When Sarah’s Team Stopped Trusting the Algorithm: A Case Study in Rebuilding AI Trust in Project Management
- AI Project Management can streamline project management, but trust issues can arise when human context is overlooked.
- Communication and transparency are vital in building trust in AI recommendations.
- Neurodivergent team members may experience unique challenges with AI systems.
- Implementing a hybrid approach can help balance automation and human expertise.
- Redefining team roles can foster a collaborative relationship with AI tools.
The Breaking Point: When Promises Meet Reality
Sarah’s story began like many others in 2024. Her 12-person hybrid team at the Pacific Wildlife Conservancy was drowning in competing priorities—grant deadlines, volunteer coordination, field research schedules, and donor relations all demanding attention simultaneously. When their board approved an AI-powered project management platform, it felt like salvation.
For three blissful weeks, the honeymoon period delivered on its promises. Tasks were automatically assigned based on availability and skill matching. Resource allocation happened overnight. Status reports practically wrote themselves. Sarah finally felt like she could focus on strategy instead of constantly juggling administrative chaos.
Then came the incident that shattered everything.
The AI, in its algorithmic wisdom, assigned their most critical quarterly donor report to Maya, a 22-year-old environmental science graduate who’d been volunteering for just six weeks. The platform’s logic was sound on paper: Maya had availability, strong writing skills, and had successfully completed smaller reports. What it couldn’t understand was that this particular donor, the Davidson Foundation, required delicate handling of a relationship that had taken three years to build after a previous fundraising mishap.
The fallout was swift and painful. The Davidson Foundation’s program officer, expecting Sarah or David to personally present the quarterly update, instead received an earnest but inexperienced Maya who couldn’t answer complex questions about long-term conservation strategies. By the end of the call, the Foundation had “paused” their partnership pending a review of the organization’s operational maturity.
“One misallocation by the AI and trust is gone,” Sarah later shared in a LinkedIn project management group, a sentiment that resonated with hundreds of comments from fellow team leaders facing similar challenges.
Research supports Sarah’s experience. While 62% of organizations now use AI routinely in their operations, only 38% trust AI recommendations for complex decisions involving human relationships or strategic priorities. The gap between adoption and trust has created what researchers call AI supervision overhead—teams spending more time verifying automated decisions than they save through automation.
Quick Win: 3 Signs Your Team Has AI Trust Issues
Before diving deeper into Sarah’s rebuild strategy, here are three warning signs that your team may be experiencing AI trust erosion:
- Constant second-guessing of automated decisions: Team members regularly override or debate AI recommendations instead of implementing them.
- Shadow systems emerging: People create parallel spreadsheets, informal tracking methods, or workarounds alongside the AI tool.
- Increased meetings to “verify” AI recommendations: Your calendar fills up with “quick syncs” that are really AI oversight sessions.
If you recognize these patterns, you’re not alone. Sarah’s team exhibited all three symptoms, plus an additional challenge specific to neurodivergent team members.
The Neurodivergent Dimension: Why Standard AI Breaks Different Brains
What made Sarah’s situation particularly complex was the neurodivergent composition of her team. Three team members, including Sarah herself, had ADHD. Two others were on the autism spectrum. One colleague managed dyslexia. This wasn’t coincidental—conservation work attracts passionate, detail-oriented, systems-thinking individuals who often happen to be neurodivergent.
For neurotypical users, AI unpredictability might be mildly annoying. For Sarah’s team, it was cognitively destabilizing. “Maya and I both need to understand the ‘why’ behind decisions to feel comfortable executing them,” explained Sam, their field research coordinator who has autism. “When the AI kept changing its logic based on new data inputs, it felt like the ground was shifting under us every day.”
The team’s trust breakdown revealed deeper issues about how AI in project management trust intersects with neurodivergent work styles:
- Predictability needs: Team members with autism required consistent patterns, but the AI’s machine learning meant its decision-making evolved unpredictably.
