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aimed at a diverse demographic is going to
certainly add speed and scale to the busi-
ness model.
Jordan Thomas, marketing manager,
QuickLink: For broadcasters and produc-
tion organizations, we have seen an inno-
vative approach in which manufacturers
and solution providers are utilizing these
AI advancements and applying them to
both existing and new solutions. These
advancements are not only streamlining
workflows but also allowing us to elevate
video and audio quality. The ability to re-
move video artifacts, correct eye con-
tact, and automatically frame the shot of
remote guests is revolutionary when it
comes to creating high-quality content
that engages audiences.
Costa Nikols, strategy advisor, media
and entertainment, Telos Alliance: In au-
dio, AI is unlocking new creative options
and helping make the unmanageable more
manageable — from improving sound clari-
ty in challenging environments to enhanc-
ing dialogue normalization at scale for
global audiences. These advancements
can reduce the manual workload for pro-
duction teams, enabling them to focus on
storytelling and creative processes rather
than the mundane. Automating the mun-
danity is where AI thrives — and where it
can deliver most impact today.
Sam Bogoch, CEO, Axle AI: AI has ma-
tured into a critical tool for broadcasters,
enabling real-time applications such as
scene understanding with semantic search,
automated tagging, speech-to-text tran-
scription, and metadata generation. These
advancements simplify media asset man-
agement, streamline workflows, and en-
hance production speed, allowing teams to
deliver high-quality content faster.
Noa Magrisso, AI developer, TAG Video
Systems: For broadcasters, this means ac-
cess to tools that automate captioning, en-
hance audience analytics, and streamline
video editing. AI agents are revolutioniz-
ing workflows by autonomously managing
tasks like scheduling, content tagging, and
even real-time audience interactions. The
rise of multimodal AI is also a game-chang-
er, enabling seamless integration of text,
images, and audio within a single model.
Simon Parkinson, managing director,
Dot Group: Within broadcasting, there are
many competitive advantages that AI can
help businesses to realize, be it through
video editing, content generation, or auto-
mating industry-agnostic challenges that
free up employees to work on being cre-
ative. The possibilities are endless.
How is AI actively being used in broadcast
production workflows? In real applications,
not just as a proof of concept?
Peyton Thomas: AI is being used in
auto-tracking/auto-framing of robotics
and robotic cameras. During the election
broadcast we saw AI being used to trig-
ger graphics via voice prompts. AI is trig-
gering back-end automation to encode
and tag data during and after a produc-
tion is complete.
Yang Cai, CEO and president, Visua-
lOn: AI is actively used in broadcast pro-
duction workflows to enhance efciency
and quality. It automates repetitive tasks
like transcription, metadata tagging, and
content indexing, significantly speed-
ing up production timelines. Addition-
ally, AI-driven tools optimize live video
streams by increasing compression ra-
tio through technologies such as con-
tent-adaptive encoding, enable real-time
language translation, and improve visual
quality through upscaling, color correc-
tion, and noise reduction.
Bob Caniglia: AI is being actively utilized
to enhance efciency and simplify complex
tasks. For example, by using smart reframe
for social media, broadcasters can easily
create square or vertical versions of their
footage for Instagram and other apps, with
AI technology automatically identifying ac-
tion and repositioning the image inside a
new frame so the team doesn’t have to do
it manually. Additionally, there’s real-world
applications of AI-powered facial recogni-
tion that streamline footage organization by
sorting clips based on people in the shot.
Steve Taylor, chief product and technol-
ogy ofcer, Vizrt: From a Vizrt perspective,
we have been using AI ML for a long time as
a key advantage for our sports and graph-
ics solutions. This includes to support color
keying on any background, without the need
for a green screen. AI ML have also been
used at Vizrt to make augmented reality and
virtual reality more realistic, as well as to
quickly process live sports content to iden-
tify players.
