OCN NI AI Week Seven

• AI
• Higher Education (non-degree)

OCN NI AI Week Seven

Learning Outcome:

1. Summarise the technical preconditions which should be in place prior to AI implementation.

a) AI Eco System

b) Technology Readiness Levels

c) Information Architecure

d) Data Readiness 

Technical Preconditions For AI 

Before implementing Artificial Intelligence (AI), establishing a robust AI ecosystem is essential. 

This ecosystem encompasses the interconnected components, stakeholders, and processes required to develop, deploy, and manage AI systems effectively.

Core Components of The AI Ecosystem

Cloud Infrastructure

Cloud and data centre providers supply the computational power and connectivity necessary for AI operations.

This includes hardware like GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) for efficient data processing and model training

Core Components of The AI Ecosystem

Data Collectors and Curators

Reliable, high-quality data is critical for training accurate AI models.

Data must be collected, labelled, annotated, and pre-processed to ensure its utility and reduce bias

Core Components of The AI Ecosystem

Deployers and Users

These are entities or individuals who implement and utilise AI applications in real-world scenarios, such as chatbots for customer service or predictive maintenance systems in manufacturing.

Supporting Infrastructure 

Computer Infrastructure

This includes specialised hardware (e.g., GPUs, TPUs) and software for managing large-scale data processing and model training.

Supporting Infrastructure 

Telecommunications and Energy Infrastructure

Reliable connectivity between data centres and users is crucial for real-time AI applications. 

Additionally, energy-efficient infrastructure supports sustainable operations.

Supporting Infrastructure 

Human Expertise

Skilled professionals such as data scientists, machine learning engineers, and domain experts are essential for developing effective AI systems

Technology Readiness Levels (TRL)

The Technology Readiness Level (TRL) framework is a widely used system to assess the maturity of a technology, from its initial concept to its deployment in real-world environments. Originally developed by NASA, the TRL scale consists of nine levels, each representing a stage in the development and readiness of a technology.

Technology Readiness Levels (TRL)

Key Features of the TRL Framework:

1. Provides a structured roadmap for technology development from concept to deployment.
   
2. Ensures technologies are rigorously tested at each stage to reduce risks during implementation.
   
3. Widely adopted across industries like aerospace, AI development, and renewable energy.

Information Architecture 

Information Architecture (IA) is the practice of organising, structuring, and labeling content in a way that ensures users can easily find and navigate information. It is essential for creating intuitive and efficient digital environments, such as websites, apps, and intranets.

What Are The Core Components of IA?

Data Readiness

Data Readiness refers to the state of being fully prepared to use data effectively for analysis, decision-making, and operational purposes. 

It ensures that data is accurate, accessible, and aligned with organizational goals, forming the foundation for successful AI implementation and other data-driven initiatives.

Data Readiness (Quality)

Accuracy: Data must reflect real-world situations without errors.

Completeness: All necessary data points should be present, with minimal gaps.

Consistency: Data must be uniform across systems and free from conflicting values.

Timeliness: Data should be up-to-date and relevant to current needs.

Example: A healthcare organization ensuring patient records are accurate and complete for AI-powered diagnostics.

Data Readiness (Intergration)

Eliminating silos by consolidating structured, unstructured, and semi-structured data.

Using tools like ETL (Extract, Transform, Load) platforms or real-time integration systems (e.g., Apache Kafka).

Example: Integrating customer data from CRM systems and social media platforms to provide personalized marketing insights.

Data Readiness (Governance)

Defining roles and responsibilities for data management.

Ensuring compliance with regulations like GDPR or Data Protection Act.

Implementing access controls to protect sensitive information.

Example: A financial institution enforcing strict governance policies to ensure compliance with regulatory standards.

Data Readiness (Accessibility)

Providing secure access through role-based permissions.

Using cloud infrastructure for scalability and remote accessibility.

Example: A retail company using cloud-based dashboards to give managers real-time access to sales performance metrics.

Data Readiness (Infrastructure)

Scalable storage solutions (e.g., relational databases like MySQL or NoSQL databases like MongoDB).

High-performance processing systems for handling large datasets efficiently.

Example: An e-commerce platform using distributed databases to store customer transaction data.

Data Readiness (Compliance)

Protecting user privacy by complying with regulations like GDPR or Data Protection Act.

Maintaining audit trails for accountability in data handling processes.

Example: A healthcare provider ensuring compliance with HIPAA when storing patient health records.

How These Components Work Together

These components:data quality, integration, governance, accessibility, infrastructure, and compliance are interdependent and collectively ensure that an organisation’s data is ready for effective use in AI systems or other advanced analytics applications.

By addressing these core areas systematically through a structured assessment process (e.g., Data Readiness Assessments), organisations can identify gaps in their current capabilities and develop a roadmap for improvement, ultimately enabling smarter decision-making and successful AI adoption.

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