Insights

AI Content and Brand Safety in Bangladesh

AI can speed drafts and variants. It cannot own brand risk. Organisations that win with AI write the rules before they scale the output.

Define allowed use cases

Always-on social variants differ from regulated claims in finance or food. A rule that's safe for a seasonal social post — generate ten variants of an approved caption, pick the best — is not automatically safe for a claim about a loan rate or a nutritional benefit. Write the allowed-use list by risk category, not by department: what a model can touch without review, and what always needs a named human sign-off regardless of who requested it.

Keep human approval gates

Especially for public-facing commercial video and packaging-adjacent content. The gate has to be a real decision point with authority to reject, not a formality someone rubber-stamps on the way to publishing. A governance model without a person who can actually say no is not governance, it's paperwork.

The risk register

Five specific things a model will not catch on its own, and a human review has to: likeness — using a real, identifiable person's face or voice without consent, even generated rather than filmed; unverifiable claims — a model can generate a confident-sounding statistic or product claim that nobody actually checked; undisclosed synthetic talent — presenting an AI-generated presenter or voice as if it were a real person, without telling the audience; competitor packaging in a generated aisle or shelf scene — a model trained on public imagery can inadvertently reproduce a real competitor's pack design in the background; and cultural missteps — a reference, gesture or visual choice that reads as fine in the training data's source market and reads badly in Bangladesh, which a model has no way to know unless a local reviewer catches it first.

Measure quality, not volume

More assets are useless if they dilute meaning or create compliance issues. A governed AI workflow that produces fifty variants nobody can distinguish from each other has succeeded at the wrong metric. The measure that matters is whether the output that actually ships still says one clear thing, not how much content the pipeline generated.

Work with production-literate partners

Teams that already ship TVC and OVC to professional standards adapt AI more safely. The judgement that catches a bad take on a live shoot — knowing what a claim can and can't say, what a client's brand voice actually sounds like, what will read as off in this market — is the same judgement that needs to review AI output before it goes out. A partner who has never run a live production has no comparable instinct for where the risk actually sits.

About the author

Azizul Hoque Shiplu is Founder & Film Director of Libanza Films and Managing Director of Libanza Limited in Dhaka, with more than two decades in brand communication and film production.