Sam Bogoch: Our company has seen mul-
tiple real-world uses of our Axle AI Tags plat-
form, ranging from large national broadcast-
ers using AI (including RTM, in Malaysia) to
make their news content searchable, to Hol-
lywood promo houses (including MOCEAN,
in Hollywood) using AI to sift through the
massive amount of dailies footage they re-
ceive. In both these cases, AI makes it prac-
tical to search the large amount of relevant
footage for the first time.
Beyond real-world implementation, what is
likely next to use AI or ML?
Stefan Lederer, CEO and co-founder,
Bitmovin: Something we’re exploring and
developing is an AI-powered solution that
translates American Sign Language (ASL)
text into client-side sign-language signing
avatars. Currently, this is strictly an innova-
tion piece that we’re collaborating with the
deaf community on to understand how and
if the technology could help make video
streaming more inclusive. Beyond that, I ex-
pect companies to explore diferent ways to
make content more accessible for all view-
ers. For example, AI could be used to ana-
lyze video content and narrate key visual el-
ements, such as facial expressions, settings,
and actions, in real-time, which will help to
automate the creation of audio descriptions
for visually impaired viewers.
Steve Taylor: The use of AI to auto gen-
erate subtitles and captions, as well as to
translate languages is definitely an area
that is growing. This is also true for AI’s
use in identify workflow optimizations,
through studio automation. In a produc-
tion environment, it can optimize work-
flow by automating repetitive tasks, en-
abling the team to confidently focus on
other areas of the production.
Noa Magrisso: The next phase of AI
and ML involves advancing collaboration,
personalizing content, and seamlessly le-
veraging multimodal AI to integrate text,
images, and audio. Emerging applications
include adaptive learning tools, healthcare
diagnostics, and immersive media experi-
ences.
How can emerging technologies improve
efciency in news gathering and reporting?
Siddarth Gupta: Emerging technologies
let reporters quickly filter vast data sets
to help them pinpoint the most relevant
information. Automated tools help reduce
Continued from previous page
tedious tasks such as transcription, trans-
lation, and summarization. This not only
speeds up production but allows news
teams to focus more deeply on research
and improve accuracy and turnaround
time.
Bob Caniglia: Innovative AI-driven tech-
nologies are driving greater efciency in
news gathering and reporting by automat-
ing repetitive tasks and optimizing work-
flows. AI tools, like automatic transcrip-
tion and smart sorting, enable journalists
to manage content faster and improve ac-
curacy under tight deadlines. This allows
news teams to dedicate more time to in-
depth reporting and delivering compelling
stories to their audiences.
What are the potential challenges of
integrating AI in newsroom workflows?
Peyton Thomas: While many may ar-
gue that integrating AI in the newsroom
removes human-manned jobs, I believe it
can be used to automate repetitive tasks
while creating an opportunity for end-us-
ers to be more creative and try things they
haven’t been able to do before.
Yang Cai: From a newsroom perspec-
tive, integrating AI can be challenging due
to concerns about maintaining journalis-
tic integrity and ensuring the accuracy of
AI-generated content. Compatibility with
existing newsroom systems and work-
flows may require significant technical
adjustments. There’s also apprehension
among journalists about balancing auto-
mation with editorial oversight and pre-
serving the human element in reporting.
Jordan Thomas: Adopting AI driven
technology within newsroom workflows
requires overcoming resistance to change
and ensuring seamless integration with
existing systems. Another fundamental
challenge is addressing ethical use of AI
and ensuring that it is not misleading view-
ers. This is particularly the case when it
comes to video and audio content that may
be altered by AI tools.
Steve Taylor: There are certainly two big
challenges that we hear about a lot. One is
the trust factor for the content workflow
— the question of whether information is
coming from a legitimate source or if it
was generated by AI? Second is whether
the output of AI is breaking any copyright
or licensing contracts, such that it is not
legally seen as new content owned by the
person who requested AI to generate it.
This this will keep lawyers busy for a long
time!
