For centuries, the em dash has quietly served writers as one of the most expressive tools in punctuation—a simple line capable of interrupting thought, adding emphasis, or shifting tone mid-sentence. Born in the early days of typography and embraced by literary giants from Emily Dickinson to Virginia Woolf, the em dash has long been a hallmark of polished writing.

Yet in a curious twist of the modern digital age, this centuries-old punctuation mark has recently gained an unexpected reputation: some readers now see it as a sign that artificial intelligence may have written the text.

In an era suddenly obsessed with detecting machine-generated prose, even a piece of punctuation has become suspect.

This article explores the origins, evolution, and enduring usefulness of the em dash—along with the strange cultural moment that has turned a classic punctuation mark into an alleged AI fingerprint.

A Long Line Through History

There are few punctuation marks as quietly powerful as the em dash. It’s long, dramatic, and a little theatrical. It can interrupt a thought, insert a revelation, or punch up a sentence with sudden emphasis. For centuries, writers have used it as a stylistic flourish. Today, however, it has developed an entirely new—and somewhat strange—reputation: some readers now view it as a signal that a piece of writing might have been produced by artificial intelligence.

Which raises an odd question.

How did a piece of punctuation that predates the modern novel become associated with machine-generated text?

To understand that paradox, we need to go back several centuries—back to the birth of typography itself.

What Exactly Is an Em Dash?

An em dash (—) is the longest of the common horizontal punctuation marks. It is longer than both the hyphen (-) and the en dash (–).

The name comes from typography. In traditional typesetting, an “em” is a unit of measurement equal to the point size of the font being used. In 12-point type, for example, one em equals 12 points.

Historically, this measurement corresponded roughly to the width of a capital “M” in metal type, which is where the name originates.

That typographic origin is important. The em dash wasn’t invented by grammarians—it was invented by printers.

Hyphen vs En Dash vs Em Dash

These three marks are often confused, but they serve different functions.

The Earliest Origins of the Dash

The conceptual ancestor of the modern dash dates back nearly a millennium.

One early precursor appears in the work of Boncompagno da Signa, an 11th-century Italian scholar who experimented with punctuation systems in medieval Latin manuscripts. His mark, called the virgula plana, resembled a long horizontal stroke similar to today’s em dash.

The mark’s early role was not stylistic—it was structural. Boncompagno used it as a flexible pause or separator within text.

However, the dash did not become widely standardized until much later.

The Dash Enters the Printing Age

When movable type printing spread across Europe in the 15th and 16th centuries, printers needed ways to represent pauses, interruptions, and rhetorical shifts in text.

By the early 1600s, long dashes began appearing in printed literature. Early examples appear in printed editions of Shakespeare’s plays, where they were used to signal interruptions in speech or sudden shifts in thought.

These early dashes were not standardized. Printers used different lengths and sometimes even composed them by stringing together multiple hyphens.

But the concept was there.

The dash had entered the written language.

The 18th Century: The Dash Finds Its Voice

If the dash had a literary champion, it would probably be Laurence Sterne, author of The Life and Opinions of Tristram Shandy, Gentleman (1759).

Sterne used dashes with wild enthusiasm. They appear throughout his novel, interrupting sentences, mimicking speech patterns, and creating dramatic pauses. His use of the dash helped legitimize it as a stylistic tool rather than merely a printer’s convenience.

The dash became a way to imitate thought itself—erratic, interrupted, and nonlinear.

Later writers embraced it as well:

• Emily Dickinson filled her poems with dashes.
• Victorian novelists used them for dramatic dialogue.
• Modernists like James Joyce experimented with dash-based dialogue formatting.

By the 19th century, the dash had firmly embedded itself in literary style.

Famous Writers Who Loved the Em Dash

The em dash has been embraced by some of literature’s most distinctive voices.

Emily Dickinson

Dickinson’s poetry is perhaps the most famous example of em dash usage. Her dashes create pauses, uncertainty, and rhythm that feel closer to spoken thought than structured grammar.

Example:

Because I could not stop for Death —

He kindly stopped for me —

Her use of dashes was so distinctive that editors later struggled to standardize her punctuation without altering the feel of her poetry.

Virginia Woolf

Woolf used dashes to reflect interior thought and shifting perspectives in stream-of-consciousness narration.

Example style:

She had the oddest feeling—that something had just slipped away.

The dash becomes a psychological pivot point in the sentence.

Kurt Vonnegut

Vonnegut often used the dash to inject conversational timing and humor into his prose.

Example style:

He was a perfectly good engineer—until someone asked him to manage people.

The dash functions almost like a comedic pause.

Herman Melville

In Moby-Dick, Melville frequently used dashes in dialogue and narration to create dramatic interruptions.

Example style:

“Look ye now,” said Queequeg—“what you say?”

How Editors and Style Guides Tamed the Dash

For all its expressive power, editors have long had a complicated relationship with the em dash.

Most major style guides eventually formalized its use.

The em dash is typically used to:

1. Insert a parenthetical aside.
2. Indicate an abrupt shift in thought.
3. Replace commas, parentheses, or colons for emphasis.
4. Introduce lists or summaries.

Example:

She had three priorities in life—family, curiosity, and good coffee.

Editorial styles vary slightly.

• Chicago Manual of Style recommends closed em dashes (no spaces).
• Associated Press style often prefers spaced dashes.

Despite these differences, the em dash remained a hallmark of polished editorial writing.

You would routinely find it in:

• Newspapers
• Literary fiction
• Magazine essays
• Academic prose
• Opinion columns

In other words, the em dash lived where edited writing lived.

Then the Typewriter Ruined Everything (Sort Of)

The 19th-century typewriter created an unexpected problem.

Most typewriters lacked dedicated keys for en dashes and em dashes. Writers were forced to approximate them using double hyphens (–). Over time, this convention carried into early word processors and digital writing systems.

Modern software eventually restored the true characters through auto-formatting.

But the em dash never quite regained its ubiquity in everyday writing.

Casual communication—emails, texting, social media—favored simpler punctuation.

And then something unexpected happened.

The Em Dash and the Rise of AI Writing

Around 2024 and 2025, an unusual cultural observation began circulating online.

Readers noticed that some AI-generated text—particularly text produced by ChatGPT—frequently used em dashes. Social media users jokingly referred to them as the “ChatGPT hyphen.”

The idea spread quickly:

“If a sentence contains an em dash, it must be AI.”

Of course, that claim is not actually true.

But it reflects a fascinating cultural shift.

Why AI Uses the Em Dash So Often

The explanation is surprisingly mundane.

Large language models are trained on enormous corpora of written text. Much of that text comes from sources such as:

• Books
• Journalism
• Essays
• Edited web content

These are precisely the environments where em dashes historically appear.

In other words, AI didn’t invent the em dash.

It simply learned from writers who were already using it.

Ironically, as everyday writing moved toward shorter, more conversational formats (texts, Slack messages, tweets), the em dash became less common in casual human communication. That created a strange perception gap.

To some readers, the mark now feels oddly formal.

Or suspiciously polished.

The Paradox of the Em Dash

This creates an unusual modern dilemma.

The em dash is:

• Grammatically correct
• Historically established
• Stylistically expressive

Yet its presence can now cause readers to suspect that the writing might be artificial.

Some human writers have even begun avoiding the em dash deliberately so their writing does not appear AI-generated.

That is a remarkable reversal.

For centuries, the dash signaled sophistication.

Now, it can trigger skepticism.

What to Use Instead of an Em Dash (If You’re Trying to Avoid the “AI Look”)

If you suddenly notice that a piece of writing contains an unusual number of em dashes, the solution is not necessarily to delete them all. In many cases they are being used correctly. However, if you want the writing to feel more natural—or simply avoid triggering the increasingly common “AI radar”—there are several easy substitutions.

Replace the Em Dash With a Comma

Many em dashes simply introduce a brief aside that can be handled with commas.

Example with an em dash:

The project—originally scheduled for March—was delayed.

Rewritten with commas:

The project, originally scheduled for March, was delayed.

Use Parentheses for True Side Notes

Example with an em dash:

The proposal—still in draft form—will be reviewed next week.

Rewritten:

The proposal (still in draft form) will be reviewed next week.

Break the Sentence Into Two

Example with an em dash:

The team completed the migration—an effort that took nearly six months.

Rewritten:

The team completed the migration. The effort took nearly six months.

Use a Colon for Introductions

Example with an em dash:

She had three priorities—speed, reliability, and simplicity.

Rewritten:

She had three priorities: speed, reliability, and simplicity.

Use a Period for Emphasis

Example with an em dash:

There was only one option left—start over.

Rewritten:

There was only one option left. Start over.

When Writing Got Faster

One theory from editors and linguists is that this phenomenon reflects a deeper change in how people write.

Traditional publishing environments—books, newspapers, magazines—had editors who refined prose and encouraged expressive punctuation.

Modern digital writing often prioritizes speed, brevity, and clarity.

Short sentences.

Minimal punctuation.

Fast communication.

In that environment, the em dash can feel almost luxurious.

A relic of a slower editorial world.

How to Type an Em Dash on Any Device

Despite its long history, the em dash can sometimes feel oddly difficult to produce. That confusion largely comes from the typewriter era, when most machines lacked a dedicated key and writers improvised using double hyphens.

