Category: Events & Trade Shows

Inside the Broadcast Revolution: Key Takeaways from DCMUG’s Night at Monumental Sports

Post author By laversa
Post date March 20, 2026
No Comments on Inside the Broadcast Revolution: Key Takeaways from DCMUG’s Night at Monumental Sports

On March 9th, the DC Media Users Group (DCMUG) gathered for what turned out to be one of its best events yet. Hosted by CHESA and a roster of top-tier technology sponsors, the evening kicked off at Clyde’s of Gallery Place before moving into an exclusive behind-the-scenes tour of the Monumental Sports Network Production Studio and Control Rooms at Capital One Arena. The night capped off with a suite for the Washington Capitals vs. Calgary Flames matchup.

The real main event, though, happened in between the tour and the puck drop: a wide-open Q&A session featuring a panel of practitioners who pulled no punches about what it actually takes to build, run, and future-proof a modern broadcast facility. The conversation touched on IP infrastructure, workforce evolution, cybersecurity, and the age-old question of when cutting-edge technology is the right call, and when it isn’t.

Here’s a deep dive into everything that came out of the room.

MEET THE VOICES IN THE ROOM

Leading the discussion was Jon Bednar, Founder and Principal Consultant of Codeso, and the architect behind the Monumental Sports Network facility, which the group had just toured. Jon is SMPTE 2110 Certified, a former Navy broadcast engineer and instructor. He has designed IP-based broadcast environments for clients ranging from the United Nations and the NFL to HHS and the US Department of State. His real-world candor set the tone for the entire conversation.

The CHESA team, rounding out the panel, included:

Patrick Johnson, Director of Federal Sales at CHESA, opened the event and kept the conversation moving.
Jason Paquin, CEO of CHESA, moderated the Q&A and brought context from years of client-facing discovery and integration work.
Jason “Pep” Pepino, Director of Media Systems Design & Engineering at CHESA, weighed in on the technical and design side throughout.
Roger Sherman, Senior Solutions Consultant at CHESA and former Chief Broadcast Technology Officer at Voice of America, offered a rare federal practitioner’s perspective on the 2110 decision.

The audience was a mix of federal agency broadcast professionals, including teams from HHS and HUD, and commercial media operators from around the DC metro area. The back-and-forth was as honest as it gets.

THE HARDEST PART OF A 2110 BUILD ISN’T THE TECHNOLOGY

When Jason Pepino asked Jon Bednar what the biggest challenge was in upgrading the Monumental Sports facility, a project that ran from conceptual design in 2021 through its launch in May 2024, the answer was immediate.

“Honestly, the people.”

The facility had been the NBC RSN operation, running on legacy SDI infrastructure for 10 to 12 years. The engineering team knew that world intimately. The upgrade took them from a traditional Grass Valley-based routing infrastructure to Panasonic’s Kairos production switcher and an EVS-based IP routing environment. It would be hard to find a more dramatic technology transition in the broadcast world.

“A lot of legacy engineers lived in the SDI world for so long, and then everything changed,” Jon explained. “You can install the best, most well-engineered platform in the world, but if you don’t have people that can operate and maintain it, it’s only as good as the people.”

The philosophical shift between SDI and 2110 is significant. In an SDI environment, troubleshooting is reactive and tactile: plug in a meter, see the video, hear the audio, wait for something to break, and fix it. In 2110, that approach doesn’t work.

“With 2110, you have to be proactive. You constantly have to monitor and massage it. If something breaks, you can’t just put a meter on it. You have to know where the packet goes, where it was lost, what the fail rate is, whether it’s the red or the blue side.”

What training methodology worked best? Shadowing. Walking engineers through the commissioning process in real time, letting them ask questions, observe, and build intuition alongside the system as it came to life, proved far more effective than formal classroom instruction alone.

The goal Jon described for a well-designed 2110 environment is elegantly simple: an operator who has spent their entire career on SDI should be able to sit down at the console and not know the difference. The route button makes the route. Switch takes the camera. Fader up means louder. The IP world underneath is invisible to the production operator.

But for the engineers maintaining it? They need to think like network professionals. And some people’s brains, he acknowledged honestly, simply aren’t wired for that — and that’s okay. Those individuals can still contribute in production engineering roles that don’t require deep packet-level troubleshooting.

MONITORING IN A 2110 WORLD: A LAYERED APPROACH

Jason Paquin pushed Jon to talk specifics about monitoring, because it looks fundamentally different in a 2110 environment than in the SDI playbook. What Jon described across both the Capital One Arena and COA North (the off-site production facility) was a layered diagnostic stack, each tool serving a distinct purpose.

EVS serves as the orchestration platform and sits at the foundation. Its APIs integrate with Cisco NDFC and Arista EOS, providing the first level of visibility: bandwidth utilization per port, multicast flow tracking, and signal routing analytics. When a route fails to take, Jon’s first check is bandwidth saturation — “if it’s at 96%, you know why the route dropped.”

Telestream’s Prism Inspect is the next layer. When a signal looks off, routing it into Inspect immediately reveals the full ST 2110 flow: STP file comparisons between the red and blue redundant paths, audio presence, and stream metadata. With the ability to monitor roughly 32 signals simultaneously, it provides a broad at-a-glance health check.

TAG sits on the monitor wall, delivering alarm-based monitoring with penalty boxes and configurable thresholds, with nearly 1,000 alarms available out of the box. It gives operators a broad “something’s wrong” signal. Crucially, though, TAG tells you that you lost video, not why. That’s where the next layer comes in.

Providious handles deeper network-level packet analysis, called in when packet drops or RTP errors need investigation at the multicast level.

