How To Use AI for Project Management: Tools & Real Examples

If you're drowning in status updates and schedule changes, understanding how to use AI for project management might be your lifeline.

Artificial intelligence is becoming a real advantage for project teams of all sizes. By taking over the repetitive parts of your workflow, like scheduling, reporting, tracking progress, AI lets you focus on the things that actually move your projects forward: strategy, communication, and leadership.

Imagine having a digital assistant that never forgets a deadline, spots risks before they become problems, and even helps you make smarter decisions based on real-time data.

11 real-world use cases of AI in project management

AI is already delivering measurable results across industries. Here are eleven proven ways organizations are using artificial intelligence to transform their project management processes.

Legal case management and deadline tracking

Law firms use AI to automatically track case deadlines, manage discovery documents, and prioritize urgent matters. The technology analyzes historical case data to predict timelines and identify potential scheduling conflicts before they cause problems.

Key capabilities:

• Automatic monitoring of court filing deadlines and statute of limitations dates
• Document review that reduces weeks-long processes to days
• AI-flagged relevant precedents and case strategy suggestions
• Early conflict detection with enough lead time to adjust priorities

Construction project scheduling and resource optimization

Project management for construction companies using AI involves optimizing material delivery schedules, predicting weather-related delays, and allocating equipment efficiently across multiple job sites.

Impact achieved:

• Significant reduction in project overruns
• Improved safety outcomes through better workload management
• Dynamic project schedules that adapt to weather patterns, supplier lead times, and equipment utilization
• Automatic alternative sequencing suggestions when delivery delays threaten the critical path

Software development sprint planning

Development teams use AI to analyze past sprint velocity, predict story point estimates, and identify potential bottlenecks in the development pipeline.

What AI analyzes:

• Each developer's productivity patterns
• Complexity of different code modules
• How various work types impact team velocity
• Realistic story point assignments during planning sessions

Results: Fewer failed sprint commitments after implementing AI-assisted planning.

💡Planning a software development project? Use our SDLC project plan template to structure your development lifecycle from concept to deployment.

Healthcare project coordination

AI can help healthcare organizations coordinate multidisciplinary projects, manage compliance requirements, and optimize staff scheduling.

Where it helps:

• Electronic health record system implementations
• Hospital expansion projects
• Regulatory requirement tracking
• Credential expiration and mandatory training schedule management

Outcome: Reduction in project delays related to regulatory oversights.

Marketing campaign management

Marketing teams use AI to analyze campaign performance data, predict content engagement, and automatically adjust resource allocation based on real-time results.

AI monitors:

• Campaign metrics across all channels
• Content types and messaging that drive the strongest engagement
• Underperforming elements that need budget reallocation

Performance gains:

• Faster optimization cycles
• Improved return on marketing spend

Educational program development

Educational institutions can manage curriculum development projects, track student outcome metrics, and coordinate resources across departments with AI.

Applications:

• New degree program development
• Learning technology initiative implementation
• Milestone tracking across multiple workstreams
• Resource constraint identification and timeline adjustments

AI helps education administrators manage complex stakeholder requirements and accreditation standards while keeping initiatives on schedule and within budget.

Manufacturing process improvement initiatives

AI empowers manufacturers to systematically identify, prioritize, and execute process enhancements. It analyzes production data to uncover inefficiencies and ensures projects deliver measurable results by monitoring performance in real-time.

Financial services compliance projects

Financial project management involves implementing AI to manage complex compliance initiatives, track regulatory changes, and ensure documentation meets audit requirements.

Key benefits:

• Continuous monitoring of regulatory updates
• Automatic mapping of regulations to affected business processes
• Scoped remediation effort assistance
• Audit-ready documentation generated throughout project execution

This reduces compliance risk while significantly streamlining project delivery and reporting burden.

Retail expansion and store opening projects

Retail companies can benefit from using AI when coordinating multi-location store openings, managing vendor relationships, and tracking inventory setup progress.

Coordination complexity AI handles:

• Dozens of vendors across multiple locations
• Local permitting requirements
• Inventory arrival timing with staff training completion
• Early identification of locations falling behind schedule
• Resource reallocation suggestions to keep grand opening dates on track

Product launch coordination

Product teams use AI to manage complex cross-functional launches, coordinate marketing and sales activities, and predict launch readiness. This leads to smoother product introductions and better market timing.

