AI Readiness Assessment Checklist for Bangladesh Businesses

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?

Most AI projects fail not because the AI is weak but because the business wasn't prepared — wrong data, weak infrastructure, unclear processes, no internal owner. A proper AI readiness assessment for Bangladesh businesses catches these gaps before they become a seven-figure failure you have to explain to the board.

An AI readiness assessment is a structured evaluation of whether a business has the data, infrastructure, processes, talent, budget, and governance in place to successfully implement AI. For Bangladesh businesses, readiness also includes local realities — power reliability, connectivity, cloud access, BDT-scaled budgets, and compliance under the Bangladesh National AI Policy 2026-2030.

This checklist walks through six readiness dimensions with yes/no items, a self-scoring system, three maturity tiers, and a 60–90 day action plan for whichever tier you land in.

How to Use This AI Readiness Checklist

The checklist has six sections, each with 4–7 yes/no items. Score every item:

• Yes = 2 points — you meet the criterion fully
• Partial = 1 point — you partly meet it (common for SMEs in hybrid paper/digital states)
• No = 0 points — you do not yet meet it

Maximum score is 60 points. Your total tells you which tier you sit in:

Work through each section honestly. "Getting Ready" is not a failure — it tells you where to invest the next BDT 5 lakh before committing BDT 50 lakh to a vendor. A 15–20 minute self-assessment is cheap insurance against a failed project.

Section 1 — Data Readiness (14 points max)

Data is the foundation of every AI project. MIT CISR research puts enterprise AI project failure rates at 60–85%, and data quality is the most cited cause. In Bangladesh, where many SMEs run hybrid paper/digital operations, the data readiness gap is usually the largest one.

• We have a defined primary dataset for our AI use case — 12+ months of transactions, support tickets, quality records, or inventory history
• Our data is stored digitally, not in paper or disconnected Excel files — paper operations need a digitisation phase first
• We have 1,000+ labelled examples for supervised learning use cases — labelled defect images, tagged customer records, or similar
• Our data is exportable in a structured format (CSV, JSON, SQL) — data trapped in a legacy ERP with no export is effectively unusable
• We have basic data governance — someone owns our data and we know where it lives — even a simple spreadsheet catalogue counts
• Data quality is acceptable — under 10–15% missing or corrupted records in the primary dataset
• We comply with applicable data privacy requirements for customer, financial, health, or HR data

Bangladesh-Specific Data Note

Many Bangladesh SMEs run hybrid operations — purchase orders on paper, accounting in Tally, HR in Excel, customer support in WhatsApp. A digitisation phase is a common Phase 0 requirement. Budget BDT 2–8 lakh ($1,800–$7,000) and 2–6 months depending on scale. Local accounting systems often have limited structured export capability; verify you can actually get data out before committing to an AI timeline. Our data strategy service covers this preparation phase.

Section 2 — Infrastructure and Technology Readiness (12 points max)

AI runs on compute, connectivity, and reliable power. In Bangladesh, these deserve specific attention before a project starts.

• Reliable internet connectivity at the AI location — minimum 10 Mbps up/down; batch workloads can tolerate slower connections if planned
• Backup power (generator or UPS) for at least 4 hours — cloud inference is recoverable from outages; on-premise processing is not
• We can access cloud services (AWS, GCP, Azure, or BDIX-connected local providers) without organisational or regulatory restrictions — some regulated entities face data localisation constraints
• Our existing systems (ERP, CRM, accounting) have APIs or export capabilities — integrations with API-less systems are expensive and fragile
• We have a technically capable liaison between the AI vendor and operations — a capable IT manager or operations analyst is enough; not a data scientist requirement
• Our team's devices can run modern web applications — low bar, but it matters in field operations

Cloud vs. Local Infrastructure in Bangladesh

As of 2026 there is no AWS, Google Cloud, or Azure data centre inside Bangladesh. Latency to the nearest regions (Singapore for AWS/GCP, Hong Kong for Azure) is 60–120ms — fine for most business AI, a factor for real-time use cases. BTRC data localisation guidelines apply to certain regulated industries; confirm with legal counsel if your sector is affected. BDIX-connected local providers offer lower-latency hosting for storage but are not yet full AI compute platforms.