And underlying all of it: PTP timing. Precision Time Protocol is the heartbeat of any 2110 plant, and as Jon put it with a laugh, “It’s easy until it’s not.” A disproportionate number of mysterious signal issues can be traced back to PTP drift.

Roger Sherman raised a great observation: SDI alarm systems would often just flag “illegal video” — technically accurate but diagnostically useless. The 2110 monitoring ecosystem, by contrast, actually points you in the right direction. It doesn’t always hand you the answer, but it gives you a direction to start digging.

CONSTRUCTION DELAYS, THE CAPS’ WIN STREAK, AND ADAM SANDLER

One of the lighter, but genuinely instructive, threads of the evening was Jon’s account of what it took to actually complete the build.

The conceptual design started in 2021. The facility launched in May 2024. The biggest delays? Construction, on the heels of COVID, with its material and parts shortages. Arista had lead times of up to 11 months at one point.

But the arena portion of the build had a uniquely Washington problem. The agreement with the city required maintaining operational continuity throughout construction. The start date was locked. It could not move.

“Every time the Caps won, our timeline got shorter and shorter,” Jon said. “My wife would ask what was wrong, and I’d say, ‘They won again.’ Every win pushed us further.”

When the Capitals finally lost, he was the only person in Washington celebrating.

The first live event in the newly completed facility? An Adam Sandler show. Not exactly a stress test for the broadcast infrastructure, but the team used it to run some routing and cameras, a warm-up before the real thing.

Monumental has since built a Verizon dark fiber loop connecting the arena, the Capitals’ practice facility, and the Mystics’ arena. JPEG XS is traversing that loop today, with plans to eventually move all 2110 traffic across all facilities from a centralized production hub.

2110 VS. NDI: THE HONEST ANSWER IS “IT DEPENDS”

One of the most valuable portions of the evening was a direct question from Jason Paquin: setting budget aside, what are the actual deciding factors between going 2110 versus SDI?

The three panelists each offered a different lens.

Jason Pepino’s answer was scalability. In a traditional SDI router environment, a 128×128 frame is essentially maxed at day one. Adding capacity means a second router, tie lines between them, and rapidly escalating costs. With 2110, scaling is adding a network switch. For organizations with growth ambitions, that flexibility is meaningful even if the upfront investment is higher.

Roger Sherman’s answer came from experience at Voice of America. The driving factors there weren’t prestige or future-proofing for its own sake; they were practical. Distributing gateways and endpoints across the facility meant they weren’t pulling all signal paths back to a single central location, saving on copper runs, core holes, and installation labor. A second driver was resolution flexibility: some VOA services operated in SD, while others, particularly Eastern European bureaus, were pushing 4K. A single 2110 environment handled both simultaneously.

But Sherman was equally clear about the limits. He recalled a conversation with TV Martí, a much smaller operation that wanted to pursue 2110. His advice was direct: don’t. “It was prohibitive for their scale and their needs.”

Jon Bednar’s framework was the simplest: always ask why. If a client says they want 2110, his first question is what problem are they trying to solve. He described a client in New York City who wanted a full IP infrastructure, and when pressed, couldn’t articulate why. They ended up with NDI and SDI, and it worked perfectly for them. “They have no roadmap to go to 4K. They’re not scaling across multiple facilities. Save the money and put it somewhere else.”

For organizations making major capital investments, particularly federal customers who may not see a comparable budget for a decade or more, Jon and the panel were aligned on one thing: invest in the fiber backbone now, regardless of your current technology decision. The labor cost of the pull is the significant cost. The incremental cost of pulling 512 strands instead of 96 is comparatively small, and fiber is future-proof in a way that no endpoint device is. The Monumental team pulled 512 strands to each redundant rack. They needed 96 on day one.

THE ENTERPRISE NETWORKING PROBLEM: BROADCAST AND IT STILL DON’T SPEAK THE SAME LANGUAGE

An attendee from a federal agency raised a challenge that clearly resonated with most of the room: their broadcast and networking teams are siloed, they’re operating on enterprise networks not designed for video, and getting approval for the specific switches needed for a media production environment, even for NDI, is an uphill battle.

Jason Pepino was direct: broadcast media networks and enterprise IT networks have to be physically separated. Not VLANs; separate switches. The bandwidth profiles differ, the multicast requirements differ, and the update cadence for broadcast systems (where an OS may be intentionally frozen to maintain certification and stability) conflicts directly with enterprise IT’s security patching cycles.

“Corporate IT guys are going to ask why you’re passing so much bandwidth. And you still have to keep up on security, but some of these systems can only get to a certain point because the provider only brought the OS up so far.”

Jon added a practical tool for navigating internal budget conversations: engage Cisco and Arista directly. Both companies have media-specific technical teams with documentation that explicitly explains why a general-purpose enterprise switch won’t work on a broadcast media network, and why the media-optimized variant is required. That documentation can be decisive when you’re trying to make the case to an IT procurement team or an agency budget officer.

Roger Sherman reframed the underlying problem: it’s a trust and language issue as much as a technical one. If a broadcast engineer can walk into a conversation with enterprise IT and demonstrate security fluency, speak to how the media network is segmented, how threats are mitigated, what the exposure surface actually looks like, they have a much better chance of getting the hardware and support they need.

“Once you can speak the language, you can get them to trust you. Work with them together.” He also noted the challenge that many in the room nodded at: just when you build that trust with someone on the IT side, they get promoted or leave.

CYBERSECURITY: THE CONVERSATION THE BROADCAST INDUSTRY CANNOT IGNORE

Perhaps the most sobering thread of the evening was cybersecurity. As broadcast infrastructure migrates to IP, the attack surface expands, and bad actors are already active.

“Do you guys witness bad actors frequently?” someone asked.

“Frequently,” Jon replied. “Every facility I’ve ever worked at, there are metrics where it was 10,000 hits a day on the external firewall.”

