The Enterprise AI Guide for Media and Entertainment
Leveraging artificial intelligence for digital transformation, big data, and customer engagement
This guide offers practical insight for media and entertainment leaders seeking to understand how enterprise AI solutions can drive profitable revenue and improve efficiency. Regardless of your technical proficiency, this material is relevant to domains involved in revenue management or optimization.
- Customer experience
- Product development
The State of Artificial Intelligence in Media and Entertainment
Anyone working in media and entertainment is familiar with the hype around artificial intelligence (AI). In fact, 75% of C-suite executives fear going out of business entirely if they don’t scale AI in the next five years.
The challenge facing organizations today is to close the gap between aspirational AI and effective implementation — to move beyond the talk, break from the status quo, and pioneer AI solutions that boost the bottom line.
Of executives fear going out of business if they don't scale AI within next 5 years
True digital transformation is a practice, not a concept. Media and entertainment organizations possess a wealth of data that can drive customer engagement, but often lack the tools to take advantage of it.
There is very little actionable guidance on how to implement and scale promising AI technologies in the media and entertainment business. This guide provides the information you need to understand what enterprise AI can do and how to leverage it in your organization.
The imperative is clear: Media and entertainment leaders must better understand how to integrate forward-looking AI technology into their operations to compete.
Nobody knows exactly what the coming years will bring in the changing world of streaming media and digital entertainment. But you can be sure that the companies with the greatest data intelligence capabilities will be able to navigate the future from a position of strength.
What is Enterprise Artificial Intelligence (AI)?
In a business setting, AI describes the ability of a computer system to simulate human intelligence at scale. Definitions of AI vary by context; the term is used to describe a branch of computer science, a theoretical concept, technological systems, and more.
In this guide, the terms “artificial intelligence” and “AI” refer to the business definition of AI.
Modern AI technology has been programmed or “trained” by humans to solve increasingly complex problems with precision, speed, and autonomy. Artificial intelligence systems today can also teach themselves to solve problems with machine learning.
Artificial intelligence is a class of technology. “Enterprise AI” is the commercial application of that technology.
Enterprise AI refers to products (software, cloud platforms, applications) that harness AI technology for business purposes. The core functions of enterprise AI are to analyze, understand, and learn from data.
On a high-stakes level, businesses use enterprise AI to grow revenue, expand profit margins, and reduce cost.
In media and entertainment, enterprise AI functionality runs the gamut of industry-specific tasks: auditing agreements, analyzing subscriber behavior, measuring content interactions. Enterprise AI is a means to an end. The true value of enterprise AI is its output: insight derived from data that is used to drive better business performance.
What is Machine Learning (ML)?
The term “machine learning” (ML) causes some confusion. Is machine learning different from artificial intelligence? How well do non-technical leaders need to understand it?
Put simply, machine learning is an artificial intelligence technique: the ability of an AI system to learn from data autonomously, thus self-improving its algorithms. For example, a ML-capable enterprise AI solution can learn to better identify signals that indicate a free trialist’s intent to convert to a paid subscription, without necessarily being told (programmed) what those signals are or where to find them.
Most enterprise AI solutions possess machine learning capabilities. As AI-powered data intelligence becomes integral to more and more business functions, it is important for non-technical leaders to understand the basic capabilities and applications of machine learning.
There are two primary machine learning techniques: supervised and unsupervised.
Supervised ML involves humans teaching machines through models based on existing knowledge. Unsupervised ML is when AI develops models without human prompting.
Suppose a business wants to utilize machine learning to address customer churn. Their AI system must develop a model to predict churn risk. In a supervised ML model, humans label a historical customer data set (“training data”) to indicate who churned and who did not churn. The AI system ingests the training data and learns which attributes correlate to churn. It builds a predictive model based on that information, then tests the model’s accuracy on an unlabeled data set (“test data”). Once the algorithm is validated, the machine independently applies the model to new data sets going forward.
But human capacity to formulate hypotheses based on known information can constrain insight. What if there are churn risk factors that haven’t been identified yet? Unsupervised ML-capable AI systems detect unspecified correlations, unexpected patterns, and unknown anomalies in messy data.
To continue the churn risk example, an unsupervised ML-capable solution can build and continuously improve its predictive model without human input. Rather than being programmed to predict churn based on factors known to have caused churn in the past, the AI system learns to detect churn risk factors autonomously.
Supervised and unsupervised ML techniques are both useful in different scenarios; one is not inherently better. Supervised ML answers specific business questions, or “known unknowns” (Who will churn?). Unsupervised ML surfaces new risks and opportunities, or “unknown unknowns” (What churn risk factors haven’t been identified?). Supervised and unsupervised ML combine to deliver powerful business insight.
Advanced AI Techniques
Machine learning is not the only AI technique. Deep learning, for example, employs a logic structure that more closely mimics human decision-making. Topological data analysis (TDA) studies the “shape” of data. Robust enterprise AI solutions utilize a variety of techniques to solve business problems.
AI Is Disrupting Business Intelligence
Traditional business intelligence (BI) employs humans and tools to analyze, interpret, and visualize data. A financial analyst may use software to forecast next year’s revenue based on historical performance. A data science team may develop an algorithm to personalize content recommendations for VOD subscribers.