Modern devices, fortunately, make it much easier.

On Mac

Option + Shift + Hyphen

On Windows

Alt + 0151 (numeric keypad)

In Word or Google Docs

Two hyphens typed between words may automatically convert into an em dash.

On iPhone or iPad

Press and hold the hyphen key to reveal dash options.

On Android

Long-press the hyphen key to select different dash characters.

The Hidden Rhythm of the Em Dash

One reason the em dash has endured for centuries is that it does something most punctuation marks cannot: it captures the rhythm of thought.

Commas organize sentences. Periods stop them. Colons introduce structure.

The em dash does something more fluid.

It mirrors the way people actually think and speak.

A sentence begins in one direction—then pivots.

A thought is interrupted—then resumed.

A writer realizes something mid-sentence—and the dash lets the reader experience that realization at the same moment.

Consider the difference:

She had finally made a decision, although it took months.

vs.

She had finally made a decision—although it took months.

The dash introduces a pause and emphasis that feels closer to natural speech.

In Defense of the Em Dash

The recent suspicion surrounding the em dash is a little ironic.

For centuries it has been used by some of the most thoughtful writers in the English language. It allows sentences to breathe, pivot, and surprise the reader.

Few punctuation marks are as flexible.

It can replace commas.

It can replace parentheses.

It can even replace a colon.

And sometimes it simply does what no other punctuation mark can do—capture the way a thought actually unfolds in the mind.

If the em dash is suddenly suspect, perhaps the real question isn’t about punctuation at all.

Perhaps it’s about how our expectations of writing are changing in the age of AI.

The Em Dash Isn’t the Villain Here

If the em dash has suddenly become suspicious, the punctuation itself isn’t really the problem.

What we are witnessing is a cultural shift in how writing is produced, consumed, and judged. For centuries, polished writing passed through editors, proofreaders, and publishing houses. Today, much of our daily communication happens quickly—emails, chat messages, social media posts—often written in seconds and rarely edited.

In that faster environment, the em dash can stand out. It feels deliberate. Almost literary.

Artificial intelligence didn’t invent the em dash; it simply learned from the same sources human writers have relied on for generations: books, essays, journalism, and other forms of edited prose.

The irony, of course, is that avoiding the em dash entirely might make writing feel less natural—not more.

After all, the mark has survived more than a thousand years of evolving language, printing technologies, and editorial standards.

Blaming the em dash for AI writing is a bit like blaming the comma for emails.

It’s not the punctuation that changed.

It’s the world around it.

And if a single horizontal line can suddenly spark debates about authorship, authenticity, and artificial intelligence—perhaps the em dash is still doing exactly what great punctuation has always done: make us pause and think.

The post The Em Dash: From Printing Press to AI Tell appeared first on CHESA.

This discussion focused on one of those debates: as artificial intelligence becomes increasingly capable of observing, interpreting, and reacting to live signals, should AI remain an advisory tool within broadcast workflows, or begin operating inside the control loop; making real-time decisions that influence cameras, graphics, audio, and other elements of live production.

AI, Authority, and Real-Time Decision-Making in Live Production

For decades, live broadcast production has operated on a simple principle: humans make the decisions.

A director chooses the shot.

An operator triggers graphics.

An audio engineer adjusts the mix.

Infrastructure carries out human intent.

Every decision inside the signal chain had a person attached to it.

But that model is beginning to evolve.

Artificial intelligence can now detect speakers, identify key moments, trigger graphics automatically, translate audio in real time, and even adjust production elements dynamically based on what is happening inside the program feed.

The question is no longer whether AI can assist production workflows.

The question is whether it should be allowed to act inside the control loop.

At CHESA Fest 2026, Vendor Panel 4 examined what happens when AI moves from being a recommendation engine to becoming part of the live production decision stack.

Not whether AI belongs in broadcast workflows. But how much authority it should have when the program is already on air.

The Panel

The discussion was moderated by Jason Pepino, Director of Media Systems Design & Engineering at CHESA, and brought together representatives from several companies whose technologies operate at different layers of the live production chain.

Panelists included:

• Chuck Davidson, Partner Account Manager at LiveU
• Steve Cooperman, Regional Sales Manager at Vizrt
• Kyle Phillips, VP of Global Sales Enablement at AI-Media
• Dan Griffin, Pro AV Territory Manager at Netgear

Together, the group represented multiple points of the broadcast signal path, from edge contribution and transport, to networking infrastructure, to graphics automation and AI-driven captioning systems.

Rather than discussing AI as a general productivity tool, the panel focused on a much more specific architectural question: if AI systems can observe, interpret, and act in real time, should they be allowed to make production decisions while a broadcast is live?

Or should they remain advisory systems that support—but never replace—human control?

AI Is Already Inside the Production Stack

One of the first themes to emerge from the discussion was that AI is no longer theoretical in broadcast environments.

In many cases, it is already actively shaping production workflows.

Steve Cooperman of Vizrt pointed to sports production as an example where AI-driven technologies are already operating inside live broadcasts.

Sports analytics tools can track players on the field, generate real-time visual overlays, and automate graphical elements that previously required extensive manual effort.

For example, AI-assisted visual cutouts allow broadcasters to isolate athletes from the playing field and integrate those elements into augmented graphics environments in real time.

In situations like this, the speed and complexity of the task often exceed what a human operator could realistically execute manually.

In those cases, AI is not replacing the creative team—it is enabling effects that would otherwise be impossible.

But even in those scenarios, Cooperman emphasized that human oversight remains essential.

The system may automate the visual effect, but the production team still needs the ability to override or disable it if something behaves unexpectedly.

Where Automation Makes Sense

Across the panel, most participants agreed that the question is not whether AI should exist inside production systems, but where its authority should begin and end.

Dan Griffin of Netgear described this balance through the lens of live audio production.

In multi-speaker environments—panel discussions, talk shows, or live events—automatically adjusting microphone levels can actually be easier for machines than for humans.

An AI-driven system can react instantly to changes in speech patterns, detecting who is speaking and adjusting levels accordingly.

“It’s a hard job to sit and try to push mics up and down for a bunch of talking heads,” Griffin noted.

In scenarios like that, automation can improve both efficiency and consistency.

But the stakes of the broadcast matter.

If the event is high-profile or mission-critical, such as an emergency broadcast or a major live sporting event, human oversight becomes much more important.

Even if AI handles the majority of the operational workload, a human operator still needs to monitor the system and intervene if necessary.

The challenge is not eliminating humans from the process.

It is deciding where human intervention remains essential.

Designing Boundaries for AI

Kyle Phillips of AI-Media introduced a concept that captured much of the panel’s thinking: bounded autonomy.

Rather than giving AI unrestricted control, systems can be designed with defined operational limits.

AI might be allowed to adjust audio levels within a narrow range, automatically place captions on screen, or reposition elements dynamically to avoid overlapping with graphics.

But those actions occur inside parameters defined by human designers.

“You design what it’s able to do,” Phillips explained. “And what AI does really well is repetitive tasks.”

In other words, the role of AI is not necessarily to replace creative judgment.

It is to accelerate the mechanical tasks that surround it.

When those boundaries are designed carefully, AI can dramatically increase speed and efficiency without introducing unacceptable risk.

Responsibility When AI Fails

As the conversation turned toward governance, the panel addressed an uncomfortable but necessary question: if AI makes a mistake during a live broadcast, who is responsible?

Phillips framed the answer bluntly.

“You can’t blame the machine,” he said. “You have to blame the person who sets the parameters around the machine.”

In other words, responsibility ultimately rests with the humans who design and deploy the system.

Chuck Davidson of LiveU pointed to another layer of the solution: compliance and monitoring tools that track broadcast outputs in real time. LiveU’s acquisition of Actus, a compliance monitoring platform, provides one example of how oversight systems can serve as a safety net as AI becomes more embedded in the broadcast chain.

Originally designed to monitor broadcasts for regulatory compliance, those platforms may also evolve into governance layers that monitor AI-driven systems and detect anomalies in real time.

As AI capabilities expand, the need for visibility and auditing may become just as important as the automation itself.

Guardrails Inside the Control Loop

If AI systems are going to operate inside the signal chain, the panelists agreed that guardrails must be built directly into the architecture.

Steve Cooperman offered a practical example from Vizrt’s production tools: gaze correction technology.

The feature automatically adjusts a presenter’s eye direction so they appear to be looking directly at the camera, even if they are reading from a screen below.

In most situations, the effect works seamlessly.

But in certain edge cases, such as when a presenter moves their head rapidly, the automated correction can produce unnatural results.
In those situations, the production team needs the ability to disable the feature immediately.

That principle applies across most AI-driven broadcast tools. Automation can enhance production quality, but systems must always allow human operators to override the result.

Dan Griffin reinforced the same point from a network engineering perspective.

Even if AI assists with network design or configuration, engineers still need to verify the results before deployment.

Automation may accelerate the process, but it cannot replace the responsibility of validating the final system.

Davidson also noted that much of the hesitation surrounding AI mirrors earlier technology transitions in broadcast. When the industry moved from tape to digital workflows, many broadcasters resisted abandoning physical media. Over time, however, those changes became standard practice.

