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zation is not building and training its own
AI models, but leveraging third-party AI
models through APIs, and exercising that
model on its own dataset or content. Hav-
ing a clear understanding of the range of
potential outcomes using easier-to-assess
metrics such as visible quality, time taken
to deliver assets, or even factors like band-
width utilisation, will help qualify valuable
use cases and help avoid disappointment.
An example evaluation model
for AI integration
There are a number of factors that need
to be considered when evaluating an AI
solution for your organization. To illus-
trate the point, below is an example eval-
uation model for assessing whether or not
to leverage AI in an encoding workflow.
This evaluation model looks at five key
factors:
1. Total cost of ownership (TCO)
AI incorporated into encoding will save
distribution bandwidth but will come with
the cost of additional compute resources
and potential software licensing costs. A
good evaluation model will consider not
only the savings in distribution bandwidth
cost, but also the additional cost of infra-
structure to manage the increased compu-
tational load. Having a benchmark of costs
for an existing process based on a few sim-
ple metrics such as ‘time taken’ or ‘asset
processed’ to compare to an AI workflow
can help with TCO calculations. But al-
ways remember: AI processes go through
both model and workflow improvements
that tend to provide incremental benefits
through subsequent versions, so TCO is an
evolving calculation.
2. End user impact
Additional AI-based processing may in-
troduce latency into the encoding work-
flow. If the AI solution introduces multiple
seconds of latency for a live stream, the
impact on viewer experience may mate-
rially impact the business. In some cases,
the AI solution may also have impacts on
the client side, which may not be accept-
able. A good evaluation model will consid-
er all end-user impacts in implementing
the solution and have a clear threshold for
acceptable performance.
3. Operational impact
Any impacts on the day-to-day opera-
tions should be well understood. Is there
additional monitoring required to ensure
sustained performance of bandwidth sav-
ings and/or picture quality? Do staff need
to be re-trained to understand any new
performance metrics, configurations and
settings? Are there sustainability impli-
cations that need to be evaluated against
the organization’s ESG initiatives due to
increased power consumption?
4. Systemic risks
Are there other systems in the video en-
coding and distribution workflow that also
use automation and/or AI? Are the end-
to-end system risks well understood to
mitigate any business-impacting events?
Could there be potential cascading effects
of a malfunctioning system feeding into
another AI-enabled system, and are the
current failsafes and redundancies suffi-
cient? Running workflows initially in test
and development environments as well as
simulating failures is a great way to under-
stand how failsafes and redundancy fare
ahead of production deployment.
5. Ethical and privacy considerations
Ethical
and
privacy
considerations
should always be part of every evaluation
model. Can the system alter the content in
any way? Is there any possibility that the
AI-powered system could touch customer
data? For example, there could be AI-en-
abled encoding systems that have built-
in mechanisms for automated language
dubbing or in-frame brand detection and
replacement for monetization purposes.
Ensuring appropriate controls and per-
missions to preserve content owner/cre-
ator rights is critical.
Piloting the use case
Once a use case is selected, develop
gradual modes of introduction into the
organization. Constrain the initial imple-
mentation so the implications to the or-
ganization are well understood, as well
as the potential for achieving the desired
outcomes.
Media companies like the BBC have
successfully adopted this approach, pilot-
ing multiple AI-driven initiatives in limited
internal settings. For example, content
personalization features were launched in
controlled settings before deploying them
to a wider audience. The BBC also ensures
that all initiatives are governed by core
principles, which inform their own inter-
nal evaluation models.
It is also useful to consider scenarios
where the system may perform very well
as a pilot but run into significant problems
at scale. Define potential issues that may
affect scaling your AI-enabled solutions as
part of the evaluation model and consider
if rollback mechanisms may be needed.
Positioning for success
in a changing landscape
AI is not just a tool — it’s fast becoming
a strategic imperative for the media and
entertainment industry. By adopting a me-
thodical approach — starting with clearly
defined use cases, supported by robust
evaluation frameworks, and conducting
thoroughly tested pilots in controlled en-
vironments — media companies can lever-
age AI to drive both efficiency and innova-
tion.
From early adopters, it’s clear that AI
isn’t a one-size-fits-all solution. Companies
that excel in harnessing AI are those with
a deep understanding of media workflows,
technical applications, and industry pain
points. These pioneers are best equipped
to utilize AI effectively, customizing its ca-
pabilities to their specific needs.
Another key takeaway is the low cost
of experimentation. By running pilots in
parallel or within non-production environ-
ments, companies can explore AI’s poten-
tial without disrupting ongoing operations.
Crucially, this trial-and-error process not
only fine-tunes AI implementations but
also develops critical internal AI literacy
that will drive long-term value.
Make no mistake — AI is already trans-
forming the industry. A 2023 Gartner poll
of more than 1,400 executive leaders re-
vealed that 45% are piloting generative AI
solutions, and 10% have already deployed
them in production. This is a sharp rise
from just 15% piloting and 4% in produc-
tion the previous year, underscoring the
urgency with which companies are em-
bracing AI to stay competitive.
As the digital landscape rapidly evolves,
those who act now to explore AI’s possibil-
ities, while building the foundational skills
and strategies, will be best positioned
to unlock new growth opportunities and
deepen audience engagement. AI isn’t just
the future — it’s the key to staying ahead in
a fast-changing world.
With over 20 years of executive
experience in the media and telecoms
space including TandbergTV, Ericsson,
and Mediakind, Narayanan Rajan has led
transformation and integration initiatives
in engineering and operations roles across
multiple organizations. As CEO of Media
Excel, he now leads an organization
developing cutting edge technology for
encoding and transcoding, including AI
based enhancements to improve encoding
performance.
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