The Flow7 AI Project Framework
For Business Managers Planning AI Integrations and Solutions
A Simple Planning and Building Tool For AI Agent & Data Projects
Ideal For Business Managers Looking To Plan, Sort Data and Action An AI Build Solution
What Is the Flow7 AI Project Framework?
The Flow7 AI Project Framework is a structured planning and implementation tool designed for business managers who need to build AI solutions but aren't AI specialists. It breaks down complex AI implementation into seven manageable steps, helping you:
- Plan your AI project with a clear checklist
- Identify and assess your data sources
- Ensure data quality and compliance
- Build your AI solution
- Test your AI system properly
- Deploy with confidence
Start with a Plan: Create Your AI Project Plan.
Establish Your Project Foundation with Security Before Any Technical Work Begins
Before starting any AI project, you need a clear plan. Just like you wouldn't build a house without blueprints, you shouldn't build an AI system without knowing what you're creating and why. This isn't just about project management—it's about creating a framework that protects your business and end users (customers, clients, patients, students etc) while enabling AI innovation that will improve how you operate and save costs.
Key Questions to Answer:
- What business problem are we solving with AI?
- What data will we need, and how sensitive is it?
- Who needs access to what information?
- What regulatory requirements must we meet?
- How will we measure success beyond just functionality?
- What is our timeframe?
Suggested Deliverables:
- Project Plan
Next Step: With your foundation in place, move to Step 1 to classify and make an inventory of your data sources.
STEP 1 : AI Sources
Map Every Data Source and Classify Its Security Level
What Will Feed Your AI? What knowledge and data will power your AI system?
Every AI system needs data to function. Before you can build anything, you need to understand where that data comes from and whether it's suitable for your purposes. This step transforms vague data concepts into a concrete, classified inventory that drives all subsequent security decisions.
Your Data Discovery Process:
- Inventory all potential data sources - CRMs, databases, documents, emails, third-party systems
- Classify data sensitivity levels - Public, Internal, Confidential, Restricted
- Document current access controls - Who can access what data today?
- Identify compliance requirements - GDPR, HIPAA, industry regulations
- Map data relationships - How does data flow between systems currently?
Classify Your Data for Security/Privacy:
- Green (Safe for AI): Public information, general company data
- Orange (Conditional): Internal data that may need anonymisation
- Red (Restricted): Personal data, financial records, confidential information
- Black (Prohibited): Data that should never enter AI systems
Critical Success Factor: Every data source must be classified before feeding it through your Live AI!
Matching Data to AI Function
It's not enough to just know where your data lives. You need to understand what the AI will do with that data:
- If you're building a customer service chatbot, it needs access to product information, FAQs, and customer history
- If you're creating an AI for invoice processing, it needs historical invoices and accounting rules
- If you're automating report generation, it needs access to your business metrics and data sources
This step prevents you from building an AI system that can't access the information it needs to work properly.
Suggested Deliverables:
- Data Source Inventory
- Security Foundation & Classification Document
Next Step: Assess the quality and compliance risk of your classified data sources in Step 2.
STEP 2 : AI Data Quality
Ensure Your Data is AI-Ready and Legally Compliant
How good is your system(s) data that will feed the AI? How will you ensure your AI is safe, accurate, and compliant?
Poor data quality is one of the biggest reasons AI projects can fail. An AI system is only as good as the data you feed it. If your data is incomplete, inaccurate, or inconsistent, your AI will produce unreliable results.
What to Check in Your Data
Completeness
- Are there gaps in your data?
- Do you have enough historical information?
- Are critical fields missing values?
Accuracy
- Is the data correct and up to date?
- Are there obvious errors or inconsistencies?
- When was the data last verified?
Consistency
- Is information formatted the same way throughout?
- Are there duplicate records?
- Do different systems use different naming conventions?
Compliance and Security
Before using any data in your AI system, you need to ensure:
- GDPR compliance - If you're handling personal data in the UK or EU
- Data protection - Who has access and how is it secured?
- Usage rights - Are you allowed to use this data for AI purposes?
- Retention policies - How long can you keep this data?
Backing Up Your Data
Always create complete backups of your data sources before starting any AI project. This ensures you can recover if something goes wrong during the implementation process.
Suggested Deliverables:
- Data Quality Assessment
- Compliance Risk Doc
Next Step: Use your data assessment results to help create your secure AI Master Data Set STEP 3
STEP 3 - The AI Master Set
Create Your AI Data Master Set - The Single Source of Truth
AI Data Master - Pulling together the data for what your AI will actually do and how will it behave?
Now that you've identified your data sources and assessed their quality, it's time to create your "master dataset" - the definitive collection of information your AI will use. RAG is an AI approach where the AI accesses your own data set to provide answers rather than just its own knowledge, which is crucial for businesses use. Your master set is the collection of your controlled source data and then depending on your AI platforms will either be uploaded into a Knowledgebase or Data source that is available with platforms like MindStudio, Voiceflow, Vapi or Retell may be added to a specific "Vector" database service connected to the AI.