The system also analyzes pre-launch activities to assess true launch readiness, helping teams avoid premature releases that could damage market reception.

Corporate real estate projects

Real estate teams deploy AI to manage property acquisitions, coordinate renovation projects, and optimize space utilization.

Applications across the lifecycle:

• Space utilization pattern analysis for office design decisions
• Predictive maintenance timelines for proactive planning
• Vendor performance management across multiple properties
• Lease negotiation analysis

Benefits of AI in project management

The adoption of AI in project management is driven by tangible benefits that impact your bottom line. Here's what organizations are experiencing when they integrate intelligent automation into their project workflows.

Enhanced decision-making through predictive analytics

AI analyzes historical project data to identify patterns and predict future outcomes. Instead of relying solely on intuition or limited data sets, project managers can make better decisions backed by comprehensive analysis of thousands of data points.

The technology examines past project performance, team velocity, resource utilization, and external factors to forecast project trajectories with remarkable accuracy. 

When planning a new software development project, for example, AI can predict completion dates based on similar past projects, accounting for team composition, technology stack complexity, and organizational factors that historically impact delivery speed.

This predictive analytics capability extends to risk assessment, where AI can flag potential issues weeks or months before they become critical problems. Teams receive early warnings about budget overruns, timeline slippage, or resource conflicts, giving them time to implement corrective actions proactively rather than reactively. 

👉Need to calculate your project's resource requirements accurately? Check out our Critical Path method calculator to optimize your team's capacity.

Significant time savings through intelligent automation

Project managers spend countless hours on administrative tasks: updating status reports, scheduling meetings, sending reminders, and tracking progress across multiple systems. 

AI eliminates this burden by automating routine activities, freeing up your time for strategic thinking and stakeholder management.

Intelligent automation goes beyond simple task scheduling. AI can automatically categorize incoming requests, assign work items based on team member expertise and availability, generate progress reports by synthesizing data from multiple sources, and even draft status updates based on actual project activity. 

What once took hours now happens in seconds, allowing project managers to focus on high-value activities that truly require human judgment.

Time savings breakdown:

The cumulative impact is substantial. Project managers using AI automation report reclaiming 10-15 hours per week - time they redirect toward coaching team members, building stakeholder relationships, and strategic planning that genuinely moves projects forward.

Improved resource allocation and workload balancing

One of the most challenging aspects of project management is ensuring your team members are neither overwhelmed nor underutilized. AI continuously monitors workload distribution, skill sets, and project priorities to recommend optimal resource allocation.

The technology considers factors human project managers might overlook: individual work patterns, task dependencies, skill development opportunities, and even time zone differences in distributed teams. 

The system can also identify when team members would benefit from upskilling opportunities or when external resources might be needed to maintain project momentum. Organizations using AI-driven resource allocation report 37% improvement in employee experience.

Real-time risk detection and mitigation

Traditional project risk management reacts after problems start. AI flips this by constantly watching project signals and spotting issues before they escalate.

AI reviews things like communication patterns, task progress, budget burn, and dependency chains. When something drifts off track, it raises an alert you’d normally miss, such as:

• A sudden drop in commit frequency from a key contributor
• A cluster of missed deadlines in one workstream
• Budget spending is rising faster than the actual value delivered

When AI flags a risk, it doesn't just notify you. It also suggests practical fixes based on what worked in similar past scenarios. For example:

• If a core contributor looks overloaded, AI might suggest redistributing tasks
• If timelines look tight, it can recommend adjusting sprints or adding support
• If budget concerns appear, it can propose scope review or reprioritization

The result is simple: problems surface sooner, and you get clear guidance on how to handle them before they become real issues.

👉 Want to further ensure your projects deliver maximum benefit? This value realization template can help.

Better stakeholder communication and transparency

Keeping stakeholders informed without overwhelming them with details is an art form. AI helps strike this balance by automatically generating tailored reports for different audiences, highlighting the information most relevant to each stakeholder group.

• For executives, AI can provide high-level dashboards showing project health, key milestones, and budget status, which is the strategic view needed for portfolio decisions. 
• For technical teams, it can deliver detailed progress reports on specific work streams, including code review metrics, test coverage, and technical debt trends. 
• For clients, it can generate customer-friendly updates that explain project status in business terms rather than technical jargon.

