Revolutionizing Business Documentation: Gen AI vs Traditional BA Approach in Retail Expansion

Introduction:

In today's rapidly evolving business landscape, documentation processes are undergoing a dramatic transformation. With Gen AI at the forefront of working as a Co-pilot, it can significantly transform the workflow of Business Analysts by revolutionizing the documentation process. Gen AI can be leveraged to automate complicated documentation processes that traditionally required referencing numerous documents, turning hours of manual work into minutes of automated efficiency.

The true power of Gen AI lies in its dual capabilities: not only can it generate comprehensive documentation, but it can also validate and review documents before passing them to Business Analysts. This transformative approach means Business Analysts can focus on what truly matters - providing value-added insights and critical analysis rather than spending countless hours on information gathering and document compilation.

I will try write the blogpost keeping in mind the scenario below:

A mid-sized retail chain planning to expand from 100 to 150+ locations across five states. BA has been tasked with creating relevant documents, procedures, manual etc for the new store. Traditionally, this would require Business Analysts to:

• Review thousands of pages of existing documentation
• Conduct numerous stakeholder interviews
• Manually cross-reference regulatory requirements
• Spend weeks creating standardized documentation

With Gen AI as a co-pilot, Business Analysts can now:

• Automatically generate base documentation from existing sources
• Instantly incorporate location-specific requirements
• Validate compliance across multiple jurisdictions
• Focus on strategic insights and critical business requirements

This shift represents more than just automation; it's a fundamental transformation in how Business Analysts work. By eliminating the time-consuming tasks of document creation and validation, Gen AI enables Business Analysts to evolve their role from document creators to strategic business partners, focusing on:

• Adding critical business context
• Enhancing strategic elements
• Validating business rules
• Ensuring stakeholder value alignment

Now, let's go through each of the layer keeping in mind the context of the use case and understand the steps.

1. Input Sources: The Foundation Layer

Think of this layer as the raw materials feeding into our intelligent document factory. Just as a master chef needs quality ingredients for a great meal, our AI system needs quality inputs for excellent documentation.

Key Components:

1.1 Historical Documents:

The AI analyzes past documentation, learning from successful patterns and structures. Imagine having a virtual expert who has read every document your organization has ever created!

1.2 Regulatory Data:

Automatically stays updated with the latest compliance requirements and legal frameworks.

1.3 Location Specific Data:

Customizes content based on regional requirements and local market needs.

1.4 Process Documents:

Maintains standardization while allowing for necessary variations.

1.5 Stakeholder Requirements:

Ensures alignment with business goals and user needs.

??Note: The quality of your input sources directly impacts the output quality. Invest time in organizing and maintaining your document repository.

2. Gen AI Processing: The Intelligence Hub

This layer is where the magic happens - transforming raw data into structured, meaningful content. AI is carrying out the work behind the scene by following mainly following steps

Key Activities:

• Data Collection & Analysis: Automated gathering and initial processing of all inputs
• Pattern Recognition: Identifying successful document structures and requirements
• Template Selection: Choosing appropriate base templates
• Content Generation: Creating initial document drafts
• Initial Validation: First-pass quality checks

Data Collection & Analysis:

Process Step AI Action Output Data Gathering Automated collection from all sources Consolidated data repository Initial Analysis Pattern recognition and categorization Structured information sets Relevance Checking Filtering and prioritizing information Prioritized data elements

2.1 Pattern Recognition & Learning:

Input Analysis example could be:

Detected standard store layout section (20 pages), identified 35 location-specific procedures, found 12 outdated regulatory references, marked 8 sections for update.

Pattern Identification example:

All successful store openings follow 6-step documentation sequence: Location Analysis → Compliance Check → Staff Requirements → Operations Setup → Safety Protocols → Launch Checklist.

Learning Integration:

State specific store templates now automatically include state-specific employee break policy section after analyzing 15 successful store openings in the state.

Pattern application:

Generated Boston Downtown store manual using mall-specific pattern: Extended hours section added, local emergency contacts inserted, specific loading dock procedures included based on previous successful mall location patterns.

