Development AI Workflow Builder - EvalCommunity Academy

Development AI Workflow Builder | EvalCommunity Academy

Problem
Definition

Data
Assessment

Solution
Design

Resource
Planning

Implementation
Roadmap

Monitoring &
Evaluation

Step 1: Define Your Problem Clearly

A well-defined problem is half solved. Be specific about what you want to achieve and why AI might help.

✅ Problem Definition Checklist

I can describe my development challenge in one clear sentence

I know who is affected by this problem and how many people

I understand why current approaches aren't sufficient

I can articulate what success would look like (specific outcomes)

I've identified stakeholders who need to be involved

📝 Problem Definition Template

Fill in this template to clarify your challenge:

Key Questions to Answer:

Question	Why It Matters	Your Answer
What specific decision or task should AI help with?	AI is a tool for specific tasks, not general problem-solving
How is this currently done without AI?	Establishes baseline for measuring improvement
Why might AI be better than current methods?	Validates that AI is the right approach
What would a 20% improvement look like?	Sets realistic success metrics

📖 Case Study: Community Health Worker Scheduling

Initial vague problem: "We need AI to improve our health program."

Refined specific problem: "Our 50 community health workers manually plan their visits, often traveling to households where no one is home. We want AI to predict optimal visit times and routes based on household patterns, reducing wasted trips by 30% and allowing 40% more family visits per week."

💡 Pro Tip: If you can't clearly explain your problem to a colleague in 2 minutes, it's not yet well-defined enough for AI implementation. Spend more time on this step—it's the foundation of everything else.

Step 2: Assess Your Data Situation

AI learns from data. Understanding what data you have (or can get) is critical to project success.

Data Availability Assessment:

✅ What Data Do You Currently Have?

We collect data regularly (daily/weekly/monthly)

Our data is stored in a structured format (not just paper forms)

We have at least 6-12 months of historical data

Our data includes outcomes/results, not just inputs

Data quality is reasonably good (< 20% missing/errors)

📊 Data Inventory Template

Data Quality Requirements:

AI Approach	Minimum Data Needed	Quality Requirements
Machine Learning	1,000-10,000+ labeled examples	Structured, minimal missing values, accurate labels
NLP	500-5,000+ text samples	Clean text, consistent language, representative samples
Computer Vision	500-10,000+ images per category	Good lighting, consistent format, clear labels
Predictive Analytics	6-24 months historical data	Complete records, known outcomes, relevant variables

🚨 Red Flag Alert: If you don't have enough quality data, DON'T give up on AI! Options include: 1) Start collecting data now for future AI use, 2) Use transfer learning (adapting pre-trained models), 3) Partner with organizations that have similar data, 4) Use synthetic data for initial testing.

📖 Case Study: From Data Gap to AI Success

An education NGO wanted AI to predict school dropout risk but only had 3 months of attendance data. Instead of waiting years, they: 1) Started systematic data collection, 2) Used transfer learning from a similar model trained in another country, 3) Began with a simple rule-based system while collecting training data. After 12 months, they had enough data to build a custom model with 82% accuracy.

Step 3: Design Your AI Solution

Match your problem and data to the right AI approach and map out how it will work in practice.

Solution Architecture:

🏗️ Solution Design Canvas

✅ Solution Design Validation

The solution addresses the core problem we defined

We have (or can get) the data this approach requires

The workflow fits how people actually work

Users have the devices/connectivity needed

We've considered privacy and ethical implications

There's a backup plan if AI predictions are wrong

Ethical Considerations:

⚠️ Critical Questions:

Could this AI system discriminate against certain groups?
What happens if the AI makes a mistake? Who is responsible?
How will we protect sensitive data about vulnerable populations?
Do users understand they're interacting with AI, not humans?
Can users opt out without losing access to services?
How will we ensure transparency in AI decision-making?

Step 4: Plan Your Resources

Estimate what you'll need in terms of budget, people, time, and technology.