Section 3 — Process and Use Case Readiness (10 points max)

AI works best on a specific, well-defined problem. Vague ambitions produce expensive failures. If your use case is "we want to use AI," stop here and fix that first.

• We have identified a specific, measurable business problem for AI to solve — "reduce tier-1 support response time from 48 hours to 4 hours" is a use case; "use AI" is not
• The process we want to automate is documented — undocumented or highly variable processes are hard to automate; even a simple flowchart qualifies
• We can measure success — a baseline (current defect rate 3.2%, ticket volume 400/month) and a target we would accept as success
• The use case does not require a capability that does not yet exist — "predict with 95% accuracy which customers buy next week" is not currently achievable
• End users have been consulted and are supportive — AI tools rejected by end users produce zero value; change management is a readiness factor

Most Common First AI Use Cases for Bangladesh SMEs

A reference list if you are stuck on where to start:

• Customer support automation — particularly WhatsApp chatbots for tier-1 query resolution
• Document processing and data extraction — purchase orders, invoices, LC documents, often built as a RAG system over your own documents
• Demand forecasting and inventory optimisation — especially relevant for RMG exporters and FMCG distributors
• Quality control inspection — image-based defect detection in manufacturing
• HR candidate screening — for SMEs running frequent high-volume recruitment

Section 4 — Talent and Organisational Readiness (10 points max)

You do not need an in-house data science team. You do need a named owner and a realistic plan for maintaining what you build.

• There is a named owner for the AI initiative — one accountable person with decision authority, not a committee
• Senior leadership has explicitly prioritised this project — projects without visible leadership commitment die in procurement or lose budget
• We have (or can hire) someone with basic Python or data analysis skills — part-time is fine; a capable business analyst can manage a vendor and validate outputs
• Our team can commit 5–10 hours per week during implementation — "the vendor handles everything" is a red flag
• We have a plan for vendor transition risk — ask upfront about documentation, model portability, and transition support

If you are still working out the right team model, our guide on how to hire AI developers in Bangladesh walks through freelancer, agency, and in-house trade-offs with local salary benchmarks.

Section 5 — Budget and Financial Readiness (10 points max)

AI projects are investments. Knowing what you can realistically spend prevents both underspending (failed pilots) and overspending (enterprise tools for SME problems).

• We have a defined budget range — BDT 10–25 lakh for a chatbot or document automation pilot, BDT 50 lakh–1 crore for a custom ML system; vendors who refuse any range without a signed contract are a red flag
• We understand ongoing costs after go-live — API usage, retraining, monitoring, and maintenance typically run 15–25% of initial build cost annually
• We have a directional ROI case — "if this saves 40% of support team time, that is BDT X per year against BDT Y implementation"
• We are not expecting payback in the first month — realistic Bangladesh SME payback is 3–12 months for process automation, 12–24 months for predictive applications
• We have considered grant funding or government incentives — KOICA's $96M Bangladesh AI investment, ICT Division programs, and some BIDA incentives may apply

For a full cost breakdown by project type, see our AI consulting costs in Bangladesh guide.

Section 6 — Strategy and Governance Readiness (10 points max)

AI governance sounds like a large-company concern. It is not. Responsible AI deployment is a growing compliance expectation under Bangladesh's National AI Policy 2026-2030, and it starts with simple decisions every business can make now.

• We have agreed what AI will and will not decide autonomously — "AI flags high-risk transactions for human review; it does not block them automatically" is a decision to make before, not after, implementation
• We understand the limitations of the AI we plan to use — LLMs hallucinate, ML models drift, image recognition fails in unusual lighting; knowing failure modes prevents operational surprises
• We have a plan for monitoring AI performance after go-live — who checks, how often, what the escalation threshold is
• Our use case does not create unacceptable risk if it fails — a chatbot with wrong FAQs is recoverable; an unsupervised AI making credit decisions is not
• We are aware of Bangladesh's National AI Policy governance expectations — responsible AI, data governance, and transparency are named compliance expectations

Our AI governance framework guide covers these items in operational detail if this is your lowest-scoring section.

