In TTS’s recent client conference that focused on embracing AI and Large Language Models (LLMs) in assessments, we asked more than 100 IO Psychologists, industry insiders, HR professionals, and expert consultants to discuss the skills and mindsets that would be critical in AI-enabled workplaces.
Our data collection strategy involved dividing these experts into 10 discussion groups, facilitated by TTS consultants. The discussions were recorded, along with notes made by facilitators and participants.
All of the above data was then collated using both LLMs and human judgement. Overlaps and discrepancies were discussed and resolved, and finally, a set of emerging themes identified.
The participants were asked to discuss skills and mindsets they thought would distinguish high performers in an AI-enabled workplace. Related to this central point was the question of emerging skills that are potentially underestimated, but likely to become vital as AI integrates more seamlessly into everyday work.
In this article, we summarize the thinking that emerged across these expert groups: points of convergence, areas of tension, and occasional outlying ideas that may prove to be definitive of future trends.
Adaptability and learning agility
One of the strongest signals across discussions was a shift away from discrete technical capabilities toward broader psychological orientations to work.
Adaptability surfaced repeatedly. Participants spoke about it as an expectation rather than a response to a world-of-work that will continue to be unstable, ambiguous, and continuously evolving.
In such a workplace, high performers are not those who have to wait for clarity before acting, but who can function effectively with limited understanding.
Included in this orientation is that ability to rapidly learn and “unlearn”. This requires a willingness to test approaches, discard what no longer works, and move on without becoming excessively attached to past methods.
Several groups noted that giving up on traditional approaches is often harder than learning new ones, particularly for experienced professionals whose identities are tied to established expertise.
Closely linked to the above is the construct of learning agility.
This refers not only to openness to new information, but the speed of learning new things as well as capability of applying new knowledge across a variety of contexts.
In this paradigm, high performers are not necessarily individuals with encyclopedic knowledge, but know what they are looking for, where to find it, and how to evaluate informational relevance.
In this way, high performance is becoming less about what professionals know, and more about how fluidly they move between knowing, questioning, and applying.
Comfort with ambiguity and cognitive demand
Many groups focused on the psychological demands of modern work. AI, much like all other digital innovations that preceded it, does not necessarily make work easier. In fact, it may well increase choice, complexity, and therefore, the cognitive demands placed on the worker.
High performers will be adept at tolerating ambiguity and complexities without becoming rigid, anxious, or overly dependent on leadership or systems for certainty.
This requires resilience, but not resilience understood as endurance at all costs. Rather, it reflects an orientation of psychological flexibility: the tendency to experiment, recover quickly from missteps, and remain creative under pressure.
Along these lines, mental endurance and emotional regulation were identified as key performance enablers.
In addition, the underlying foundation of psychological wellbeing was not framed as a separate HR concern, but as integral to sustained effectiveness in high-velocity, high-complexity environments.
Prompt engineering
Prompt engineering appeared frequently in our discussions, but it was described as a cognitive and judgement-based skill rather than related to technical ability.
Effective prompting requires clarity of intent, contextual awareness, and the ability to interrogate outputs critically.
Participants repeatedly noted that AI output quality is highly dependent on input quality. The differentiator lies in knowing what to ask, how to ask it, and when to challenge (or even reject) the response.
This positions AI literacy less as tool usage and more as a method of how problems are framed. High performers will not simply “ask ChatGPT,” but will work with such technologies as a thinking partner while retaining accountability for the final product.
Aligned to this capability is the importance of knowing when AI is wrong, incomplete, or ethically problematic. Courage to override AI outputs, rather than defer to them, was emphasized as a critical leadership capability.
Judgement and subject matter expertise
A recurring tension in our discussions concerned expertise. While AI considerably widens access to information, participants expressed concern about the risks of generating superficial knowledge and a notable overconfidence driven by polished outputs rather than deep insights.
Sophisticated subject matter expertise was repeatedly identified as a differentiator in this regard.