An Industry Still in the Early Stages

When the discussion opened to audience questions, the conversation turned toward a broader question: how far along is the industry in adopting AI-driven production systems?

The panelists agreed that broadcast technology is still in the early phases of this transition.

Griffin noted that AI tools have improved dramatically even within the past year, evolving from novelty features into genuinely useful production tools.

Phillips added that financial incentives will accelerate adoption. As broadcasters look for new ways to monetize archival content and expand into new markets, AI-driven translation, localization, and restoration technologies may unlock entirely new revenue streams.

Cooperman pointed to sports broadcasting as a clear example of rapid innovation.

Over the past year alone, the volume of real-time analytics, augmented graphics, and AI-assisted visual effects has increased dramatically across live sports coverage.

And that trend is unlikely to slow down.

So, Should AI Be Allowed Inside the Control Room?

The panel’s answer was nuanced.

AI is already entering the signal chain, analyzing content, triggering workflows, and assisting operators in real time.

But authority remains a human responsibility.

Automation can improve speed, reduce repetitive workloads, and enable new types of production effects.

Yet live broadcast environments still require human oversight, editorial judgment, and operational accountability.

The most likely future is not one where machines replace production teams.

It is one where humans design the boundaries, machines operate within them, and the control room evolves into a collaboration between the two.

And as AI becomes more capable, the most important role may not be deciding what the machines can do.

It may be deciding what they should never be allowed to do at all.

The post When Machines Enter the Control Room appeared first on CHESA.

This discussion focused on one of those debates, how the rapid acceleration of automation and AI-driven tooling is reshaping operational control inside media workflows, and where human
judgment must remain as pipelines become increasingly autonomous.

Where Human Judgment Still Matters in Media Operations

For decades, automation in media workflows meant something very specific.

Machines executed instructions.

Humans made the decisions.

Files were transcoded, assets were moved, QC checks were triggered, and workflows advanced step by step through carefully designed pipelines. Automation increased speed, but authority still belonged to the people designing and operating the systems.

That line is beginning to blur.

Today, automation doesn’t just execute tasks. Increasingly, it evaluates conditions, suggests edits, flags problems, and triggers decisions that once required human review. AI-assisted tools summarize content, generate metadata, recommend creative adjustments, and in some cases even assemble media outputs automatically.

The question is no longer whether automation can accelerate media workflows. That battle was won years ago.

The real question is what happens when automation begins to make operational decisions.

At CHESA Fest 2026, Vendor Panel 3 examined the tension emerging inside modern media pipelines: as AI-driven systems become more autonomous, where does operational authority actually reside? Are we simply building faster deterministic workflows, or are we gradually transferring judgment itself to software?

The discussion revealed that while automation continues to expand rapidly, the role of human judgment inside media operations remains far from obsolete.

In fact, it may be becoming more important than ever.

The Panel

The conversation was moderated by Felix Coats, Solutions Architect at CHESA, and brought together experts representing different layers of the modern media pipeline; from workflow orchestration and infrastructure automation to AI-assisted creative tooling.

Panelists included:

• Erik Zindulka, Senior Sales Engineer at Telestream
• Sarah Semlear, U.S. Sales Lead at Hiscale
• Greg Holick, VP of Business & Channel Development at Helmut US
• Dave Helmly, Director of Strategic Development – Professional Video at Adobe
• Scott Eik, Senior Engineer at Scale Logic
• Jason Whetstone, Senior Product Development Engineer at CHESA

Together, the panel explored a critical architectural question: as automation platforms become more intelligent and AI-driven tooling becomes embedded across production pipelines, what decisions must remain human, and which ones can safely be delegated to machines?

Automation Is Expanding — But Not Evenly

The panel opened with a deceptively simple question: by 2030, what percentage of media operations will be fully automated?

Even defining that percentage proved difficult. Several panelists noted that automation happens unevenly across organizations and workflows, with some environments already heavily automated while others still rely on largely manual processes.

The answers that followed reflected that uncertainty.

Dave Helmly of Adobe offered perhaps the most aggressive prediction. From his vantage point inside the creative tooling ecosystem, the direction of travel appears clear.

“I’m going to say 99 percent,” Helmly said. “Because there’s always that one person holding out the last one percent.”

Helmly’s reasoning wasn’t based on replacing creative professionals, but on eliminating the production tasks that consume enormous amounts of time while contributing little creative value.

In large-scale media operations, generating deliverables can quickly multiply from a single asset into hundreds of variations—different languages, aspect ratios, captions, and regional compliance edits. Increasingly, those variations are being processed automatically.

In that model, automation does not replace creativity. It removes the operational friction surrounding it.

Not everyone on the panel was ready to go that far.

Greg Holick of Helmut suggested that the industry is still early in the automation curve. Today’s AI systems are already effective at tasks like orchestrating pipelines, managing localization, or moving assets between systems. But that does not mean the entire production lifecycle is ready to run autonomously.

“I think right now we’re around the twenty percent phase,” Holick said. “AI is great at handling the mundane tasks that humans shouldn’t be doing in the first place.”

Holick estimated that by the end of the decade, automation could realistically reach 50 to 70 percent of media operations, but he emphasized that creative judgment will continue to require human involvement.

“There are things AI just isn’t aware of,” he explained. “Creative intent, cultural context, subtlety. Those are human capabilities.”

Sarah Semlear of Hiscale framed the issue in simpler terms. The real goal of automation is not to eliminate people from the process—it is to eliminate the work nobody wants to do.

Automation, Semlear argued, should function like a calculator: removing tedious effort and allowing people to focus on higher-value work. If that happens, the outcome is not a fully automated industry. It is a more enjoyable one.

The Accountability Problem

From there, the discussion shifted toward a more fundamental question: what operational decisions cannot safely be automated today?

Several panelists returned to the same underlying issue: accountability.

Erik Zindulka of Telestream referenced a line from early computer science training that still resonates decades later.

“A computer cannot be held accountable,” he said. “Therefore it cannot make a management decision.”

In media workflows, that principle still matters.

Automation can analyze files, detect patterns, and trigger processes, but responsibility for the final output remains human. Editorial standards, brand identity, and compliance obligations ultimately belong to the organizations producing the content.

“You come to a media outlet because you expect a certain type of output,” Zindulka explained. “That’s defined by the people behind it.”

Greg Holick added a practical layer to that idea: legal responsibility.

If an automated workflow publishes the wrong content, pulls the wrong advertisement, or distributes media incorrectly across regions, the consequences are not theoretical. Those decisions carry contractual, regulatory, and financial implications.

“And AI can’t be held responsible for that,” Holick said. “Only a human can.”

Automation may accelerate production, but ownership of the outcome remains human.

In that sense, the question is not whether AI will participate in decision-making. It already does. The real question is where organizations draw the boundary between automation and authority.

The Morality Question

As the conversation deepened, the panelists began exploring another dimension of AI-driven workflows: ethical judgment.

Felix Coats raised the question directly. If AI systems can be trained to follow rules, remove bias, and enforce guidelines, does human judgment still need to remain in the loop?

Sarah Semlear argued that morality is too contextual to encode into software.

According to Semlear, “Morality depends on culture. It depends on the country you’re in, the situation you’re in, and the people involved.”

Greg Holick agreed, noting that even sophisticated systems struggle with nuance.

“You can tell AI the rules,” he said, “but it doesn’t understand the cultural references or the creative intent behind something.”

Dave Helmly approached the issue from another angle: the way AI shapes content consumption. As recommendation systems become more sophisticated, they increasingly learn individual user behavior and tailor what content people see.

“It’s going to know me better than it knows me now,” Helmly said. “And it’s going to feed me the things it thinks I want.”

That dynamic introduces a new layer of responsibility for organizations deploying AI-driven media systems.

The issue is not simply whether automation can produce content. It is whether it can shape perception responsibly.

The Rise of Low-Code Workflows

The conversation then pivoted toward another emerging shift in media operations: the rise of low-code tools and AI-assisted scripting.

Modern workflow platforms increasingly allow operators to design complex orchestration visually, often without writing traditional code. At the same time, generative AI tools now allow users to produce scripts or automation logic through simple prompts.

In theory, that democratizes automation.

In practice, it also introduces risk.

Scott Eik of Scale Logic pointed out that operators who run AI-generated scripts without understanding what they do can create serious operational problems.

“If you don’t know what’s happening in the background,” he said, “you can end up with systems that break and nobody knows how to fix them.”

Dave Helmly raised another concern that organizations are only beginning to grapple with: intellectual property.

If AI generates code or workflows, the origins of that code may not always be clear.

“Where did that code come from?” Helmly asked. “You could end up using something that was effectively copied.”

Yet despite those risks, panelists broadly agreed that AI-assisted development is inevitable.

Jason Whetstone of CHESA described two ways engineers are currently using these tools. One approach treats AI as a substitute for research or manual work. The other treats AI more like a collaborator.

Whetstone compared the latter approach to pair programming, where developers work together to solve problems and learn from one another.

“When I use these tools,” he said, “I treat them as a partner. But I still have to define the problem and judge whether the result actually makes sense.”