- Executive function support: Rather than reducing cognitive load, the AI created additional overhead of verification and anxiety management.
- Sensory processing: Visual thinkers needed to see connections between tasks and goals, not just receive automated assignments.
This is where traditional project management platforms fundamentally miss the mark. They optimize for efficiency metrics while ignoring the human factors that make efficiency possible. Sarah realized they needed a different approach—one that prioritized transparency and cognitive accessibility alongside automation.
Leantime’s approach tackles this challenge head-on with features designed specifically for neurodivergent work styles. The platform’s task sentiment tracking allows team members to express not just what they’re working on, but how they feel about their assignments. Visual thinking support through multiple views—kanban boards, timeline visualization, and calendar integration—helps different cognitive styles understand project relationships. Most importantly, the goal-to-task connection visualization shows the “why” behind work assignments, addressing the transparency gap that broke trust in Sarah’s original system.
Sarah’s Rebuild Strategy: From Digital Rebellion to Partnership
Faced with a team in open revolt against their AI system and a major donor relationship damaged, Sarah could have abandoned automation entirely. Instead, she chose a more nuanced path: rebuilding trust through radical transparency and hybrid decision-making.
Phase 1: Radical Transparency (Weeks 1-2)
Sarah’s first move was counterintuitive—instead of hiding the AI’s decision-making process, she exposed it completely. She created an “AI Decision Log” accessible to all team members that tracked:
- What decisions the AI made each day
- What data it used to make those decisions
- Which recommendations the team accepted or overrode
- The outcomes of both AI and human choices
“People trust what they can verify,” became Sarah’s mantra. Every Monday morning, instead of debating AI recommendations, the team spent 30 minutes reviewing the previous week’s decision log. Patterns emerged quickly: the AI excelled at resource scheduling based on availability but failed miserably at understanding stakeholder relationship nuances.
Weekly “AI audit” meetings initially felt like group therapy sessions for a betrayed team. But gradually, they evolved into productive discussions about the appropriate boundaries for automation. Team members began to see the AI not as a replacement for their judgment, but as a tool that excelled in specific, predictable domains.
Phase 2: Hybrid Approach (Weeks 3-6)
With transparency established, Sarah could begin redistributing responsibilities between human and artificial intelligence. The division wasn’t about technical capabilities—it was about what each party could do reliably:
AI Responsibilities:
- Resource scheduling based on availability and workload
- Deadline tracking and automated progress reminders
- Routine status updates and reporting
- Data aggregation for decision support
Human Responsibilities:
- Stakeholder communication requiring relationship context
- Creative problem-solving for novel challenges
- Strategic prioritization involving multiple competing factors
- Relationship management and donor stewardship
This division aligned with broader industry trends. Research shows that hybrid methodologies have become the norm as organizations recognize that pure automation creates as many problems as it solves. The key insight was role clarity—both AI and humans needed to understand their distinct contributions.
Sarah’s approach mirrors what makes Leantime distinctive in the project management space. While other platforms push toward full automation, Leantime’s My Work Dashboard supports executive function without replacing human decision-making. The platform helps neurodivergent team members focus on their priorities while maintaining visibility into how their work connects to larger goals—exactly the kind of hybrid support that Sarah’s team needed.
Phase 3: Role Redefinition (Weeks 7-12)
The final phase involved a fundamental shift in how team members related to AI recommendations. Instead of seeing themselves as “AI supervisors” constantly checking the algorithm’s work, team members became “AI coaches” who provided feedback to improve future recommendations.
This shift was psychological as much as operational. When the AI suggested that Maya handle a complex donor relationship, instead of overriding the assignment, the team began discussing what support Maya might need to succeed. That small shift in perspective allowed the whole team to re-engage with the AI—transforming it from an antagonist into a partner.
Citations
Source: Personal account from Sarah Martinez, member of the Pacific Wildlife Conservancy.
Research statistics from various project management studies conducted in 2024.