Sam Bogoch: Challenges include adapt-
ing legacy systems to integrate with AI
tools, although increasingly the AI tools
can catalog existing media and storage
repositories (both on-premise and cloud).
Training staf to take full advantage of rap-
idly-evolving AI capabilities is also critical;
even the best technical solutions have lim-
ited value if there isn’t buy-in and adoption
from the wider team.
What are the biggest barriers to adopting
AI in broadcast production?
Siddarth Gupta: Adopting AI in broad-
cast production often requires extensive
infrastructure and specialized talent, both
of which drive up implementation costs.
Models trained on limited or non-rep-
resentative data can often struggle with
real-time scenarios, leading to out-of-dis-
tribution (OOD) errors. These compound-
ing technical and financial hurdles have
forced broadcasters to rigorously scruti-
nize and justify their potential ROI before
committing to AI implementation.
Yang Cai: The biggest barriers to adopt-
ing AI in broadcast production include
high implementation costs, the complexity
of integrating AI with existing workflows,
and a lack of technical expertise among
staf. Additionally, concerns about data pri-
vacy, reliability, and resistance to change
within organizations can hinder adoption.
Overcoming these challenges requires in-
vestment in training, infrastructure, and
building trust in AI solutions.
Kathy Klinger: Ensuring quality and au-
thenticity remains a challenge, as AI lacks
the nuanced understanding and emotional
depth of human creators. Ethical and legal
concerns, including intellectual property,
data privacy, and bias, further complicate
its adoption, particularly in news and fact-
based content. To navigate these issues,
the industry must balance AI’s efciency
with human creativity, establish responsi-
ble frameworks, and uphold transparency
to maintain trust and content efcacy.
Jordan Thomas: Often, a lack of tech-
nical expertise and concerns about job
displacement may hinder full-scale adop-
tion, however, this can be overcome by
preparing and providing insightful training
to workforces. One misconception is often
the barrier of cost and complexity of inte-
grating AI-driven tools. However, this isn’t
always the case. Solutions like QuickLink
StudioEdge utilizes AI-technology pow-
ered by Nvidia to enhance video and au-
dio quality of remote guest contributions,
ofered at no additional cost, and can be
seamlessly integrated into workflows.
Ken Kobayashi, business manager,
Sony Electronics: One of the biggest bar-
riers in camera operation is the “skills
transfer.” Customers already have their
own established or inherited skills, and
sometimes they don’t want to use auto-
mated features such as auto-focusing. If AI
cameras have room to train or implement
customer’s skills about PTZ speed/fram-
ing etc. through deep-learning algorithms
in the future, they would be more widely
used in broadcast production.
What role does AI play in improving live
event production and broadcasting?
Yang Cai: AI enhances live event pro-
duction and broadcasting by enabling re-
al-time analytics, automated camera con-
trol, and intelligent content curation. It
improves viewer experience with features
like real-time language translation, per-
sonalized recommendations, and adaptive
bitrate streaming. AI also assists in de-
tecting and correcting errors during live
broadcasts, ensuring seamless delivery
and high-quality output.
Kathy Klinger: AI enhances live event
production and broadcasting by optimiz-
ing workflows and enabling real-time ad-
justments to improve both quality and
efciency. It can automate tasks such as
camera switching, highlight detection, and
audience analytics, allowing production
teams to focus on creativity and storytell-
ing. This combination of automation and
insight elevates the viewing experience
and ensures events reach audiences with
greater impact.
Zeenal Thakare: Broadcasters and live
event productions are going to focus on
creating more refined and engaging con-
tent. What that means is faster reaction
times during live events, as well as im-
mersive and interactive experiences. AI
is helping push the boundaries in the art
For broadcasters, this
means access to tools
that automate captioning,
enhance audience
analytics and streamline
video editing.
Continued on next page
Adopting AI in broadcast
production often requires
extensive infrastructure
and specialized talent,
both of which drive up
implementation costs.