Organizations implementing AI-driven stakeholder communication report an 82% reduction in administrative burden, communication-related delays, and significantly higher stakeholder satisfaction scores. The transparency created by consistent, relevant updates also builds trust and reduces the frequency of impromptu status meetings that disrupt team productivity.

Top AI tools for project management

The market offers numerous AI-powered project management solutions, each with unique strengths. Here's an objective comparison of leading platforms to help you choose the right tool for your team's needs.

Dart 

Best for: Teams seeking a comprehensive AI-first project management platform that adapts to any workflow

Key differentiators: Natural language filling, intelligent task prioritization that learns from your behavior, automated status reports that synthesize information from multiple sources, and predictive resource allocation that prevents bottlenecks before they occur. 

Dart's AI understands context, allowing you to manage projects through conversational commands rather than complex workflows.

The platform continuously learns from your team's work patterns, automatically adjusting task priorities as deadlines approach, resource availability changes, or dependencies shift. Teams report that Dart anticipates their needs, surfacing the right information at the right time without requiring manual dashboard configuration or report generation.

Ideal for: Teams across industries who want to spend less time managing tools and more time delivering results. Particularly valuable for organizations managing multiple concurrent projects with shared resources.

Monday.com

Best for: Visual project tracking with AI-enhanced automation

Key strengths: Intuitive visual interface, extensive integration ecosystem, and flexible board structures that adapt to various project methodologies. The AI features help automate notifications and suggest workflow optimizations based on team behavior patterns.

The platform's strength lies in its visual approach to project tracking, with color-coded boards and timeline views that make project status immediately apparent. The AI layer adds intelligence to these visualizations, highlighting items that need attention and predicting which tasks might miss their deadlines based on current progress rates.

Best suited for: Marketing teams, creative agencies, and operations groups that value visual project tracking and need extensive customization options.

Asana

Best for: Task management with smart recommendations

Key strengths: Clean interface, powerful task dependencies, portfolio-level reporting, and AI-driven workload management that helps prevent team burnout. Asana's AI analyzes workload distribution and alerts managers when team members are overallocated.

The platform particularly excels at managing complex task dependencies across large projects. The AI monitors these dependency chains and provides early warning when upstream delays threaten downstream deliverables, giving teams time to adjust plans before cascade failures occur.

Best suited for: Software development teams, product organizations, and companies running multiple cross-functional initiatives simultaneously.

ClickUp

Best for: All-in-one productivity with AI writing assistance

Key strengths: Comprehensive feature set, AI writing assistant for documentation, customizable views, and time tracking capabilities built into the platform. The AI writing features help teams generate project documentation, meeting notes, and status updates more efficiently.

ClickUp's breadth of features makes it attractive for teams wanting to consolidate multiple tools. However, this comprehensiveness comes with increased complexity; teams should expect a longer learning curve compared to more focused alternatives.

Best suited for: Small to medium businesses looking to consolidate multiple tools into a single platform, particularly those that need strong documentation capabilities alongside project management.

Wrike

Best for: Enterprise project management with AI-powered risk prediction

Key strengths: Advanced reporting capabilities, risk prediction algorithms, enterprise security features, and sophisticated resource management tools. Wrike's AI excels at analyzing portfolio-level data to identify trends and risks across multiple projects simultaneously.

The platform is built for enterprise scale, with features supporting complex approval workflows, detailed audit trails, and integration with enterprise systems. The AI layer helps large organizations maintain visibility and control across hundreds of concurrent projects.

Best suited for: Large enterprises managing complex project portfolios, particularly in regulated industries that require detailed audit trails and governance controls.

Feature comparison matrix

Key tips for successful AI project management

Implementing AI in your project management workflow requires more than just selecting the right tool. Success depends on how thoughtfully you integrate the technology into your existing processes and culture. 

Here are some essential strategies for maximizing the value of AI in your project environment.

Start with clear objectives and measurable outcomes

Before implementing any AI solution, define specific problems you're solving and how you'll measure success. Vague goals like "improve project management" lead to disappointing results.

Instead, identify concrete challenges. For example:

• "Reduce time spent on status reporting by 60%"
• "Improve project deadline prediction accuracy by 40%"
• "Decrease resource allocation conflicts by 50%"

These specific objectives guide your tool selection and help you evaluate whether the technology delivers value. They also help explain the change to your team in terms they understand - not "we're adopting AI" but "we're eliminating three hours of weekly administrative work."

