THE numbers tell a paradoxical story as McKinsey's latest global research finds that 78 per cent of organizations now use artificial intelligence in at least one business function, making it one of the fastest-adopted technologies ever tracked.
Yet only around five per cent qualify as genuine high performers who see meaningful bottom-line impact from their AI investments. Gartner has warned that at least 30pc of generative AI projects are abandoned after proof of concept, and predicts that over 40pc of so-called agentic AI projects will be cancelled by the end of 2027.
One widely cited MIT study put the failure rate of generative AI pilots as high as 95pc. These figures should concern every boardroom in Pakistan. Our businesses, from textile mills and banks to retailers, hospitals and logistics firms, are under growing pressure to 'do something with AI'.
Vendors are knocking, competitors are announcing pilots, and the temptation to launch a chatbot or an analytics dashboard for its own sake is strong. But the global evidence is unambiguous: adopting AI is easy; creating value with AI is hard.
The difference is not the sophistication of the models. It is the discipline of the strategy. Having spent two decades between academic research and applied AI delivery, including over a hundred projects across more than twenty countries, I have seen the same pattern repeatedly. Organizations that succeed do not start with technology.
They start with a structured assessment of need, value, feasibility and risk, and they implement in measured stages. Start with need, not hype.
Every sound AI journey begins with a deceptively simple question: what problem are we actually trying to solve? A genuine need assessment walks the organization's value chain and asks where time, money or quality is being lost; which decisions are made slowly or inconsistently; and which processes are repetitive, rulebound and data-rich, the natural habitat of automation.
The output is a map of business pains expressed in the language of operations and finance, not of algorithms. Cast a wide net for use cases. Most industries have fifteen to twenty well-recognized AI use cases, from fraud detection and demand forecasting to invoice processing and 1Op-ed submission | Business & Finance Approx. 1,050 words customer-service automation.
The richest candidates often come from the shop floor rather than the boardroom: the invoice that takes three departments to validate, the phone orders transcribed by hand, the documents reviewed line by line. These unglamorous processes are frequently the highest-return ones. Evaluate on two axes.
Every candidate must then be scored on two independent dimensions: the business value it can create and the feasibility of actually delivering it. Value flows through four channels: financial impact (cost reduction and revenue growth), improved quality, time saved, and reduced human intervention.
Feasibility is broader than most managers assume. It covers organizational readiness (are the processes digitalized, does usable data exist?), management readiness (is top leadership committed, is there money for the run and not just the build?), the willingness of the departments concerned to adopt and adapt, and how frequently the process actually occurs. Gartner attributes most AI failures to poor data quality and expects 60pc of projects lacking AI-ready data to be abandoned through 2026.
A use case that is high value but low feasibility is a research project; one that is feasible but trivial is a distraction. Take risk seriously. Before selection, every shortlisted use case should pass a structured risk review: what happens when the AI is wrong, how dependent the solution is on external large language models whose pricing and behaviour can change overnight, and where the organization's data travels.
That last question alone often dictates whether the system should run on-premises, in the cloud or in a hybrid arrangement, a choice with major cost consequences. Implement in stages, with kill-switches.
Selected use cases should move through a gated pipeline: a proof of concept of a few weeks to validate the idea on real data, a minimum viable product, a bounded live pilot measured on business indicators such as cycle time and error rate, and only then scaling and full deployment with ongoing maintenance.
Each gate carries pre-defined success criteria and a willingness to stop. A project killed cheaply at the proof-of-concept stage is a success of the methodology, not a failure. The organizations stuck in what consultants call pilot purgatory are usually running many shallow experiments instead of a few deep deployments.
Close the loop. The rarest discipline of all is the honest review: comparing the value that was projected with the value actually realized in production, and feeding the lessons 2Op-ed submission | Business & Finance Approx. 1,050 words into the next initiative. This single habit converts AI from a faith-based expenditure into a managed portfolio. One concern deserves special emphasis: the choice of technology partner.
The build cost of an AI system is only the entry ticket. The recurring running cost, the deployment cost, the maintenance cost and the deployment model collectively determine whether the initiative becomes an asset or a liability. A generative AI solution that delights in the demo can bleed money in production if every transaction triggers expensive model calls that a smarter design would have avoided. Seasoned teams do not merely develop a solution; they develop a cost-effective solution, using smart algorithms, right-sized models and careful architecture to minimize recurring cost.
The gap between a naive design and an optimized one is frequently five to ten times in operating cost, often the difference between positive and negative returns on the same use case. Boards should demand delivered outcomes with numbers, an honest feasibility assessment before any quote, and stage-gated delivery with explicit exit points.
For Pakistan, the stakes are larger than any single firm. Our IT sector has shown it can deliver AI for clients across the world; the bigger prize is for domestic industry, government and services to apply the same discipline at home. The AI era will not reward the organizations that move first. It will reward those that move deliberately, with a cleareyed assessment of need, value, feasibility and risk, a staged implementation discipline, and the right partner beside them.
