Why Emotional Intelligence Is Becoming a Critical Skill in the AI Era
Written by Genna Barbara Zimmel, Guest Writer and Executive Contributor
AI is becoming increasingly capable of reasoning, creating, and automating complex tasks. But as technology becomes more powerful, the skills that determine whether organizations can successfully use it are becoming increasingly human.
The future of AI will not only be defined by better models. It will be defined by the people who understand context, communicate clearly, build trust, and know how to translate human needs into effective systems.
AI will automate more execution, but that will increase the value of human judgment. Emotional intelligence is becoming a critical implementation capability because organizations do not adopt technology. People do.
What happens when AI takes over the thinking work?
For the last two years, the conversation around AI has centered on capability: what it can reason through, create, and automate. But as that capability keeps expanding, a strange thing is happening in parallel: the demand for distinctly human skills isn't shrinking. It is accelerating.
LinkedIn's 2026 Skills on the Rise report found that the fastest-growing skills in the U.S. workforce now split into two nearly equal tracks: technical AI capabilities like prompt engineering, and human-centered skills like executive communication, leadership influence, and cross-functional collaboration. A Harvard Business School analysis of nearly every U.S. job posting from 2019 through March 2025 found something that captures this shift precisely: structured, repetitive cognitive roles declined 13%, while demand for analytical, creative, and leadership-intensive work grew 20% over the same period.
The World Economic Forum's Future of Jobs Report 2025 projects that employers expect 39% of workers' core skills to change by 2030. The same report places empathy and active listening among the skills that complement its top 10 core competencies, alongside technological literacy and lifelong learning, reflecting what the report's authors describe as the need to balance hard and soft skills as AI adoption accelerates.
SHRM's 2025 research adds a sharper data point: 80% of HR professionals say active listening is an acceptable skill for candidates to have developed through non-degree pathways, such as work experience or coaching, rather than formal credentials, placing it among the most widely recognized interpersonal skills in hiring today. A companion SHRM analysis found that critical thinking, active listening, coordination, and judgment and decision-making are increasingly valued specifically as AI takes on more routine work.
Emotional intelligence: An implementation skill, not just a leadership one
Most conversations about emotional intelligence at work still frame it as a leadership trait: the ability to manage a team well, read a room, stay calm under pressure. That framing is incomplete now.
Through my work exploring AI implementation, automation, and digital transformation, one pattern has become increasingly clear: the biggest challenges are rarely only technical. They are behavioral, organizational, and human. As AI systems increasingly interact directly with customers, employees, and decision-makers, understanding human context, intent, and hesitation determines whether that system creates real value or just sits unused.
This shows up constantly in practice. An AI agent can be built correctly and still fail if nobody accounted for how a stressed employee will actually talk to it, or what a hesitant customer needs to hear before they trust an automated response. Reading that correctly and designing for it is an emotional intelligence skill applied to a technical problem. It's quietly becoming one of the most valuable things an implementation specialist can bring to the table.
The real advantage belongs to people who can bridge both worlds
LinkedIn's 2026 data introduces a useful term for this: "skill stackers". These are professionals who combine a technical core (AI literacy, data fluency) with a human layer (communication, judgment, stakeholder management) rather than specializing narrowly in either. Neither layer alone creates a sustainable advantage. Together, they become significantly more valuable.
This tracks with what's happening on the ground. Upwork's 2026 In-Demand Skills report found that skills explicitly tied to applying AI within existing roles grew 109% year-over-year, not AI development in isolation, but AI applied by people who already understand the domain it's being used in. Domain knowledge, communication, and judgment aren't soft additions to technical work anymore. They're what determines whether the technical work actually lands.
AI is increasing the value of human connection, not reducing it
There's a reasonable fear that automation quietly makes human connection less necessary. The data doesn't support that. Upwork's research found that even in categories widely assumed to be vulnerable to automation, such as coding, creative work, and customer support, demand for human expertise has remained consistently strong rather than declining, even as AI-enabled skill demand more than doubled in the same period.
What seems to be happening instead is a redistribution, not a replacement. As automation absorbs more information-processing and execution work, the things AI still cannot fully replicate, such as building trust, reading unstated needs, navigating disagreement, and collaborating across teams with competing incentives, become sharper differentiators.
Microsoft's 2026 Work Trend Index captured this directly: when asked which human skills are becoming more important as AI takes on more work, professionals ranked quality control of AI output and critical thinking at the top of the list. Judgment isn't being automated away. It's being asked to do more.
Successful AI adoption depends on psychology as much as technology
This is the piece most organizations still underestimate. Multiple industry reports have highlighted that AI initiatives often struggle to deliver expected business value, with common challenges including unclear objectives, poor integration into workflows, insufficient organizational readiness, unclear communication, unmanaged resistance to change, and a failure to build genuine trust in the new systems being introduced.
I've seen this pattern firsthand in CRM and AI implementation work. The technology is rarely the hard part. The hard part is helping people trust a new system enough to actually change how they work, and that requires exactly the skills this article started with: empathy, clear communication, and the ability to read what people aren't saying out loud.
Organizations that treat AI adoption as a purely technical rollout are the ones most likely to struggle. The ones that treat it as a change management process, grounded in real understanding of fear, uncertainty, and behavior, are the ones actually positioned to make it work.
Carol Dweck, the Stanford psychologist behind the concept, distinguishes between a fixed mindset, which views ability as static, and a growth mindset, which recognizes that capability can be developed through learning, effort, experimentation and exposure to challenges. This concept has become widely influential in business and leadership education, including my own MBA studies.
The WEF's finding that resilience, flexibility, and agility are among the fastest-growing skills employers demand can be viewed as a reflection of this mindset at an organizational level. The ability to learn, adapt, and evolve will increasingly determine whether individuals and organizations successfully navigate AI transformation or resist it.
The human advantage in an AI-enabled workplace
AI can process information at a scale and speed beyond human capability. The things AI still cannot fully replicate, such as building trust, reading unstated needs, navigating disagreement, and collaborating across teams with competing incentives, become sharper differentiators. That interpretive work, the very human work of meaning-making, is becoming one of the defining competitive advantages of the AI era.
The professionals who thrive over the next decade won't be the ones who avoided learning AI, and they won't be the ones who learned AI but nothing else. They'll be the ones who understood, early, that the technology was never the whole job.
If your organization is navigating this shift and the human side of the transition hasn't gotten the same attention as the technical side, I'd welcome the conversation.
Read more from Genna Barbara Zimmel
Genna Barbara Zimmel, Guest Writer and Executive Contributor
Genna Zimmel is a Technical Project Manager and founder of Torus Solutions, specializing in AI implementation, CRM architecture, and digital systems transformation. She works with organizations exploring how AI can be integrated into real-world workflows, combining technical systems thinking with a focus on human behaviour, adoption, and organizational change. Her work explores the intersection of artificial intelligence, human communication, and practical implementation, with a focus on building systems that are not only technically capable but meaningful and usable.
