On 26 June 2026, someone ran the same question through the same AI engine, on the same day, in two languages. The question was about payroll software for an Indonesian company that needs to handle BPJS and PPh 21 compliance. Nothing else changed. Same engine, same day, same underlying need.
The Bahasa Indonesia version of the prompt returned three vendors: Gadjian, SatuHR and GajiHub. The English version returned one: Mekari Talenta. Not one name overlapped between the two answers. If you only monitor your AI visibility in English, and your Indonesian competitors are visible only in Bahasa, you are both confidently wrong about your market position, just in opposite directions.
Why This Happens, in Plain Terms
AI engines answer a prompt by retrieving content that seems relevant to it, then summarising what they find. If the content that discusses Indonesian payroll compliance in depth is written in Bahasa Indonesia, on Indonesian publishers, in Indonesian forums, that is what gets retrieved for a Bahasa prompt. An English prompt pulls from a different pool entirely: English-language ASEAN roundups, regional comparison articles, and international sources that may mention Indonesia only in passing.
These are not the same content pool with a translation layer between them. They are two separate information ecosystems that happen to be answering superficially similar questions. A vendor with excellent English content and no Bahasa presence is, for practical purposes, invisible to half the buying conversation happening about their own product.
The Supply-Side Evidence Behind the Demand-Side Finding
The Citable audit is a single run, not a longitudinal study, and it should be read that way: a strong, direct signal rather than a settled statistic. But it lines up with something measurable on the supply side. Ahrefs data shows the Indonesian search volume for "software akuntansi" sits at roughly 1,900 searches a month, while "accounting software Indonesia," the English equivalent, sits at around 90. A ratio of roughly 21 to 1, for the same underlying product category.
Nobody has published a formal study measuring the language of B2B AI-prompting in Indonesia specifically. What is measurable is the sheer imbalance in available content supply, and AI systems retrieve from the supply that exists. If a category has twenty times more Bahasa content than English content, a Bahasa prompt has a materially deeper pool to draw an answer from, independent of anything about how buyers personally prefer to phrase questions.
| Search Term | Language | Monthly Volume |
|---|---|---|
| software akuntansi | Bahasa Indonesia | 1,900 |
| accounting software Indonesia | English | 90 |
| Ratio | - | ~21 to 1 |
Why Gemini Matters More Here Than the Global Numbers Suggest
Global market share data for AI chatbots tends to put Gemini behind ChatGPT and sometimes behind Perplexity, depending on the measurement. That global picture understates Gemini's practical importance in Indonesia, for one structural reason: Android integration.
A large share of Indonesian smartphone users are on Android, and Gemini sits closer to the operating system than any competitor does anywhere else. This does not show up cleanly in global chatbot usage statistics, which tend to measure app downloads or web traffic rather than default-assistant usage baked into a device. The practical implication for a SaaS vendor: a monitoring programme that tracks ChatGPT and ignores Gemini is missing a channel that behaves differently from what the global rankings imply.
There is a widely discussed claim that Perplexity has a bundling arrangement with a major Indonesian telecommunications provider, which if real would meaningfully raise Perplexity's local usage share too. That specific claim could not be independently confirmed in the research behind this article. It is included here as a live open question worth checking directly, not as an established fact.
What Indonesian AI Platforms Actually Favour
Buyers using AI on a phone, inside a chat interface, are not reading ten blue links and clicking through. They expect a direct answer, and the engines respond accordingly. Gemini in particular tends to favour editorial roundups, listicles and structured buyer guides over long-form narrative articles, when a query has a clear comparative shape.
This has a direct content implication that is easy to miss if your content strategy was built around Google-style long-form authority pieces: the format that wins in Bahasa-language AI retrieval is closer to a well-structured comparison guide than an essay. Long-form narrative still has a place for depth and authority signals, but it is not what gets extracted when someone asks "software HR terbaik untuk startup" on their phone.
The Thin-Coverage Problem That Makes This Worse
Enterprise B2B software brands globally rank for thousands of keywords and still appear in only around 3% of the AI Overviews relevant to their category. In most markets, that gap gets partially filled by a dense layer of independent comparison sites, review aggregators and analyst coverage. In Indonesia and the wider ASEAN region, that third-party layer is thinner.
Thinner third-party coverage amplifies the language gap rather than compensating for it. Fewer independent Bahasa-language comparators exist to cite in the first place, which means whichever vendor actually invests in building that comparison content, the pillar articles, the local case studies, the Bahasa-native documentation, becomes disproportionately likely to be the source an engine falls back on. The absence of coverage is not just a gap. It is an opening with a shorter queue than most people assume.
What Happens When the Same Bahasa Prompt Hits Four Different Engines
The Citable audit's headline finding, zero vendor overlap between Bahasa and English on one engine, is striking on its own, but the cross-engine picture adds a second layer worth understanding separately. Running the same Bahasa prompt across ChatGPT, Perplexity, Gemini and Claude does not produce four identical Bahasa-language answers either. Each engine draws on a different index and a different weighting of Indonesian versus regional sources, which means a vendor visible in one engine's Bahasa answer is not automatically visible in another's.