Action steps:

• Create baseline measurements of your current state before implementation
• Track how long reports currently take and their perceived value
• Involve stakeholders in defining success criteria
• Get alignment on what makes the implementation successful for executives versus team members

Ensure data quality and consistency

AI systems are only as effective as the data they analyze. Incomplete, inconsistent, or inaccurate project data produces unreliable insights that teams quickly learn to ignore.

Before implementing AI, audit your current practices:

• Do team members consistently log time?
• Are tasks updated regularly with accurate status?
• Do project plans reflect actual work being performed?

Establish clear governance standards:

• Define required fields for tasks
• Standardize how teams categorize work
• Create consistent naming conventions across projects
• Make data entry as frictionless as possible

When one team calls something a "bug" and another calls it a "defect," the AI treats them as different work types, fragmenting its analysis. Choose tools with mobile apps, browser extensions, and integrations that capture data naturally as work happens rather than requiring separate data entry sessions.

Train your team thoroughly and address resistance early

The most sophisticated AI tool becomes worthless if your team doesn't understand how to use it or refuses to adopt it.

Start with the "why" before the "how:"

• Help team members understand what problems AI solves for them personally
• Project managers might care about faster status reporting
• Team members might value better workload balancing that prevents burnout
• Tailor your messaging to each group's concerns

Provide role-specific training:

• Project managers need to understand AI-generated insights and how to act on them
• Team members primarily need daily task management skills
• Don't waste developers' time showing them executive dashboard features they'll never use

Create internal champions:

• Identify AI experts who help colleagues troubleshoot issues
• These champions bridge the gap between IT support and end users
• They gather feedback about what's working and what needs adjustment

Address resistance directly:

• Some team members will fear that AI replaces them or mistrust automated recommendations
• Acknowledge these concerns respectfully
• Explain how AI augments rather than replaces human judgment
• Show concrete examples of AI handling tedious tasks so people focus on interesting, strategic work

Begin with pilot projects before full-scale rollout

Attempting to implement AI across your entire project portfolio simultaneously creates unnecessary risk and chaos.

Choose pilot projects that:

• Are important enough to matter, but not mission-critical
• Have engaged, tech-savvy teams
• Run for 60–90 days (long enough to see patterns, short enough to maintain momentum)
• Have clear success criteria defined upfront

Avoid these pilot mistakes:

• Using struggling projects as pilots (unclear if outcomes result from AI or underlying issues)
• Skipping documentation during the pilot phase
• Not sharing results broadly, including both successes and challenges

Document everything:

• What works well?
• What causes confusion?
• Which AI features deliver the most value?
• Which ones do teams ignore?

When teams see peers succeeding with AI, resistance decreases, and enthusiasm builds organically.

Monitor performance and iterate based on results

AI implementation is an ongoing process of optimization and refinement.

Establish review cadences:

• Monthly reviews: Examine key metrics like on-time completion rates, administrative overhead, and AI recommendation adoption
• Quarterly reviews: Assess strategic impact, like overall project delivery efficiency and success rates

Pay attention to actual usage:

• Which AI features do teams actually use versus ignore?
• High-value features with low adoption might need better training or simplified interfaces
• Low-value features that consume resources might need retirement

Gather qualitative feedback:

• Surveys and interviews reveal why metrics move
• A team might ignore AI schedule recommendations not because they're inaccurate, but because they're presented at the wrong time or in unhelpful formats

Don't expect perfection immediately. AI systems improve over time as they gather more data. Judge the trajectory of improvement, not just the current state.

Balance AI recommendations with human judgment

AI provides valuable insights and recommendations, but project managers must maintain their professional judgment and contextual understanding.

Teach teams to question AI recommendations:

• The AI might suggest delaying a seemingly urgent task
• Investigation might reveal a hidden dependency that would block progress anyway
• Conversely, the AI might miss context that makes a recommendation inappropriate

Establish clear escalation paths:

• Resource allocation recommendations might be implemented automatically for routine adjustments
• Major changes affecting multiple projects should require human approval
• Define these thresholds explicitly

Use AI to inform discussions, not end them:

• When AI predicts a project will miss its deadline, that's the start of a conversation about mitigation strategies
• The prediction creates an opportunity for proactive problem-solving

Recognize AI limitations:

• AI operates on patterns from historical data
• When facing genuinely novel situations without historical precedent, AI recommendations become less reliable
• Lean more heavily on human judgment while documenting outcomes so AI can learn

Create feedback loops for continuous AI improvement

AI systems improve through learning from outcomes. Establish structured feedback mechanisms that help AI refine its models.