In that model, AI becomes an amplifier for expertise rather than a replacement for it.

The “Wild West” Phase of AI

Several panelists suggested that the industry is currently experiencing an early and chaotic phase of AI adoption.

Semlear compared the moment to the early days of YouTube.

When online video platforms first appeared, traditional media organizations often dismissed them as amateurish or disruptive. But over time, the ecosystem matured. Production standards improved, and entirely new professional roles emerged around the technology.

AI may follow a similar trajectory.

“We’re in the wild west right now,” Semlear said. “But it will calm down.”

Over time, the technology will likely settle into the same role many other tools have played in the evolution of media production: an infrastructure layer that becomes invisible once it matures.

Where Human Oversight Evolves

As the panel moved toward its conclusion, the discussion turned to how human roles might evolve as AI becomes embedded across production systems.

Most panelists agreed that automation will not eliminate oversight. But it will change its nature.

Even highly automated systems still require humans monitoring outcomes, validating decisions, and stepping in when automation produces unintended results.

Scott Eik emphasized the importance of guardrails. AI systems produce far better results when they operate within clearly defined boundaries.

“If you give it guidelines and rules,” he said, “you get much better outcomes.”

Erik Zindulka also pointed to another emerging capability: AI-driven enrichment of media libraries during ingest and processing. Instead of relying solely on manually logged metadata, AI systems can analyze content as it enters the archive and continuously add contextual understanding over time.

Zindulka offered a simple example, searching an archive for “someone talking about topic x while wearing a red shirt and sitting on a beach” and having the system return the exact moment.

Because many media archives persist for decades, he noted that future AI systems could repeatedly analyze the same material, adding new layers of metadata each time. Over time, that process could produce some of the most richly described media archives ever created.

The Real Question

By the end of the discussion, one point had become clear.

Automation will continue to accelerate media workflows.

That much is certain.

But speed was never the real question.

As Felix Coats summarized in the closing moments of the panel:

“The question isn’t whether automation increases speed. The question is whether judgment remains human; or becomes encoded into software.”

For media organizations navigating the rise of AI-native workflows, that distinction may become one of the defining architectural questions of the next decade.

The post Automation, AI, and the Limits of Machine Decision-Making appeared first on CHESA.

This discussion focused on one of those debates; whether the traditional model of structured, relational metadata remains the foundation of modern media asset management, or if emerging
vector-based semantic retrieval and AI-driven discovery are reshaping how organizations search, understand, and govern their media libraries.

Is Structured Metadata Enough in the Age of Vector Intelligence?

For decades, media asset management systems were built on declared truth.

Structured metadata fields.

Relational databases.

Deterministic queries.

If an asset had the correct tags and identifiers, the system could retrieve it with precision. If it didn’t, the asset might as well have been invisible.

But that model is being challenged.

As AI-driven workflows gain traction, users increasingly expect systems to understand intent, similarity, and context, not just keywords. Instead of searching for exactly what has been declared, they expect systems to infer what they mean.

At CHESA Fest 2026, Vendor Panel 2 explored the architectural tension emerging inside modern MAM platforms: does structured relational metadata remain the foundation of media asset management, or do vector-based semantic systems fundamentally reshape how assets are discovered and managed?

The answer from the panel was neither simple nor unanimous. But a clear theme emerged: the future of asset management isn’t relational versus vector.

It’s relational and vector; working together in ways users may never see.

The Panel

The conversation was moderated by Felix Coats, Solutions Consultant at CHESA, and brought together a mix of technology vendors and practitioners who are actively shaping the next generation of media asset management systems.

Panelists included:

•  Jason Pattan, Media Asset Manager at Sesame Workshop, representing the client perspective from one of the most iconic media libraries in the world.
• Tim Ayris, Head of Partnerships at VIDA
• Jeff Herzog, Director of Product Management at EditShare
• Jim Cavedo, VP of Global Solutions at Orange Logic
• Sofia Fernandez, Channel Manager at Backlight (Iconik)
• Eduardo Mancz, President and CEO of Fonn Group (Mimir)

Rather than focusing on product capabilities or feature comparisons, the panel examined a deeper architectural question: how the rise of semantic search, embeddings, and vector databases may reshape the role of structured metadata inside modern MAM systems.

The discussion quickly revealed that the industry isn’t debating whether vector intelligence will arrive in media asset management; it already has.

The Foundation Still Matters

Before the discussion even began, one reality became clear: abandoning structured metadata entirely isn’t realistic.

Jason Pattan of Sesame Workshop, who joined the panel as a client practitioner rather than a vendor, framed it bluntly.

“I can’t imagine a vector-only database for a MAM,” Pattan said. “You still need things like unique identifiers. That’s the foundation of the system.”

In other words, relational metadata provides the factual backbone of asset management: IDs, rights information, timestamps, licensing rules, and governance controls.

Those attributes aren’t fuzzy concepts. They are deterministic facts.

Trying to retrieve them through semantic similarity would introduce ambiguity where none can exist.

Jeff Herzog, Director of Product Management at EditShare, echoed that distinction.

“There’s a whole set of metadata; like rights management, camera metadata, UUIDs, that can’t be fuzzy,” Herzog explained. “A fuzzy search on a UUID doesn’t make sense.”

Structured metadata, in other words, still governs the operational truth of a media asset.

But that doesn’t mean it governs discovery.

Search Is Changing

The real disruption lies not in how assets are stored, but in how users expect to find them. For decades, the people searching MAM systems were the same people who built them. Editors, archivists, and media managers understood naming conventions and metadata structures because they created them.

That generation is disappearing.

Pattan pointed out that the next generation of users approaches search completely differently.

“There’s a whole new crop of users whose only experience is talking to a chatbot,” he said. “They don’t know naming conventions. They don’t know identifiers. They just describe what they’re looking for.”

Instead of typing a specific filename or metadata tag, a user might search for something like:

“Clips where Elmo is counting with kids.”

That type of request cannot be answered by structured metadata alone.
Vector-based search, using embeddings and semantic similarity, allows systems to retrieve assets based on meaning rather than declared fields. Images, transcripts, and video context become searchable in ways that traditional schemas cannot support.

Tim Ayris, Head of Partnerships at VIDA, summarized the shift succinctly.

“If that semantic search capability isn’t there,” he said, “the pressure on the MAM will be huge.”

Complement, Not Collision

Despite the headline tension, most panelists agreed that relational and vector systems are not competing architectures. They are complementary layers.

Jim Cavedo, VP of Global Solutions at Orange Logic, described the relationship as codependent.

Users shouldn’t have to think about which system they’re querying. Instead, the platform should dynamically determine how to answer the question.

“If someone asks for Sesame Street from 1969 with rights that expire in three years, that’s relational,” Cavedo explained. “If they’re asking for a video with a certain type of moment or feeling, that’s semantic.”

The system’s job is to translate the user’s intent into the appropriate retrieval method.

From the user’s perspective, the experience should be seamless, a single interface that abstracts the complexity underneath.

Eduardo Mancz, CEO of Fonn Group (Mimir), emphasized the same principle.

“From the user perspective, who cares what database it is?” he said. “They just want to find their content.”

The Governance Problem

While semantic discovery may improve search, it introduces a new challenge: governance.

Relational databases are deterministic. A query returns the same result every time because it operates on declared data.

Vector systems behave differently.

Similarity searches are probabilistic. Two searches may produce slightly different results depending on weighting, context, or embedding updates.

That distinction matters in regulated environments.

“Good enough is the problem,” Cavedo said. “In regulated industries, good enough is never good enough.”

Legal rights management, embargo dates, licensing restrictions, and union participation rules require deterministic enforcement.

Those systems cannot rely on probabilistic retrieval.

Herzog added that explainability is another concern. In relational systems, you can trace the logic behind a query result. In vector systems, that traceability becomes harder.

“You can’t always see the work behind the answer,” he noted.

This is why governance layers are likely to remain anchored in relational systems, even as semantic discovery expands.

The Metadata Quality Crisis

Another uncomfortable reality surfaced during the discussion: many organizations don’t actually have good structured metadata to begin with.

Ayris described a scenario his team sees regularly.

Customers migrate decades of archival content into a new MAM platform; only to discover the metadata is incomplete, inconsistent, or simply wrong.

“The metadata is terrible,” he said. “If you don’t have those foundations in place, it becomes much harder to audit or govern anything.”

Vector-based enrichment may help compensate for those gaps by generating transcripts, object detection, and contextual descriptions automatically.

But that raises its own risks.

Ayris warned that relying on semantic enrichment to replace missing structured metadata could create governance blind spots.

“If you haven’t built the foundation today,” he said, “you may leapfrog straight to semantic systems.”

Convenient, perhaps, but potentially dangerous from a compliance standpoint.

Users Don’t Want to Learn Databases

One of the more entertaining moments of the panel came when the discussion turned toward user experience.

Herzog suggested that users may still need training to understand the difference between semantic search and structured filtering.

Cavedo disagreed.

“Users don’t want to be trained,” he said. “The world is an iPhone.”

In other words, users expect systems to work intuitively. They shouldn’t have to understand the architectural layers beneath the interface.

Sofia Fernandez of Backlight offered a helpful metaphor.

