Guided workshops designed to help organisations achieve better AI outcomes
Need guidance on adopting generative AI? Facing roadblocks with your current pilot program?
Generative AI demos, often seen as almost magical, have captivated us by showcasing transformative capabilities across industries. Yet the ability to quickly produce impressive results in a proof-of-concept can be deceptive. There's a striking gap between the polished performance of demos and the complex realities of real-world deployment.
When evaluating generative AI use-cases, key aspects need to be thought through: Is accuracy essential? Are consistent results required? Is data verifiability a must-have? If yes, your implementation of Generative AI will require careful consideration.
My guided workshops are designed to help organisations achieve better generative AI outcomes.
Services
Gen AI Starting Out
Everything your organisation needs to start your AI journey. The AI Beginnings workshop builds knowledge, interest, and engagement with Generative AI, for a smoother adoption process.
GenAI Scaling Your Pilot
Scaling AI from Proof-of-Concept (PoC) to real-world operation is challenging. While it is relatively straightforward to clean and constrain data for a PoC, scaling these efforts into real-world operations is a vastly more complex undertaking.
GenAI Pathways
Each organisation's AI adoption journey is unique; there is no “one-size fits all”. In this workshop, organisational priorities, resources, and readiness are all considered using an AI adoption framework.
GenAI Ethics & Governance
De-risking GenAI means addressing privacy and regulatory concerns. Unlike the PoC, which operates in a constrained environment, the operational system must handle a wide range of queries and scenarios.
GenAI Starting Out
Start your organisation's AI journey with the AI Beginnings workshop designed to begin the thinking on how to integrate GenerativeAI into your operations. The AI Beginnings builds knowledge, interest, and engagement with Generative AI.
The AI Beginnings addresses three challenges of effective GenAI adoption:
1. Identifying your starting point.
2. Choosing the right use-cases.
2. Promoting responsible adoption.
This industry-specific offering provides insights into how others in your sector are leveraging AI for transformation, with real-world examples. Ideal for organisations or start-ups looking to begin their AI journey responsibly and with a clearer view of how GenAI can be used.
GenAI Pathways
Determining the optimal level of GenAI adoption presents a significant challenge. Not all organisations require an "AI-first" approach, while others may face substantial market disruptions necessitating more significant adaptation.
Successfully adopting AI is a complex and time-consuming process. It needs careful planning and execution to maximise business benefits while minimising risks.
The GenAI Pathways framework offers a tailored approach for each organisation, recognising the unique contributions of all teams - from Board level to your front office staff.
This approach helps organisations to understand their current level of AI maturity, identify their specific needs, and determine the optimal level of AI integration for their unique circumstances.
GenAI Scaling your Pilot
While it is relatively straightforward to clean and constrain data for a PoC, scaling these efforts into real-world operations is considerably more complex. And, as the scope of initiatives widen, the financial burden of developing and deploying GenAI models is increasingly felt.
Rushing into building a PoC can lead to disillusionment within the business. Instead, begin by defining the ‘destination model’ and breaking down the problem into its components.
For example, start with mapping a single, internal use case. By focusing initially on an internal application, businesses avoid directly impacting customer relationships during the initial stages of AI implementation.
Before effectively integrating AI into workflows, it is necessary to understand what Generative AI can and cannot do, its limitations, and the ethical considerations involved.
This approach is helpful for determining which AI applications are suitable for real-world operational use, and helps build trust through a better understanding.
GenAI Ethics & Governance
Beyond traditional risk management and compliance, AI governance requires continuous adaptation to emerging capabilities, evolving regulatory frameworks, and shifting ethical considerations, from initial deployment through to ongoing operations.
Critical decisions must be made about model selection, data usage, output validation, and risk mitigation - all while maintaining operational efficiency and competitive advantage. These decisions cannot be made once and forgotten; they require constant reassessment as technology capabilities expand and regulatory landscapes shift.
The stakes are particularly high with Generative AI, where outputs can be unpredictable and consequences far-reaching.
The path to effective AI governance is continuous, not destination-oriented. Your governance framework should include a wide range of policies , and requires a clear vision that is aligned with organisational goals. For example:
Data Privacy and Security (establishing trust)
Ethical AI Use (fairness, transparency)
Model Validation and Monitoring (accuracy and relevance)
Documentation and Traceability (provenance, accountability)
Stakeholder Engagement (collaboration)
Compliance & Auditing (standards)
Risk Management (proactive mitigation)
Data Governance (quality, consistency)
