How can organizations prepare people for jobs that artificial intelligence is already beginning to change? My new paper in Strategic HR Review introduces the Skills Reset: a framework for moving from reactive reskilling toward continuous capability renewal in the age of AI.
Download the free Author Accepted Manuscript (PDF):
CLICK HERE (https://marlowguttmann.de/skills-reset-paper)
Artificial intelligence is changing work.
But are organizations changing how people learn at the same speed?
AI can draft, summarize, analyze, classify and recommend. Tasks that once required considerable human effort can increasingly be automated or augmented. Job titles may remain the same while the actual work underneath them changes substantially.
This creates one of the central challenges for HR, Learning & Development and the future of work:
How do we prepare people for roles that AI is already beginning to redesign?
This question is at the heart of my new practitioner paper:
The Skills Reset: Redesigning Learning Before AI Redesigns Roles
Published in Strategic HR Review by Emerald Publishing, the paper introduces the Skills Reset as a practitioner-oriented conceptual model for helping organizations redesign learning before AI redesigns roles.
Its central argument is simple:
Reskilling is necessary. But reskilling alone is no longer enough.
Organizations need to develop the ability to anticipate changes in work, continuously renew capabilities and strengthen human judgement before disruption becomes visible.
The problem: Learning often comes too late
- A familiar pattern can be observed in many transformations.
- A new technology is introduced.
- Processes change.
- Roles begin to evolve.
- New skill gaps appear.
- And only then does learning follow.
- With artificial intelligence, that sequence becomes increasingly problematic.
The paper argues that AI transformation is not simply a technology or reskilling challenge. It is also a workplace management challenge requiring what I describe as anticipatory capability renewal.
Instead of asking only:
“What AI training do our employees need?”
organizations should increasingly ask:
“How must our organization learn before, during and after AI changes work?”
That shift is at the heart of the Skills Reset.
What is the Skills Reset?
The Skills Reset moves workforce development away from isolated training interventions toward continuous capability renewal.
Rather than waiting for skills to become obsolete, organizations continuously examine how AI is changing tasks, roles, decision-making and value creation and what those changes mean for people.
This matters because the current AI skills debate can easily become too focused on tools.
Prompting matters. AI literacy matters. Technical competence matters.
But learning how to operate an AI system is different from knowing when to trust it, when to question it and when human judgement needs to take precedence.
The Skills Reset therefore connects technological transformation with organizational learning, workforce development, leadership, employee voice and human judgement.
The research brings together insights from AI-mediated work, task-based technological change, dynamic capabilities, workplace learning, psychological safety and employee voice and translates them into practical approaches for HR leaders, learning professionals, managers and executives.
The Skills-Reset Flywheel: learning as an organizational capability
One of the central concepts of the paper is the Skills-Reset Flywheel.
It contains six interconnected dimensions:
- Role foresight anticipates how AI may change tasks, responsibilities, role boundaries and decision rights.
- Capability diagnosis identifies which skills, behaviors and forms of judgement are becoming more important, declining or fundamentally changing.
- Learning architecture redesign moves learning beyond isolated courses toward role-based pathways, peer learning, coaching, communities of practice and learning embedded in everyday work.
- Human judgement development strengthens critical thinking, ethical reasoning, contextual interpretation and the ability to question AI-generated outputs.
- Transition support recognizes the psychological and social dimension of technological change and the importance of trust, employee voice and psychological safety.
- Feedback and renewal turns experiences from AI adoption back into learning and workforce strategy.
The idea of a flywheel is deliberate.
AI transformation does not have a clear endpoint. Technology changes, tasks evolve, new capabilities become necessary and organizations discover new possibilities for using AI.
Learning therefore needs to become an organizational capability rather than a downstream training response. That is also one of the central findings of the published paper.
RESET: five questions for HR and leadership
The broader idea is translated into a simpler implementation framework: RESET.
R – Role foresight
How will AI reshape tasks, boundaries and decision rights?
Organizations need to understand what is changing underneath existing job titles.
E – Emerging capability mapping
Which capabilities are emerging, declining or changing?
This moves skills analysis beyond today’s competency profiles.
S – Skill renewal architecture
How must learning be redesigned for continuous renewal?
The answer will rarely be another isolated training course.
E – Employee transition support
How will people be supported through role change?
AI transformation affects confidence, professional identity and perceived employability as well as skills.
T – Transformation feedback loop
How will AI adoption continuously update the skills strategy?
Learning from implementation needs to flow back into workforce development.
The model can be used in workforce planning, AI implementation, leadership development and organizational learning workshops.
From “Which jobs will AI replace?” to a better question
One of the problems with the current future-of-work debate is its fascination with entire occupations.
Will AI replace lawyers?
Will it replace HR professionals?
Will it replace consultants?
Those questions can obscure what is actually happening inside organizations.
Work consists of tasks, and different tasks within the same role can have very different relationships with AI.
The paper therefore also introduces an AI Role-Change Matrix, distinguishing between the potential impact of AI on a task and the level of human judgement required.
This produces four possible directions:
- Automate: AI impact is high while the requirement for human judgement is comparatively low.
