Practical knowledge for businesses navigating the AI landscape — from strategy to implementation.
What does a maths teacher do with 65 students, 40 minutes, and one chapter on quadratic equations? She teaches to the middle, and nobody gets individual feedback until the exam results arrive.
Search for information on AI outsourcing in Bangladesh and you get two things: directory listings that rank vendors by nothing in particular, and blog posts written by Bangladeshi firms explaining why you should hire Bangladeshi firms. Neither answers the question a CTO actually has.
In November 2026, Bangladesh stops being a Least Developed Country. That sounds like good news, and economically it is — but the margin that used to absorb a two-week port delay is about to get much thinner.
Every few months a foreign newsletter publishes a one-line take on 'the Bangladesh AI scene' — usually a sentence about KOICA money and a name-drop of bKash. Every month a directory site shuffles the same 22 company names into a new listicle. None of these tell you what anyone is actually building.
Last Eid season, a Dhaka fashion seller on Daraz received 1,800 cash-on-delivery orders in three days. Almost 600 were returned undelivered. The seller next door kept her return rate at 14% with a $40-per-month fraud scoring setup she had built in a weekend.
Bangladesh loses 15 to 30 percent of its rice crop every season to disease, weather shocks, and late advice. AI tools for crop disease detection and yield prediction now run on a Tk 12,000 Android phone, work offline, and answer in Bangla.
A 500,000-unit-per-month garment factory in Ashulia runs 15 to 25 human QC inspectors across inline stations and the final audit table. They still ship defective units. Chargebacks from buyers run between USD 0.50 and USD 3.00 per defect, plus rework and return shipping.
Most companies treating AI as urgent do not have a written strategy. They have a list of things they want to try. That is not a strategy — and the difference shows up in wasted budget, scattered pilots, and a leadership team that cannot answer why any of it is happening.
Most AI vendor selection guides read like they were written for a Fortune 500 procurement team. If you are a business owner choosing your first AI partner without a dedicated IT team and under real budget pressure, those guides are not much help.
Most AI projects do not fail at deployment. They fail at planning, because nobody ran a real proof of concept first. You do not need a six-figure budget or an in-house data science team to fix this.
Most companies approach the build vs buy AI solutions decision as if it is 2015. That framing is wrong in 2026. Foundation models have collapsed the cost of the hardest part of AI to near zero.
Most AI projects in Bangladesh don't fail because of bad technology. They fail because nobody measured the right things from the start.
Every week, a Bangladesh business owner reads that AI is transforming their industry, takes a sales call promising a 40% cost reduction, and walks into a board meeting where someone asks, "What's our AI strategy?" Before spending a single taka, there is a better question: is your business actually ready?
A hospital director in Sylhet recently told us something that stuck: "I have 300 patients a day and four doctors. I don't need a smarter doctor. I need each doctor to be able to see twice as far."
There are three things every business deploying AI needs to have: a security setup that protects against prompt injection and data leakage, a governance policy that defines how AI is used, and a review process for high-stakes AI outputs. Most businesses in 2026 have none of them.
Last year, a Dhaka-based logistics company hired three "AI developers" from a freelance platform. Six months and BDT 18 lakh later, they had a collection of Jupyter notebooks that looked impressive in demos but could not handle a single live customer request.
Your customer service team answers the same 20 questions every day. An AI chatbot for customer support handles all of them, 24 hours a day, in Bangla or English, at a fraction of what you pay a full support team.
Every business headline about Bangladesh's AI policy says the same thing: 'Government launches AI strategy.' None of them tell you which sectors get priority funding, who controls the $96 million in KOICA investment, or what your business actually needs to do about it.
Sixty-five million users. Thirty billion dollars in annual transactions. And behind every transaction on bKash, AI systems are making decisions in milliseconds -- flagging fraud, scoring credit, routing customer queries, and coaching agents in real time.
Your AI chatbot keeps making things up. A customer asks about your refund policy, and it invents one. This is the exact problem that Retrieval-Augmented Generation, or RAG, solves.
A $200,000 proposal for a chatbot. That was the quote a Dhaka-based logistics company received from a US consulting firm. The same project cost $18,000 with a South Asia-based team.
AI agents go beyond chatbots -- they reason, plan, and take action. Learn what they are, what they cost, and how South Asian businesses can deploy one.
A factory in Gazipur runs 12 production lines. Their QC team catches roughly 65% of defects at inline inspection. The rest show up at final audit, when the cost of rework triples.
From garment factories to fintech startups, AI adoption in Bangladesh is accelerating. Here's what's driving the change and where the biggest opportunities lie.
You don't need a massive budget to benefit from AI. Here's a step-by-step approach for small and medium enterprises to implement meaningful automation.
Large Language Models are reshaping how businesses operate. A no-jargon explanation of what they are, what they can do, and where they fall short.
South Asia is emerging as a significant force in the global AI ecosystem. What makes the region unique and where is it headed?
Concrete, proven approaches to using AI for cost reduction — with realistic expectations about savings and implementation effort.