This isn’t theoretical. A 2110 plant is not a closed SDI environment with copper everywhere. The orchestration platform that used to be a massive, dedicated piece of hardware is now a virtual machine. A single compromised VM could take down an entire broadcast infrastructure — audio, control, tally, routing, everything. If the facility generates revenue through live events or chargebacks, the business impact of a successful breach is severe.

Roger Sherman outlined a pragmatic approach to segmentation: certain assets, particularly ingest encoders taking feeds via SDI, can be placed in a DMZ outside the inner firewall. If someone compromises that encoder, the incoming signal is already SDI. The blast radius is limited. “I don’t care if you hack that encoder,” he said. “Put it outside the firewall. I’ve got fewer ports to worry about. You have fewer ports to worry about. And we can proceed.”

The architecture Jon used at Monumental started with the broadcast network as a complete island. Third-party signal delivery (like connectivity to Encompass in Atlanta) went over dedicated dark fiber with no shared firewall exposure — a direct line, touching nothing else. As operational needs grew and facility-to-facility connectivity became necessary, proper dual-firewall segmentation was added. Today, anything that crosses the public internet, Zixi feeds, and similar, passes through two firewalls. Monumental also hired dedicated cybersecurity staff specifically for broadcast and 2110 security.

Jason Paquin connected the cybersecurity conversation to a historical pattern: when facilities moved from SD to HD, broadcast engineering and IT had to merge for the first time, and the friction was real. He recalled being a young engineer watching a broadcast, and IT teams fighting across a conference table at WABC New York during a SAN installation, neither willing to acknowledge the other’s expertise. The current transition is the same collision, but at a higher level of complexity, with cybersecurity now in the mix.

His framing for the discovery conversation resonated throughout the room: if a client’s plan is to have their general IT team manage the broadcast network switches, someone needs to stop and calculate the cost of being down, and the cost of chasing issues with people who don’t have the right expertise. When that number starts approaching the cost of the proper solution, the conversation changes.

THE WORKFORCE IS CHANGING — AND THAT’S JUST THE TRUTH

Woven through every topic of the evening was a theme that nobody introduced directly, but that kept surfacing: the broadcast engineering workforce is in the middle of a generational shift, and the industry is moving whether people are ready or not.

“Legacy engineers leave, and they’re going to be backfilled by a broadcast IT guy,” Jon said. “A lot of the hires I see now for day-two support, it’s not the broadcast engineer from ABC. It’s a 25-year-old with an IT degree who also streams. That’s the perfect candidate for broadcast IT engineering. They understand enough about video. They understand more about networking. That’s just the blunt truth.”

The job postings already reflect this. Almost universally, broadcast engineering roles now require Cisco, Arista, and Layer 3 networking experience. They don’t ask whether you can troubleshoot an SDI frame.

Jon’s advice to his own teams, going back to when he ran AV integration in Baltimore: conduct 3 interviews a year. Not necessarily to leave, but to read the market. See what skills employers are asking for. The job requirements tell you where the industry is headed more clearly than any conference keynote.

The through line, as Jason Paquin framed it, is that IP migration isn’t just a technology change — it’s an operational, staffing, and cultural change, all at once. Organizations that treat it as a technology procurement project and ignore the people side will find themselves with a world-class system they can’t fully operate or maintain.

THANK YOU TO OUR SPONSORS

This DCMUG event was made possible by the generous support of our sponsors. Here’s a brief introduction to each:

Backlight

Josh Norman (President & CRO) and Alex Burke joined us, representing Backlight, makers of Iconik — one of the leading media asset management platforms in the broadcast industry. Iconik was referenced throughout the evening as a go-to MAM solution for media organizations managing large volumes of content.

EVS

Bevan Gibson (North American Operations) and Will Walz (Northeast) represented EVS. You likely know EVS for sports replay — if you’ve watched any live sport, you’ve seen their technology at work. EVS has a significant installation at Monumental Sports, including the EVS Neuron conversion platform that Jon discussed extensively during the Q&A. They also do control, orchestration infrastructure, and robotics.

LiveU

Mike Mahoney (VP of Growth Markets, US & Canada) and Jared Brody represented LiveU. Best known for broadcast-grade bonded cellular encoding and transmission, LiveU is now pushing into bonded IP over WAN and LEO satellite connectivity, with AI-enhanced workflows in development. If you’re watching a live news report from a field location, there’s a good chance LiveU is how it’s getting back to the studio.

Studio Network Solutions (SNS)

Chance Hayworth (Northeast Territory Manager & DoD Territory Manager) represented SNS, a company specializing in high-performance shared storage and complete workflow solutions. SNS also serves as the OEM manufacturer for Ross Video devices and works closely with the CHESA Federal team on a range of opportunities.

Telestream

Bob Barnshaw and engineer Dave Norman represented Telestream. As Jason Pepino noted to close out the sponsor introductions: “You’d be hard pressed to find a broadcast facility without something Telestream inside.” Their Prism Inspect platform was central to the monitoring discussion all evening. They also offer transcoding, test and measurement tools, and Stanza, their captioning application.

LucidLink

Rich Warren introduced LucidLink — a cloud-based storage collaboration platform that mounts as local, shared storage and is globally accessible. The short version: it puts everyone in the same studio, regardless of where they physically are.

ABOUT DCMUG

The DC Media Users Group holds quarterly events in the DC metro area, bringing together federal and commercial broadcast professionals to share what’s working and what isn’t. The format is deliberately practitioner-focused: not vendor pitches, but real conversations from people in the trenches.

Coming up: DCMUG will have a presence at NAB in Las Vegas, followed by an event alongside the Bits by the Bay Conference, held right on the Chesapeake. If you haven’t been to Bits by the Bay before, it’s worth looking into.

