How Will Indonesia’s Copyright Rules Affect AI Licensing for Businesses in 2026?

Direct Answer: Indonesia Does Not Yet Have a Single AI Licensing Regime

Indonesia does not presently have one universal “AI copyright licence” that companies can buy to cover training data, generated output, voice cloning, music, images, or product launches. The operative rules are a combination of copyright law, contract terms, technology-provider terms of service, personality and data protections, and sector-specific requirements. A licence normally covers only the rights expressly granted, the assets described, the territories named, and the period authorized; it does not automatically settle ownership of every output produced by an AI system.

Also worth reading: Indonesia Crypto Compliance in 2026: What Businesses and Investors Need to Know? · How Can Small Businesses in Indonesia and Southeast Asia Scale AI Operations Without Breaking Their Budgets? · What is the UU PDP AI data mapping tool and how does it help businesses comply with Indonesia's AI data governance regulations?

As of 26 September 2026, reported proposals concerning copyright and AI should be treated as legislative or policy developments unless an enacted provision and its effective date can be verified in Indonesia’s official legal database. The existing Copyright Law remains No. 28 of 2014, and its implementing rules continue to matter during any transition. Businesses should distinguish an enacted amendment from a draft bill, an academic article, an industry proposal, or a political commitment reported in the media.

For an Indonesian B2B AI transaction, the safest answer is that there is no standard tariff or government clearance process comparable to a blanket media licence. Instead, a company should identify each relevant copyright category, select the necessary rights, document provenance, and decide whether output ownership and commercial use are contractually guaranteed. This market remains fragmented, so a platform subscription, enterprise API agreement, content licence, and bespoke permission may govern different parts of the same project.

How Indonesian Copyright Applies to AI Training and Output

Indonesia’s Copyright Law protects original works and gives the creator exclusive rights including economic rights and certain moral rights. Copyright can attach to literary works, artistic works, music, audiovisual works, photographs, software, and other subject matter. AI complicates the analysis because a model may process many works, reproduce recognizable elements during training or retrieval, and generate material whose degree of human authorship is unclear.

The law does not offer a simple numerical test saying that an output is protected because a person supplied 10%, 30%, or 50% of its prompts. A court or authority would need to examine the creative contribution, the source material, contractual terms, the final work, and the circumstances of creation. Likewise, the fact that an image was generated by a machine does not by itself grant unrestricted commercial rights or automatically remove copyright risk from recognizable protected material.

There is also a major distinction between copying, adapting, and creating. Training or retrieval may implicate reproduction, communication to the public, and adaptation rights even when the model does not display an entire source work. An output may separately infringe if it reproduces substantial protected expression; conversely, an output may be contractually attributable to the user while presenting unresolved copyright questions about training and inputs. Businesses therefore need two analyses: rights to use the inputs, and rights to commercialize the outputs.

The 2022 US case involving “A Recent Entrance to Paradise” is useful as a comparative example, not as Indonesian binding law. The work was created with the Midjourney system, entered the US public domain in 2022, and later sold at auction, while the US Copyright Office declined to grant it a copyright claim. The case illustrates that ownership of a digital file, copyrightability of AI output, and legal permission to use source material are separate questions. It cannot safely be imported as a rule for Indonesian courts.

What a Proper Indonesia AI Copyright Licence Should Specify

A defensible licence should identify the licensor, licensee, permitted assets, media, territory, exclusivity, duration, users, model or system, training status, and commercial activities. It should also state whether the licence covers ingestion, feature extraction, fine-tuning, retrieval, caching, prompt processing, output generation, redistribution, modification, sublicensing, and internal use. Without those details, a statement such as “licensed for AI” is too vague to support a transaction.

The document should allocate responsibility for underlying rights, warranties, takedowns, claims, and indemnities. A licensor can warrant that it controls the rights it grants, but cannot sensibly warrant that every output will be non-infringing if the underlying training corpus is undisclosed. A licensee may prefer stronger protection; a publisher may only offer a narrow licence limited to a supplied repository, retrieval system, or named campaign. Price is often connected to those risk allocations rather than to the number of prompts alone.

Output provisions deserve particular care. The agreement should distinguish contractual rights from any copyright that may or may not exist, address whether the licensee or provider claims ownership, and clarify whether the provider can reuse prompts, embeddings, outputs, and feedback. A promise that all outputs are “copyrightable” is unreliable because protection depends on the actual work and applicable law. A promise that the output is “unique” is also weak unless paired with enforceable warranties, a dispute process, and meaningful remedies.