She compared the system to a coffee machine.

“You store the milk in one place and the coffee somewhere else,” she said. “But when the user presses ‘latte,’ the machine figures out how to combine them.”

In modern MAM architecture, relational metadata may be the milk while vector intelligence supplies the coffee.

But the user should only see the latte.

The Cost of Intelligence

While the conversation often focused on capability, Mancz raised a less-discussed issue: cost.

Vector search systems rely on embeddings that must be stored, updated, and occasionally regenerated as models evolve.

That process, known as re-indexing or re-vectorization, can become computationally expensive as libraries grow.

“Very few discussions are happening about how much this will cost,” Mancz said.

In large archives containing millions of assets, refreshing embeddings or retraining models could become a significant operational consideration.

This reinforces the idea that vector intelligence will augment existing metadata structures rather than replace them outright.

Metadata Isn’t Disappearing

In the closing round, the panel returned to the original question: how does structured metadata evolve as AI-native workflows expand?

The consensus was clear.

Structured metadata isn’t going away.

But its role is changing.

Instead of being the primary mechanism for discovery, it becomes the framework that ensures governance, identity, and operational truth.

Pattan shared how Sesame Workshop recently revisited its own taxonomy to prepare for this shift.

“If we get the structure right,” he said, “then we can leverage it in any system; relational or AI-driven.”

Vector intelligence may generate massive volumes of contextual data; transcripts, object detection, sentiment analysis, but that information still needs structured anchors to connect it to the operational world.

So, Is Structured Metadata Enough?

No.

But it’s still essential.

Vector-based retrieval is transforming how media assets are discovered. Semantic search allows systems to surface content based on meaning, context, and similarity rather than explicit tagging.

Yet governance, rights management, compliance, and operational workflows still rely on deterministic data structures.

The future of MAM isn’t a choice between relational or vector architectures.

It’s a layered system where relational metadata defines truth, vector intelligence expands discovery, and applications orchestrate both behind the scenes.

Users may never see the difference.

But under the hood, the architecture of media asset management is quietly evolving.

The post The Next Evolution of Media Asset Management appeared first on CHESA.

This discussion focused on one of those foundational debates; how the rise of object storage and cloud-native architectures is challenging long-standing assumptions about the role of the file system in media production, and whether traditional file-based workflows remain the
operational backbone of modern environments or are evolving into a performance layer within an increasingly object-native ecosystem.

The Performance Tier in an Object-Native World

For years, the file system has been the unquestioned center of gravity in media production. If you were editing, finishing, transcoding, or archiving, you mounted a volume and got to work. It wasn’t debated. It was assumed.

But that assumption is quietly being tested.

Object storage now underpins nearly every cloud workflow. SaaS creative tools are training a generation of professionals to think in applications, not directories. APIs are becoming first-class citizens inside production software. And at CHESA Fest 2026, Vendor Panel 1 took the question head-on:

If applications can increasingly interact directly with object storage, what happens to the file system?

Not whether object storage works.

Not whether cloud workflows are viable.

But whether the file system remains the architectural core, or becomes something more specialized inside a broader object-native stack.

The Panel

The discussion was moderated by Tom Kehn, Vice President of Solutions Consulting at CHESA, and brought together storage and workflow leaders from across the media technology ecosystem to examine how modern production environments are evolving as object storage becomes more deeply integrated into creative workflows.
Panelists included:

• Rich Werhun, Senior Solutions Engineer at LucidLink
• Ryan Servant, Senior Director of Channel & Alliances at Suite Studios
• Dave Simon, Senior Director of Media & Entertainment Alliances at Backblaze
• Nathan Halverson, Manager of Solutions Architecture at Spectra Logic

The conversation also included contributions from additional practitioners and audience members, including Dave Helmly, Director of Strategic Development for Professional Video at Adobe, whose perspective from the application layer helped frame how creative tools may evolve as storage architectures shift.

Together, the group explored a question quietly emerging across the industry: as object storage continues to scale and applications increasingly interact with data through APIs rather than mounted volumes, does the traditional file system remain the center of gravity for media production workflows, or is it evolving into a performance layer within a broader object-native architecture?

The discussion quickly moved beyond simple “file versus object” comparisons and into deeper territory: how modern workflows balance performance, governance, lifecycle management, and the expectations of creative users who increasingly care less about where data lives and more about how quickly they can access it.

The File System as Abstraction

The first meaningful pivot in the discussion came when Rich Werhun of LucidLink reframed the premise entirely.

“I see the file system as the abstraction layer. That’s what it is.”

That statement reshapes the debate.

If object storage continues to win on scalability and economics—and it clearly is—something still has to translate object semantics into something creative tools can consume. Even if applications eventually speak S3 natively, they won’t be the only systems interacting with that data.

Workflows are ecosystems. They include transcoders, QC tools, AI engines, review platforms, and automation frameworks. Remove the abstraction layer entirely and you don’t simplify the system—you destabilize it.

Too many tools across the production ecosystem still depend on file semantics to function reliably.

In that framing, the file system isn’t disappearing. It’s evolving into middleware.

Werhun also noted that this layer of infrastructure is still early in its lifecycle. Technologies that present file semantics over object storage are only beginning to gain traction, and as adoption grows, more vendors are likely to emerge building solutions in that space.

Creatives Don’t Design Around Infrastructure

Architecture aside, the human factor quickly entered the conversation.

“The file system is sticking around as long as users are accustomed to using it,” said Dave Simon of Backblaze.

Simon pointed to years of experience working with media organizations and user groups. Sports teams, broadcast crews, and post-production houses expect folder hierarchies. They expect mounted volumes. They expect naming conventions that feel tangible and familiar.

Even platforms like Google Drive replicate file system views because familiarity drives productivity.

Ryan Servant of Suite Studios reinforced the point more bluntly. Creatives don’t necessarily care what’s underneath the hood. They simply want to see their files immediately, regardless of tier, location, or lifecycle state.

And that is where the tension sharpens.

Object-native infrastructure may be architecturally elegant. But architecture rarely wins arguments on its own.

If lifecycle policies introduce latency at the wrong moment, if a file has been tiered down when an editor suddenly needs it, the user doesn’t see optimization. They see friction.

The industry is no longer optimizing solely for storage efficiency.

It is optimizing for creator delight.

The Workflow Reality Check

Theoretical architectures always look clean on diagrams. Real production environments rarely do.

Simon offered a practical scenario: imagine a field shoot generating terabytes of camera card data. In a purely cloud-object workflow, that material must first be uploaded before editing can begin.

“If Premiere starts reading S3 natively, that’s great,” Simon said. “It’s great for my business. But you still have to get the media there first.”

It wasn’t anti-object rhetoric. It was operational math.

High-performance disk tiers still solve ingest bottlenecks. Local caching still protects edit timelines. Certain transcode engines still require mount semantics to read growing files during processing.

Many production environments also continue to rely on traditional shared file systems delivered through NAS and SAN infrastructure. These architectures provide deterministic performance for demanding workloads like high-resolution editing, finishing, and broadcast playout, and they remain a foundational part of many on-premises media workflows.

Even in five years, there will be components inside applications that rely on traditional file behavior.

Object-native does not automatically mean performance-native.

Another practical consideration came from Dave Helmly of Adobe, who noted that interacting directly with object storage introduces operational layers that traditional file systems abstract away. Accessing S3 requires credential management, client configuration, and secure key handling; processes most creative applications were never originally designed to manage internally.

Helmly also pointed to the importance of caching technologies and emerging workflow techniques like Time Addressable Media (TAMS), which allow editors to work with proxy-style representations of media while the underlying files remain distributed across storage tiers.

These approaches help maintain timeline responsiveness while bridging the gap between object storage architectures and the real-time expectations of editing systems.

In other words, the industry isn’t simply replacing file systems with object storage.

It is building new layers that preserve the editing experience while allowing storage architectures to evolve underneath.

At the same time, object storage providers are actively working to close the performance gap. Simon noted that Backblaze has developed the ability to perform live reads of growing files directly from object storage, allowing systems to access media even as it is still being written.

And in media production, performance still wins arguments.

Archive Isn’t Disappearing — It’s Moving Closer to Production

When the discussion turned toward archive, the existential question deepened. If object storage makes data increasingly accessible, does archive even remain a meaningful category?

Nathan Halverson of Spectra Logic brought nuance to the answer.

Object storage introduced lifecycle tiering—hot, cool, deep archive—across hybrid environments. But that flexibility increases complexity.

“Everyone says S3 is S3,” Halverson noted. “It’s a lot more complex than that.”

Retrieval policies vary. API implementations differ. Latency characteristics shift depending on tier.

What appears to be a single namespace can behave very differently depending on how lifecycle policies and storage classes are configured.

Archive isn’t vanishing.

It is becoming programmable.

It is no longer a passive vault; it is an actively orchestrated layer in the stack. And as workflows grow more distributed, that orchestration becomes more strategic, not less.

The Governance Headache

Perhaps the most forward-looking moment of the session came from the audience.

Jason Whetstone, Senior Product Development Engineer at CHESA, observed that younger media professionals increasingly think of their data as living inside applications rather than on shared file systems.