If we compare with manual process vis a vis new process:

Manual Process: Boston Store Opening Documentation

• BA reviews 5 previous store documents
• Manually extracts relevant sections
• Individually updates each procedure
• Hand-checks compliance requirements

Approximate time: 2-3 weeks

Automated Process: Boston Store Opening Documentation

1. Input Analysis: "Processing 100 store documents, identified 85% relevant content for Boston location"
2. Pattern Identification: "Detected mall-specific requirements pattern, city-specific compliance pattern"
3. Learning Integration: "Applied Northeast region best practices, incorporated MA-specific requirements"
4. Pattern Application: "Generated Boston-specific document with 98% accuracy, highlighting 3 areas for BA review" Approximate Time: 4 hours

2.2 Template Selection:

2.3 Content Generation:

Process Flow:

• Template Population

- Insert standard content

Store Operations Overview

? Standard operating hours: [AI inserts corporate standards

- Add location-specific details

? Mall hours: 10 AM - 9 PM

- Include regulatory requirements

Employee break rules: 30-min break per 6-hour shift

• Content Customization

- Apply business rules

- Add context-specific information

- Format according to standards

Example on the above step could be

• Initial Assembly

- Compile sections

- Apply formatting

- Insert references

The above steps compile document and create the output in desired format.

2.4 Initial Validation:

3. AI Validation Layer: Quality Assurance

3.1 Structural Validation:

Checks Performed:

• Document organization
• Section completeness
• Format consistency
• Reference integrity
• Template compliance

3.2 Content Validation:

Validation Elements:

• Language accuracy
• Technical correctness
• Terminology consistency
• Content completeness
• Logic flow

3.3 Compliance Check:

3.4 Quality Validation Metrics:

• Readability Scores
• Consistency Checks
• Completeness Verification
• Reference Validation
• Format Compliance

4. BA Strategic Review: The Human Touch

This is where Business Analysts add their crucial value, transforming good documentation into great business assets.

4.1 Strategic Analysis:

Existing documentation will help to create documents based on existing rules and past data. Those pieces of information need to be put into the context of changing landscape and oriented towards the future. Some areas that BA can add value to the document added by Gen AI are following Business impact assessment (e.g. Operation time window vs stuff allocation) Risk evaluation (e.g. new regulation impact on product packaging label) Value proposition analysis (e.g. keeping store one hour beyond current hour could serve local community better)

4.2 Business Context Enhancement:

Market insights (e.g. recently there are two new store come in that area and could impact footfall) Operational implications (e.g. change in regulation could impact current operation process) Strategic alignment (e.g. new store operation can align with other few states to simplify the store management)

4.3 Stakeholder Value Assessment:

User value delivery (e.g. if all stakeholders are represented in new operational procedure) Process improvement impact (e.g. assessment of impact of changes to stakeholders) Implementation feasibility (e.g. Ai created process steps can run into feasibility of implementation given the resource limitation)

4.4 Final Approval:

Stakeholder sign-off (e.g. lead the collaboration with stakeholders to get the sign off) Implementation clearance (i.e. moderate discussion with technical team to get go ahead)

5. Document Management: The Lifecycle Guardian:

The final layer ensures proper maintenance and distribution of documents.

Key Components:

5.1 Version Control: Tracking changes and maintaining history

5.2 Distribution: Controlled release to relevant stakeholders

5.3 Repository Storage: Secure and accessible storage

5.4 Continuous Monitoring: Ongoing performance tracking

Future Outlook:

The future of document automation looks promising with:

• Enhanced natural language processing
• Improved pattern recognition
• Real-time compliance updates
• Advanced customization capabilities

Conclusion:

Gen AI-powered document automation isn't just about saving time; it's about transforming how Business Analysts work. By automating routine tasks and enabling focused strategic input, this technology empowers BAs to deliver more value to their organizations.

Implementation Checklist:

? Assess current documentation processes ? Organize input sources ? Set up AI processing framework ? Define validation criteria ? Train BAs on new workflow ? Monitor and optimize