From Org Charts to Work Charts: Why the AI Shift Must Include Human Well-Being
Work charts may define what gets done—human-centered design will define how well we thrive doing it.
This issue is the second in a two-part series focused on a new way of working. It outlines a four-part framework design to improve production and ensure people don’t get lost along the way. (Read part one here.)
Org charts are crumbling. Work charts are emerging. But what happens to the humans inside them?
When it comes to implementing AI in the workplace, there’s no voice I respect more than that of Liza Adams, CMO of GrowthPath Partners.
In a recent newsletter, she offers one of the clearest breakdowns I’ve seen of how AI is transforming organizational structure. Instead of organizing around roles and functions, forward-thinking companies are aligning around workflows, with AI acting as the connective tissue.
It’s fast. It’s smart. And it’s happening now.
But here’s the critical piece we must not overlook:
As AI breaks silos and redefines how teams collaborate, it also introduces new stressors: cognitive overload, role confusion, decision fatigue, and the quiet erosion of identity.
At the AI Technostress Institute, we see this shift as both a technical evolution and a human challenge. The transition from org charts to work charts will only succeed if we design with wellbeing, governance, and psychological sustainability in mind.
Liza’s maturity model shows the path forward. Our framework helps ensure people don’t get lost along the way.
👉 Read her brilliant piece here.
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Deep Dive
AI is changing not just the tools we use—it’s changing how organizations are structured, how teams collaborate, and what it means to get work done.
Liza describes this shift in vivid terms: We’re moving from org charts that show who knows what to work charts that center on what needs doing.
It’s a powerful reframe. But if we want this transformation to be sustainable, not just scalable, we must consider a parallel shift: from seeing AI as an operational upgrade to recognizing it as a psychological disruptor.
Let’s unpack Liza’s maturity model and map it to the real risks of AI-induced technostress. Then, we will discuss how leaders can navigate this moment's opportunity and emotional complexity.
The Work Chart Revolution: A Quick Overview
According to Liza, AI is actively dissolving traditional departmental boundaries. Expertise is no longer bottled up inside roles or hierarchies—it’s available on demand, via AI systems that can generate content, analyze data, make recommendations, and even execute actions.
In this new world:
Org charts define who knows what (departments, titles, authority).
Work charts define what needs doing (jobs-to-be-done, workflows, and goals).
She outlines a 4-stage AI Work Chart Maturity Model:
Traditional Org + AI Tools – AI is used in isolated, uncoordinated ways within departments.
Traditional Org + AI Teammates – Teams begin to embed AI systems to augment specific roles.
Connected Workflows – AI acts as connective tissue between departments, enabling cross-functional collaboration.
Work Chart Organization – Organizations organize around workflows, not roles. Teams become flexible, ephemeral, and outcome-driven.
Adams’ model is pragmatic, action-oriented, and valuable. But there’s an unspoken tension: What happens to the humans inside these systems?
The Hidden Cost: AI-Induced Technostress
At the AI Technostress Institute, we study the psychological toll of rapid AI integration in the workplace. While AI's strategic upside is undeniable, our research reveals a darker undercurrent: technostress—the emotional strain people experience when overwhelmed by technology.
When we map Liza’s model against our framework, we see some clear parallels and pressure points emerge:
Stage 1: Traditional Org + AI Tools
Technostress Risk: Shadow AI, isolation, and inconsistent literacy
In the earliest stage, AI tools are introduced as one-off productivity enhancers. A marketing coordinator uses ChatGPT to write a campaign brief, and a sales rep uses an AI-powered email generator. But none of this is coordinated, governed, or shared.
This is where Shadow AI thrives. Employees use tools independently, often without disclosure or support. The result?
Low consistency
High cognitive load
Fear of “getting it wrong”
Technostress Trigger: Lack of training, unclear policies, and tool overload can create feelings of anxiety, inadequacy, or impostor syndrome.
Stage 2: Traditional Org + AI Teammates
Technostress Risk: Role confusion and “cyborg stress”
Here, AI is no longer just a tool; it's a teammate. In Adams’ example, Dice built a 45-person marketing team with 25 humans and 20 AI agents.
But without clear guidelines, human workers start to ask:
What is my role vs. the AI’s?
If the AI can do this faster, am I still needed?
How do I stay valuable?
We call this “cyborg stress”—the mental friction of collaborating with an algorithm whose speed and precision can feel helpful and threatening.
