Which problems are worth solving with AI, in what order, and with what constraints. Most organisations skip this step entirely.
Most organisations approach AI backwards. They evaluate products before defining problems. They deploy before establishing baselines. They measure activity (tools adopted, hours saved) instead of business outcomes. Then they wonder why the ROI case is hard to make.
This topic covers the decisions that come before any technology evaluation: how to identify which problems AI can solve, how to think about the real cost of AI systems, and how to position AI as a coherent capability.
Written for executives and mid-market owners making first AI investments, people who need the strategic logic.
How do you identify the right AI use case before buying anything?
Define the business problem first. The right use case is repeatable, measurable, data-rich, and bounded. It is rarely the most exciting one. It is the one where a failure costs the least while you learn.
What does AI cost and how do you read vendor proposals?
AI costs sit in three places: model inference, data infrastructure, and human oversight. Most vendor proposals only show the first. The total cost of ownership depends on all three.
How do you build a coherent AI strategy instead of running disconnected pilots?
Start with a capability assessment across your operations. Identify the highest-return constraint. Deploy once, prove ROI, then expand. Coherent strategy comes from sequenced investment, not parallel experiments.
What does it mean to "compete on AI" when models are commodities?
It means competing on your data, your processes, and your speed of learning, not on model access. Everyone can access the same models. Your advantage is using them on better data, in better workflows, faster than competitors.
The price per token falls every quarter while the AI bill rises, because volume, tokens per task and model choice grow faster than prices fall. How to tag every model call to the use case that ran it, build a cost-per-outcome line that sits beside the benefit, and run a monthly AI cost review on three numbers. Part 4 of the AI Benefit Realisation series.
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Marc Andreessen says the gap between what AI-native startups and established companies believe AI can do is the widest of his career. The evidence mostly supports him. Here is why the gap exists inside incumbents, where his argument goes too far, and what a board can do about it this quarter.
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In October 2026 one developer released seven open-source Adobe alternatives written with Claude, and both large AI labs shipped their own documents, slides and animation tools inside the chat window. The software most exposed is the kind most people use only lightly. For enterprises, the bigger question is who governs the software a model builds on demand.
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An AI pilot rarely runs alone. Routing changes, campaigns and new hires land in the same quarter, and a before-and-after comparison credits all of it to the model. How holdouts, staggered rollouts, switchbacks, synthetic controls and trend-adjusted comparisons separate the AI's effect, what each costs, and which to use when no control group is possible. Part 3 of the AI Benefit Realisation series.
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A baseline taken after an AI pilot starts is a reconstruction, and reconstructions flatter the pilot. What to measure, for how long, how much the number moves on its own, and who signs the record before the first production model call. Part 2 of the AI Benefit Realisation series.
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Most AI programmes cannot prove what they returned because nobody recorded the starting point, nobody agreed how to separate the AI's effect from everything else, and the running cost sits in a cloud bill no one maps to a use case. A framework the CFO and the Chief AI Officer run together: baselines, attribution, inference cost accounting and stage-gates that stop funding for pilots that miss their targets.
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Being late is the dominant fear in the mid-market right now. Two in three CEOs say the risk of falling behind has pushed them to buy technology before they understood its value, and half say the pace has left them with piecemeal systems. Here is what being late costs, and an order of operations that protects a budget from its owner's anxiety.
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The fuel station is one of the first places in retail where AI, the connected car and new payment rails meet at scale. What changes at the pump and in the shop, where the risks sit in pricing, fraud and customer data, and what operators should do in the next twelve months.
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DBS reported about S$1 billion in economic value from data analytics and AI in 2025. The figure blends revenue, cost avoidance and losses averted, is measured with control groups, and sits outside the audited accounts. Here is what it counts, what it leaves out, and what a board should demand before quoting its own number.
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In 2018 DBS reorganised into 33 platforms, each led jointly by a business head and a technology head on one scorecard, funded as a standing unit and reviewed on live dashboards. Here is how the model works, what it added in 2021, and how a smaller organisation can adapt it.
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Most agents have adopted AI and half of them report no difference to the business. The gain sits in the first hour after an enquiry and in scoring leads on behaviour, and every AI output a buyer sees still needs a licensed human to sign it off.
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The mandate arrives written as strategy and starts as the fear of being seen as behind. Three questions tell you which one you have, and there is a way to answer that costs nobody their standing.
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Most AI programmes fail the same way products used to fail, by betting big before anyone tested the smallest version of the idea. Here is how Lean's build-measure-learn discipline makes AI adoption safer.
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IAB, Emplifi, and NIM data from 2026 show a widening gap between how positively marketers believe audiences feel about AI-generated creative and how audiences feel. What's driving the distrust, and what specifically closes the gap without giving up AI's production advantages.
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A corrected scaling law, real data on correlated model error, and the economics of compute scarcity all point the same way: near-total AI capability does not translate into near-total substitution. The five numbers that set the ceiling, and the one most enterprise business cases leave out.
