds.
The productivity gain is real.
BUT the developmental loss could be enormous for generations to come.
A second trend is emerging beneath the talent pipeline discussion and it may prove even more significant over time.
The risk is not that people stop working.
The risk is that they stop thinking.
For decades, junior roles acted as a training ground for professional judgment. Employees learned by analysing information, wrestling with ambiguity, making decisions, defending recommendations, and learning from mistakes. These activities were often time-consuming and inefficient but they were also developmental.
Today, many of those same activities can be delegated to AI.
When used well, AI accelerates learning.
When used poorly, it can replace it.
The danger for organisations is the gradual erosion of critical thinking, problem-solving, business judgment, and decision-making capability across an entire generation of talent. Employees become highly proficient at prompting AI, but less capable of evaluating the quality of its output, challenging assumptions, identifying risks, or knowing when the answer is wrong.
Organisations that moved too aggressively are encountering what no AI capability roadmap prepared them for: the irreplaceable value of human judgment, empathy, and complex problem-solving in the situations that matter most.
The question is not whether people are needed. It is how to develop them when the traditional entry point no longer exists. The tasks that once quietly built junior capability are being automated. The judgment those tasks were developing is not. And without a deliberate replacement model, organisations are eliminating the very experiences that produced their future leaders.
This is one of the most consequential gaps we see forming in organisations right now. The decisions being made about entry-level hiring in 2026 will show up as a leadership pipeline problem in 2029 and 2030, and by then, the window to address it through deliberate development will have narrowed significantly.
In the AI era, the most valuable employees may not be those who can generate answers fastest.
They will be those who know when the answer should not be trusted.
Our survey reveals several emerging trends in early career development:
Clear plan for early career development in an AI-enabled workplace
Hiring fewer entry-level roles but have not redesigned early career development
Believe declining entry-level roles will create a leadership capability gap within five years
Current early career programmes were designed for a pre-AI environment and have not been meaningfully updated
Source: Simitri Learning Trends Survey 2027
Accenture offers a live example of this choice being made in real time. In March 2026, CEO Julie Sweet confirmed the firm was increasing entry-level hiring even as AI absorbed the routine analytical work that once defined junior roles, reshaping those roles around problem-solving, strategic thinking, and communication rather than eliminating them. Accenture's graduate training has been updated accordingly: new hires now build AI fluency alongside a deliberately stronger emphasis on the judgment and interpersonal capability AI cannot replicate.
The signal is unambiguous. When automation compresses the tasks that once trained judgment, the organisations that win are not the ones that cut the pipeline. They are the ones that redesign it.
Deloitte's NextGen Leaders programme takes a similar approach, building cohort-based development, cross-functional exposure, and structured mentoring into the earliest career years, explicitly designed to develop the leadership judgment and interpersonal capability that the firm's research shows are the true predictors of long-term performance. The programme was significantly redesigned following the acceleration of AI tool adoption within the firm, not to accommodate AI, but to develop the human capabilities that AI adoption made more important.
The programmes rebuilding the early career pipeline most effectively combine four elements:
Experience without structure produces inconsistent development. Structure without experience produces theory. The combination designed intentionally, sustained over time produces leaders.
This trend represents one of the most important strategic conversations L&D leaders should be having with executives today.
How do we prepare people to use AI?
How do we ensure AI does not replace the developmental experiences that create future leaders?
L&D leaders should challenge their organisations to think beyond productivity gains and workforce reductions.
If AI is performing the work that previously developed judgment, where will that judgment now come from?
This positions L&D at the centre of a critical business issue: protecting the future leadership pipeline.
To do this, learning leaders should focus on four priorities:
Create structured experiences that intentionally build critical thinking, decision-making, stakeholder management, communication, and business judgment rather than relying on traditional task-based learning.
Teach employees how to challenge AI outputs, identify weaknesses, test assumptions, and apply human reasoning rather than simply accepting generated answers.
Move emerging talent earlier into customer interactions, cross-functional projects, problem-solving activities, and decision-making environments where judgment can be developed through experience.
Track the development of critical thinking, decision quality, collaboration, adaptability, and leadership readiness with the same rigour applied to AI adoption metrics.