Resource Estimation:

Resource Category	What You Need	Estimated Cost Range
Technology/Tools	- AI platform or service - Cloud computing/storage - Development tools - Mobile devices (if needed)	$500 - $10,000+/month (varies widely by approach)
Human Resources	- Technical lead/data scientist - Project coordinator - Field staff training - Ongoing support	$3,000 - $15,000/month (depending on expertise level)
Data	- Data collection costs - Data cleaning/preparation - Labeling services - Storage and management	$1,000 - $20,000 (one-time or periodic)
Testing & Iteration	- Pilot testing - User feedback sessions - Model refinement - Accuracy validation	$2,000 - $10,000 (over 3-6 months)

💰 Budget Planning Template

💡 Budget-Friendly Strategies:

Start with free tiers of cloud AI services (Google, Microsoft, AWS all offer credits)
Partner with universities for technical expertise (student projects, research collaborations)
Use open-source tools instead of commercial platforms when possible
Apply for technology grants from foundations focused on development innovation
Join AI for development communities to share resources and lessons learned

Skills Needed:

✅ Team Capability Assessment

Someone who understands the development problem deeply

Data analyst or person comfortable with spreadsheets/data

Technical person who can work with AI platforms/APIs

Project manager to coordinate implementation

Field staff who can test and provide feedback

Community members for co-design and validation

Step 5: Build Your Implementation Roadmap

Create a realistic timeline with clear milestones and deliverables.

Recommended Phased Approach:

Phase 1: Preparation
(Months 1-2)

Finalize problem definition
Assess and prepare data
Assemble team
Select tools/platforms
Set up infrastructure

Phase 2: Development
(Months 3-5)

Build/train AI model
Test accuracy
Create user interface
Integrate with systems
Internal validation

Phase 3: Pilot
(Months 6-8)

Deploy in limited context
Train users
Collect feedback
Measure results
Refine and iterate

Phase 4: Scale
(Months 9-12)

Full deployment
Scale to all locations
Ongoing training
Monitor performance
Document lessons

📅 Detailed Timeline Template

⏱️ Reality Check: Most AI projects take 6-12 months from start to full deployment. If someone promises you results in 6 weeks, be skeptical. Good AI implementation requires time for data preparation, testing, user training, and iteration based on feedback.

📖 Timeline Example: Health Clinic Chatbot

Month 1-2: Collected 2,000 real patient questions, defined 50 most common topics
Month 3-4: Trained NLP model, built simple interface
Month 5-6: Piloted in 3 clinics with 10 staff
Month 7-8: Refined based on feedback, improved accuracy from 75% to 88%
Month 9-12: Scaled to 25 clinics, trained 80 staff, handled 5,000+ patient queries
Result: 60% reduction in wait times, staff focus on complex cases

Step 6: Plan for Monitoring & Evaluation

Define how you'll measure success and continuously improve your AI system.

Key Performance Indicators (KPIs):

KPI Category	What to Measure	Example Metrics
Technical Performance	AI accuracy and reliability	- Prediction accuracy (%) - False positive/negative rates - System uptime - Response time
User Adoption	How people actually use it	- Number of active users - Frequency of use - User satisfaction score - Training completion rate
Development Impact	Real-world outcomes	- Lives improved/saved - Time/cost savings - Program reach increase - Quality improvement
Sustainability	Long-term viability	- Cost per beneficiary - Local capacity built - System maintainability - Funding sustainability

📊 M&E Framework Template

✅ M&E Readiness Check

We have baseline data from before AI implementation

Success metrics are specific, measurable, and realistic

We've assigned responsibility for data collection

Dashboard/system for tracking metrics is ready

Regular review meetings are scheduled

We have a plan for learning and iteration

🔄 Continuous Improvement: AI systems aren't "set and forget"—they need ongoing monitoring and updates. Plan for quarterly model retraining, regular user feedback sessions, and annual comprehensive reviews. The best AI implementations improve steadily over time.

🎉 Your AI Workflow is Complete!

You now have a comprehensive framework for planning your AI project. Remember: successful AI implementation is a journey, not a destination.

🛠️ Development AI Workflow Builder