“What’s a file?” he asked, half rhetorically.

It was both humorous and revealing.

SaaS editing platforms, generative AI tools, and cloud-native collaboration systems increasingly encapsulate media within application boundaries. From a creative perspective, that feels efficient.

From a governance perspective, it creates fragmentation.

Instead of one authoritative namespace, organizations now face dozens of application-bound silos.

Servant acknowledged the tension candidly. Creatives open their preferred tool and expect their assets to be there instantly.

If governance policies move something to a lower tier, they don’t see lifecycle optimization; they see disruption.

Another audience participant pointed out an additional complication: files in professional environments rarely belong to a single application. Assets are routinely accessed by multiple tools across the production pipeline.

“More than one app needs to access that file,” Whetstone noted.

Which is precisely why shared storage semantics remain important.

Audience member Nina Smith raised another important reminder: media workflows are rarely uniform across an organization. Editing teams, archive teams, and operations groups often have very different requirements. Understanding who the system is truly serving is essential before designing a single unified architecture.

Where the Center of Gravity Really Lives

Late in the session, Nathan Halverson of Spectra Logic offered an insight that reframed the debate entirely. The center of gravity, he suggested, may not reside in the file system; or in object storage at all. Instead, it lives in the application layer.

Users don’t interact with storage tiers, APIs, or lifecycle engines. They interact with tools. From the user’s perspective, the application defines the experience, while the infrastructure behind it remains largely invisible.

That perspective reorganizes the architecture of the stack. Object storage becomes the durable namespace, file systems act as performance and compatibility layers, and lifecycle engines orchestrate movement across tiers. Applications, ultimately, define how all of those systems are experienced.

In that sense, the crown isn’t simply passing from file systems to object storage.

It’s moving upward.

So, Is the File System Dying?

No. But it may be losing its throne.

The file system is unlikely to disappear anytime soon. Too many workflows rely on its semantics, too many tools depend on its behavior, and too many users expect the familiarity of mounted volumes and directory structures.

What is changing is its role.

Rather than serving as the unquestioned foundation of media infrastructure, the file system is increasingly becoming a high-performance edge tier sitting atop object-native storage architectures.

Object storage continues to rise. Governance complexity is increasing. Applications are becoming more storage-aware, and lifecycle strategy is becoming a central architectural concern.

In that evolving stack, the file system remains essential. Not as the monarch of the infrastructure layer, but as its mediator; bridging the expectations of creative tools with the realities of modern storage systems.

And in contemporary media workflows, mediation may ultimately prove more valuable than domination.

The post Is the File System Dying? appeared first on CHESA.

Under the contract, Chesapeake Systems has been pre-qualified to support the agency on an as-needed basis through individual task orders that may include the design, installation, upgrade, and maintenance of AV and broadcast systems across secure government facilities. The contract supports technology environments such as hearing rooms, conference and event spaces, and broadcast production facilities.

“This award reflects the confidence placed in our team’s technical expertise, operational rigor, and ability to deliver complex solutions within highly regulated and secure environments,” said Lance Hukill, Chief Commercial Officer at Chesapeake Systems. “We are honored to support the federal government by providing reliable, future-ready AV and broadcast systems that meet evolving operational requirements.”

The IDIQ contract establishes a multi-year framework through which task orders may be issued for specific projects as needs arise. Each task order is evaluated based on technical merit, past performance, and best value, ensuring consistent quality, accountability, and performance throughout the life of the contract.

With decades of experience designing and integrating professional AV, broadcast, and media workflow systems, Chesapeake Systems supports organizations that require dependable technology in mission-critical environments. The company’s expertise includes IP-based video and audio systems, control systems, infrastructure modernization, and long-term system support.

This award further reinforces Chesapeake Systems’ position as a trusted technology partner to government and enterprise organizations nationwide.

About Chesapeake Systems

Chesapeake Systems (CHESA) designs, builds, integrates, and supports advanced media workflow, broadcast, and audio-visual solutions for organizations across government, sports, media, and enterprise sectors. Headquartered in Baltimore, Maryland, CHESA is known for delivering scalable, secure, and high-performance systems tailored to each client’s operational needs.

The post Chesapeake Systems Awarded Multi-Year IDIQ Contract for Federal Government AV and Broadcast Services appeared first on CHESA.

IP Workflows Come of Age (ST 2110 & Beyond)

One clear theme was the evolution of IP-based workflows for broadcast production. It’s no longer hype – IP infrastructure is now a practical reality for studios large and small. Our partner Imagine Communications underscored this by showcasing SMPTE ST 2110 in action as the backbone of next-gen facilities. Imagine’s demonstrations in their booth (W2067) highlighted how far IP video transport has come: uncompressed signals flowing seamlessly over COTS networks, with their Selenio Network Processor (SNP) and Magellan control system simplifying the transition from SDI to IP. In fact, Imagine’s John Mailhot noted that this tried-and-tested IP combo has “made IP transformation practical for any size operation, enabling more efficient live production across the industry — even for projects incorporating HDR and UHD”. For CHESA and our clients, the takeaway is clear – IP workflows are maturing. We’re seeing broadcasters gain the flexibility to scale and reconfigure systems without the limitations of SDI routers, which means our integration strategies must ensure new systems can seamlessly route signals over IP networks. The health of the industry was on full display: standards like ST 2110 are broadly adopted, and CHESA is already leveraging that momentum to design future-proof, hybrid IP systems that protect clients’ existing investments while opening the door to cloud and UHD workflows.

Immersive & Interactive Broadcast Experiences (XR + Social Media)

Another show highlight was the rise of immersive, interactive broadcast experiences – blending augmented reality, virtual production, and even social media integration to captivate audiences in new ways. A stunning example came from Vizrt. At their booth, Vizrt (in partnership with startup blinx) demonstrated a world-first: an extended reality (XR) virtual studio where the audience could drive the content in real time via TikTok Live. In this proof-of-concept stream, viewers’ TikTok “gifts” weren’t just icons on a screen – they actually transformed the on-screen environment. For instance, a user sending a virtual “Galaxy” gift would cause the studio background to explode into a galactic 3D animation, even displaying that viewer’s name within the scene – a dynamic, real-time shoutout. This clever fusion of gaming-like interactivity with live broadcast graphics had NAB attendees buzzing. Vizrt’s team emphasized that such XR-driven engagement isn’t just gimmickry; it opens up new revenue models. With TikTok users spending in the hundreds of millions on virtual gifts, a live production that taps into that participatory energy can “drive transactions with deeply immersive entertainment opportunities… without the hard sell”. From CHESA’s perspective, this trend signals that broadcasters and content creators are keen to merge traditional production quality with interactive tech to win over younger, online-native audiences. Whether it’s integrating Unreal Engine-driven virtual sets or connecting social media APIs to on-air graphics, we anticipate more projects where CHESA will be asked to connect these technologies. The goal will be to create seamless workflows that allow our clients to deliver immersive storytelling – where viewers don’t just watch, but actually influence the story in real time.

AI-Powered Workflows: Smarter Captioning, Metadata & Creativity

If one trend permeated every hall at NAB 2025, it was the influence of artificial intelligence on media workflows. From automating rote tasks to augmenting creative decisions, AI-driven tools are rapidly becoming mainstream in our industry. A prime example came from Telestream: they unveiled new AI-powered automation for captions, subtitles, metadata tagging, and even content summaries in their Vantage platform. This means a video file ingested into a workflow can have high-quality speech-to-text captions generated almost instantly, multilingual subtitles prepared, descriptive metadata auto-populated, and short synopsis content drafted – all via AI. It’s a game-changer for efficiency: think of compliance captioning, localization, and content indexing being done in a fraction of the time, with less manual effort. Our integration partner SNS (Studio Network Solutions) offered a complementary peek at AI’s role in creative asset management. At SNS’s booth, they set up an on-premises “AI Playground” – a hands-on demo where attendees could explore AI’s power in media management. We tried out tools that let you search a massive media library by describing a scene, or automatically identify duplicate images and even pinpoint specific moments in video by their content. For example, an editor could query, “find all clips where the CEO appears on stage at CES,” and an AI engine would sift the archives to find those shots – no manual tagging needed. SNS’s approach here is to show how AI can enrich metadata in situ and trigger complex workflows behind the scenes. In fact, their upcoming integration with Ortana’s Cubix orchestration platform will let users kick off automated tasks (like file moves or cloud backups) just by setting a tag in the SNS ShareBrowser MAM – essentially using AI and orchestration to connect storage, MAM, and cloud services intelligently. “These new integrations highlight our commitment to providing users with flexible tools that enhance collaboration and drive efficiency,” said SNS co-founder Eric Newbauer, underscoring that the end goal is an end-to-end workflow where mundane tasks are handled by smart systems and creative people can focus on higher-value work.