Leadership Imperative: Set clear boundaries. Define what AI is for, and what humans are uniquely qualified to do.
Stage 3: Connected Workflows
Technostress Risk: Cross-functional chaos and decision fatigue
As AI enables workflows that span departments, the pace and complexity of work increase. Information flows faster, but without strong alignment, it can feel like constant context-switching.
Imagine being pulled into AI-accelerated marketing → sales → onboarding → retention flows. You’re not just doing your job; you’re doing three jobs, simultaneously, with machines in the loop.
This is classic techno-overload, a well-documented technostress factor that leads to burnout, irritability, and a sense of drowning in too much information.
Solution: AI governance frameworks like shared prompt libraries, defined handoffs, and centralized model oversight can reduce chaos and restore confidence.
Stage 4: Work Chart Organization
Technostress Risk: Identity erosion and loss of control
In the most advanced model, org charts are replaced by fluid teams that form around workflows. Human and AI experts temporarily come together, complete a task, and then disband.
While this is efficient, it also presents an existential question: Where do I belong?
When everything is transient, and AI teammates are ever-present, employees may struggle with purpose, team identity, and long-term career visibility.
Mitigation Strategy: Leaders must invest in human continuity, even within ephemeral structures. That includes:
Coaching programs
Wellness support
Feedback loops
Clear growth pathways, even in AI-augmented roles
What We Propose: Merging the Models
We suggest combining Liza’s Maturity Model with our AI Technostress Framework to make the Work Chart's future sustainable. Here’s how:
By integrating these human-centered safeguards, leaders can pursue productivity gains without sacrificing psychological sustainability.
Productivity Without Burnout
Liza is absolutely right: AI is ushering in a new era of organizational design. The organizational chart gives way to the work chart. Speed, agility, and cross-functional collaboration are the new hallmarks of success.
But if we don’t also update how we care for the humans inside those work charts, we risk building high-speed systems that leave people behind.
The future of work isn’t just about what needs to be done. It’s also about how people feel while doing it and whether the systems we build empower them to thrive or quietly wear them down.
The companies that recognize both sides of this equation—structure and stress, efficiency and empathy—will move faster and last longer.
Want to assess where your team stands?
Use our AI-Induced Technostress Self-Assessment Tool with your team and book a free consultation with Paul Chaney, founder of the AI Technostress Institute.
Are organizations adopting AI without the accompanying stress, or do the two go hand in hand? Leave a comment and let us know your thoughts.
PS: Be sure to show your support for Workleap. Their sponsorship and your support help keep the newsletter coming every week!
Paul, thank you for articulating this so clearly and thoughtfully. I deeply resonate with your core point: implementing AI isn't simply about productivity or efficiency, it's fundamentally about how we take care of human well-being, psychological safety, and meaning as we navigate these rapid changes.
In my own experience, I've seen firsthand how easy it is for organizations to chase the productivity gains of AI while neglecting the real cognitive and emotional strain it creates. When we focus too narrowly on efficiency, we overlook the critical skills humans uniquely bring clarity, reflection, judgment, empathy, and coherence. The real question isn't just how quickly we adopt new tools, but how intentionally we create environments that genuinely support people as they integrate those tools.
Your emphasis on a combined approach, merging workflow efficiency with human-centered safeguards, is exactly what we need. Real productivity isn't just about doing more, but about creating space for people to think, reflect, and thrive alongside these powerful tools.
Thank you for sharing this invaluable perspective. It's essential that we keep this conversation alive, explicitly prioritizing human coherence as we adopt AI into our organizations.
Very nice breakdown Paul.
Most companies I work with hit a wall right around Stage 3. They've got AI tools talking to each other across departments, data flowing between systems, and workflows that span marketing, sales, operations, and customer success. On paper, it looks seamless.
In practice, it's a complete mess.
Just as an eg, Marketing's AI generates leads based on certain criteria, which automatically triggers sales outreach sequences, which feeds into onboarding workflows, which impacts customer success metrics. It all sounds efficient until a major client complains about inappropriate messaging. Who's responsible? Marketing says their AI was working within parameters. Sales points to the automated handoff. Customer success blames the onboarding sequence. Everyone's technically right, but nobody owns the outcome. That's the biggest issue I am seeing.
Traditional org structures, for all their flaws, had clear escalation paths.
Hope you are having a good Wednesday!