Track predictions versus actual outcomes:

• Did the three-day task actually take five days?
• Did the predicted risk materialize or was it a false alarm?
• Feed this outcome data back into the system

Encourage teams to rate AI recommendations:

• Simple thumbs-up/thumbs-down feedback helps the system learn 
• Over time, AI prioritizes recommendation types that resonate with your organization

Document exceptions and edge cases:

• When teams override AI recommendations, capture why
• This context helps AI understand situational factors it might be missing
• Prevents it from persistently recommending actions that don't fit your culture

Share learnings across projects:

• If marketing discovers AI task estimates need 20% buffers for creative work, apply that learning to all creative projects
• Insights from one project improve AI performance across your entire portfolio

Common challenges and how to overcome them

Despite its transformative potential, AI implementation in project management isn't without obstacles. Understanding common challenges and their solutions helps you navigate the adoption journey more successfully.

#1 Resistance to change from team members

The challenge: Team members who are comfortable with the existing project management approaches may resist adopting AI-powered tools, viewing them as unnecessary complexity or threats to their autonomy and job security.

How to overcome it: Address both emotional and practical concerns through empathetic change management. Start by acknowledging that current approaches work. You're not implementing AI because people are failing, but because the project environment has become more complex and demanding.

#2 Data privacy and security concerns

The challenge: AI systems require access to project data that may include confidential information about strategies, budgets, client details, or proprietary methodologies. Organizations rightly worry about data security, especially when using cloud-based AI tools.

How to overcome it: Conduct thorough due diligence on your AI vendor's security practices before implementation. Review their certifications (SOC 2, ISO 27001), data encryption standards, access controls, and incident response procedures. For sensitive projects, confirm that data remains within specific geographic regions to comply with data sovereignty requirements.

#3 Integration difficulties with existing systems

The challenge: Most organizations use multiple systems for different aspects of work: communication tools, code repositories, financial systems, CRM platforms, and existing project management tools. Getting AI to work well across this fragmented technology landscape proves challenging.

How to overcome it: Prioritize AI tools with robust integration ecosystems that connect with your existing technology stack. Most modern AI project management platforms offer integrations with popular tools like Slack, Microsoft Teams, Jira, GitHub, Salesforce, and others. Evaluate integration depth; some are superficial, while others enable true bidirectional data flow.

#4 Accuracy and reliability of AI predictions

The challenge: AI recommendations are probabilistic, not deterministic. The system might predict a task will take three days when it actually takes five, or flag a risk that never materializes. When predictions prove inaccurate, teams lose confidence in the technology and revert to trusting their own judgment exclusively.

How to overcome it: Set realistic expectations from the start. Explain that AI provides probabilistic guidance based on available data, not absolute certainty. Frame predictions as "best estimates given current information" rather than guaranteed outcomes.

#5 Initial implementation costs and resource requirements

The challenge: AI project management tools often require significant upfront investment: software licensing fees, implementation consulting, staff training, and the opportunity cost of productivity dips during transition periods. For smaller organizations or those with tight budgets, these costs can seem prohibitive.

How to overcome it: Build a comprehensive business case that accounts for both costs and benefits. While implementation requires upfront investment, calculate the ROI from time savings, improved project success rates, and reduced overhead. Most organizations see positive ROI within 6–12 months when accounting for efficiency gains.

What's next: Future of AI in project management

Today's AI capabilities are just the beginning. Emerging trends point toward sophisticated, autonomous systems that will fundamentally reshape project management.

Autonomous agents handling routine coordination

Future AI won't just recommend actions, but it'll execute them. Imagine an agent that automatically reschedules conflicting meetings after checking participant availability, or reassigns tasks when team members become unavailable based on skills and workload. 

These virtual coordinators will handle mechanical tasks while humans focus on strategy and relationships. Early versions already exist in development environments, where AI reviews code and generates tests automatically.

Months-ahead risk prediction

While current AI flags risks weeks in advance, future systems will detect problems months early by analyzing employee sentiment, market trends, technology shifts, and organizational changes. 