This compounds the monitoring problem rather than simplifying it. A monitoring programme that tests only one language on only one engine is missing at least three other combinations that could tell a materially different story about actual visibility. The realistic minimum monitoring matrix for a market like Indonesia is language multiplied by engine, not language alone, even though that quadruples the operational effort involved.
A separate, related question is whether Indonesian enterprise buyers actually trust an AI-generated software recommendation enough to act on it, versus treating it as a starting point for further research. The honest answer, based on the sources reviewed for this piece, is that this has not been measured specifically for Indonesian B2B software buyers. What is documented more broadly is that AI recommendation trust tends to track general AI adoption comfort, and Indonesia's AI tool usage has grown quickly enough that treating an AI answer as background noise is no longer a safe assumption.
There is a second, sharper risk worth naming directly: AI systems do not distinguish reliably between accurate and outdated information about a product. A software vendor that fixed a compliance issue eighteen months ago can still be described using language from an old, unresolved complaint, simply because that complaint is what the retrieval system found. This compounds with the thin third-party coverage problem above: with fewer independent sources to draw from, a single old, negative mention carries disproportionate weight.
Building the Content Pool, Not Just a Few Pages
Closing a gap this specific does not mean writing three Bahasa-language landing pages and calling it solved. AI retrieval favours depth and volume of credible, consistent coverage over any single page, however well optimised. The realistic target is a content pool: comparison guides, FAQ-structured pages, documentation translated and rewritten natively rather than machine-translated, and where possible, presence on the .id publishers and local forums the Citable audit found dominating the Bahasa-language answer.
This is slower and less glamorous than a single hero landing page, and it is also the only approach consistent with how the underlying retrieval mechanism actually works. A vendor that builds five genuinely useful Bahasa-language resources over two quarters will typically outperform one that builds a single, heavily designed page and stops, because the AI system is drawing from a pool, not ranking a list.
What This Means for a Monitoring Programme
A monitoring set that only queries in English will report a false sense of visibility or invisibility, depending on which language actually carries your category's content pool. The practical fix is running the same core prompts in both languages on a fixed schedule, logging results separately rather than averaging them into one score. Averaging hides exactly the asymmetry this article describes, which defeats the purpose of monitoring in the first place.
The Deeper Pattern This Points To
This finding is not really about one payroll software category. It is a demonstration of something GEO practitioners are still under-appreciating broadly: language is a retrieval variable, not a translation task. It changes which content pool an engine draws from, not just which words appear on the page.
For any SaaS company selling into Indonesia or a similarly multilingual Southeast Asian market, the practical takeaway is to stop treating "we have an English site" as visibility and start treating each language as a genuinely separate GEO channel, measured separately, with separate content investment. Our book Cited or Silent covers this exact dynamic in more depth, including the platform-by-platform playbook for testing it in markets beyond Indonesia. This finding sits alongside a broader shift in how software gets shortlisted before a demo is ever booked, covered in our companion piece on SEO for SaaS.
Frequently Asked Questions
Should our GEO strategy be in Bahasa Indonesia or English?
Test both for your specific category rather than assuming either. A controlled audit found zero overlapping vendors between the two languages on the same engine, same day, which is too large a gap to leave to assumption.
Is machine translation a reasonable shortcut?
No. Machine-translated content is measurably down-ranked relative to natively written content across the sources reviewed here. Native Bahasa content, written for Bahasa search behaviour, performs meaningfully better.
Why does Gemini matter more in Indonesia than global data suggests?
Android integration puts Gemini closer to the operating system than competitors, in a market with high Android penetration. Global chatbot usage rankings do not capture this device-level default advantage.
Is the Perplexity and Telkomsel bundling claim confirmed?
No. It is widely discussed but could not be independently confirmed in the research behind this article. Treat it as an open question worth checking directly, not an established fact.
What content format works best for Bahasa-language AI retrieval?
Structured comparison guides and editorial roundups tend to be favoured over long-form narrative, particularly for Gemini, which is used heavily on mobile chat interfaces where a direct comparative answer is expected.
How thin is third-party coverage in this market, really?
Thin enough that enterprise B2B brands appear in only around 3% of relevant AI Overviews globally, and the independent comparison layer that partially compensates in larger markets is noticeably sparser across Indonesia and ASEAN.
How do we test this for our own category, quickly?
Run your core buying question through ChatGPT, Perplexity and Gemini in both Bahasa Indonesia and English, and log which vendors appear in each answer. This takes under an hour and produces data specific to your category rather than a general assumption.
Sources & References:
- Citable cross-market audit, 26 June 2026 - controlled Bahasa vs English prompt test, four engines, three markets.
- Ahrefs - Indonesian keyword volume data, arfadia.com exports, July 2026.
- Sahabat-AI (Indosat and GoTo) - Indonesian-language AI model context.
- Arfadia Digital Indonesia - State of SEO Indonesia 2026. arfadia.com/resources