If you work in broadcast, media production, or AV integration in the DC metro area — whether in a federal agency, a commercial facility, or somewhere in between — this is a community worth being part of. The conversations are real, the people have done the work, and you’ll almost certainly walk out with something you can use.

Well, and there’s usually a sports game or concert involved. That doesn’t hurt either.

Tags 2110 migration, and federal broadcast engineering., broadcast cybersecurity, broadcast engineering, broadcast facility design, broadcast monitoring tools Broadcast IT engineer, broadcast network segmentation, DC Media Users Group, DCMUG, EVS Neuron, IP broadcast infrastructure, media production network, Monumental Sports Network, NDI vs 2110, PTP timing broadcast, SDI to IP migration, SMPTE 2110, Telestream Prism Inspect

Events & Trade Shows

When Machines Enter the Control Room

Post author By admin
Post date March 10, 2026
No Comments on When Machines Enter the Control Room

The final vendor panel at Chesafest 2026 saved its most time-sensitive question for last.

For the first three panels, AI was a topic you could discuss at a philosophical remove. The file system will evolve over years. MAM architectures will shift over decades. Human oversight in media operations will be negotiated gradually. But in live broadcast, there is no gradually. A shot is taken or it isn’t. A graphic fires or it doesn’t. Audio goes to air or it goes silent. The decisions happen in real time, and the consequences arrive just as fast.

Moderated by Jason “Pep” Pepino, Director of Media Systems Design and Engineering at CHESA, Panel 4 brought together representatives from LiveU, Vizrt, Netgear AV, and AI Media to answer a question that gets more urgent every year: should AI be allowed to make real-time production decisions inside a live control loop, or should it remain strictly advisory?

The answer, as it usually does, landed somewhere in the middle. But the journey to get there was worth the trip.

MEET THE PANEL

Chuck Davidson — Partner Account Manager, LiveU

Chuck describes himself as an optimist, with a “glass half full” orientation toward technology in general and AI in particular. LiveU’s work in the bonded IP and remote production space means AI decisions at the transmission layer carry real operational stakes, and Chuck brought that weight to the conversation while maintaining his characteristic forward-looking energy.

Dan Griffin — Territory Manager, Netgear AV

Dan’s background is in live production audio, which gives him a different instinct than most people in a broadcast technology conversation. He showed up to Chesafest as a self-described skeptic (his wife, who works in tech, had been actively working on his conversion) and moved visibly toward realist over the course of the discussion. His perspective on AI in network design and audio mixing was among the most practically grounded of the session.

Kyle Phillips — VP of Sales Enablement, AI Media

Kyle acknowledged upfront that his pro-AI position at a company called AI Media was not exactly a surprise. What he brought beyond the predictable enthusiasm was specificity: real deployment context for live caption automation, guardrails design, and the practical limits of what AI can handle when breaking news or live sports throws something unexpected at the system.

Steve Cooperman — Sales Manager, Vizrt

Steve came in as the panel’s pragmatist, with 20 years of experience across Panasonic, Canon, and now Vizrt spanning cameras, live production, and software. He’s seen enough real-world deployments to know where AI delivers and where it overpromises, and he wasn’t shy about either.

Jason “Pep” Pepino — Director of Media Systems Design and Engineering, CHESA (Moderator)

Pep opened by declaring himself an accelerationist, the most enthusiastic position on the AI spectrum, and framed the panel accordingly. He had just finished building CHESA’s first SMPTE 2110 studio and had personally entered thousands of IP addresses by hand. His enthusiasm for an AI agent that could do that work someday was, as he put it, “real.”

SHOULD AI MAKE REAL-TIME PRODUCTION DECISIONS?

Pep opened by laying out what AI can already do in a live production environment. It can identify key moments. It can select camera angles. It can trigger graphics automatically. It can translate and localize audio in real time and adjust levels based on speaker detection. The question isn’t capability anymore. It’s authority.

Should the AI decide, or should it advise?

Steve Cooperman came in with a real-world example that illustrated both sides of the question simultaneously. Vizrt’s Libra product brings sports analytics into live production, powering the kind of on-screen overlays that have become standard in sports broadcasts. But beyond data visualization, the platform also handles AI cutouts: cleanly separating a player from the background in real time, handling edge cases like dark uniforms on grass, enabling 3D effect automation without a compositor doing it by hand.

“AI is really helpful for that cutout, and then automating it. Of course, we could always override it. But that’s a real-world example of production applications that some sports productions are using today.”

The override option is the tell. Even in a case where the AI is clearly adding value, the ability to override it is treated as non-negotiable. The automation runs unless a human says otherwise.

Chuck Davidson framed LiveU’s approach as one of intentional flexibility. Their CTO’s current development focus is something called an AI connector, essentially a configurable entry point into the LiveU ecosystem that lets each customer define which AI agent they want to use and how much authority it gets. The premise: there is no universal right answer for how much AI authority is appropriate. It depends on the customer, the content, and what’s at stake.

“We can’t assume that everybody’s going to want to have the same parameters or the same mindset for how they want to integrate AI.”

Dan Griffin brought the audio mixing perspective, and it was one of the most honest assessments of the session. When it comes to managing microphone levels for a group of talking heads, he said flatly, machines can do it better than humans. They react faster. They don’t get fatigued. They don’t miss a cough. For that specific task, in that specific context, AI authority isn’t a philosophical question. It’s just more reliable.

But the right answer shifts dramatically when you change the context. Life-critical broadcasts, high-stakes live events, anything where a muted microphone could mean something goes out wrong or doesn’t go out at all: those require human readiness to intervene, even if AI is handling the moment-to-moment operation.

