Business case (ideate)

Discovering the scope of the potential pilot shaped by the value and aims of the enterprise digital transformation strategy and the Generative AI business case.

Months 1 - 3

Identify and appoint resources based on the competencies needed to meet the ‘Discovery Pilot’ OKRs. Establish proof of concepts and validate organisation competencies.

Months 3 - 6

Transition pilot lessons learnt and functionality to the formal business case, as subsequent roadmap as the initiative gathers momentum and employs an ethical monitoring strategy and governance structure.

Months 6 onwards

Scale value created by the Generative AI initiative through the implementation of additional use cases and optimising both its ethical and governance capabilities.

Organisational Design

GEN AI Pilot Team (Prototyping Use Case #1)

Light customisation of the Client’s own data. Focus on a specific low-risk Use Case . Update the pre-trained models as part of an iterative process.

Team consisting (minimum): Product Owner, Data Specialist/Scientist, Automation SME, Governance SME and a Hardware Specialist

Centre of Excellence: Infrastructure & Ops Provisioning

Ensuring the provisioned infrastructure is capable of meeting the high demand of the use cases and the COE establishing good practices prior to scale-out. Including, where needed cloud infrastructure to meet variable processing demand.

Centre of Excellence:  Data Integrity & Governance

Dedicated COE that is responsible for constantly evolving the organisations regulatory defintion in response to the performance of the pilot.

Ensuring trust, unbiased, fairness, explainability of the Generative AI solution.

An iterative approach enables the models to reflect the constantly changing technology landscape and ensuring strong data rules.

Ethics committee

Form a committee that holds responsibility for ensuring organisational and regional GEN AI mandates are applied. Achieved through establishing three core practices:

Methodology - Publish tools and practices for that must be adhered through the Generative AI life cycle.

Adoption - Over see the introduce of these methodologies across the organisation.

Governance - Continually evaluate how the core practices are being adopted and provide guidance where improvements are required.

Discovery to Value Creation

Creativity Potential

Discover the true potential of Generative AI

• rapidly verify new concepts and assumptions in a controlled manner. 

• Identify and exploit previously unforeseen potential of Generative AI technology

• focused and rapid research cycles strengthen a business case 

• shaping a clear implementation path towards a healthy return on investment 

• validate the suitability of its current competencies, processes, and tools prior to scale-out

Validation Efficiencies

Rapidly validate new ideas and concepts

• quickly validate both technical and commercial capabilities for Generative AI implementation and

• Ensure suitable solutions are found to ethical factors and challenges as part of

• Constantly capture and analysis customer feedback across data, automation, usability and ethical factors

• Ensure the team employs a tight governance and financial management

Continuous and Incremental Value Creation

Lean development life cycle

• A clear and prioritisation mechanism

• Accelerate Generative AI return on investment

• Quickly securing market share generates income earlier 

• Constantly adapt the ways of working to meet the capabilities of:

  • Generative AI technology

  • internal capabilities

  • external landscape as appropriate

Critical Success Factors

Product Backlog (baseline)

• Foundation and LLM selection

• Establish Ethics & Governance Committees

• Pilot Objectives. & Key Results

• Stakeholder Management

• DevSecOps Strategy

• Communication Plan

• Colocation Space (Teams & Squads) Provisioning

Value Creation Life Cycle

• Early Value Realisation & Technology Suitability

• Validation Efficiencies

• Continuous Investment in a Lean delivery cycles

• Team capability & engagement

• Suitable & accurate KPIs

• Creativity potential & risk tolerance

GEN AI Commercial Readiness for Product Management

The MONETICAL Generative AI Commercial Readiness Assessment covers the following core characteristics of the initiative: AI ethics & regulation, AI initiative OKR & KPI, AI Stakeholders, AI post deployment and AI use cases.

GEN AI Technology Readiness for Product Management

The MONETICAL Generative AI Technology Readiness Assessment covers the following core characteristics of the initiative: foundation & LLM, ethical capabilities and data governance, privacy and security factors, human oversight and technical capabilities.