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On the journey to a productive workplace relationship between AI and employees, it can often feel like there are far more questions than answers.
What operating models must be re-evaluated? Should training and development programs focus on technical or human skills? How can leadership build trust when there is so much fear?
One leader with a blueprint for successful adaptation and adoption is Victoria Pelletier, Global EVP of Enterprise Transformation at Kyndryl, and a People, Process, Value & Change Practice Leader.
She speaks to HR Grapevine about the concept of organizational readiness for AI, shifting employees from fear to partnership, including the vital roles of holistic reskilling, change management, and leadership role modelling.
What does organizational readiness for AI look like at the business level?
AI readiness at the organizational level is a fundamental shift in how the business works, rather than just a checklist of tools and governance. Yes, governance, risk, and ethics frameworks must be in place, and yes, organizations must close critical skills gaps. But without rethinking operating models and ways of working, AI remains an unrealized promise.
The real measure of readiness is whether employees adopt AI into the flow of daily work, with trust and fluency. Until AI is embedded in processes, decision-making, and culture, organizations won’t capture the value. In other words, readiness is less about “installing AI” and more about designing a system where people, skills, and technology evolve together.
How about at the employee level?
At the employee level, readiness is about more than just technical fluency. It’s about feeling seen, supported, and trusted. Whole Human Leadership plays a pivotal role in this regard.
Employees need transparency in communications, so they understand not only what AI means for the business, but also what it means for their careers. They need a clear commitment to career development, with reskilling and upskilling programs that demonstrate the organization’s investment in them, not in replacing them.
The real measure of readiness is whether employees adopt AI into the flow of daily work, with trust and fluency
Most importantly, they need leaders who balance accountability with empathy – leaders who coach resilience, model curiosity, and create safe spaces for employees to experiment with AI. When employees feel their growth and humanity are prioritized, adoption becomes less about compliance and more about empowerment.
How can organizations embed change management across the business to drive employee adoption?
Too often, organizational change management (OCM) is treated like a sidecar on the vehicle of transformation. A nice to have, but not essential. It should be the engine.
Without a deliberate, enterprise-wide approach to change, the value of AI will remain largely theoretical. Embedding change management involves establishing the structures, rituals, and communication channels that enable employees to shift their mindset, from fear of replacement to confidence in augmentation, seeing AI as a trusted co-pilot in their work.
Think of OCM as the organization’s value assurance function. Just as financial audits ensure money is being used effectively, robust change management ensures that AI investments translate into adoption, behaviour change, and measurable outcomes.
When employees understand the “why,” have transparency into the journey, and feel supported through new ways of working, adoption accelerates. Only then do the promised productivity gains, innovation, and competitive advantage become real.
What does effective up-skilling or re-skilling look like? Who should lead this within global businesses?
Reskilling can’t live in the HR silo. It must be driven by the business, aligned directly to strategic priorities, and the skills that unlock growth. That means line leaders own defining the roles, capabilities, and future profiles they need. At the same time, HR becomes the enabler, building the platforms, learning pathways, and career models to scale those efforts globally.
Equally critical is governance. With AI, reskilling goes beyond capability to include compliance, ethics, and risk. This is where the Chief Risk Officer (CRO) and other governance leaders come in, ensuring that new skills are developed and applied responsibly, with guardrails in place for data use, bias, and resilience.
When done well, reskilling becomes a three-legged stool: business-led demand, HR-enabled delivery, and risk-governed oversight. That balance ensures programs don’t just teach technical skills, but embed trust, value realization, and long-term sustainability.
Beyond technical AI skills, what “soft” skills or capabilities do employees need & how can they be coached?
In the AI era, technical prowess is table stakes – but it’s our human capabilities that truly unlock value. The most in-demand skills of 2025 are adaptability, emotional intelligence, creativity, collaboration, communication, resilience, and critical thinking top the list.
The age of AI demands leaders who are both technically fluent and deeply human. Traditional command-and-control won’t cut it
Meta-skills – our ability to learn, self-reflect, and adapt rapidly – are increasingly essential. This includes self-awareness, resilience, and creativity, all of which enable employees to thrive in a fast-moving culture.
To coach these power skills effectively, organizations need to move beyond traditional classroom training and invest in blended methods.
That includes scenario-based learning, with real-world simulations that challenge creative problem-solving and resilience; peer coaching and mentoring to foster the development of emotional intelligence, active listening, and teamwork; leadership modelling, setting the tone with empathy, transparency, and adaptability; and reflective practices, such as journaling, debriefs, or peer circles, which help individuals build self-awareness and learning agility.
When coaches and managers invest in developing these human capabilities alongside technical skills through hands-on experiences, feedback loops, and role modelling, employees grow not just in competence, but in confidence and curiosity.
How does this impact the type of leadership skills needed?
The age of AI demands leaders who are both technically fluent and deeply human. Traditional command-and-control won’t cut it. Leaders now need skills in foresight, resilience design, ethical AI evaluation, and data stewardship, but those alone also aren’t enough.
Whole Human Leadership becomes the differentiator. Leaders must balance candor with compassion, being radically transparent about the opportunities and risks of AI while modelling curiosity and adaptability themselves. They must create environments of psychological safety where employees feel safe to experiment, fail, and learn. Empathy, vulnerability, and inclusivity are the glue that builds trust during times of uncertainty and rapid change.
In short, effective leaders in the AI era are translators of data into business value, and of fear into confidence. They ensure their organizations don’t just implement technology but adopt it in ways that unlock both performance and people’s potential.
How do HR/leadership teams build trust with their workforce at a time when there is so much fear?
Trust is built through radical transparency, empathy, and inclusion. Workers need to see AI not as a threat but as a co-pilot. That requires leaders to openly acknowledge their fears, set clear boundaries (on how AI will and won’t be used), and invest visibly in their people through upskilling and career pathways. Recognition programs and career models that include AI-enabled roles reinforce the message: “We’re not replacing you with machines—we’re equipping you to thrive alongside them.”