- Redesign: Both AI impact and human judgement are high, creating a need for deliberate human-AI collaboration and clear accountability.
- Streamline: AI impact and judgement requirements are relatively low, allowing proportionate efficiency improvements.
- Protect and develop: AI impact is lower while human judgement remains high, making expertise, mentoring, ethical reasoning and professional development particularly important.
The point is not to classify people.
It is to understand work more precisely.
Human judgement could become more valuable, not less
There is a paradox at the center of AI transformation.
As artificial intelligence becomes more capable, it is tempting to assume that human capability becomes less important.
In many situations, the opposite may happen.
AI can generate an answer.
- But someone still needs to determine whether that answer makes sense.
- Someone needs to understand the context.
- Someone needs to recognize uncertainty.
- Someone needs to consider ethical consequences.
- And ultimately, someone needs to remain accountable.
This is why human judgement occupies such a central position in the Skills Reset.
The future of work should not only be about teaching people how to use AI.
It should also be about strengthening the distinctly human capabilities required to question, contextualize and take responsibility for AI-supported decisions.
AI transformation is also a question of fairness
AI-driven skill transformation affects more than productivity.
It can influence employability, professional identity, access to opportunity and perceptions of fairness.
Employees do not enter AI transformation from equal starting positions. Access to learning, digital confidence, professional experience and opportunities to experiment differ.
If organizations redesign work without redesigning learning, technological transformation may therefore contribute to capability polarization.
That is why the Skills Reset emphasizes employee involvement and transition support alongside technological adoption.
Emerald summarizes this social implication explicitly: responsible AI adoption means involving employees, supporting transitions and protecting human judgement.
A framework to test, not a finished answer
There is an important limitation to the Skills Reset.
It is a conceptual practitioner model and has not yet been empirically validated.
That distinction matters.
Future research should test the Skills-Reset Flywheel across industries, occupations and different organizational sizes. Questions around capability measurement, AI-related skill uncertainty and potential capability inequality deserve particular attention. Longitudinal and comparative case studies could help establish the model’s boundary conditions and unintended consequences.
So I do not see the Skills Reset as a finished answer.
I see it as a framework to use, challenge, test and develop further.
And that is one reason why I want the research to be accessible beyond the journal paywall.
Read “The Skills Reset” Open Access
The official Version of Record of my paper was published by Emerald Publishing in Strategic HR Review on 10 September 2026. The publication currently spans pages 1–7 and is available through Emerald Insight.
Official publication on Emerald Insight
At the same time, Emerald’s Green Open Access policy allows authors to make their Author Accepted Manuscript (AAM) publicly available on their personal or company website immediately following official publication, without an embargo period.
Publication details
Guttmann, Marlow D. (2026). “The skills reset: redesigning learning before AI redesigns roles.” Strategic HR Review, pp. 1–7. Emerald Publishing.
Article type: Practitioner Paper
Published: 10 September 2026
Author: Marlow D. Guttmann
Corresponding author: Marlow D. Guttmann
DOI: 10.1108/SHR-06-2026-0069
Download the free Author Accepted Manuscript (PDF):
CLICK HERE (https://marlowguttmann.de/skills-reset-paper)
Abstract
Artificial intelligence is changing tasks, role boundaries and decision-making faster than many organisations are changing learning. While organisations invest in AI tools, pilots and productivity programmes, learning often remains a downstream response: a course after implementation, a reskilling initiative after role expectations have shifted, or an AI literacy module detached from work redesign. This practitioner paper introduces the Skills Reset as a strategic process through which organisations continuously reassess, renew and reconfigure the capabilities required for meaningful work in response to AI-mediated changes in tasks, roles and value creation. It presents the Skills-Reset Flywheel as a practical conceptual model with six dimensions: role foresight, capability diagnosis, learning architecture redesign, human judgement development, transition support, and feedback and renewal. The paper translates research on task-based technological change, organisational learning and dynamic capabilities into a usable framework for HR, learning and leadership practice. Its central argument is simple: in the age of AI, reskilling is necessary, but too narrow. Organisations need learning systems that anticipate role change, protect employability and strengthen human judgement before AI redesigns work.
Keywords: Artificial intelligence; Skills Reset; Reskilling; Organisational learning; Human judgement; HRD; Workforce transformation; Future of work.
Open Access and reuse
I want the ideas behind the Skills Reset to be accessible to researchers and practitioners and to be tested, discussed and developed further.
I am therefore making the Author Accepted Manuscript available under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) in accordance with Emerald Publishing’s Green Open Access policy.
Under this license, anyone may distribute, adapt and build upon the AAM for non-commercial purposes, subject to full attribution. Commercial reuse requires separate permission.
So if you are working in HR, Learning & Development, organizational development, leadership, AI transformation or future-of-work research, you are welcome to engage with the Skills Reset within these license terms.
If you test the Skills-Reset Flywheel, RESET model or AI Role-Change Matrix in research or organizational practice, I would be very interested to hear what you learn.
Because ultimately, the future of work will not be determined by technology alone.
Before AI rewrites roles, organizations need to rewrite how they learn.
Licensing statement
This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please visit Emerald Publishing’s Marketplace.