On the content creation side, AI is also stepping up to tackle one of the industry’s perennial challenges: making content accessible to broader audiences. Perhaps the most jaw-dropping example we saw was AI-Media’s debut of LEXI Voice, an AI-powered live translation solution. Imagine broadcasting a live event in English and, virtually in real time, offering viewers alternate audio tracks in Spanish, French, Mandarin, or over 100 languages – without an army of human interpreters. AI-Media’s LEXI Voice does exactly this: it listens to the program audio and generates natural-sounding synthetic voice-overs in multiple languages with only ~8 seconds of latency. The system impressed many broadcasters at NAB by showing that a single-language feed can be transformed into a multi-language experience on the fly. “Customers are telling us LEXI Voice delivers exactly what they need – accuracy, scale, and simplicity, at a disruptive price,” shared James Ward, AI-Media’s Chief Sales Officer. For global media companies and even event producers, this AI-driven approach could break language barriers and dramatically cut the cost of multi-language live content (AI-Media estimates up to 90% cost reduction versus traditional methods) while maintaining broadcast-grade quality. For CHESA, which often helps clients integrate captioning and translation workflows, these AI advancements are exciting. We foresee incorporating more AI services – whether it’s auto-captioning for compliance, cognitive metadata tagging for asset management, or AI voice translation for live streams – as modular components in the solutions we design. The key will be ensuring these AI tools hook seamlessly into our clients’ existing systems (MAMs, DAMs, playout, etc.), so that captions, metadata, and even creative rough-cuts flow automatically, saving time and enabling content teams to do more with less.

Cloud, Streaming & Remote Production Breakthroughs

NAB 2025 also reinforced how much cloud and remote production technologies have advanced. Over the past few years, necessity (and yes, the pandemic) proved that quality live production can be done from almost anywhere – and the new gear and services on display cemented that remote and cloud-based workflows are here to stay. For instance, our partner Wowza showcased updates that make deploying streaming infrastructure in the cloud or hybrid environments easier than ever. Their streaming platform can now be spun up in whatever configuration a client needs – on-premises, in private cloud, or as a service – while still delivering the low-latency, scalable performance broadcasters expect. This kind of flexibility is crucial for CHESA’s clients who demand reliability for live events but also want the agility and global reach of cloud distribution. We witnessed demos of Wowza’s software dynamically adapting video workflows across protocols (from WebRTC to LL-HLS) to ensure viewers get a smooth experience on any device. The message was clear: cloud-native streaming has matured to the point where even high-profile, mission-critical streams can be managed with confidence in virtualized environments.

On the live contribution and production side, LiveU made a strong showing with its latest remote production ecosystem. LiveU has been a pioneer of cellular bonding (letting broadcasters go live from anywhere via combined 4G/5G networks), but this year they took it up a notch. They unveiled an expanded IP-video EcoSystem that is remarkably modular and software-driven. “The EcoSystem is a powerful set of modular components that can be deployed and redeployed in a variety of workflows to answer every type of live production challenge,” explained LiveU’s COO Gideon Gilboa. In practice, this means a production team can spin up a configuration for a multi-camera sports shoot in the field, then re-tool the same LiveU gear and cloud services the next day for a totally different scenario (say, a hybrid cloud/ground news broadcast) without needing entirely separate kits. One highlight was LiveU Studio, a cloud-native vision mixer and production suite that enables a single operator to produce a fully switched, multi-source live show from a web browser – complete with graphics, replays, and branded layouts. Another headline innovation was LiveU’s new bonded transmission mode with ultra-low delay: we’re talking mere milliseconds of latency from camera to cloud. Seeing this in action was impressive – it means remote cameras can truly be in sync with on-site production, opening the door to more REMI (remote integration) workflows where a director in a central control room can cut live between feeds coming from practically anywhere, with no noticeable delay. CHESA recognizes that this level of refinement in remote production tech is a boon for our clients: it reduces the cost and logistical burden of on-site production (fewer trucks and crew traveling) while maintaining broadcast quality and responsiveness. We’ve already been integrating solutions like LiveU for clients who need mobile, nimble production setups, and at NAB we saw that those solutions now offer even greater reliability, video quality (e.g. 4K over 5G), and cloud management capabilities.

Even the traditionally hardware-bound pieces of broadcast are joining the cloud/remote revolution. Companies like Riedel – known for studio intercoms and signal distribution – showed off IP-based solutions that make communications and infrastructure more decentralized. Riedel’s new StageLink family of smart edge devices, for example, lets you connect cameras, mics, intercom panels, and other gear to a standard network and route audio/video signals over IP with minimal setup. In plain terms, it virtualizes a lot of what used to require dedicated audio cabling and matrices. We see this as “smart infrastructure” that eliminates traditional barriers: an engineer can extend a production’s I/O simply by adding another StageLink node to the network, rather than pulling a bunch of copper cables. For remote productions, this means field units can tie back into the home base over ordinary internet connections, yet with the robustness and low latency of an on-site system. Riedel also previewed a Virtual SmartPanel app that puts an intercom panel on a laptop or mobile device. Imagine a producer at home with an iPad, talking in real time to camera operators and engineers across the world as if they were on the same local intercom – that’s now reality. For CHESA, whose projects often involve tying together communication systems and control rooms, these developments from LiveU, Wowza, Riedel and others mean we can architect workflows that are truly location-agnostic. Whether our client is a sports league wanting to centralize their control room, or a corporate media team trying to produce events from home offices, the technology is in place to make remote and cloud production feel just as responsive and secure as traditional setups.

Smart Infrastructure & Workflow Orchestration

The final theme we noted is a bit more behind-the-scenes but critically important: the growth of smart infrastructure and orchestration tools to manage all this complexity. As integrators, we know that deploying one shiny new product isn’t enough – the real value comes from how you connect systems together and automate their interaction. At NAB 2025, many vendors echoed this, introducing solutions that orchestrate workflows across disparate systems. We’ve already touched on Riedel’s IP-based infrastructure making physical connections smarter, and SNS’s integration platform leveraging AI and tags to automate tasks. To expand on the SNS example: they announced a native integration with Ortana’s Cubix workflow orchestration software that takes automation to the next level. With SNS’s EVO storage plus Cubix, a media operation can do things like: automatically move or duplicate files between on-prem storage, LTO archives, and cloud tiers, triggered by policies or even a simple user action in the MAM; or enrich assets with AI-generated metadata in place (send files to an AI service for tagging as they land in storage); or spin up entire processing jobs through a single metadata tag. In a demo, SNS showed how setting a “Ready for Archive” tag on a clip could kick off a cascade: the file gets transcoded to a preservation format, sent to cloud object storage (with a backup to a Storj distributed cloud for good measure), and the MAM is updated – all without manual intervention. This kind of event-driven orchestration is incredibly powerful. It means our clients can save time and reduce errors by letting the system handle repetitive workflow steps according to rules we help them define. CHESA has long championed this approach (we often deploy orchestration engines alongside storage and MAM solutions), and it was validating to see so many partners focusing on it at NAB.

“Smart” infrastructure also refers to hardware getting more integrated smarts. We saw this in Riedel’s new Smart Audio Mixing Engine (SAME) – essentially a software-based audio engine that can live on COTS servers and apply a suite of audio processing (EQ, leveling, mixing, channel routing) across an IP network. Instead of separate audio consoles or DSP hardware, the mixing can be orchestrated in software and scaled easily by adding server nodes. This aligns with the general trend of moving functionality to software that’s orchestrated centrally. For CHESA’s clients, it means future facilities will be more flexible and scalable. Need more processing? Spin up another virtual instance. Reconfigure signal paths? Use a software controller that knows all the endpoints. The days of fixed-function gear are fading, replaced by what you might call an ecosystem of services that can be mixed-and-matched. Our job as an integrator is to design that ecosystem so that it’s reliable and user-friendly despite the complexity under the hood. The good news from NAB 2025 is that our partners are providing great tools to do this – from unified management dashboards to open APIs that let us hook systems together. We came away confident that the industry is embracing interoperability and orchestration, which are key to building solutions that adapt as our clients’ needs evolve.

Conclusion: From Show Floor to Real-World Workflows

After an exciting week at NAB 2025, the CHESA team is returning home with fresh insights and inspiration. We want to extend our thanks to our key technology partners – Imagine Communications, Vizrt, Telestream, SNS, Wowza, LiveU, Ai-Media, and Riedel – for sharing their innovations and visions with us at the show. Each of these companies contributed to a clearer picture of where media technology is headed, from IP and cloud convergence to AI-assisted creativity and immersive viewer experiences. For CHESA, these advancements aren’t just flashy demos; they’re the building blocks we’ll use to solve our clients’ complex workflow puzzles. Our role as an integrator is ultimately about connecting the right technologies in the right way – turning a collection of products into a seamless, tailored workflow that empowers content creators. NAB Show 2025 reinforced that we have an incredible toolbox to work with, and it affirmed CHESA’s commitment to staying at the forefront of media tech. We’re excited to take what we absorbed at NAB and translate it into real-world solutions for our clients, helping them create, manage, and deliver content more efficiently and imaginatively than ever. In the fast-evolving world of media workflows, CHESA stands ready to guide our clients through the innovation – from big picture strategy down to every last system integration detail – just as we have for over twenty years. Here’s to the future of media, and see you at NAB 2026!

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Identify the Market and Roadmap

Every MAM vendor follows a development cycle influenced by feature requests from sales teams, solutions architects, or client engagements. These product roadmaps are driven by the need to fulfill use case requirements. Some MAMs have robust features designed for image-based workflows, while others are tailored for video management. Yet, each vendor will claim their product is the best, within their defined market, of course. To narrow your options, start by identifying the types of assets and files you need to manage and the features required for your workflows.

