Florence AI Blog
Insights on AI strategy, adoption, and making technology work for your organisation, from the team at Nightingale HQ.
Bootstrap your AI strategy from the documents you already have
You probably already have a strategy deck, a business plan, or a board update sitting in a folder. Here is how to turn them into an AI strategy without retyping any of it.
ISO/IEC 42001 explained: what SMEs actually need to know
A plain-English guide to the AI management system standard - what it covers, who needs certification, and how to borrow its structure even if you don't.
What's the right AI operating model?
Centre of excellence, federated teams, or hub-and-spoke? A guide to picking the shape that fits your organisation.
How much will AI cost, and what's the ROI?
Budgeting for AI includes tech, data, people, external consultants, and ongoing operations. Here's how to estimate without pretending it's precise.
How do you measure AI strategy success?
Blend leading indicators (pilot completion, data readiness, adoption) with lagging indicators (cost savings, revenue, efficiency).
How to get people to actually adopt AI
Change management, communication, training, and involving staff early. Underestimating these is why AI strategies fail.
What team and skills do you need for AI?
AI strategy needs more than data scientists. Here's the roster and why each role matters.
How to prioritise and sequence AI projects
Common criteria: strategic fit, feasibility, data availability, cost, risk, and time to value. Here's how to combine them into a roadmap.
Should we build AI in-house or buy it?
The honest answer is 'a portfolio of all three'. Here's how to decide which mode each initiative should use.
Data, governance, and risk: what AI strategy must cover
Strategy needs to state what you will and will not do with AI, not just what you can build.
How AI maturity models turn gut feel into a plan
IBM's Big Data & AI Maturity Model gives you a structured way to score readiness across strategy, governance, data, culture, and technology.
Do we really need a business model canvas first?
Yes, usually. Without it, AI strategy optimises the wrong things. Here's what the canvas exposes.
How to find the right AI use cases
The best AI portfolios combine top-down business priorities with bottom-up ideas from people who know the work.
Where do we even start with AI strategy?
Skip the use-case brainstorm. The better first move is aligning on business goals, readiness, and what success actually looks like.
The three modes of AI strategy development, and when to use each
Top-down mandates, small leadership groups, and crowdsourced completion each have a place. The mistake is picking only one.
Crowdsource AI ideas, but build the management process too
Open idea capture unlocks energy across the organisation. Without triage, it also drowns the team responsible for delivery.
How the IBM Big Data and AI Maturity Model sharpens your strategy
Maturity models get a bad reputation. Used well, they turn vague readiness debates into concrete investment decisions.
Why you need a business model canvas before you build an AI strategy
AI strategies that skip the business model tend to optimise the wrong things. Here is what to map first, and why.
A practical guide to creating an AI implementation roadmap
A step-by-step approach to turning AI ambition into a sequenced, funded, and accountable delivery plan.