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Not cloud infrastructure, not copilots bolted onto existing software. A benchmark of the AI-native companies whose entire business is the AI product itself, what they charge for, and why the pricing model is the real signal to watch.
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Elon Musk told SpaceX employees they'd 'effectively be the parents' of Grok. Take that seriously and sixty years of parenting research becomes strangely relevant to AI governance. What decades of studying real children tell us about instilling values in something that will eventually outgrow every rule we write.
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Regulation gets the headlines, but it isn't what stalls most enterprise AI programmes. Security, unit economics, data infrastructure, and reliability are, and each one compounds if left unaddressed.
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McKinsey found 88% of companies use AI regularly, but only about a third have grown it company-wide. MIT NANDA found only 5% of AI test projects lead to lasting profit. Three studies agree: the problem is how work is set up, not how smart the AI is.
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A 2025 government report shows AI use by Singapore small businesses tripling to 14.5%. But the gap with big companies grew to 48 points. Five easy categories explain who is catching up, and one you should not trust yet.
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Training your staff to use AI tools is not the same as having an AI plan. Data from McKinsey, BCG, Deloitte, and PwC all shows the same gap: it's top leaders and boards who need to understand AI.
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Corporate technology has run on a seven-layer OSI model for forty years. Generative AI needs a governed cognitive layer, what I call Layer 8, that turns unpredictable reasoning into governed, auditable enterprise action.
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Companies are building high-tech AI infrastructure before they've upskilled a single employee to use it. That sequencing error is why the tech spend is massive and the adoption is near zero.
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The AI honeymoon phase is ending. What's left is a less glamorous question: can this run at scale, on a budget, without falling over. Five architectural moves for teams past the pilot stage.
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For SMEs, AI results depend less on which tools they pick than on how well leaders agree on goals, how clearly processes are defined, and how firmly the work is phased.
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Businesses built on 1990s assumptions now face serious operating risk. As geopolitics, cybersecurity, and AI converge, the leaders who thrive by 2030 will be the ones who questioned every assumption in 2026.
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Enterprise AI transformation fails not because the technology is flawed, but because organisations suspend discipline in response to ambition. The fix is structural, not motivational.
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MIT research is explicit: most AI pilot failures come from poor use case selection, not model quality. Here is the six-criterion framework for choosing right the first time.
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Before buying any AI tool, you need to know whether your business can use one. This diagnostic draws on the same readiness framework used in enterprise deployments, reduced to five questions any business owner can answer.
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The software AI market is crowded and margins are compressing. The next large commercial opportunity in AI is embedded intelligence: AI built directly into hardware, appliances, and physical infrastructure.
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Better prompts are table stakes. The real productivity gains come from structured AI loops: chained, recursive, self-improving workflows where Claude operates as an execution engine.
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Agent frameworks are engineering infrastructure, and deploying them without a validated process baseline transfers accountability to a system that carries none. The correct sequence is tool fluency before agent deployment.
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Hosting large language models yourself is right only under certain conditions. The break-even point depends on more conditions than most buying conversations admit.
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Enterprise AI programmes in Asia are speeding up. The models are running. But the way they are measured is still tied to the wrong layer of the technology stack, so decisions get made on incomplete information.
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Organisations deploying AI at scale are accumulating token debt at pace. Without deliberate cost architecture, inference spend becomes the single largest barrier to production ROI.
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The conversation around AI in enterprise has largely been shaped by two competing anxieties: the breathless pace of adoption, and the fear of being left...
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A bad process, accelerated by AI, produces bad results faster and at scale.
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The current AI market is characterised by rapid iteration, short product lifecycles, and an overwhelming number of seemingly similar offerings. The barrier...
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Many SMEs are being pushed to “adopt AI” through grants, vendor pitches, and digitalisation roadmaps. They deploy a chatbot on their website, bolt an AI...
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A Zero to One AI business sets out to create something new instead of making an existing solution a little better. The...
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In AI strategy, 'disruptive' and 'destructive' are not buzzwords.
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The question of what it takes to achieve and sustain growth is not theoretical. It is a daily operating concern across multi‑billion‑dollar infrastructure...
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We're witnessing a fundamental shift that most organisations have not yet fully grasped.
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Except it's never done. And the biggest line item is something that rarely makes it into the budget: people.
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Knowledge used to be power. AI changed that. As AI gets better at generating expertise instantly and practically for...
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Most AI projects don't work out. We're talking 95% of enterprise AI pilots delivering basically zero ROI. And in 2025, 42% of companies threw in the towel...
Read article →The AI Governance & ROI Executive Programme takes the five-dimension framework and applies it to your specific organisation, producing a ranked list of use cases and a twelve-week roadmap in half a day.
Frameworks deployed for national infrastructure operators, government agencies and mid-market enterprises across Asia and the Middle East.
The line on this page I believe most, 'the right use case is rarely the most exciting one,' is also the one clients push back on hardest, because nobody gets promoted for shipping the boring pilot. I've learned to let that tension sit. Choose the boring, bounded, low-consequence use case first anyway. The exciting ones are still there waiting once you've learned something real from the first one.