Define Your Use Cases

To find the right MAM for your organization, begin by defining your specific use cases and how your workflows operate. Detail the system functionalities and requirements you need. Weigh these functional requirements with a measurable metric, which will help during the system assessment and ultimately determine deployment success, KPI achievements, and ROI.

Understand Workflows and Integrations

Consider what legacy or future technology is part of your environment. Using the 3-5-7 Flywheel methodology from our previous blog, evaluate how your workflows have evolved. What new codecs or systems are you implementing? What languages and API parameters will be necessary for smooth cross-application functionality? Identify your “source of truth” for data and how it flows throughout the data landscape. How do you want your workflows to operate, and how should users progress through them? What storage types are being used, what connectivity and protocols are being used, and where are those storage located? These considerations are vital to ensure functional requirements align with use cases and that the system integrates well within your ecosystem.

Engage Stakeholders and Measure Fulfillment

Involving key stakeholders is crucial. Make sure you gather feedback from a diverse range of users, not just the typical producers and editors. Then, create a matrix to assess how well the system fulfills your requirements, and another to evaluate usability. Some systems may seem like an obvious choice on paper, but may impose rigid processes that users find difficult to adapt to. When users fail system acceptance tests or create workarounds, ROI and KPIs suffer.

Seek Professional Guidance

Most organizations have existing relationships with systems integrators or IT providers—use these resources to bridge knowledge gaps. Engage with engineering teams, ans subject matter experts to gather additional insights, and document key takeaways to explore during testing or proof of concept (POC). When conducting a POC, involve the vendor’s professional services team. A simple integration built by the vendor can reveal their responsiveness and ability to meet your needs.

Conclusion

As the saying goes, “Fail to plan, plan to fail.” This is especially true when choosing and implementing a MAM, DAM, or PAM. With careful planning and attention to the steps mentioned, you’ll be on track to selecting the best system for your organization.

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Scalability: Meeting the Demands of a Growing Industry

Elastic Scalability in the Cloud

Cloud platforms like AWS, Google Cloud, and Microsoft Azure offer elastic scalability, enabling businesses to expand or contract resources based on demand. During peak events such as live sports or major show premieres, these platforms allow broadcasters to handle traffic surges without investing in physical infrastructure.

Key benefits include:

• Real-time scaling during high-demand periods.
• Cost-effective global content distribution with low latency.
• Seamless streaming performance for millions of concurrent users.

Hybrid Cloud for Tailored Flexibility

A hybrid cloud model blends on-premises systems with cloud services, ensuring scalability while maintaining control over critical assets. For example:

• On-premises systems handle latency-sensitive or high-security tasks.
• Cloud platforms manage tasks like rendering and storage of non-critical assets.

This balanced approach optimizes resource usage while preserving security and performance.

Scalability for Real-Time Media Delivery

Media companies increasingly rely on real-time delivery for live broadcasts and interactive content. Cloud-based architectures distribute workloads efficiently across global regions, reducing latency and ensuring uninterrupted service to a dispersed audience.

Cost Management: Reducing Expenses and Boosting Efficiency

Pay-As-You-Go Flexibility

Unlike traditional on-premises systems, cloud platforms utilize a subscription-based model. Media companies pay only for the resources consumed, leading to significant cost reductions:

• Avoid capital investments in underutilized hardware.
• Allocate resources dynamically to prevent waste.

Optimized Resource Allocation

For episodic projects like live broadcasts or film productions, cloud infrastructure eliminates the need for permanent, high-cost hardware. Teams can scale resources for tasks such as rendering and media storage, then scale down afterward, saving operational costs.

Automated Workflows for Efficiency

Cloud platforms incorporate AI and ML tools to automate repetitive tasks, reducing human workload and improving efficiency:

• Metadata tagging.
• Content encoding and transcoding.
• Automated file backups and organization.

This automation allows creative teams to focus on higher-value activities, streamlining operations and reducing overall costs.

Improved Collaboration and Faster Time-to-Market

Global Collaboration with the Cloud

The decentralized nature of modern media production requires seamless remote collaboration. Cloud platforms enable:

• Simultaneous project access for geographically dispersed teams.
• Faster production cycles through shared real-time workflows.

Hybrid Solutions for Security and Flexibility

Hybrid infrastructures empower companies to store sensitive data on-premises while leveraging the cloud for demanding tasks like real-time editing and rendering. This blend ensures security without compromising production speed.

Disaster Recovery and Content Security

Resilient Disaster Recovery Systems

Cloud infrastructure ensures business continuity through data replication across geographically diverse servers. Key advantages include:

• Rapid recovery during outages.
• Built-in redundancy to safeguard content.

Enhanced Security with Hybrid Infrastructure

For sensitive content, hybrid solutions offer robust protection by keeping critical data on-premises while leveraging cloud scalability. This model supports:

• Advanced encryption.
• Digital rights management (DRM).
• Prevention of unauthorized access.

Future Technologies Enhancing Scalability and Cost Management

Edge Computing for Low-Latency Delivery

Edge computing processes data closer to end-users, reducing latency and enhancing experiences for live streaming and interactive media.

5G for Seamless Media Delivery

The rollout of 5G networks complements cloud and hybrid infrastructures by:

• Enabling faster content delivery.
• Supporting high-bandwidth applications like ultra-HD streaming and immersive VR experiences.

Conclusion

The adoption of cloud and hybrid infrastructures is revolutionizing the media and entertainment industry. With elastic scalability, cost-efficient operations, and robust security, these technologies provide the foundation for a future-ready, competitive landscape. Companies embracing these innovations today will enjoy enhanced flexibility, reduced costs, and the agility to navigate an ever-evolving digital ecosystem.

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Cybersecurity Breaches

With the growing reliance on cloud-based workflows and digital collaboration tools, media organizations have become prime targets for cyberattacks. Hackers exploit vulnerabilities in content storage and distribution systems, leading to data theft, intellectual property leaks, and operational disruptions.

Disrupted Content Pipelines

The rise of global crises, including political conflicts and environmental disasters, has hampered location-based productions and delayed delivery schedules. These disruptions have forced companies to rethink their approach to content creation, remote production and planning.

Complex Rights Management

As media companies expand their offerings across multiple platforms and regions, managing licensing agreements and royalties has become increasingly complicated. Mismanagement of intellectual property (IP) rights can lead to legal disputes and revenue loss. Organizations are also rewriting Personal Data Policies to include image and likeness, directly affecting retention and archival policies.

Technology Fragmentation

The integration of new technologies such as AI, VR, and 5G has created both opportunities and challenges. Legacy systems often struggle to keep up with these innovations, resulting in inefficiencies and compatibility issues within the media supply chain.

Regulatory Pressures

Heightened scrutiny over data privacy, content moderation, and intellectual property rights has added another layer of complexity. Compliance with regional and global regulations demands significant resources and operational agility.

Strategies to Address Media Supply Chain Vulnerabilities

Adopting End-to-End Digital Workflows

The transition to cloud-based, fully digital workflows can streamline content production and distribution while improving scalability. Advanced media asset management (MAM) systems allow real-time collaboration and ensure secure content storage and transfer.

Strengthening Cybersecurity Measures

Media companies must adopt robust cybersecurity protocols, such as encryption, multi-factor authentication, and regular audits. Partnering with cybersecurity firms and leveraging AI-driven threat detection tools can help mitigate risks.

Enhancing Production Resilience

To combat disruptions, media organizations should diversify production locations and leverage virtual production technologies. Virtual sets and AI-assisted post-production tools can reduce dependency on physical environments and accelerate timelines.

Optimizing Rights and Royalty Management

Blockchain technology offers a transparent and efficient way to manage licensing agreements and royalty payments. Automating rights management systems can reduce errors, ensure compliance, and provide real-time tracking of revenue streams.

Investing in Interoperable Systems

To overcome technology fragmentation, media organizations should adopt interoperable tools and standards that integrate seamlessly with existing systems. This ensures smooth workflows and reduces downtime when implementing new technologies.

Navigating Regulatory Compliance

Proactive engagement with policymakers and industry groups can help media companies stay ahead of regulatory changes. Establishing dedicated compliance teams and leveraging AI for real-time monitoring of content and data usage can streamline adherence to legal requirements.

The Role of Collaboration and Innovation

The media supply chain is no longer a linear process—it is a dynamic ecosystem requiring collaboration across stakeholders. Partnerships with technology providers, production houses, and distribution platforms can drive innovation and unlock new revenue streams. Additionally, fostering a culture of experimentation with emerging technologies like generative AI, immersive media, and personalized content delivery can create competitive advantages.

Conclusion

The challenges of 2024 have revealed critical vulnerabilities in the media supply chain, but they have also highlighted opportunities for transformation. By embracing technology, fostering collaboration, and prioritizing resilience, media organizations can turn these challenges into catalysts for growth.

In an industry where change is the only constant, the ability to adapt and innovate will define the leaders of tomorrow. Now is the time for media companies to fortify their supply chains, ensuring they are prepared to meet future disruptions head-on.

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