Building the Master Dataset
This involves:
Consolidating Data
Bringing together information from multiple sources into one structured format that your AI can understand. Your knowledgebase!
Cleaning and Standardising
- Remove duplicates
- Fix formatting inconsistencies
- Fill in missing information where possible
- Standardise naming conventions
- Creating Vector datasets if required - e.g. Scrape your website data into LLM ready data
The "One Version of Truth" principle: Your "Master Set" becomes the authoritative source for AI training and RAG operation. No AI system should access data outside this controlled environment without explicit approval and security review.
Why This Matters
Without a properly managed master dataset:
- Your AI might get conflicting information
- Results become unpredictable
- Testing becomes impossible
- You can't track what data the AI is actually using
Think of this as creating the instruction manual that your AI will follow. If the manual keeps changing without your knowledge, the AI won't work as expected.
Suggested Deliverables:
- Secure Data Master Set - Knowledgebase
- Change Control Procedure Doc
Next Step: Define how users will interact with your AI system in Step 4.
STEP 4 - The AI Front End(s)
Understand Your AI Target System - How Will People Actually Use It
Where Will AI Output Be Consumed? Where and how will people interact with your AI?
Your AI doesn't exist in isolation - it needs to connect to systems where people will actually use it. This step is about understanding how information flows in and out of your AI and is how and what is presented to the end users. Your customers, clients, staff etc.
Understand and Define the User Experience
Consider how users will interact with your AI:
- Interface type - Web page, mobile app, chat window, email, or API?
- User journey - What steps do users take to get information from the AI?
- Input questions and requests - what questions will be asked or what processes need to run?
- Output format - Text responses, reports, automated actions, or data updates?
Data Flow Architecture
Map out the complete journey of data:
- Input - User makes a request or triggers the AI
- Processing - AI retrieves information from your master dataset
- AI model interaction - Data is processed by the AI engine
- Output - Results are presented to the user or system
- Feedback loop - Information may flow back to update records
Security and Access Control
Consider:
- Who should have access to the AI?
- What data should different users be able to see?
- How will you prevent unauthorised access?
- Where will data be stored and how will it be encrypted?
Understanding these elements before you start building prevents costly redesigns later.
How Do These Interactions Fit Your Business Processes?
- Where does AI fit in existing workflows?
- What processes need to be updated?
- How will staff training need to change?
- What new procedures are required?
Your AI "Target System" Components:
- User Interface: The front-end where people interact (website, app, chat platform, phone)
- Business Integration: How AI connects to existing systems and processes
- Output Management: How AI responses are delivered and used
- User Experience: The complete journey from question to answer
Align Business Processes/User Answers to AI Capabilities: This step helps you understand how your business processes will work with AI and creates the foundation for your testing scenarios - just like understanding a target system in data migration.
Suggested Deliverables:
- AI Summary Target System Sheet (Business Process Alignment)
- User Answers List
STEP 5 - The AI Build
Build Your AI Data Flows and Automations
Building & Mapping Flows, Tools and Automations. How will information and tasks flow through your AI system?
Now is the physical build of your AI agent or automation. This process involves mapping the flow of data between the sources and the final AI Front End and building out the workflows and all the other elements that are required for a functioning AI agent or chat bot.
This is where you actually build the AI system. Think of it like building a pipeline - you're connecting your clean, organised data from Step 3 to the user interactions you planned in Step 4. The AI sits in the middle, processing questions and delivering answers.
What You're Building:
This is where you actually construct your AI system. With proper planning from the previous steps, the building process becomes much more straightforward.
Mapping Workflows
Create visual diagrams showing:
- How data moves from sources to the AI
- What happens when users interact with the system
- Decision points where the AI needs to make choices
- Error handling when something goes wrong
Building the AI Agent/Process
The actual construction involves:
Connecting Data Sources
Setting up secure connections between your master dataset and the AI system.
Configuring the AI Model
- Choosing the right AI model for your use case
- Training it with your specific data
- Setting parameters for how it should respond
Creating Automations
Building the workflows that handle repetitive tasks automatically, such as:
- Processing incoming requests
- Updating records
- Sending notifications
- Generating reports
Integrating with Existing Systems
Making sure your AI works alongside your current business tools without disrupting existing processes.
Keep It Simple to Start:
Don't try to build everything at once. Start with core functionality and add features incrementally. You can always add more sophisticated features later once the basic system is proven and working well.
- Reduces risk of major failures
- Allows for testing and refinement
- Lets you learn from early results
- Makes problems easier to identify and fix
Suggested Deliverables:
- Working AI System
- Data Flow Documentation
Next Step: Test everything thoroughly before launch in Step 6.
STEP 6 - AI Testing
Test Your AI Before Launch
Make Sure Your AI Works Properly Before Anyone Uses It?
Testing is where you discover problems before your users do. Thorough testing prevents embarrassing failures, data errors, and user frustration. You've built your AI system with data flowing from source to front-end. Now you need to test everything thoroughly to ensure you have a smooth AI launch and determine your go-live plan.
Simple Testing Approach:
1. Test Each Business Process/User Answer
For each business process/user answer that you identified in Step 4 (your Target Step), create a simple UAT Testing Document and test:
- Does the AI give the right answers?