Expertise enables professionals to distinguish fact from fiction, evidence-based insight from popularized or “pop” psychology, and relevance from irrelevant noise. The well-known example of “Dr Google” was raised as a cautionary parallel: access to information does not equal access to understanding (or prudent application of information).
Participants identified judgement and discretion as central capabilities.
When AI offers multiple plausible answers, the user task becomes choosing appropriately based on context, consequences, and ethical considerations. Several groups expressed concern about over-trust in AI systems. Knowing when to override an automated recommendation, when to ask a better question, and when to take responsibility rather than blame “the system” were seen as underestimated leadership skills.
This requires confidence, ethical grounding, and accountability, qualities that are not typically developed through technical training alone. In this sense, AI does not replace expertise. It raises the bar for what experts need to be aware of in their work.
Sense-making and cognitive discipline
If AI provides more information, then sense-making may become a bottleneck.
Participants often referred to the ability to integrate disparate inputs into a coherent whole as a defining high-performance skill. This includes separating signal from noise, managing cognitive demands, and translating insight into action.
Several of our groups noted the risk of “dashboard fatigue,” where metrics are abundant but meaning is scarce. High performers can decide what matters, simplify complexity, and focus attention accordingly.
Importantly, data is not neutral. Participants highlighted that data includes opinions, assumptions, and values, not just numbers. Effective sense-making therefore requires critical thinking and contextual awareness, not blind trust in outputs.
Interpersonal fluency
Despite the conference’s focus on AI and technology, human skills featured strongly in all our group conversations.
Emotional intelligence, empathy, communication, and influence were repeatedly identified as differentiators of high performance.
As automated systems take over transactional interactions, moments requiring genuine human engagement become more salient and impactful. As digital interactions increase, the value of genuine human connection intensifies. Participants argued that compassion and the ability to hold a human conversation when systems fail are becoming increasingly important.
Such skills are operationally critical in moments of anxiety, frustration, and uncertainty, which are likely to increase as work systems become more complex.
Related to the above insight is an emphasis on conflict management, particularly in hybrid, cross-functional, and AI-mediated work environments.
Notably, there were strong reservations against framing younger generations as deficient in interpersonal skills. While concerns were raised about reduced face-to-face exposure, younger professionals were also described as open, adventurous, and comfortable with experimentation.
Inter-generational tolerance
High performers were consistently described as those who can bridge generational differences without defaulting to hierarchy or stereotype.
This includes humility, openness to being challenged by juniors, and the ability to combine experience-based intuition with AI-enabled insight.
Psychological safety for upward challenge was seen as critical, as was low-ego leadership.
Leadership, in this paradigm, shifts from control to facilitation and emphasizes cooperative creation rather than top-down instruction.
Effective communication
A particularly strong theme concerned effective communication and the ability to translate complex AI or data-driven insights into language that makes sense to non-technical stakeholders.
This includes building confidence among workers whose roles may change, and contextualising AI use in environments far removed from corporate or knowledge work.
In this sense, good communication is less about presentation skills and polish, and more about helping others understand and make sense of complexity.
Ethical reasoning
Finally, ethical reasoning emerged as both a mindset and a skill that is essential but often underestimated. Participants argued that ethics cannot be outsourced to policy, IT, or compliance alone.
Instead, true ethical literacy involves spotting bias, questioning assumptions embedded in systems, and understanding the human consequences of automated decisions. Given common fears of AI being used to make people or their roles redundant, a sensitivity to such perils is more important than ever.
This competence requires ongoing vigilance, and needs to be contextual while also adhering to professional standards and practices.
Final thoughts
Across both questions, a consistent picture emerged. High performance in an AI-enabled workplace is less about technological mastery and more about psychological and cognitive maturity.
One certainty is that technological tools will continue to evolve and become ever-more sophisticated. IO Psychologists and talent professionals will need to evolve alongside such technologies to remain relevant to the businesses they serve.
If you’d like to know how TTS can help you assess emergent and critical future skills and capabilities such as those we discussed in this article, reach out to us at info@tts-talent.com.