Continuous Improvement Processes

Why is a Continuous Improvement Mindset and Regimen critical to the success of your new AI/ML Digital Transformation?

2024-05-22 Business Transformation - LinkedIn Post

If you’re NOT already focused on continuous improvement processes (CIP), better data and modeling, and articulating challenges to the norm, then downstream, your systems will degrade or get fat; requiring even larger steps to avoid complete atrophy and poor competitive situations.

So what CIP regimen and tools do we need to avoid system atrophy after the Transformation ??

• Target Setting: CIP doesn’t happen overnight - clearly define and set your Strategy, Objectives, and Measures from the Get-Go.
  Tools and Tactics: Schedule regular performance reviews of your AI/ML systems and models to assess their efficacy, accuracy, relevance to business goals, drift, and corrective action approaches.

• Refine Business Models: Continuously refine and update machine learning models to adapt to changing data distributions and evolving business requirements.
  Tools and Tactics: Experimentation platforms and sensitivity analysis aid in tracking experiments, parameters, and results. 

• Data Quality Assurance: Implement governance and validation processes/models for ensuring data quality, integrity, and relevance throughout the AI/ML lifecycle.
  Tools and Tactics: Build in data quality and governance tools that preferably use real-time intervention and feedback to assist in data cleansing, transformation, and validation. Ensure your tools are ‘Collaborative’ and data owners can be involved in a timely manner.

• Continuous Learning and Skills: Analyse OCM Impacts of all processes and decisions, and encourage continuous learning and skills development amongst team members so that they stay abreast of the latest AI/ML techniques and best practices.
  Tools and Tactics: Design the UI/UX with a Low Cognitive Load in mind and back it up with online learning material and data validation platforms.

• Automate Testing and Validation: Implement automated testing and validation processes to ensure the robustness and reliability of AI/ML systems.
  Tools and Tactics: Use CI/CD tools such as Jenkins and GitLab CI/CD et al and establish adjustable rules within your validation process to automate the building, testing, and deployment processes. 

Through adhering to this CIP regimen and leveraging the appropriate tools, you can prevent system atrophy in your AI/ML transformations and ensure their long-term success and effectiveness.

Contact Us and read CIP Data Maturity Stages