AI Is Almost Everywhere in Media. Almost Nobody Has It Working.

The media industry has no shortage of artificial intelligence. Speech recognition, machine translation, image recognition, summarisation, automated logging and generative tools are widely available.

Yet the presence of AI is not the same as its successful implementation.

In reality, most media companies we talk to are experimenting with AI services, running proofs of concept, or giving individual users access to general-purpose tools. Some vendors promise fully automated editing, but the results don’t live up to expectations when applied to real production requirements. Far fewer organisations have embedded AI into their workflow in a way that is context-aware, reliable, repeatable and the results measurable.

That distinction matters. A successful demonstration proves that an AI model can produce an impressive result under controlled conditions. It does not prove that the same model can watch and process thousands of hours of content, apply the correct production context, respect access permissions, preserve timing information and deliver editorial consistency.

That the technology may work on its own, doesn’t mean the editor has access to a more effective workflow.

A good approximation is not the same as a correct result

Most contemporary AI services are probabilistic. They are trained to generate results that appear most likely based on the information available to them at that point.

This can produce remarkably convincing output. A speech recognition model can transcribe an interview. A language model can summarise a scene. An image recognition service can describe images and identify objects and locations. Another transformer model can propose a synopsis or assemble a rough story structure.

But as long as AI service are disconnected from the production context, operated as a simple black box, it is navigating blindly. It may at best produce a lookalike of the result, but it will often miss the mark and remain prone to errors.

A name mentioned in dialogue may be spelled incorrectly because the service doesn’t know what is a call sheet. A scene may be interpreted incorrectly because the system has no access to the script or shooting schedule. A allegedly useful summary may omit the essence as it doesn’t understand continuity notes.

The diagnosis is not a model failure. We are dealing with workflow flaws, rooted in the quixotic expectation that AI can infer information which already exists elsewhere in the production, but has never been learned how to do so.

More services, more fragmentation

We often discover the following operational patterns. Transcription takes place in one service, translation in another and image recognition in a third. Scripts and call sheets live in Airtable, spreadsheets or specialist production systems. Media files sit in shared storage, while comments, approvals and editorial decisions are exchanged through email or messaging applications.

Each individual service may save time. The production workflow as a whole does not become more manageable. Instead, producers and editors find themselves moving files and metadata between disconnected systems, correcting inconsistent output, and trying to establish which version is authoritative. AI increases the volume of information, but does not necessarily improve its quality or accessibility.

This is why many experiments fail beyond pilot stage. A demonstration is easy. Operationalising it means coherently dealing with media ingest, metadata, permissions, storage, orchestration, human validation and integration with editorial systems. That work is less spectacular, but it is where most of the value is created.

Context changes the result

AI becomes substantially more reliable and applicable when it is embedded in the production workflow and has access to the information generated upstream.

Consider a scripted production. By the first frames become available for editing, the production already holds vast amounts of structured information:

• Scripts and scene breakdowns
• Cast and character lists
• Call sheets
• Shooting schedules
• Location information
• Slate and take identifiers
• Continuity notes
• Production reports

Similarily, in factual production, the available context may include research documents, interview schedules, contributor information, subject lists, archive descriptions and editorial notes. Entertainment and reality productions can provide running orders, contestant information, camera assignments, live set notes, story beats and detailed production logs.

This information may come from Airtable, production planning software, script systems, spreadsheets or other sources. Wherever it originates, it should not remain isolated from the AI services processing the resulting media.

If transcription and logging services can draw on cast lists, scripts, call sheets, production notes and verified metadata, they can recognise names, interpret recorded material and identify relevant content far more accurately. The ultimate goal is to use that intelligence to prepare a structured, multi-track timeline in the edit, taking care of much of the manual ingest and offline preparation and giving editors a genuine head start. This is especially valuable in complex multicamera productions, where synchronising, organising and selecting material can otherwise consume substantial editorial capacity.

The guiding principle is simple: do not ask AI to guess information the production already knows.

Trust is part of the implementation

Accuracy is not the only concern. In automated production environments, even a minor error can propagate through the workflow and have dramatic consequences downstream, raising critical questions about provenance, transparency and human responsibility.

A Gartner survey found that half of US consumers would prefer to give their business to brands that do not use generative AI in consumer-facing content.

That research concerns marketing, but the underlying warning applies equally to media. Audiences are becoming more conscious of synthetic and AI-assisted content. Trust can be damaged when AI is used without transparency, clear editorial responsibility or adequate human oversight.

For professional media production, AI should therefore support the creative and editorial process, not operate as an unaccountable substitute for it.

The relevant questions are not simply which model was used or how quickly it generated an answer. The production also needs to know:

• Which source material informed the result?
• Which metadata and production records were used?
• What confidence can be assigned to the output?
• Who reviewed or corrected it?
• How can those corrections improve subsequent processing?
• Can the original media and decisions be traced?

Without those safeguards, faster output may simply mean faster propagation of errors.

From isolated tools to an intelligent workflow

Limecraft treats AI services as first class citizens participating in the production workflow rather than as a collection of disconnected services.

Media, production metadata and editorial decisions are brought together in a shared environment. Information from scripts, call sheets, Airtable records, live set notes and other upstream sources can be associated with the recorded material. AI services then work with this context to support transcription, translation, logging, search, captioning and story building.

Human validation remains an important part of the process. Corrections are made where the material is reviewed, without repeatedly exporting and re-importing files between separate applications. Verified results can flow onwards to Avid, Adobe or other production and delivery systems. It makes AI output better informed, easier to verify and substantially more useful.

It also makes the result operational. Instead of a series of impressive but isolated point solutions, AI becomes part of a controlled workflow that can be repeated across episodes, productions and customers.

The real implementation challenge

The media organisations struggling to move beyond AI pilots are not necessarily using the wrong models. In the majority of cases, they use models without proper context, workflow integration, or human oversight required to make the results dependable.

Buying access to another middleware will not solve that problem. The more durable solution is to connect AI to the information the production already creates, embed it at the right points in the workflow and maintain clear responsibility for validating the output. That assumes a broad and deep understanding of the purpose and the workflow one wants to establish.

This is particularly important in scripted, factual and entertainment production, where substantial amounts of valuable production data already exist. Used properly, that information turns AI from a plausible guessing machine into a genuinely useful production assistant.

AI does not fix a fragmented workflow. Left unattended, it scales the fragmentation. Embedded properly, it will deliver on its promise.

See it in action at IBC

This year’s IBC, Limecraft will demonstrate how production data, AI-assisted logging and workflow automation can turn complex rushes into a structured, multi-track timeline, giving editors a genuine head start. Book a meeting with us at IBC to discuss how this approach could support your next scripted, factual or multicamera production.