- Can users easily interact with it?
- Are the security rules working?
- Does it connect properly to other systems?
2. Create Your Testing List
- Make a simple list of all the things your AI needs to do
- Test each one systematically
- Record the results - pass or fail
- Keep a global list to track your progress
3. Test With Real Users
- Get actual staff or customers to try the AI
- Watch how they use it
- Fix any problems they find
- Make sure they understand how to use it properly
What to Test:
- Basic Functions: Does the AI answer questions correctly?
- User Access: Can the right people log in and use it?
- Security: Does it protect sensitive information?
- Performance: Does it work fast enough?
- Integration: Does it connect to other business systems?
Simple Pass/Fail Approach:
Each test either passes (works correctly) or fails (needs fixing). Don't launch until everything passes.
Your Go-Live Decision:
Once all tests pass and users are happy, you're ready to consider Going Live with your AI system to everyone.
Suggested Deliverables:
- UAT Sheet(s)
- UAT Issue Log
Next Step: Launch your tested AI system in Step 7.
STEP 7 - AI Go Live
Launch Your AI System
You've Finished Testing - Now It's Time to Launch Your AI
Once testing is complete and you're confident the AI works correctly, it's time to deploy and go live with your AI. But before you do, make sure to have your AI Go Live Plan and Checklist ready and make sure your team is fully notified, especially related to support and issue resolution. This time can be stressful - keep your cool! Decide on your roll out type, phased or full. Follow your plan with your team on board.
Core Tasks for the AI Go Live Milestone:
A) Conduct the Pre-Launch Final Preparation Checks
- All testing completed and passed
- User training materials ready
- Support team briefed and ready
- Backup and rollback procedures tested
- Go-live checklist reviewed and approved
B) Manage the Launch Window Itself
- Turn on the AI system for users
- Monitor initial user interactions
- Watch for any immediate problems
- Be ready to fix issues quickly
- Keep stakeholders updated on progress
C) Sign Off to Go Live!
- Confirm the AI is working as expected
- Verify users can access and use the system
- Check that security controls are working
- Get formal approval that launch is successful
- Announce to the wider organisation
D) Conduct Post-Launch Validation
- Review first day/week of AI usage
- Check that all functions are working correctly
- Gather initial user feedback
- Verify data flows are operating properly
- Confirm performance meets expectations
E) Post Go Live Monitoring
- Set up ongoing monitoring and reporting
- Establish regular review meetings
- Create procedures for handling user issues
- Plan for future improvements and updates
- Maintain security and compliance monitoring
Simple Go-Live Success Criteria:
- Users can successfully access and use the AI
- The AI provides accurate and helpful responses
- No security or data privacy issues
- System performance meets business needs
- Training Support processes are working effectively
Training and Support
Make sure users know:
- How to use the AI effectively
- What it can and cannot do
- Who to contact if something goes wrong
- How to provide feedback
Deliverables:
- Go-Live Plan
- Launch Signoff
- Technical & Maintenance Documentation
- User Guides
- Support Contact
Congratulations: Your AI system is now live and helping your business!
If you have found this helpful, please share...
Why Use the Flow7 AI Project Framework?
Structured and easy to follow
This framework is based on real-world experience implementing business systems and data migrations. The structured approach reduces risk and increases your chances of success.
Suitable for Non-Technical Managers
You don't need to be an AI specialist to use this framework. It's designed for business managers who need to deliver AI projects but want a clear, understandable process to follow.
Reduces Common AI Project Failures
Many AI projects fail because of:
- Poor planning and unclear objectives
- Low-quality or unsuitable data
- Lack of testing before deployment
- No consideration for how users will interact with the system
The Flow7 framework specifically addresses each of these failure points.
Adaptable to Different AI Projects
Whether you're building:
- Customer service chatbots
- Business process automation
- Data analysis tools
- Document processing systems
- Predictive analytics
The same seven-step framework applies. You adapt the details to your specific project while following the same proven structure.
Getting Started with Your AI Project
If you're planning an AI implementation and need help working through these steps, I can guide you through the process. Whether you're at the initial planning stage or stuck partway through a project, the Flow7 framework provides the structure you need to move forward confidently.
The key to successful AI implementation isn't using the most advanced technology - it's following a systematic process that ensures your AI actually solves your business problems with reliable, high-quality data.
Frequently Asked Questions
Do I need technical expertise to use this framework?
The framework is designed for business managers. You will likely need technical people to build the AI, (but there are many great AI tools and agent platforms for different use cases ) but the framework helps you plan and manage the project effectively.
Can I use this for any type of AI project?
Yes - whether you're building chatbots, automation tools, or AI agents, the seven-step process applies. The specific details change, but the framework remains the same.
What's the most common mistake in AI projects?
Skipping the data quality assessment (Step 2). Poor data quality causes most AI failures, but many projects rush past this step to start building quickly.
How do I know if my AI project is worth doing?
If you have a clear business problem, suitable data sources, and users who will benefit from the solution, it's likely worth pursuing. The planning stage helps you evaluate this properly.