The organisations that gain the greatest advantage from AI will not be those that automate the most work.
They will be those that use AI to amplify human capability while continuing to develop the judgment, curiosity, and leadership potential that technology cannot replace.
Because while AI can accelerate expertise, it cannot develop wisdom.
That remains a uniquely human capability.
"The organisations that outperform in the AI era will not be those with the most learning programmes. They will be those with the strongest capability ecosystems."
As AI takes over more routine, analytical, and process-driven work, a paradox is emerging. Organisations may require fewer people in certain roles, but the people they do retain will need to operate at significantly higher levels of judgment, adaptability, leadership, collaboration, and innovation.
The future workforce will not simply need to do more. It will need to do what AI cannot.
Creativity. Strategic thinking. Human connection. Ethical judgment. Influence. Collaboration across increasingly complex and interconnected environments.
These capabilities do not emerge from a single training programme, leadership workshop, or digital learning platform. They are developed through a broader organisational ecosystem that intentionally supports growth at every stage of the employee experience.
Leading organisations are therefore shifting their focus away from individual learning interventions and towards the design of enterprise-wide capability ecosystems.
These ecosystems connect leadership development, manager capability, coaching, mentoring, career pathways, skills measurement, talent processes, learning culture, performance management, and workforce planning into a coherent whole. Rather than operating as separate initiatives, each element reinforces the others, creating an environment where development becomes continuous rather than episodic.
The distinction is important.
Our research highlights this challenge clearly. While 61% of organisations report that their capability-building efforts are effective at the individual level, only 34% believe they have a coherent and connected capability development strategy across the enterprise.
This finding suggests that the challenge facing most organisations is not a lack of learning activity. It is a lack of organisational alignment.
Employees attend programmes. Managers receive training. Leaders participate in coaching. Skills are assessed. Yet these activities often operate independently, without a unifying architecture that connects development to organisational priorities and long-term workforce capability.
The result is fragmented investment rather than cumulative capability.
This matters because the organisations creating sustainable competitive advantage are increasingly distinguished by the quality of their human systems.
Deloitte reports that 88% of leaders believe the ability to effectively orchestrate people and work is critical to organisational success, yet only 7% believe they are making significant progress. The gap is not primarily a skills gap. It is an ecosystem gap.
Gallup's research reinforces this point. The strongest predictor of successful AI adoption is not the technology itself, but whether managers actively support and encourage employees to use it. Human capability remains the multiplier that determines whether technology investments create value.
The World Economic Forum's Future of Jobs Report 2025 points in the same direction. Leadership, social influence, systems thinking, curiosity, lifelong learning, and self-awareness continue to rise in importance. As AI assumes more technical and transactional work, uniquely human capabilities become more valuable, not less.
Our survey findings reflect this reality.
percent of respondents believe human capability development will be their most important source of competitive advantage by 2027.
report growing demand for leadership development, facilitation, coaching, and behavioural change initiatives.
The implication is clear.
The future belongs to organisations that treat capability development not as a collection of programmes, but as an enterprise capability ecosystem. One that continuously develops talent, accelerates adaptation, and ensures that every investment in people contributes to a larger organisational capability strategy.
In the age of AI, technology may provide scale. But it is the capability ecosystem that determines whether an organisation can translate that scale into sustained performance.
Salesforce's AI Fluency Playbook, launched in January 2026 by Chief People Officer Nathalie Scardino, offers one of the most explicit public models of this integration. Rather than treating AI skills and human capability as separate tracks, the framework defines fluency in three progressive stages: Engagement, Activation, and Expertise, with Expertise explicitly combining AI proficiency with human skills such as adaptability and judgment. The approach reflects a deliberate bet: that AI adoption succeeds or fails on the human layer built around it, not the technology itself.
Standard Chartered is a useful example precisely because it is not a finished blueprint. It is an organisation mid-transition, and that may be the more instructive case for 2027. The bank already runs strong capability pathways at opposite ends of the career spectrum: an eighteen-month International Graduate Programme built to develop future business leaders from entry level, and, at the senior end, a 2026 programme with the University of Cambridge Institute for Sustainability Leadership, whose inaugural cohort of Market Heads and Team Leaders spanned Singapore, Hong Kong, Dubai, and the UK.