The AI might warn of team dysfunction based on communication patterns before traditional metrics show issues, or flag technology obsolescence risks before significant work begins. This transforms project management from reactive problem-solving to proactive opportunity identification.

Conversational interfaces replacing traditional tools

Project managers will interact entirely through natural language: "Show me projects at risk of missing Q3 deadlines" or "What if we moved launch forward two weeks?" Voice interfaces will enable project management while driving or between meetings, making oversight continuous rather than scheduled. 

This dramatically lowers barriers for teams who find traditional PM tools intimidating.

Immersive VR project environments

Virtual reality combined with AI will create immersive project spaces where you walk through visual representations - work streams as physical spaces, dependencies as visible connections. AI highlights issues through visual cues: delayed work streams appear red, overworked team members show warnings, and critical tasks pulse. 

Distributed teams will collaborate in shared virtual spaces, with AI facilitating coordination and conflict resolution.

Industry-wide collective learning

Future AI will anonymously aggregate learnings across thousands of organizations, identifying best practices and pitfalls across industries. 

Your assistant might note "similar projects succeeded by prioritizing stakeholder communication early" or warn "this profile has 80% scope creep probability." Privacy-preserving technologies enable knowledge sharing without exposing proprietary information.

IoT integration for real-time monitoring

For physical projects, like construction, manufacturing, product development, IoT sensors with AI will provide real-time visibility. Construction sites might monitor concrete curing and structural stress, with AI predicting quality issues. 

Manufacturing lines could adjust schedules automatically based on equipment performance. This eliminates lag between reality and tracking systems, enabling decisions based on current data rather than outdated reports.

Transform your project delivery with intelligent automation

AI is defining what's possible for high-performing teams. Organizations that embrace intelligent automation are delivering projects faster, with greater accuracy, and with less burden on their teams.

Dart offers the most intuitive path to AI-powered project management. Purpose-built for intelligent automation rather than traditional project management with AI features bolted on, Dart understands how modern teams actually work. 

Natural language interaction, automatic prioritization that learns from your behavior, and predictive insights that keep projects on track - all without the complexity that makes traditional PM tools feel like obstacles rather than enablers.

Ready to experience the future of project management? 

• Contact our team and see how Dart turns data into a strategic advantage.
• Download the project management tool and stay secure on the go.
• Find the right solutions to empower your team.

FAQs

How does AI actually improve project management compared to traditional methods?

AI improves project management through three primary mechanisms: automation of repetitive tasks, predictive analytics that identify risks early, and data-driven recommendations that optimize resource allocation.

What data does AI need to provide accurate project management insights?

AI project management systems require four key data types: 

• historical project performance (timelines, budgets, outcomes)
• real-time status updates (task completion, time tracking, blockers)
• resource information (skills, availability, workload)
• contextual constraints (dependencies, deadlines, budget limits)

Can AI project management tools work for small teams or only large enterprises?

AI delivers value for teams of all sizes, though benefits vary by scale. AI handles coordination, tracking, and reporting automatically, letting small teams maintain professional project governance without dedicated administrators.

How long does it typically take to see ROI from AI project management implementation?

Most organizations see positive ROI within 3–6 months, with full ROI typically achieved within 12 months. Quick wins appear in the first month: time saved on status reports, faster scheduling, reduced email volume. These immediate gains start offsetting costs almost immediately.

What happens to project managers' roles when AI handles routine tasks?

AI doesn't eliminate project management roles; it transforms them from tactical coordinators to strategic leaders. As AI handles mechanical aspects of project management, project managers have more capacity for high-value activities that truly require human judgment, emotional intelligence, and strategic thinking.

Is AI project management suitable for agile methodologies or just traditional waterfall projects?

Yes, AI particularly excels at sprint planning. The technology analyzes historical velocity, story point accuracy, and team capacity to suggest realistic sprint commitments. During sprint planning, AI can flag when proposed work exceeds typical team capacity or identify dependencies that might create mid-sprint blockers.

How does AI handle unexpected project changes or crises that weren't in historical data?

When facing situations without direct historical analogues, AI uses transfer learning, applying patterns from similar situations even if not identical matches. If your organization hasn't managed a project during a pandemic before, AI might draw on patterns from other major disruptions: economic downturns, natural disasters, or periods of rapid organizational change.

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