Kyle Phillips introduced a concept that became one of the most useful frameworks of the session: bounded autonomy. You define the space in which AI is allowed to act, and the machine operates confidently within that space. The boundaries are the human decision. The execution within them is the machine’s.

“You design what it’s able to do. When you can replace manual, repetitive tasks with AI, you get efficiency and speed. But you give it parameters. It can adjust levels a few decibels, but it can’t go from zero to twenty all at once.”

The design phase is where human judgment lives. The operational phase is where the machine works. Keeping those two things clearly separated is the architectural foundation of responsible AI deployment in live production.

WHEN AI FAILS: WHO’S RESPONSIBLE?

Pep shifted the conversation to accountability, and the panel’s first response was telling: someone immediately said “the systems integrator,” then immediately acknowledged they were not supposed to say that.

The laughter that followed said something real. In the live production chain, when something goes wrong, the question of who owns it is genuinely complicated.

Kyle Phillips was direct: if the AI is failing, it’s ultimately on the vendor. But the more important variable is how the system was designed and what parameters were set. You can’t blame the machine for operating within the boundaries someone gave it. The accountability traces back to whoever set those boundaries.

Chuck Davidson took a different angle. LiveU’s acquisition of Actus (a compliance monitoring platform) started making sense to him in this context in a new way during the panel. Actus was built for FCC compliance monitoring, essentially an automated oversight layer that watches what goes to air and flags violations. As AI takes on more production authority, a compliance layer like that becomes part of the answer to the accountability question. It’s governance infrastructure for an AI-driven environment.

Pep offered his own ground-level perspective: having just spent significant time manually entering IP addresses to configure CHESA’s 2110 studio, he’s acutely aware of how much room there is for AI to help with the configuration and commissioning process, and equally aware of how much human verification that work currently requires. The AI can assist. The engineer still has to verify.

GUARDRAILS: WHAT MUST EXIST IF AI IS IN THE CONTROL LOOP?

The panel’s final formal question was the most practical: if AI is operating inside the signal chain, what guardrails must be in place?

The consensus was rapid and clear: operator override is non-negotiable. Every panelist said some version of it.

Steve Cooperman used Vizrt’s gaze correction feature on the TriCaster as a live illustration. The feature automatically adjusts a speaker’s eye line to maintain direct-to-camera contact even when they’re looking down at a monitor. It works well most of the time. It does not work well when someone is moving erratically, and a malfunctioning gaze correction in the middle of a live broadcast creates a deeply unsettling viewer experience. The human has to be able to turn it off. Immediately. Without friction.

“You need a human, presumably, to be able to override or to monitor. If any technology goes bad, you want the ability to turn it off if it’s not working in that environment properly.”

Kyle Phillips described the guardrails in AI Media’s captioning deployments in specific terms. AI handles the placement of captions in real time, dynamically repositioning them so they don’t block on-screen text like lower thirds or score bugs. That’s clean, bounded automation. But then there’s the harder layer: topic models and content filters that prevent certain words from appearing in captions when a speaker has a particular accent or when a live sports moment generates unexpected language. Those filters need to be configurable, auditable, and human-adjustable in the moment.

Dan Griffin brought it back to the network layer. Netgear’s value-add includes free network design services. AI can help design a network much faster than doing it manually. But an engineer still puts eyes on every design before it goes to a customer. Not because the AI is unreliable, but because the stakes of a poorly configured live production network are too high to skip the human review, regardless of how good the last ten designs were.

“The thing to fear is getting too comfortable. You always have to look and make sure you’re monitoring what it’s doing and providing feedback as needed.”

Chuck Davidson closed the guardrails discussion with the framing that felt most honest about where the industry actually is: this is a change management problem as much as a technology problem. The resistance to AI in live production isn’t always about legitimate technical concerns. Sometimes it’s just that change is scary, and AI is a category of change that the industry has no prior template for. The tape-to-digital transition felt just as existential at the time. Many broadcasters refused to let go of physical tape long after digital was the better answer.

“Part of the challenge is that change is scary, and AI is a very powerful tool that this industry has never seen before. Part of our job on the technology side is how do we harness it and how do we manage it to eliminate the fear.”

WHERE ARE WE ON THE INNOVATION CURVE?

An audience question brought the session to its most forward-looking moment: where are we on the innovation curve for visual AI in live broadcast?

Pep didn’t hesitate: we’re at the very beginning. The capabilities visible today will look primitive compared to what five to ten years will produce. The panel agreed.

Dan Griffin noted that even the most basic AI research tools (looking up someone’s background before a meeting, pulling a bio from the web) were significantly worse just six months ago than they are now. The trajectory of improvement is steep. Broadcast-specific applications are more complex and more critical than general research tools, which means they’ll take longer to mature. But the same rate of improvement will get there.

Steve Cooperman pushed back slightly on “very beginning.” In live sports specifically, the volume of AI-driven sports tech visible on broadcast in the last year is roughly ten times what it was before. Not all of it is AI in the strict sense, but the category of computer-assisted production technology has exploded, and AI is a meaningful part of that acceleration.

Kyle Phillips connected this to the economics of linear broadcast. Traditional linear television is under revenue pressure, and that pressure is creating urgency around monetizing existing content in new ways. Old archives, up-rezzed to modern quality, localized for new markets, offered on emerging platforms (he pointed to retro TV services running on antenna signals with commercials as a surprisingly significant revenue generator) represent a category of AI-driven value that is very real, very current, and still early.

A voice from the audience painted one of the most vivid pictures of where this heads. Imagine taking old television series and not just up-resing them, but giving them new language tracks where the original actors’ voices are preserved but given the phonetic quality of the target language’s native speakers. Not a new voice actor. The original voice, delivered as if the original actor had learned to speak Hindi or Spanish natively. The legal questions around that are still being worked out. The technology to do it exists now.

