TL;DR

AI projects differ from traditional projects in key ways: uncertainty is higher, iteration is essential, and success depends on data quality. Successful AI project management requires realistic scoping, flexible planning, strong cross-functional collaboration, and honest communication about what AI can and can't do.

Why it matters

Most AI projects fail—not because of technology, but because of management issues: unclear goals, unrealistic expectations, poor data, and inadequate resources. Good project management dramatically improves AI project success rates.

How AI projects differ

Higher uncertainty

Traditional projects: "Build feature X with specification Y"
AI projects: "Can we predict X? How accurately? We'll find out."

Implications:

  • Plan for exploration and iteration
  • Set ranges, not fixed targets
  • Build in decision points
  • Expect pivots

Data dependency

AI projects live or die by data:

  • Data quality determines model quality
  • Data availability shapes what's possible
  • Data problems surface late in projects
  • Data work takes longer than expected

Implications:

  • Front-load data assessment
  • Budget significant time for data work
  • Establish data quality gates
  • Have contingency plans for data issues

Iteration required

AI development is inherently iterative:

  • First attempts rarely work well
  • Improvement comes through experimentation
  • Real-world testing reveals issues
  • Continuous refinement is normal

Implications:

  • Plan for multiple iterations
  • Build feedback loops
  • Reserve time for refinement
  • Don't lock in early approaches

Project phases

Phase 1: Discovery and scoping

Objectives:

  • Clarify the business problem
  • Assess feasibility
  • Define success criteria
  • Identify risks

Key activities:

  • Stakeholder interviews
  • Data availability assessment
  • Technical feasibility analysis
  • Similar project research

Outputs:

  • Clear problem statement
  • Feasibility assessment
  • Success metrics
  • Risk register
  • Go/no-go decision

Phase 2: Data preparation

Objectives:

  • Acquire and organize data
  • Assess and improve data quality
  • Prepare data for modeling

Key activities:

  • Data collection
  • Data cleaning and preprocessing
  • Feature engineering
  • Data quality validation

Common pitfalls:

  • Underestimating data work (budget 50-80% of project time)
  • Discovering data problems late
  • Assuming data is ready to use
  • Skipping quality validation

Phase 3: Model development

Objectives:

  • Build and train models
  • Evaluate performance
  • Select best approach

Key activities:

  • Experimentation with approaches
  • Model training and tuning
  • Performance evaluation
  • Fairness and bias testing

Management approach:

  • Define clear evaluation criteria
  • Set performance thresholds
  • Plan for multiple experiments
  • Document what works and doesn't

Phase 4: Integration and deployment

Objectives:

  • Deploy model to production
  • Integrate with existing systems
  • Ensure operational readiness

Key activities:

  • Infrastructure setup
  • Integration development
  • Testing and validation
  • Monitoring setup

Critical considerations:

  • Performance at scale
  • Error handling
  • Rollback capability
  • Operational documentation

Phase 5: Monitoring and improvement

Objectives:

  • Track production performance
  • Identify issues
  • Continuously improve

Key activities:

  • Performance monitoring
  • User feedback collection
  • Model retraining
  • Continuous optimization

Success factors

Clear business value

Start with the problem, not the technology:

Good: "We need to reduce customer churn by identifying at-risk customers early."
Bad: "We want to use AI for something."

Questions to clarify:

  • What business outcome do we want?
  • How will we measure success?
  • What's the value of improvement?
  • What decisions will this inform?

Realistic expectations

Set achievable goals:

Unrealistic Realistic
"AI will solve this completely" "AI will assist human decision-makers"
"We'll achieve 99% accuracy" "We'll aim for meaningful improvement over baseline"
"3 months to production" "3-6 months for initial version, ongoing refinement"

Cross-functional collaboration

AI projects need diverse skills:

Essential roles:

  • Business stakeholder (problem owner)
  • Data scientist/ML engineer (technical)
  • Data engineer (data infrastructure)
  • Domain expert (business context)
  • Project manager (coordination)

Collaboration requirements:

  • Regular cross-functional meetings
  • Shared understanding of goals
  • Clear communication channels
  • Joint decision-making

Iterative approach

Plan for learning and adaptation:

Iteration structure:

  1. Hypothesis (what we think will work)
  2. Experiment (try it)
  3. Evaluate (measure results)
  4. Learn (what did we discover?)
  5. Adapt (adjust approach)

Risk management

Common AI project risks

Risk Likelihood Impact Mitigation
Data quality issues High High Early data assessment
Performance doesn't meet needs Medium High Set ranges, have alternatives
Integration challenges Medium Medium Involve systems early
Scope creep High Medium Clear scope, change control
Stakeholder misalignment Medium High Regular communication

Go/no-go checkpoints

Build in decision points:

After discovery:

  • Is this feasible?
  • Do we have data?
  • Is the value clear?

After data prep:

  • Is data quality sufficient?
  • Are we on track?
  • Should we continue?

After model development:

  • Does performance meet thresholds?
  • Are risks acceptable?
  • Ready for production?

Communication

Managing expectations

Be honest about uncertainty:

  • "We're exploring whether X is possible"
  • "Early results suggest Y, but we need more testing"
  • "We've identified challenges with Z"

Avoid:

  • Overpromising early
  • Hiding problems
  • Technical jargon without translation

Stakeholder updates

Regular, clear communication:

  • Progress against milestones
  • Key learnings and discoveries
  • Risks and issues
  • Upcoming decisions needed

Common mistakes

Mistake Consequence Prevention
Skipping discovery Building wrong thing Invest in understanding problem
Underestimating data work Schedule overruns Budget 50-80% for data
Fixed plans Can't adapt to learning Iterative approach
No success criteria Can't measure success Define metrics upfront
Technical-only team Missing business context Include domain experts

What's next

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