For teams using music, likeness, text, or branded characters, a separate personality-rights and publicity analysis may be required. Copyright permission alone may not authorize every commercial use of a person’s voice, likeness, or persona. Likewise, a content licence does not automatically clear personal data, confidential information, trade secrets, platform rules, or sector regulation. The contract is one layer in a wider rights assessment.

Practical Comparison of Licensing Routes

No single row is automatically best. A news-summary pilot may begin with an enterprise search licence covering a carefully selected corpus, while a campaign requiring precise music synchronization may need a direct licence from the publisher or rights administrator. A local chatbot using internal documents may rely on enterprise retrieval terms plus internal rights controls, but it should not infer that the vendor has cleared every uploaded file for model training. An open licence can reduce acquisition cost while introducing obligations that may be harder for an enterprise to satisfy.

The comparison also exposes why there is no defensible universal Indonesian AI licence price. Vendors commonly charge by subscription tier, token or usage volume, document count, query volume, or enterprise seat count, while content owners may quote by asset, collection, audience, territory, duration, or campaign. A bespoke package can combine a platform fee with a content fee and legal review. Any advertised “free AI commercial licence” should be checked for non-commercial restrictions, attribution rules, acceptable-use policies, output disclaimers, and exclusions.

Steps an Indonesian Business Should Take Before Launch

First, create an asset and use-case register. Record the model or API, version where available, data sources, licensor, contract, intended purpose, users, geography, retention period, and release date. Separate public-web material, licensed publisher content, customer uploads, employee material, and commissioned work. A threshold of one copyright owner is not the only trigger: a single recognizable song, photograph, book passage, or character can create disproportionate exposure, especially in advertising.

Second, obtain written evidence of the rights chain. Receipts, invoices, contributor releases, publisher permissions, dataset documentation, and model terms should be stored with the production version. A procurement spreadsheet saying “approved” is not enough unless the reviewer can point to the relevant grant and scope. For material collected from creators, record whether they assigned copyright, licensed it, retained moral rights, and authorized AI processing, adaptation, publicity, and international distribution.

Third, run a documented review of prompts and outputs before publication. Search for exact phrases, visual matches, logos, faces, voices, lyrics, and distinctive characters, and use human legal review for material intended to be published at scale. Automated similarity tools can help prioritize review but do not replace a legal determination. A 30-day pre-launch review may be sensible for a major campaign, while a lower-risk internal prototype can use a shorter triage process if it remains non-public and non-commercial.

Finally, establish incident procedures. The team should know who receives a takedown, who can pause generation, which logs are preserved, and who contacts the licensor or platform. The process should address defective outputs, source disputes, customer-uploaded material, and requests from Indonesian creators or rightsholders. In a production deployment, a named owner should be assigned even if ordinary business operations are handled by product, legal, and procurement teams separately.

Common Mistakes That Create False Confidence

A frequent mistake is treating a report about a copyright amendment as an already effective law. Commentary titled around an “Indonesia Draft Copyright Law 2026” may analyze plausible reforms, but media coverage cannot establish enactment, promulgation, transition periods, or commencement. The implementation register atjdihn.go.id and official parliamentary records should be checked for the actual status. As at 26 September 2026, proposals should not be used in a board paper to state a new mandatory licensing duty unless the provision is verified.

Another mistake is assuming that generated material is free of copyright because no human artist was paid. A model can be trained on protected expression, and an output can reproduce recognizable features. The opposite error is equally damaging: assuming every output is owned by the enterprise because employees selected the tool. Contract terms may grant output rights, but the legal status of the particular expression can remain uncertain. Teams should describe output rights as contractual permissions unless a competent authority has decided otherwise.

Businesses also confuse access with ownership. A subscription may allow use of a service without transferring copyright in its model, datasets, interface, or brand. A downloaded file is not necessarily freely reusable, and a platform’s public availability does not clear commercial advertising use. A model supplier can change its terms or discontinue a feature, so version changes and material changes should trigger procurement review. Silence from a rightsholder is not a licence, and a small creator is not less likely to enforce rights than a major publisher.

