Mumbai: The FICCI EY Report conducted a survey of Indian M&E CXOs in December 2025.
AI is already in production
Indian media firms are increasingly using AI, particularly for content creation and processing, with 60% of surveyed companies having adopted it. With 75% of respondents noting a measurable impact, AI has transitioned from experimentation to delivering concrete business value.
67% of respondents reported cost reduction as the key benefit of implementing AI. AI budgets remain meaningful but conservative for most firms, with 46% of respondents allocating less than 5% of their IT budget to AI. As a result, spend is focused on near-term, high confidence use cases while organizations build stronger data, capabilities, operating models and ROI evidence before they plan to scale.
AI adoption is strongest in core content workflows, with content creation and content processing leading adoption at 60% of respondents. Core implementation was around standardised workflows, repetitive tasks and multi language requirements that made automation valuable and efficient, resulting in faster turnaround times and low-mid production cost reduction opportunities.
AI is fast extending across the content lifecycle, with: 47% of respondents reported AI usage in content planning and greenlighting 40% in distribution and marketing Personalization, product feature enhancement and software development life cycle were also highlighted as areas where organisations have deployed AI in a meaningful way
Results are mixed:
AI has moved past experimentation and is beginning to deliver tangible business value, with most respondents (75%) indicating measurable impact. Value realisation is currently concentrated in modest performance improvements, with a significant share of respondents (59%) indicating improvements in the up to 10% range. Just 16% of respondents saw clear benefits as of now.
Efficiency and speed are driving early value realisation:
Efficiency outcomes dominate measurable value, with 67% of respondents reporting cost reduction, 47% citing higher content throughput, and 40% noting faster time-to-market.
Limited monetisation impact across ad sales, engagement and marketing ROI could suggest that commercial value will emerge only as organisations move from isolated pilots to more scaled, data-rich use cases that link content efficiency gains to revenue-facing functions
Budgetary caution prevails despite strategic intent:
AI budgets remain meaningful but conservative for most firms, with 46% of respondents allocating less than 5% of their IT budget to AI and 40% allocating between 5% and 15%. This signals that most firms are taking a measured investment approach, allocating limited AI budgets while they assess nighttime value and strengthen the foundations needed for broader deployment.
A significant cohort of 14% has committed aggressively towards allocating more than 15% of their IT budget to AI, primarily driven by mediatech companies that view AI as central to their competitive strategy. Investment is largely directed towards high confidence use cases while organisations build the necessary data infrastructure to support largescale deployment.
Shape of the future
Content operations will become the most experimented and prioritized area for AI enablement within media organisations, as 80% of respondents continue to believe it still has not been efficiently automated. Priorities for the year ahead will center on enabling AI in core workflows, with firms focussing investment on content creation, operations and distribution to unlock repeatable, scalable value.
The primary bottleneck to scaling AI investments will be the lack of clear use-case prioritisation, which 60% of respondents cite as the leading constraint.
Significant unrealised potential exists in value-chain automation:
‘Content operations’ is the most experimented and prioritized area for AI enablement within media organisations. However, it remains largely untapped due to fragmented data, an underdeveloped vendor ecosystem, difficulty in mapping outcomes to ROI, and ongoing talent and bandwidth constraints.
Scaling will remain a significant gap even where AI adoption exists, with 60% of respondents noting that multi-language content generation at scale remains a challenge due to quality and consistency issues. Almost half the respondents (47%) flagged real-time ad-decisioning and predictive content performance as high-potential but unaddressed areas, indicating that real-time and predictive use cases will remain complex and difficult to implement.
Future priorities are shifting from pilots to industrialized operations: Priorities for the year ahead will center on enabling AI in core workflows, with firms focussing investment on content creation, operations and distribution to unlock repeatable, scalable value.
60% of respondents aim to prioritise subscriber growth and engagement, signaling a strategic move beyond content pilots into end-to-end execution and monetisation levers such as personalization, churn prediction and commerce.
Respondents plan to focus on AI-led editing, post-production, promo generation and metadata tagging as key operational priorities for the next 12 months.
Strategic ambiguity and data readiness are impeding scale: The primary bottleneck to scaling will be the lack of clear use-case prioritisation, cited by 60% of respondents as the leading constraint. It signals that organisations have limited strategic, governance, and cross-team alignment required to turn AI from experimentation into enterprise value.
Inability to track ROI, talent issues and poor data quality were cited by a majority of respondents as other issues that could impact their AI scale-up. Although advancements in AI models are reducing many technological infrastructure and data-related challenges, the absence of a clear roadmap aligned to future needs and capabilities is leaving teams directionless, resulting in scattered pilots and POCs across numerous AI use cases rather than a focussed, prioritised strategy.