What changed in January 2026 is more revealing than either programme on its own: the bank created a new Global Head of HR role for Technology and Operations, with an explicit mandate to align capability, culture, and investment across the function, rather than leaving each to develop on its own track. We read that appointment as evidence of a distinction most organisations have not yet made: that strong individual programmes and a connected capability system are two different achievements, and closing the gap between them requires someone with the authority, and the job title, to own it.
What Salesforce and Standard Chartered share, at different stages, is the same underlying recognition: individual programmes develop individuals. A capability architecture develops an organisation. Salesforce shows what that architecture looks like once built. Standard Chartered shows the harder, more common moment: the point at which an organisation with genuinely strong individual programmes decides it needs one connected system, and gives someone the authority to build it.
The organisations making the most progress are not running more programmes. They are connecting the programmes they already have into a coherent system, where leadership development, manager investment, early career design, skills measurement, and learning culture reinforce each other. The question is not what to add. It is how to align what already exists around a clear, shared picture of what great capability looks like at every level. That alignment is the work. And in our experience, it is the work that changes everything, not just for L&D, but for the organisation as a whole.
The human layer of performance is not a programme. It is an organisational capability system and building it coherently is the defining L&D leadership challenge of 2027. Facilitation, behavioural change, leadership development, coaching, and human connection are not supplementary to an AI strategy. They are the conditions that determine whether an AI strategy delivers. AI will not differentiate organisations. Capability will. And capability, at its deepest, is a human achievement built person by person, team by team, over time, with intention.
Every week seems to bring a new announcement, a new capability, or a new prediction about how work will be transformed. Yet leaders should be careful not to confuse hype with reality.
AI is undoubtedly a powerful technology, but not every organisation needs to be at the bleeding edge of adoption. While a small number of companies gain competitive advantage by moving first, the majority benefit more from taking a thoughtful and evidence-based approach. The goal should not be to adopt AI because everyone else is doing so; it should be to understand where it can create genuine value for customers, employees, and the business.
Our advice is simple: observe carefully, research rigorously, and separate facts from forecasts. Build a clear understanding of which AI applications are relevant to your organisation and where the technology can meaningfully improve performance.
Most importantly, focus on preparing your people. Technology will continue to evolve long after today's AI tools have been replaced by the next wave of innovation. The organisations that thrive will not necessarily be those with the most advanced technology, but those with the most adaptable workforce. New technologies should be adopted to enhance human capability, not simply replace it.
Rather than preparing employees only for AI adoption, prepare them for continuous adaptation. That capability will outlast any single technology trend.
No sources are drawn from competitor organisations or commercially conflicted publishers. Survey data: Simitri Learning Trends Survey 2027 · 847 respondents · Asia Pacific, Middle East, Europe, Americas.
Gallup State of the Global Workplace 2026 - 140+ countries · Q1 2026 · gallup.com
Deloitte 2026 Global Human Capital Trends - 9,000+ business and HR leaders · 89 countries · March 2026 · deloitte.com
WEF Future of Jobs Report 2025 - 1,000+ companies · 22 industries · 55 countries · weforum.org
Grant Thornton 2026 AI Impact Survey - ~1,000 senior business leaders · United States · 2026 · grantthornton.com
McKinsey AI Trust Maturity Survey 2026 - Global · March 2026 · mckinsey.com
Gartner Talent Management Trends 2026 - 919 employees aged 22–27 · Q2 2025 · gartner.com
Gartner Talent Acquisition Trends 2026 - October 2025 · gartner.com
Gartner Future of Supply Chain 2026 - 509 global supply chain leaders · July–October 2025 · gartner.com
Gartner AI Job Displacement Research - February 2026 · gartner.com
Resume.org 2026 Business Leaders Survey - ~1,000 US business leaders · resume.org
HiBob Leadership Learning Report - January 2026 · hibob.com
TechClass · Measuring the ROI of Leadership Development Initiatives - February 2026
Tapaswee Chandele · SVP Global Talent · Coca-Cola · Publicly documented programme discussion
Cisco Employee Pulse Survey & Engagement Practices · Publicly documented
Microsoft Work Trend Index 2024–2025 · microsoft.com