Numeric claims need the same discipline. No reliable general Indonesian threshold converts a certain prompt length, image count, revenue level, or company size into immunity. The June 2023 Constitutional Court decision No. 91/PUU-XXI/2023 concerning material created through artificial intelligence is an important part of the domestic context, but a court’s analysis of conventional intellectual-property provisions should not be expanded into a universal AI exception. The resulting reasoning should be reviewed in the actual decision and applied to the company’s specific workflow.

When to Act, and What It May Cost

Act before public release when the system will process licensed publications, reproduce creator work, clone a voice, use recognizable people, create advertising, or support a revenue-generating product. Internal research deserves review if outputs are shared outside the company, used to train another system, or connected to customer data. A time-boxed pilot can reduce delay, but its data should be tagged, access-limited, and prevented from production use until rights are confirmed. For time-sensitive news or commerce work, begin review in parallel with prototyping rather than after the asset is live.

Cost depends on the route. Public or open-licence content may be free to access, but review and compliance still have labor and technical costs. Enterprise API and retrieval products often use monthly or annual subscriptions, while bespoke commercial licences can range from modest per-project fees to substantial enterprise contracts involving guaranteed rights, takedown support, warranties, or indemnity. There is no authoritative Indonesian market-wide average, and quoting one without a defined scope would be misleading.

The biggest cost may be operational. Rights review can slow release; unclear records can block enterprise sales; repeated provider changes can require retesting and revalidation. By contrast, a small upfront review may prevent a campaign withdrawal, a takedown, or a dispute over training data. Procurement should compare the full cost of the licence, review, monitoring, storage, response time, vendor dependence, and legal exposure rather than just the invoice.

For Indonesian and Southeast Asian teams, the pragmatic objective is a repeatable rights ledger rather than a promise that AI law has become simple. Market-intelligence and knowledge-operations products should make source identity, permitted use, approval status, territory, expiration, and output review visible to business users. That discipline is useful across sectors, although it does not replace a licence, legal advice, or the judgment needed for a novel use case.

The Defensive Position for 2026 and Beyond

Indonesia’s evolving copyright debate puts pressure on AI vendors and content owners, but reported proposals are not a substitute for enacted law. Existing copyright, contract, technology terms, and related rights remain the foundation until an amendment takes effect. International law and overseas cases may influence policy and interpretation, yet they do not automatically determine rights in Indonesia. The relevant question for a business is therefore not “Does AI need one Indonesian licence?” but “Which rights, for which assets, uses, parties, territories, and period, have actually been granted?”

Companies should establish a controlled path for low-risk experimentation and a stricter approval path for commercial publication. The controlled path can use approved tools, synthetic or owned inputs, restricted access, and no public distribution. The strict path can require verified permissions, a human review record, release approval, takedown contacts, and a contract that addresses output use. Contracts with creators should state compensation, attribution, revocation, downstream licensing, and what happens if the work is used in model training or derivative datasets.

The next legislative steps should be monitored through official records, including any final wording on AI, compensation schemes, collective rights, transparency, and exceptions. A 2026 proposal that includes a licensing mechanism should not be assumed to be operational on the date it is announced. Businesses may need 30 to 90 days of transition work after a verified change, depending on how the rule affects existing models, datasets, and contracts. Until then, documented permissions and conservative deployment are more reliable than speculation.

Bottom-Line Guidance for Buyers and Rights Holders

For buyers, begin with a rights inventory and a scoped licence request. Ask the supplier to identify what it can license, what it cannot, and what warranties it will provide. Prefer written terms over sales assurances, preserve the contract with the relevant asset, and test the output before release. Do not confuse a model provider’s terms with the copyright status of the model, a dataset, a generated file, or a person’s likeness.

For rights holders, a clear permission instrument can be commercially useful, but it should not promise unlimited ownership of every output. Separate source-material rights from output allocation, define permitted uses, and price by scope. A low-cost pilot licence, a paid enterprise licence, and a fully negotiated risk package serve different purposes. The strongest current practice is not a single government licence; it is a chain of evidence showing how each material component entered the system and what the parties agreed.

In short, Indonesian AI copyright licensing is presently a contract-and-governance challenge, not a standardized statutory purchase. The proposed 2026 debate makes monitoring essential, but it does not justify inventing a new rule. Businesses that act before launch, keep precise records, and route high-risk uses to qualified Indonesian counsel will usually be better prepared than those that rely on generic “commercial use allowed” language.