Discussions about artificial intelligence and OSINT often get pulled towards the same question: can AI do what an experienced analyst can do?
The answer, at least for now, is clearly no. AI can misunderstand context, conflate people with similar names, overlook important evidence and produce confident conclusions that are poorly supported by its sources. It does not have an analyst’s experience, intuition or accountability, and it remains particularly unreliable when evidence is sparse, ambiguous or deliberately deceptive.
But that may be the wrong benchmark.
AI does not need to reproduce the complete role of an analyst to transform intelligence work. The more consequential question is what happens when an experienced analyst can use AI to search, process and compare an information environment at a scale that would otherwise be impossible.
That shifts the debate away from analyst versus AI and towards something more useful: what should the intelligence process look like when human judgement is combined with machine reach?
OSINT analysts work in an environment containing more information than any person or team can comprehensively examine.
Potentially relevant material is distributed across news reporting, social media, corporate records, imagery, video, and commercial datasets. It may be multilingual, duplicated, short-lived, deliberately obscured or published in formats that are difficult to search.
Every analyst works from a selectively discovered portion of the available evidence. Search terms, language skills, platform access, prior knowledge and time constraints determine what enters the analytical process. Important information may exist but remain effectively invisible.
This is where AI has an immediate and credible role. AI can help analysts:
These capabilities do not amount to independent analytical judgement. They expand and organise the candidate evidence set on which human judgement can operate.
AI does not automatically find “the right data.” Retrieval systems make choices about what to include, exclude and prioritise. Those choices can be difficult for analysts to see.
An AI-enabled analyst might examine more information than was previously possible while understanding less about how that information was selected. Greater reach can create an impression of comprehensiveness that is not necessarily deserved.
The answer is not to reject the technology. It is to design workflows that preserve visibility over the evidence.
Most serious proposals for using AI in intelligence include some version of “human-in-the-loop.” The phrase is reassuring, but often poorly defined. A human might:
These are not equivalent forms of oversight. A meaningful human role requires enough time, expertise, information and authority to understand an output, challenge its basis and change or stop the result. An analyst cannot be accountable for an answer if they cannot inspect the sources, understand what the system searched or identify how the output was constructed.
The emerging division of labour may look something like this:
The boundary will move as technology develops. However, the principle should remain: the greater the ambiguity and potential consequence, the stronger the requirement for accountable human judgement.
The transformation is not only happening on the production side of intelligence. People are increasingly accustomed to asking a system a question and receiving a generated answer before encountering a conventional list of sources. They expect to ask follow-up questions, request summaries and receive information adapted to their immediate context.
Arguments about ChatGPT’s precise share of search traffic are difficult to resolve because different estimates count prompts and visits differently. The more important evidence is that the dominant search provider is redesigning its own experience around AI-generated answers.
Google says its AI Overviews reach more than 2.5 billion monthly users, while AI Mode has exceeded one billion monthly users. It has also connected AI Overviews directly to conversational follow-up, allowing users to move from an initial generated answer into a continuing exchange.
This is conditioning people to expect:
Intelligence consumers do not leave those expectations behind when they enter government or corporate environments.
The traditional intelligence report will not disappear. But it may potentially coexist with more interactive products: conversational evidence repositories, continuously updated assessments, automated monitoring and outputs generated for particular consumers at the point of need.
This creates an important risk. Conversational interfaces make synthesis feel like retrieval. A consumer may believe that the system has simply found an answer when it has actually selected sources, weighted evidence, resolved contradictions and constructed an interpretation. Those are analytical acts that must remain visible and contestable.
There is no single predetermined model for AI-enabled OSINT. At least four plausible operating models are emerging.
These models are not mutually exclusive. A mature organisation could eventually use all four.
AI-enabled analysts might maintain a verified evidence base. Automated monitoring could update it. Consumers could interrogate it conversationally. Different products could be generated from it without requiring analysts to reproduce the same work manually.
The challenge is ensuring that every output remains connected to a common evidential core.
Personalisation should normally change presentation and depth rather than the underlying facts or approved key judgements. Otherwise, different consumers may receive different versions of reality while believing they were given the same intelligence.
AI makes fluent synthesis cheap but it does not make reliable intelligence cheap. As generative systems become better at finding, combining and presenting information, the differentiating capabilities will increasingly be:
Source volume is not the same as corroboration. Ten articles repeating one original claim are not ten independent sources. A system may mistake synthetic repetition for consensus or merge derivative reporting into an apparently strong evidential pattern.
OSINT analysts must therefore be able to determine not only what the information says, but where it originated, how it changed and whether apparently separate sources are genuinely independent.
This will become more difficult as the information environment fills with generated text, synthetic imagery, automated personas and content deliberately designed to influence retrieval systems.
In that environment, verification is not a final quality-assurance step. It is the architecture around which AI-enabled OSINT must be built.
Organisations should stop asking whether AI can completely replace an analyst. For most serious intelligence work, that is neither the immediate prospect nor the most useful benchmark.
They should instead ask:
The future of OSINT is unlikely to be autonomous machines replacing intelligence professionals. Nor will it be analysts working exactly as they do today while ignoring technology that can process information at an otherwise impossible scale. It will be a redesigned intelligence process combining machine reach with human judgement.
The organisations that succeed will not necessarily be those that adopt the most advanced model first. They will be those that preserve the connection between every answer, the evidence behind it and the person accountable for the resulting judgement.
AI makes information abundant. The defining role of OSINT will be to make intelligence derived from that information trustworthy.