Data Readiness it is recognized as the essential "AI-readiness" step, moving beyond simple data migration to focus on making data behave intelligently rather than just storing it. Automating data cleansing, tagging, and standardizing formats to ensure AI models receive "high-grade fuel" (accurate and reliable data). is also a requisite step.

Legacy Transformation: Moving away from siloed, on-premise applications and databases toward scalable cloud or hybrid platforms. A streamlined Cloud infrastructure puts you in a position to rapidly take advantage of automation and Machine Learning capabilities.  A streamlined Cloud landscape will break down silos, integrating disparate data sources for a unified view, ensuring seamless data flow.

Involves centralizing diverse data, applying AI for automated insights (like predictive maintenance or personalization), and using integration tools (ETL/ELT) to pipeline data for real-time analytics, creating a foundation for intelligent applications and automated decision-making across the business. ​

Key steps include:​

• Centralizing storage with IBM, AWS or Azure for examples

• Customizing data ingestion using ETL tools such as AWS Glue

• Implementing meta data management and governance - AI can help in this laborious process.

• Centralizing Infrastructure: Implementing unified platforms like Data Lakes or Data Lakehouses to provide a single, consistent source of truth for both structured and unstructured data.

Develop and Train Models: Build the AI models using the prepared data, employing relevant machine learning techniques. This is an iterative process that involves training the models and fine-tuning parameters to optimize performance.

​

It is critical to next test and Validate: Rigorously test and validate the AI models using separate datasets to ensure they are accurate, reliable, and fair in real-world scenarios. This is also the stage to conduct pilot projects or a Minimum Viable Product (MVP) to gather feedback in a low-risk environment.

​

Strategy and Alignment is critical as AI Deployment is not like a traditional IT Deployment.  Success means an integrated, holistic Team across Business Leadership and Management and IT Teams. Not only will Business processes and personnel change, but the whole culture must change an adopt AI and the ongoing innovation to be successful and achieve full ROI.

​

• Define Measurable Objectives: Identify specific business problems to solve (e.g., automating 30% of customer queries) rather than adopting AI for its own sake.

• Identify High-Impact Use Cases: Prioritize projects based on feasibility, potential ROI, and alignment with overarching business goals.

• Establish Ethical Frameworks: Define governance policies regarding data privacy, security, and bias mitigation to build trust and ensure regulatory compliance.

An AI Assessment is a strategic process to identify, evaluate, and prioritize where Artificial Intelligence can add significant value to a business, helping move from idea to action by pinpointing high-impact use cases, analyzing potential ROI, assessing readiness, and creating a practical roadmap for implementation, ensuring alignment with business goals and ethical considerations. It answers questions like "Where can AI help?" and "How do we start?"

​

Can include:

• Opportunity Identification

• Impact Analysis 

• Readiness Assessment

• Prioritization

• Roadmap Creation

• Initial Use Cases Created and evaluated

​