Scoring Your AI Readiness — Three Maturity Tiers

Add up your points across all six sections. Your total (0–60) tells you where you actually stand and what the honest next step looks like.

Tier 1: Not Ready (Score 0–20)

Significant foundational gaps exist that would cause an AI project to fail before delivering value. The most common gaps are data (paper or fragmented records), undefined use cases, and missing leadership alignment.

Next 60–90 days:

1. Digitise your primary records — even Excel to a cloud database is a meaningful step
2. Define one specific, measurable AI use case and write down what success looks like
3. Identify and brief your AI initiative owner

Investment at this stage: BDT 2–10 lakh ($1,800–$9,000) in data infrastructure and process documentation, before any AI vendor engagement. Do not sign an AI vendor contract at this tier — you will pay to build something that cannot work in your environment yet.

Tier 2: Getting Ready (Score 21–40)

The foundation is there but specific gaps remain. Typical patterns: data is digital but fragmented, the use case is identified but not measured, or infrastructure is adequate but untested.

Next steps:

1. Close your lowest-scoring section first — that is your weakest link
2. Run a structured discovery with an AI consultant to translate your use case into technical scope — typically a 1–2 day AI audit engagement at BDT 1–3 lakh ($900–$2,700) that prevents much larger downstream mistakes
3. Get three vendor quotes against a written scope before committing

Investment at this stage: BDT 5–25 lakh depending on gap size.

Tier 3: Ready to Start (Score 41–60)

Your business has the data, infrastructure, process, team, budget, and governance foundations to begin an AI implementation.

Next steps:

1. Issue a structured brief to two or three AI vendors
2. Require a proof-of-concept (PoC) phase of 4–8 weeks before full build
3. Set 90-day success metrics in writing before the project begins

Expected timeline: A well-scoped AI implementation takes 2–4 months from kickoff to go-live. Plan your review cycle accordingly.

What Comes After the Assessment

The checklist gives you a number. The number tells you the honest next step.

Not Ready (0–20): Focus the next 60–90 days on data and process infrastructure. Do not hire an AI vendor yet. Retake this assessment in three months.

Getting Ready (21–40): Book a structured AI audit. A 1–2 day engagement at BDT 1–3 lakh identifies the shortest path to your first successful project and prevents BDT 20–50 lakh mistakes downstream. This is the tier where a paid assessment genuinely pays for itself.

Ready to Start (41–60): Issue vendor briefs, require a PoC phase, and set clear 90-day success metrics before full implementation. Our guide on AI automation for SMEs covers what the next eight weeks typically look like.

Our AI audit service covers your data landscape, process mapping, infrastructure assessment, vendor options, and a prioritised implementation roadmap. You receive a written readiness report and a project scope any vendor can execute against — not a lock-in to our own implementation services. If you would like a professional AI readiness assessment with a written report and roadmap, tell us about your AI project.

Final Word

AI readiness isn't binary. It is a current position on a spectrum, and every organisation starts somewhere. The point of this assessment is to find where you actually are, not where you wish you were.

The businesses that succeed with AI in Bangladesh are not the ones with the largest budgets or the most technical staff. They are the ones that identified a specific problem, had their data in order, and chose a use case where AI had a realistic chance of working. Readiness is the variable that separates the two groups.

If you scored Not Ready, that is useful information that saves you a failed project. Spend 60–90 days on the foundations. If you scored Getting Ready, a paid AI audit is the cheapest insurance policy in your pipeline. If you scored Ready to Start, write a vendor brief with a PoC phase built in. Whichever tier you are in, tell us about your project and we will help translate your score into a roadmap.