Global AI Implementation
AI Strategy, Not Hype
Designing the Right AI for the Job
Foundation Model Selection
The best foundation model isn’t the largest or newest, it’s the one that aligns with your use case, fits your infrastructure and delivers value within your constraints.
Foundation Model Selection
The best foundation model isn’t the largest or newest, it’s the one that aligns with your use case, fits your infrastructure and delivers value within your constraints.
Foundation Model Selection
The best foundation model isn’t the largest or newest, it’s the one that aligns with your use case, fits your infrastructure and delivers value within your constraints.
Data, Infrastructure, and Cost
Successful AI deployment is a balancing act, weighing performance needs against costs, while ensuring scalability doesn’t compromise security.
Data, Infrastructure, and Cost
Successful AI deployment is a balancing act, weighing performance needs against costs, while ensuring scalability doesn’t compromise security.
Data, Infrastructure, and Cost
Successful AI deployment is a balancing act, weighing performance needs against costs, while ensuring scalability doesn’t compromise security.
Governance Frameworks
Turn AI from experimentation into accountable practice balancing innovation with oversight, so scale, speed and impact never outpace control and compliance.
Governance Frameworks
Turn AI from experimentation into accountable practice balancing innovation with oversight, so scale, speed and impact never outpace control and compliance.
Governance Frameworks
Turn AI from experimentation into accountable practice balancing innovation with oversight, so scale, speed and impact never outpace control and compliance.
RAG vs “Raw” LLMs
Retrieval augmented generation (RAG) combines a language model with a trusted knowledge base, grounding responses in verified data rather than relying solely on model memory. This approach improves factual accuracy, reduces hallucinations and keeps sensitive information within organisational boundaries.
Measuring an AI business case
A credible AI business case combines hard numbers with longer term value. Teams track reduced manual effort, lower error rates and faster time to market, then tie these to revenue, capacity or avoided costs.
Measuring an AI business case
A credible AI business case combines hard numbers with longer term value. Teams track reduced manual effort, lower error rates and faster time to market, then tie these to revenue, capacity or avoided costs.
Measuring an AI business case
A credible AI business case combines hard numbers with longer term value. Teams track reduced manual effort, lower error rates and faster time to market, then tie these to revenue, capacity or avoided costs.
Integrating your AI ROI model
An ROI model looks at three lenses: efficiency, revenue and intangibles. Efficiency covers hours saved and reduced rework; revenue focuses on uplift and speed; intangibles capture long term capability building.
Integrating your AI ROI model
An ROI model looks at three lenses: efficiency, revenue and intangibles. Efficiency covers hours saved and reduced rework; revenue focuses on uplift and speed; intangibles capture long term capability building.
Integrating your AI ROI model
An ROI model looks at three lenses: efficiency, revenue and intangibles. Efficiency covers hours saved and reduced rework; revenue focuses on uplift and speed; intangibles capture long term capability building.
