Feature
AI is changing software development and organizational operations at an unprecedented pace. Teams are automating routine tasks, efficiently analyzing large datasets and delivering projects faster. The productivity improvements are measurable.
But the most persistent challenges in organizations — team dynamics, employee burnout, organizational culture and systemic bias — are not improving at the same rate. In many cases, these problems are getting worse, and an AI-first approach to organizational management may be contributing to these outcomes.
Table of Contents
- Algorithms Can't Replace Human Empathy
- Atrophy Through Automation
- When AI Reinforces Bias
- The Risks of Overreliance on AI
Algorithms Can't Replace Human Empathy
Some workplace problems happen where algorithmic approaches don’t work well.
"AI is excellent at analyzing data, automating repetitive tasks and identifying patterns, but it cannot replace human empathy, judgment and context,” said Neil Morrison, global chief people officer at Staffbase.
Consider trust deterioration between teams. “Challenges like interpersonal conflict, building trust, fostering culture and navigating complex ethical decisions are inherently human,” Morrison said.
Monitoring systems may detect dysfunction through communication pattern analysis or meeting attendance metrics. However, detection is not resolution, Morrison said. AI cannot earn trust between individuals, repair damaged working relationships or enforce accountability in ways that rebuild team cohesion. These outcomes require direct human engagement and the willingness to navigate uncomfortable conversations.
The pattern repeats with employee disengagement. People disengage for reasons that resist quantification: exclusion from decision-making, misalignment with direction, management practices that erode psychological safety. “Understanding why a team is disengaged often requires nuanced conversations and emotional intelligence that AI alone cannot provide,” Morrison said.
Atrophy Through Automation
Increased AI adoption carries an under-examined risk: the degradation of essential human capabilities through disuse.
The problem is clear to Shrinath Thube, IEEE senior member and software developer at IBM: "Building trust, resolving conflict or aligning people around a mission still requires human conversations." Yet organizations are increasingly deploying AI to mediate these interactions.
As communication and decision-making processes are delegated to automated systems, skills required for difficult interpersonal work receive less practice, Thube said. This creates a feedback loop where deteriorating human skills increase dependence on automated systems.
Thube cited the example of a venture-backed technology company in 2024. Its people analytics platform identified concerning patterns within the infrastructure engineering team: terse communication, declined meetings, reduced collaboration. The organization's AI-enabled wellness system deployed personalized resources addressing burnout prevention and work-life balance.
Six months later, three senior engineers resigned within the same week.
Exit interviews revealed the actual problems as an ineffective manager, unproductive meeting culture and absence of meaningful communication — issues that the AI-enabled wellness system entirely missed.
The system correctly identified symptomatic patterns but had no capacity to diagnose root causes. “By creating an appearance of organizational responsiveness, the automated intervention prevented human attention until the situation had become irreversible,” Thube said.
"If employees relied on AI to source all of their inquiries and problems and never engaged with fellow team members, this could lead to several challenges,” agreed Marc Booker, vice provost at University of Phoenix.
When teams route all communication and problem-solving through AI intermediaries, interpersonal conflicts remain unaddressed. AI creates what Booker described as "a temporary false sense of security" while underlying problems persist. When direct human collaboration becomes necessary, "issues could become exacerbated and inflamed,” he said.
There is also an amplification effect. "With over-estimation of confidence in answers that a tool provides without double-checking responses or source data, organizations systematically propagate errors,” Booker said. When AI systems are used without adequate data validation, erroneous information that might previously have affected one individual spreads across entire teams. “This takes an isolated issue and expands it to cause treble damage,” he warned.
When AI Reinforces Bias
AI systems do not correct for cultural or structural problems, either. Instead, they amplify them. The systems operate within existing organizational incentives and reflect the data on which they are trained. When that data includes historical bias, the algorithm learns to reproduce it.
"AI can only reflect the data it's trained on,” Morrison said. “If workplace culture tolerates inequities or if organizational data is incomplete or biased, AI may reinforce, rather than correct, those problems."
The dynamic becomes particularly dangerous because algorithmic outputs carry an appearance of objectivity. When a hiring system flags certain candidates or a performance evaluation tool generates ratings, leaders defer to these outputs, rarely recognizing that the system has learned patterns from historical data that already contained the bias.
"If the organization isn't already thinking critically about fairness or inclusion, the AI won't fix that for them,” Thube agreed. “It'll just reflect whatever's already baked into the data or decision process."
Meaningful cultural change requires individuals willing to challenge power structures and call out dysfunction. These actions require moral agency and institutional courage. Neither can be programmed.
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Even where AI is genuinely useful — identifying patterns associated with burnout or disengagement — substantial limitations remain. While Morrison acknowledges that "by analyzing patterns in communications, collaboration metrics or engagement data, AI can flag potential burnout, disengagement or workflow bottlenecks," he emphasized the critical limitation: "The critical next step, understanding context, providing support and taking action, remains a human responsibility."
That responsibility is being abdicated.
The Risks of Overreliance on AI
Ironically, the theory is that AI reduces time spent on routine tasks, saving time for mentoring, relationship-building, strategic discussion and cultural development. In practice, organizations use AI to reduce human involvement rather than redirect it toward higher-value activities.
"When organizations rely solely on AI, they risk oversimplifying complex human problems or missing context that machines cannot perceive," Morrison said. This results in "generic messaging that doesn't actually address the root causes."
However, there is no getting away from AI. "AI already shapes our daily decisions, but the real test of humanity is how long we insist on staying conscious in such choices before we let convenience quietly take over,” said Louisa Loran, a business and digital transformation consultant.
Overreliance on AI intensifies existing workplace challenges and introduces new risks if not applied thoughtfully, Loran said. Algorithms may perpetuate biases incorporated in training data, leading to unfair treatment in recruitment, promotion and evaluation. Excessive dependence on AI may also weaken human skills such as critical thinking and communication.
Critical thinking has become even more valuable in the AI era. It's now easy for someone to pass an initial competency check using large language models, but those same tools can’t dig deeper unless the human user has independent expertise.
Sometimes, manual work is simply faster and more reliable. Individuals who repeatedly ask AI to handle simple tasks often waste more time than if they had just done the work themselves.
When leaders start trusting automation more than awareness, they lose context, overconfidence builds around what looks precise but isn't and in cultures where debate is absent and psychological safety low, the human insight needed to question outputs disappears, Loran said.
"AI does not challenge the culture it learns from — it scales it,” Loran said. “In organizations where openness is limited or hierarchy overrules reflection, technology codifies existing dynamics, and the real risk is that bias becomes harder to see precisely because it appears systematized."
When efficiency or profit outweigh ethics and equity, AI deployment may reproduce structural biases, Loran warned. A lack of transparency and accountability further discourages employees from identifying and addressing ethical issues.
Editor's Note: What other questions should we be asking while we incorporate AI into our daily work?
- Waking Up to Our Power: Digital + Human Capabilities for a Future-Ready Workforce — Beyond technical know-how, the future-ready worker needs a new blend of human and digital skills — anchored in awareness.
- From Tool to Teammate: How AI Is Rewiring People Strategy and What HR Can Do to Adjust — HR leaders see AI transforming work beyond automation — reshaping teams, culture and people strategy. The future is “human-engaged” work, not human-replaced.
- If AI Is Your Best Friend at Work, It Might Be Tanking Your Social Capital — AI is becoming many of our go-to collaborators, over our colleagues. What does this do to workplace relations and what can we do about it?