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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.