Chuck Davidson offered the most memorable real-world deployment of the session: the NYPD’s Drones as First Responder program in New York City. Drones dispatched autonomously in response to 911 calls, giving officers visual intelligence on a scene before they arrive. Operational today. No lab demo. No pilot program. Running in the city.

“If you’ve never seen it, I would encourage you to watch it. It’s a great example of where we are from a technology perspective.”

It’s not a broadcast example. But it’s the clearest illustration of what bounded AI autonomy looks like when it works: a machine operating within carefully designed parameters, doing something faster and more effectively than any human alternative, with humans ready to act on what it finds.

THE THROUGH LINE ACROSS ALL FOUR PANELS

Across the four Chesafest vendor panels, the same idea surfaced in every room, in different forms and different vocabularies, and it’s worth naming it clearly.

The question was never really “AI or humans.” It was always “what do we want the humans to do?”

In storage and file systems, we want humans setting governance policy, not manually moving files. In MAM, we want humans defining taxonomy and verifying results, not logging 20-year-old archives by hand. In media operations, we want humans deciding what the work is and evaluating what comes out, not checking codec values on every ingest. In live production, we want humans making editorial decisions and ready to override, not manually adjusting audio levels for twelve talking heads who all speak at different volumes.

The machines are getting better at the things humans shouldn’t have to do. The work of the industry right now is figuring out exactly where that line is, drawing it deliberately, and building the guardrails to hold it.

That’s not a 2030 problem. It’s a now problem. And it was the right note to end Chesafest 2026 on.

ABOUT CHESAFEST

Chesafest is CHESA’s annual gathering of team members, technology partners, clients, and practitioners in the media, broadcast, and AV space. It blends the energy of a partner kickoff with substantive, practitioner-driven conversation about where the industry is actually headed.

Now in its 4th year, Chesafest has grown into something genuinely distinct: a program where CHESA’s team, its vendor partners, and its clients are all in the same room at the same time, participating in the same conversations. The panels are designed to surface real disagreement, real tradeoffs, and real-world insight. The 4th Annual Chesafest took place on February 25, 2026 in Towson, Maryland, drawing 19 vendor partners and a cross-section of CHESA’s client community.

The four vendor panels from Chesafest 2026:

Vendor Panel 1: Is the File System Dying? The Performance Tier in an Object-Native World

Featuring: Backblaze, LucidLink, Suite, and Spectra Logic | Moderated by Tom Kehn, CHESA

Vendor Panel 2: The Next Evolution of Media Asset Management: Is Structured Metadata Enough in the Age of Vector Intelligence?

Featuring: Backlight, Fonn Group, OrangeLogic, EditShare, and VIDA | With client perspective from Jason Patton, Sesame Workshop | Moderated by Felix Coats, CHESA

Vendor Panel 3: Automation, AI, and the Limits of Machine Decision-Making: Where Human Judgment Still Matters in Media Operations

Featuring: Telestream, Hiscale, HelmutUS, Adobe, and Scale Logic, with Jason Whetstone, CHESA | Moderated by Felix Coats, CHESA

Vendor Panel 4: When Machines Enter the Control Room: AI, Authority, and Real-Time Decision-Making in Live Production

Featuring: LiveU, Vizrt, Netgear AV, and AI Media | Moderated by Jason “Pep” Pepino, CHESA

This blog series covers each panel in depth. If the live production and AI authority conversation is in your world, the other sessions are worth your time too.

Tags AI Broadcast Monitoring, AI Captioning, AI Graphics Automation, AI in Broadcast, AI Live Production, Broadcast Automation, Broadcast Decision Systems, Control Room Automation, Live Broadcast Infrastructure, Real-Time Production AI

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Automation, AI, and the Limits of Machine Decision-Making

Post author By admin
Post date March 10, 2026
No Comments on Automation, AI, and the Limits of Machine Decision-Making

The third vendor panel at Chesafest 2026 started with a question that sounds deceptively simple: how much of what media operations teams do today will be done by machines by 2030?

The answers ranged from 70% to 99%. And the real conversation was everything in between.

Moderated again by Felix Coats of CHESA, Vendor Panel 3 brought together practitioners from Telestream, Adobe, HelmutUS, Hiscale, and Scale Logic, alongside CHESA’s own Jason Whetstone, for a conversation about automation, accountability, and the specific kinds of decisions that still need a human in the room. The panel covered everything from the philosophy of machine morality to a story about a guy downloading Python at the gym and submitting the output to his boss without checking a single line.

It was a good panel.

MEET THE PANEL

Scott Eik — Senior Application Engineer, Scale Logic

Scott has been in the industry for about 16 years, moving between MAM systems, archive systems, and the customer side. He joined Scale Logic at NAB the prior year and brought a grounded, operational perspective to every question.

Dave Helmly — Director of Professional Video and Audio, Adobe

Dave has been at Adobe for 30 years and leads a workflow strategy and development team of 22, the only team of its kind embedded in Adobe’s engineering organization. His philosophy: trust your customers to tell you how to make your software. He’s been working with CHESA for most of his time there.

Greg Holick — VP of Business and Channel Development, HelmutUS

Greg has been in the M&E industry for over 25 years, with deep experience helping large customers architect and orchestrate complex media workflows. He came in as the voice of measured optimism: enthusiastic about AI’s potential, clear-eyed about the things it still can’t do.

Sarah Semlear — US Sales Lead, Hiscale

Sarah came to Hiscale after spending time on the client side, deploying MAMs and transcode systems from the inside. She showed up at Chesafest the prior year as a client. She brought the most infectious energy to the panel and consistently redirected the conversation toward what matters: whether any of this is actually making work more fun.

