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AI Centre of Excellence 

Increasingly businesses are looking to take advantage of AI and the opportunities it can provide.

However, when deploying scalable AI solutions into an organisation, they will want to address the following challenges:


Prevention of unintended consequences

AI can present legal and ethical issues concerning privacy and surveillance, bias or discrimination, and philosophical challenges around the replacement of human judgment; this could have detrimental repercussions upon an organisation’s reputation and integrity.


Security of data

AI is data driven, without secure governance and infrastructure, as well as education around what should be fed into AI models, organisations are at risk of compromising sensitive data.


Environmental costs

Running AI to generate real-time prediction requires heterogeneous computing. This in turn uses large amounts of power and generates vast amounts of CO2. As well as factoring this into organisational carbon neutral pledges, businesses need to encourage and educate their organisations on the responsible use of AI in the context of climate change.


Lack of AI-focused culture

Misunderstanding and miscommunication about AI can adopt a culture that is sceptical and/or has unrealistic ideas towards AI opportunities. Managing expectations and providing a comprehensible understanding of AI will help with resistance and foster a culture that embraces realistic AI opportunities.


Not fit-for-purpose technology

Adopting and deploying AI solutions needs significant technical maturity within the organisation to ensure the infrastructure and skillset can manage the requirements of AI, particularly data management capabilities. Technical constraints can limit the scalability and available AI solutions.


Adapting the Operating model

Introducing AI solutions will have significant impacts upon the operating model. Failure to identify and address these changes in a holistic way will compromise the functionality of the model. For example, organisations may see a breakdown in the communication and efficiency between teams, decision-making and business goal alignment.


Business Case justification

Identifying appropriate business cases for AI, by assessing them against business strategy and the organisation’s AI maturity, prevents the selection of unfeasible projects that provide little or no value to the organisation.


Ideation/experimentation

Without a dedicated space to securely experiment and encourage innovation, companies will lack the ability to upskill and test AI capabilities thus constraining the scalability of AI in the organisation.

AI Centre of Excellence

A holistic and structured approach to scalable AI deployment

1

AI Ideation, Prioritisation and Business Cases

Generating AI innovation that is properly assessed and prioritised against the business’ needs and strategy

Innovation and Ideation
  • Generation/Experimentation
  • Solution Development
  • Proof of concept

Business Case Justification
  • Justification and viability assessment
  • Maturity assessment
2

Governance and Enablement

Structured frameworks and guardrails for AI deployment to ensure data security and prevention against unintended consequences

Governance and Enablement
  • AI strategy and framework
  • IT Strategy- AI CoE’s collaboration and influence on other business areas
  • AI Governance Board
  • Roles and responsibilities
  • Ethical, environmental and regulatory compliance
  • Procurement

Patterns and accelerators
  • Pattern creation

Data
  • Data Governance
  • Data Security/protection
  • Data Quality Management
3

Delivery

Deploying AI Solutions with the required tools, skills and frameworks in a controlled environment

Tooling and technical capabilities
  • Baseline technical prerequisites
  • Tooling
  • Modelling
  • Observability

Change Management
  • Organisational Change
  • Stakeholder Management
  • Communication Planning
4

Operation/Continuous Improvement

Adapting the operating model to reflect AI solutions and fostering a continuous improvement culture

Operating Model
  • Operating Model Review
  • FinOps

Continuous Improvement
  • Lessons Learned
  • Monitoring Trends and latest AI developments
  • Upskilling

Communication and Education
  • AI Training | AI Champions | Knowledge sharing

Business Outcomes of an AI Centre of Excellence


Assurance

Confidence in the holistic approach that AI is being deployed in a secure, responsible, and scalable manner


Deploy cutting-edge solutions

A structured approach that allows the organisation to take advantage of and invest in the latest solutions offered by AI technology


Ideation

Cultivate the environment and processes for generating, developing, and communicating new ideas for the application of AI across the business


Value-add solutions

Identify best-fit solutions based on business requirements and strategic goals

Structure

A comprehensive framework for securely deploying scalable AI and promoting cross-business collaboration between teams


Governance

An appropriate governance model aligned to organisational objectives that encompasses security, ethical, environmental, and legal considerations

Operating model consideration

Holistic analysis of deploying an AI solution to ensure the functionality and efficiency of the organisation’s operating model

Technical maturity

Enhance the ability to assess and manage technical change, and defined processes for upgrades and improvements


AI trends/developments monitoring

Encourages a culture that embraces and follows the evolving trends and advancements in AI technology


Consistency of approach

Establish an AI delivery lifecycle across the value chain which can be repeated