By Oliver Sam, CHRO, VDart Group
Artificial Intelligence has turned from an idea for the future to a part of everyday life. It is changing whole industries, changing how we do work, and changing people in work. Some jobs are going away, new work is being created, and organizations are going faster than ever. The real challenges, however, are still human.
Leaders have a hard job: using AI to improve performance while maintaining trust, engagement and a collective cause.
Culture Shapes the Impact of AI
The tendency is often to focus on efficiency improvements. AI can automate repetitive tasks, speed up decision-making and produce insights, that were once elusive. However, the adoption of technology provides access to gaps in culture, including how decisions get made, how teams come together, or what the employee experience is like, all change when AI is introduced.
Leadership is not challenged by how fast an organization adopts AI, but how thoughtfully AI is infused into human workflows. Culture is a key to this. Culture shapes how people respond to change, influences behaviour, and represents priorities between members. When AI is integrated into workflows, places metrics or performance indicators, culture can be bolstered or compromised. Leaders can’t leave culture to survive by a dysfunctional organization; culture must be intentionally built into the systems and rhythms of daily practice.
According to Gartner’s research, organizations that build and practice culture in its daily work, can expect a performance increase between 8 – 34%. The translation of values into observable behaviours provide cues to help behaviour, protect trust, and leverage enable technology that doesn’t unnecessarily weaken the fabric of organizations.
Balancing Efficiency and Human Experience
An effective strategy to keep this balance is the use of dual KPIs (as mentioned in Gartner’s 2026 Top Priorities for CHROs): one for the “now” and one for the “next.”
The “now” KPIs are considerate of indicators that matter in the short term such as speed, efficiency, and accuracy.
The “next” KPIs to assess organizational indicators that matter in the long-term such as cultural alignment, engagement, and adaptability.
By tracking these two types of KPIs, there is assurance that operational efficiencies do not compromise employees or organizations capability to adapt moving forward.
An example of this is how recruitment can illustrate both dimensions. AI and analytical tools can be used to screen resumes, match candidates to position, and set interviews thereby reducing times to hire. At the same time, the organization can assess the experience of candidates as well as the satisfaction of recruiters to ensure that the human aspect of the transformation is not compromised. If speed and efficiency improve but experience declines, the leaders can take suitable actions. This evaluation of the two dimensions provides balance on the pressing concern to produce immediate results while maintaining the longer-term strategic priorities.
This also engenders leaders to modify their behaviours. When trust, collaboration, and inclusion are evident in the performance metrics, culture is seen as a measurable strength. Consulting and discussing the human impact of AI changes and sharing the decision-making rationale in a transparent manner leads to a sense of belonging.
Even as artificial intelligence streamlines the routine aspects of a job, human judgment is still required. In hiring, for instance, AI can handle the repetitive parts of the job, but a team of candidate experience people still needs to be involved when empathy and nuance are required. AI “carries” the team; it does not replace the team. AI and people provide data-informed, human-led decision-making—AI offers insight and people provide meaning.
Preparing People for AI Era
Simply being more efficient will not protect an organization in the future. Instead, what creates long-lasting success is how people change and evolve with the change. AI is changing tasks faster than job titles can change, which necessitates reskilling. Employees will need to have the technical capabilities you would imagine (digital fluency, data interpretation) but will also need to develop adaptive skills (problem-solving, empathy, and the ability to collaborate with AI).
Gartner’s research asserts, organizations that invest in continuous learning ecosystems outperform those organizations that treat training as an isolated intervention. Leaders will need to embed learning as part of the work, recognize the development of skills in performance conversations, and support continuous learning by allowing and embedding AI-based recommendations for individualized learning.
The significance of psychological safety is equally essential. Organizational change driven by AI can cause anxiety about job security. Leaders create psychological safe environments when they make it safe to ask questions, reward exploration and experimentation, and treat mistakes as learning.
When employees can be curious in a psychologically safe environment, AI is less a threat and more a collaborative partner. The “next” KPI principles also applies in this context as measuring readiness, rate of learning and resiliency of the team reminds leaders to invest in people not just technology. Organizations thrive in disruption when their learning culture adapts while still maintaining the organization’s identity.
Ethics and Inclusive AI
Adoption of AI with the sense of responsibility is also dependent upon ethics and governance. Inclusive and equitable AI, in a human-centered way, requires leaders to ensure that in all AI processes there are ethical guardrails that promote fairness, transparency and accountability. There are levels of bias from algorithmic bias to the use of data, to how the system is designed.
Bias can be easily introduced unintentionally, and it is essential outcomes are continuously monitored and refined to avoid automated processes that reinforce bias. Recruitment, performance management and opportunities for learning should represent the same ethical principles. When leaders develop the value of inclusivity in AI processes, it builds trust and identifies every contribution that is valued.
Designing human-centered AI workflows takes intention. Scenario planning, pilot programs, and feedback loops are ways to envision possible operational and cultural effects prior to scaling. A culture of experimentation enables teams to be able to test, learn, and evolve without fear of being wrong, which inspires innovation and deepens psychological safety.
A Culture That Learns with Technology
Long-term resilience relies on aligning operational performance with human experience. AI has the potential to liberate people to focus on creativity, strategy, and judgment—but only if leaders are cognizant of trade-offs. The extent to which both operational and cultural measures of success are reviewed provides maximum insight into progressing past the initial position.
AI is going to continue to create new work. Some job functions will change, some will cease to exist, and new work will be created. The only guidelines that will help shape how people experience working in this new world are those leaders who support the transition with kindness and clarity.
The actual measure of AI is not task completion or speed, but whether people feel trusted, capable, and collectively purpose driven. When leaders embed skill sets, ethics, values, human decision-making, and inclusiveness into day-to-day work, AI will expand human capability and potential and not reduce it.