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NEWSCASTSTUDIO.COM
By NARAYANAN RAJAN
CEO, Media Excel
Historically, organizations that resist
disruptive technologies face a difficult path
forward. While early adopters have the
advantage of building foundational skills,
late adopters often scramble to catch up,
risking their market position. Artificial in-
telligence (AI) is particularly disruptive
because it’s a broad enabler, affecting ev-
erything from software development to
how society will function in the future. The
media and entertainment industry, with its
ever-growing demand for high-quality per-
sonalized content and never-ending cost
pressures, is an early adopter of genera-
tive AI and is now benefiting from general
AI-driven innovation across the distribu-
tion and operational spheres.
For media companies, the question has
shifted from whether to integrate AI to how
to do so effectively. Those who navigate
this transition successfully will streamline
operations and unlock new opportunities
for innovation and growth.
The technology adoption cycle:
From innovation to disruption
As AI matures, it follows a well-known
technology adoption cycle, moving from
product and solution innovation to indus-
try-wide disruption. Pioneers in the media
and entertainment industry have already
begun integrating AI and machine learning
(ML) into their workflows.
Examples of where the technology is
being used to enhance efficiency, person-
alization, and creativity are growing. Net-
flix’s AI-driven recommendation engine,
for instance, personalizes user experienc-
es to boost engagement. In addition, Net-
flix leverages AI to create compelling pre-
views for their content, identifying which
combination of highlights is most likely to
create viewer engagement. Spotify’s AI DJ
curates personalized playlists, blending
data with creativity. Spotify also uses AI
to give their “synthetic” DJ a human voice,
with the ability to change tone, accent and
gender to create greater resonance with
their subscribers. Microsoft’s Azure plat-
form offers AI-based content
moderation tools, and Azure’s
Video Indexer uses AI to ana-
lyze content and enrich the as-
sociated metadata. Freewheel
has developed AI-powered ad
insertion and targeting technol-
ogies, to increase the efficiency
of ad monetization.
These are just a few exam-
ples of how AI is quietly revo-
lutionizing the industry, and the
use cases will only continue to
grow. For media companies fac-
ing this wave of AI offerings, the real chal-
lenge isn’t whether AI can help, but how to
choose the right tools and strategies for
their needs.
Evaluating a use case:
Where to begin
The first step in integrating AI into me-
dia workflows is understanding the or-
ganization’s readiness to adopt an AI use
case. AI solutions with broad based impli-
cations, touching multiple organizational
functions and data sources, will require
complex evaluation models
to ensure all aspects of the
business are considered. Far
easier to digest are AI solu-
tions that are narrower in
scope – improving encoder
performance for example –
requiring simpler evaluation
models to determine if they
are a fit for the organization’s
needs.
The second step in deter-
mining a good use case is
to define a desired outcome
that is measurable through
clear operational KPIs. Typically, this is
clearly tied to increasing efficiency (reduc-
ing cost), improving customer experience
(reducing churn and increasing engage-
ment), or driving additional revenue.
The third step is to evaluate the robust-
ness of the available solutions and deter-
mine the threshold of performance that
would define a successful outcome for a
given use case. In most cases, the organi-
Key strategies to consider when
implementing AI, machine learning
STRATEGY
RAJAN
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