Erik Zindulka — Senior Sales Engineer, Telestream

Erik spent eight or nine years as a Telestream customer before joining the company. He described himself as “the MAM nerd in some circles at Telestream” and brought a practitioner’s sensibility to questions about automation, enrichment, and where AI fits into workflows people are already building.

Jason Whetstone — Product Development Engineer, CHESA

Jason has been at CHESA for 12 years and in the media industry for close to 18. He brought a developer’s precision to the panel: focused on what “done” actually means, why AI needs humans to define the work, and what pair programming has to teach us about working with AI tools.

Felix Coats — Solutions Consultant, CHESA (Moderator)

Felix moderated his second panel of the day and, per his own admission, had prepared a full list of questions that the panelists proceeded to answer before he could ask them. He pivoted gracefully throughout and introduced the gym story that became the thread everyone kept pulling on.

BY 2030, WHAT PERCENTAGE OF MEDIA OPERATIONS WILL BE FULLY AUTOMATED?

Felix opened with a clean, direct question and asked each panelist to answer it honestly: by 2030, what percentage of media operations in your space will be fully automated?

The answers were telling.

Dave Helmly went first and went highest: 99%. His reasoning was precise. Adobe’s AI work, particularly with Firefly Services, is focused on productivity and batch automation (resizing, reformatting, localization across 400 output variants from a single source). The jobs nobody wants. A creative still starts the job, still reviews the rejections, still makes the final call. But the volume of mechanical work being handed to machines is already enormous, and it’s only going in one direction.

Scott Eik landed at 70 to 80%, acknowledging that some human interaction will persist but that the trend is unmistakably toward automation for the operational layer.

Greg Holick took a longer view and came in at 50 to 70%. His reasoning was rooted in what AI currently lacks: creative intent, cultural inference, the subtle judgment calls that define the difference between technically correct and actually good. He’s watched the industry’s AI capabilities grow and believes they’ll continue to grow, but maintains that the creative mind brings things to the table that can’t be encoded.

Sarah Semlear declined to give a number. Her answer was better than a number: if we want the future of media to be fun, there has to be human interaction. The machines should own the tedious, horrible tasks. The calculator analogy she returned to repeatedly was perfect: a calculator doesn’t replace the mathematician. It removes the arithmetic so the mathematician can think.

“Let the machines do the tedious, horrible tasks that we don’t want to do. Then we’re focusing on the really awesome, juicy, creative, fun stuff. That’s not Skynet. That’s a utopia.”

Erik Zindulka pointed out that the “extreme majority” of media operations tasks that AI is being asked to automate are things that customers have wanted machines to handle for years. A file lands in a folder. Twenty things should happen to it automatically. Nobody should be sitting in a cubicle checking the codec and moving it to the right directory. AI is the natural continuation of automation logic the industry has been building for decades.

Jason Whetstone offered the most structurally precise answer: as long as humans are creating content and consuming content, the system can never be fully automated, and shouldn’t be. The human role shifts, but it doesn’t disappear. The job becomes defining the work, being clear with the machines about what “done” means, and reducing the exceptions that fall outside the automation envelope.

“Our job as humans is determining what the work actually is and being very clear with the machines about what the work is and how we want it done.”

WHERE HUMAN JUDGMENT IS NON-NEGOTIABLE

Felix pushed the panel on a harder question: are there operational decisions that cannot safely be automated today? And will they ever be able to be?

Erik Zindulka surfaced a quote that became a reference point for the rest of the panel, a placard from an IBM training program from 1979 that read: “A computer cannot be held accountable, therefore it cannot make a management decision.”

That sentence from nearly 50 years ago maps almost perfectly onto the AI governance debate happening right now. Accountability is the line. Wherever a decision has legal consequences, creative stakes, or reputational exposure, a human needs to be in the chain, not because machines can’t generate an answer, but because machines can’t be held responsible for the answer they generate.

Sarah Semlear picked up the accountability thread with a specific point about morality. The industry often talks about training AI to be ethical or unbiased. But morality isn’t a universal constant. It varies by culture, country, context, and situation. You can’t hand a one-size-fits-all moral framework to an AI and consider the problem solved.

Greg Holick added the copyright and compliance dimension: AI in a media environment has access to enormous volumes of protected content. Should it? The legal exposure of an AI system pulling the wrong ad, using the wrong asset, or making a rights decision it can’t justify is enormous. And the entity that gets held responsible isn’t the machine.

Dave Helmly extended this into the personalization and content consumption space: AI is already learning individual users well enough to feed them content they’ll react to. By 2030, it will know users dramatically better than it does now. That creates an obligation on the human side to question what’s being surfaced, why, and whether the information environment being constructed serves the person or just the engagement metric.

Jason Whetstone brought it back to something clean and practical: the decision to publish. You can automate the upload. You can automate the metadata. But the decision to put content in front of an audience should require a human making a deliberate choice.

“The decision to actually publish to the public should be on a human.”

Dave Helmly also noted where compliance automation actually adds value: territory-specific edits, regional restrictions, content standards for different markets. These are the jobs that no one wants to do anyway, that currently require enormous manual effort, and where AI can do the work reliably because the rules are known and explicit.

Scott Eik grounded the whole discussion with a production operations lens: someone has to QC what comes out the back end before it goes to air or to print. That checkpoint is a human checkpoint. The question isn’t whether the QC role exists; it’s whether AI can support it by catching more before it reaches the human reviewer.

THE GYM STORY: LOW CODE, UNMANAGED RISK, AND THE GUY WHO SUBMITTED THE PYTHON SCRIPT

Felix opened the third segment with a story that generated more discussion than any formal question could have.

