AI CRM, SFA approach

AI CRM, SFA approach

AI process within CRM, SFA automation, optimisation

Expected productivity improvement and cost savings of 50% vs traditional, SI based project approaches.

1. Discovery and Assessment:

• AI would be used to analyze the on-premise system’s architecture, data schema, and dependencies.

• It would automatically identify data types, relationships, and business logic within the existing system. This stage would also involve assessing the complexity and effort required for the migration.

• Can help with documentation and producing scripts to convert to diagrams.

• There would be human intervention within this process, given the complexity of most estates.

 

2. Data Extraction and Transformation:

• AI-powered tools would be used to automate the extraction of data from the source database, handling different data formats and structures.

• The core of this stage is using machine learning models to automatically cleanse, normalize, and map the extracted data to the schema of the new SaaS application on AWS.

• The AI would be trained on historical migration patterns to predict and fix common data inconsistencies, such as duplicate records or incorrect formatting.

• Human intervention required.

 

3. Application Modernization and Re-platforming:

• AI could analyze the on-premise application’s code and logic to identify components that need to be re-written or refactored for the AWS cloud environment.

• Natural Language Processing (NLP) could be used to parse requirements documents, which might reside in Confluence, Jira, or a Word document, and translate them into a technical design for the new AWS-based application.

• The AI would help in building a new front-end interface in AWS, based on the identified requirements.

• Human intervention necessary.

 

4. Automated Testing and Validation:

• AI-driven testing frameworks would automatically generate test cases based on the original system’s behavior and the new requirements.

• These tools would validate data integrity, functionality, and performance in the new AWS environment, flagging any discrepancies or errors that need human intervention.

• Integrate with existing testing tools, approaches.

 

5. Deployment and Cutover:

• The final stage would involve an automated deployment pipeline on AWS.

• The AI would orchestrate the cutover process, ensuring a smooth transition from the on-premise system to the new SaaS solution with minimal downtime.

• Human intervention, runbooks, testing, go-no decision.