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AI Strategy6 min read
AI Strategy Terms: Ambition, Readiness, Quick Wins and RICE
Part 4 of a plain-English AI terms series: AI ambition, the waves of adoption, maturity, readiness, automate vs augment, quick wins, RICE and pilot purgatory.
Why AI strategy terms matter
The words a leadership team uses about AI shape what it funds. If the only question is which tool to buy, the answer will be a tool. This is part 4 of our plain-English series on AI terms, covering the language of AI strategy and prioritisation.
The terms follow the vocabulary of the Singtel AI.dea – AI bridge programme, run with SIM Academy (Programme Architect: Michelle Yeo). The explanations are our own.
AI ambition: what AI is for in your business
An AI ambition is a short statement of the business results you want from AI, and the lines you won't cross getting there. It starts from problems, not technology: quotes take too long, customer emails go unanswered, month-end close drags on for a week.
A useful ambition turns each problem into a measurable target, such as closing the month's books in five working days instead of ten, and lists the guardrails, such as no AI-written message reaching a customer unread, and no staff records in public AI tools.
Ask: which two or three business numbers should AI move this year, and what will we not do to move them?
Waves of adoption and AI maturity
AI adoption tends to come in three waves: efficiency first, then enhancement, then transformation. Efficiency means doing today's work cheaper or faster, such as automating data entry. Enhancement means helping people make better decisions, such as sharper forecasts. Transformation means new products or business models that weren't possible before.
AI maturity describes how far along that path an organisation is, from occasional experiments with no budget or owner, through scattered pilots, to AI built into how the business runs. Most SMEs are near the start, and the useful question is what it takes to move up one step.
Ask: which wave are our current AI ideas in, and are we trying to skip straight to transformation?
AI readiness: are the foundations in place?
AI readiness is an honest check of whether your organisation can make AI work, not just buy it. It usually covers strategy and leadership, data, technology, people and skills, processes, and governance.
The weakest area sets the limit. A great tool on messy, scattered data gives messy results, and a capable team with no one owning AI drifts. Readiness scores are most useful when they are backed by evidence and point to one thing to fix first.
Ask: which of these areas is our weakest, and what would it take to fix it in the next 90 days?
Automate, augment or keep human
For each step of a process, decide whether AI should do it, help a person do it, or stay out of it.
• Automate: repetitive, rule-based steps with good data, such as logging orders or filing documents.
• Augment: steps that need judgement, where AI drafts, suggests or flags and a person decides.
• Keep human: high-stakes, sensitive or relationship-heavy steps, such as disciplinary conversations or negotiating with a key client.
• Hybrid: AI handles the routine cases and passes the exceptions to a person.
Working through a real process step by step stops AI-everywhere thinking and shows where the first gains are.
Ask: in our most painful process, which steps could AI fully own, and which must stay with a person?
Quick wins and strategic bets
Quick wins make today's work faster or cheaper within weeks or months. Strategic bets change what you offer or how you compete, and take longer. A healthy AI portfolio has both: two or three quick wins to prove value and build confidence, plus one or two bets for the future.
The common mistakes are the extremes: only quick wins, so nothing ever changes fundamentally, or only big bets, so nothing pays off for a long time and support fades.
Ask: which of our AI ideas will show a result this quarter, and which one are we backing for the long term?
RICE: scoring AI ideas against each other
RICE is a simple scoring method for ranking ideas: Reach × Impact × Confidence ÷ Effort. It comes from product management and works well for AI ideas because it forces trade-offs into the open.
• Reach: how many people or transactions it affects.
• Impact: how much it improves things for each of them.
• Confidence: how sure you are of those estimates, which keeps over-optimism in check.
• Effort: how much work it takes to get a working pilot.
The score isn't the decision; it is a way to make the conversation about evidence rather than enthusiasm.
Ask: if we scored our top five AI ideas with RICE, would the favourite still come first?
Pilot purgatory: when pilots never scale
Pilot purgatory is when AI pilots run, show promise, and never become part of normal operations. The usual causes are a pilot with no business owner, no agreed measure of success, data that only worked for the demo, or no budget for the step after the pilot.
The fix is to decide before the pilot starts what result would justify scaling it, who will own it afterwards, and what it would cost to run for real.
Ask: what result would make us roll this out, and who owns it if it works?
Further reading: recent research on AI adoption and strategy
• Ministry of Manpower, Adoption of AI Among Firms (April 2026). MOM's first survey on the topic: 71.5% of Singapore firms have yet to adopt AI, and only 3.8% are integrating it into core processes. The full report has the detail by firm size.
• McKinsey's State of AI in 2026, reported by FM Magazine (September 2026). Eight in ten respondents say AI has improved their own productivity, and 44% report scaling AI across the enterprise.
• Our own write-up of why AI projects fail, based on MIT's 2025 research on the gap between AI pilots and results.
Next in the AI terms series
Part 5, the last in the series, covers AI architecture and governance terms: RAG, fine-tuning, cloud vs edge, data residency and KPIs. Earlier parts cover core AI terms, the types of AI and AI risk terms.
To learn these with your own team, you can register your interest on the AI.dea programme page.
Frequently asked questions
What is AI readiness?
AI readiness is how prepared an organisation is to make AI work, not just to buy it. It usually covers strategy and leadership, data, technology, people and skills, processes, and governance. The weakest of these tends to limit results, so a readiness check is most useful when it identifies one area to fix first.
What is the RICE framework for prioritising AI projects?
RICE scores each idea as Reach times Impact times Confidence, divided by Effort. Reach is how many people or transactions it affects, Impact is how much it helps each one, Confidence is how sure you are of the estimates, and Effort is the work needed for a pilot. Higher scores go first.
What is pilot purgatory in AI?
Pilot purgatory is when AI pilots show promise but never become part of everyday operations. Common causes are no business owner, no agreed success measure, data that only worked in the demo, and no budget for rollout. Agreeing the success threshold, owner and running cost before the pilot starts is the usual fix.
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PreviousAI Terms for Leaders: Hyperscalers, Agents and ObservabilityNext AI Risk Terms: Hallucination, Workslop, Shadow AI and More
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