He overheard two finance professionals at the gym. One of them had been asked by his boss to produce some charts. He didn’t know how. He asked ChatGPT. ChatGPT told him to download Python. He asked how. ChatGPT told him. He installed it, ran the script, and submitted the output to his boss. His boss said great job. He was proud of himself.

Felix’s internal reaction was a list of questions he didn’t say out loud: Did you validate the code? Did you confirm it wasn’t also accessing your financial records from the last decade? Did you check what it was touching?

This is the low code moment the industry is living in right now. The tools have gotten accessible enough that people with no technical background are generating and running code that touches real systems and real data. The gap between capability and comprehension has never been wider.

Scott Eik was direct: you have unmanaged risk the moment you don’t understand what’s happening in the background. And when something goes wrong, the person who ran the script without understanding it is not equipped to diagnose or fix it.

Dave Helmly raised the IP dimension: code generated by AI may have been derived from copyrighted source material. If you don’t know math, you can’t validate the logic. If you don’t know code, you can’t validate its origins. The people who are safe in this environment, he argued, are the ones with 10,000 hours in their specialty. They’re the ones qualified to judge what the AI produced.

Greg Holick brought it back to responsibility: automation and AI are extraordinary productivity tools, but they change who’s responsible for the outcome. The ownership lands on the person who ran the process. If you deployed code that touched data you shouldn’t have touched, the fact that an AI wrote it doesn’t reduce your exposure.

“Just because you can do it doesn’t mean you should. Automation and AI change your responsibility. The ownership is still on the person doing that.”

Sarah Semlear offered the most optimistic frame. She compared the current moment to the early days of YouTube, when traditional media companies were horrified by the chaos of user-generated video flooding the internet. People posting content they shouldn’t, no standards, no guardrails. It looked like a disaster. It became an industry. The wild west always calms down.

“Everything always calms down. It’ll be fine. We’ll get to the place where it’s actually that super powerful calculator we really need.”

Erik Zindulka pushed toward the practical design goal: the end state for low code in a media environment isn’t Python scripts generated in a gym. It’s a visual workflow builder where an operator draws a flowchart, describes the production logic they want, and the system handles the execution. Bring-your-own-code for edge cases, yes. But the default should be intuitive enough that nobody has to think about scripting at all.

Jason Whetstone added the concept of AI context: an AI system is results-driven and will generate an answer as fast as possible, even if it doesn’t have all the information it needs to get the right answer. If it’s missing context, it guesses. That’s where the human has to step in: not to do the work, but to be clear about what the work actually is.

He described two models of working with AI tools. The substitutive model: you outsource a task to AI and don’t particularly care how it gets done. The assistive model, which he prefers, is pair programming. Two people working shoulder to shoulder through a problem, each learning from the other. You understand the problem. The AI understands aspects of the code you don’t. You teach each other. The outcome is better because both parties are engaged in the process.

“I have to help the AI understand what the problem is that I’m trying to solve, what I’m not trying to solve, what good results look like, what success means, and what done means.”

THE FUTURE OF HUMAN OVERSIGHT: AI MONITORING AI?

Felix closed the formal portion of the session with a question about where human oversight goes as AI-native workflows mature. Do you create new roles to supervise AI output? Do you build AI to monitor AI? Or does the oversight layer gradually get automated away too?

The panel converged on a few consistent positions.

Scott Eik: in the near term, you want humans checking everything that comes out of AI. As trust is established over time, that check can become more targeted and less constant. The progression is gradual. You don’t just flip a switch.

Dave Helmly: AI is going to take some jobs. Photoshop took jobs too. But Photoshop created entirely new categories of work. The pattern holds. The people who lose jobs will be the ones who tried to use AI as a shortcut without understanding the underlying craft. The ones who keep their jobs, and build new ones, will be the ones who can judge what the machine produced.

Sarah Semlear: you don’t need to reinvent the wheel. The organizations that respond to AI by blowing up their org charts and starting over are making the same mistake people make with every major technology shift. Find the efficiencies. Add the roles where they’re needed. Check your sources, which is not a new skill requirement. Keep humans in the loop and keep it interesting.

“If you just take a base answer of anything and you don’t look into it, if you Google one thing and go with the first result, you should probably be fired for that too. This is not something new in humanity.”

Erik Zindulka offered one of the most forward-looking points of the session: AI enrichment isn’t a one-time event. Archives and libraries persist for decades. An archive enriched by one AI tool today will be enriched again five years from now by a better one. And again five years after that. Each pass adds another layer of metadata, another dimension of searchability, another tier of context. The result, over time, is a media archive richer than anything that could have been produced by human logging alone.

Greg Holick closed with a framing that landed well: AI changes the shape of human responsibility, but not its existence. Someone still has to set up the guardrails. Someone still has to evaluate what comes out. The pre-checking that happens before automation runs may matter as much as the post-checking that happens after.

Felix added one more thought before closing: the industry might start seeing something like a “production AI supervisor,” a new role whose job is specifically to QC AI output before it hits a downstream system or a human audience. Not a developer. Not a traditional post supervisor. Something in between. It’s not here yet, but the logic is sound.

A CLOSING QUESTION FROM THE CEO

As the session wound down, Jason Paquin stepped in with one last question for the group: what guidance do you have for someone building their career in this space right now?

It was a good question to end on, and Nina Smith gave the best answer.

She said that the greatest gift you can give anyone you’re talking to is the ability to truly listen. Not to have the answer ready before the question is finished. Not to perform expertise before you’ve understood the problem. Holding back, listening, and offering real perspective when you actually have something to contribute will take you further than sounding smart ever will.

“Know who you’re dealing with. If someone wants to talk fluff, talk fluff. If someone wants to talk truth, talk truth. You will go much further by listening and learning and offering your advice when you really know something, not when you’re guessing.”

That’s good advice in any era. In an industry moving as fast as this one, it’s essential.