Managing and understanding data consumes a huge amount of resources. Deriving and disseminating insight from data requires even more resources. And yet the data supply grows along with demand for better insight. The BI tools that have dominated the sector for the past 20 years are no longer enough. Intelligence is routinely outdated, insufficient, or difficult to explain by the time it hits a decision-maker’s desk.
Enterprise AI improves quality of insight (QoI) as measured by the value that insight provides. Factors that impact the quality of insight include:
- Speed: How quickly does data produce insight?
- Frequency: Is insight consistently available?
- Objectivity: Did human bias influence conclusions?
- Depth: Was all relevant data analyzed?
- Accuracy: Are findings free of error?
- Relevance: Does insight answer the right questions?
- Accessibility: Is insight readily available to stakeholders?
- Explainability: Is it clear how a decision was reached?
- Actionability: Can the business act on the information?
Any aspect of QoI can be improved with today’s BI tools and process improvements. Enterprise AI improves every aspect of QoI simultaneously, in real time, and at greater scale. The more stakeholders who have access to high QoI, the more valuable it becomes to the entire organization. Gartner describes this as continuous intelligence:
“Continuous intelligence is a design pattern in which real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events.”
Of businesses struggle with AI operability
Continuous intelligence is difficult if AI solutions are accessible only to employees with the technical expertise to operate them. AI solutions that are designed without consideration of business users’ needs force organizations to rely on a small team of in-demand and highly compensated individuals. Employees’ ability to actually utilize AI solutions is cited as a barrier to adoption by 57% of organizations.
Modern enterprise AI solutions must be operable by individuals in technical and non-technical roles to truly democratize insight.
Enterprise AI Today
Enterprise AI promises transformational impact. Organizations that have already invested in AI consider it to be a competitive advantage. According to a Deloitte survey of global executives, “A majority of global early adopters say that AI technologies are especially important to their business success today — a belief that is increasing.”
But many executives see the window for gaining a competitive AI advantage closing, largely because the technology is becoming more affordable, scalable, and manageable via SaaS and cloud-based platforms. A Gartner CIO survey found a 270% increase in AI implementation from 2016 to 2019, and global spending on AI is expected to more double from $50 billion in 2020 to $110 billion in 2024.
Thankfully, AI technology is more approachable than ever for organizations of all sizes. Infrastructural upgrades and implementation headaches of last-generation commercial AI solutions are quickly giving way to more flexible, business-friendly solutions.
It’s clear that five years from now, enterprise AI will no longer be a competitive differentiator. Success will be determined by how effectively companies integrate AI throughout their operations and whether early adopters retain the competitive edge that they have seized.
Enterprise AI Applications in Media and Entertainment
Media and entertainment organizations are awash in data. Proliferating distribution models, monetization strategies, and revenue streams mean that managing and analyzing data is only getting more complex. Big data is here to stay.
Before organizations can monetize big data, they need to make sense of it. Enterprise AI is poised to revolutionize data-driven intelligence in media and entertainment.
McKinsey reports that the media and entertainment sector stands to capture 57% more value with AI than with other analytics techniques. For that value to materialize, industry leaders must thoroughly understand the enterprise AI use cases in media and entertainment.
In this section, we review the primary applications of enterprise AI technology to:
- Drive customer engagement
- Grow revenue and margins
- Drive operational efficiency
Enterprise AI Use Case: Data Aggregation
Data must be normalized (consistently formatted) for AI to do its job. But data is naturally messy; heterogenous formats, structures, and standards do not easily lend themselves to rapid analysis. To compound the issue, data is inevitably dispersed across silos that weren’t built to communicate with one another.
Consider just a few of the common data sources that enterprise AI could analyze to increase quality of insight:
- Content: Popularity, consumption, and engagement by segment, genre, platform, etc.
- Customer: Demographics, interactions, and preferences throughout the journey
- Payments: Cash flow, payments, budgets, contracts, receivables, accounting cycles
The more of these data sources an enterprise AI solution has access to, the greater its potential to facilitate continuous intelligence.
Aggregating, ingesting, and normalizing big data is no small undertaking. Enterprise AI solutions equipped with automated data normalization and multi-platform integration features hasten an organization’s ability to obtain insight from heterogenous data sources. The value of such solutions grows exponentially over time; data and insight are generated at equal speed.
Data normalization has indirect benefits, too. Any system or program can readily access and analyze more data sources.
Benefits of Data Hygiene
De-duplicating redundancies, centralizing and securing data storage and establishing a single source of truth deliver operational efficiencies throughout the enterprise.
Enterprise AI Use Case: Predictive and Prescriptive Insight
Few sectors have seen as much disruption as media and entertainment. In the decade since Netflix streaming and the Apple iPhone reinvented the digital landscape, sea change has become the norm. By 2020, 50% of industry executives said they could no longer rely on traditional business models. What’s next, and how can providers prepare?
Of M&E execs can’t rely on traditional business models
Enterprise AI provides the predictive and prescriptive insight needed to stabilize operations, remain agile, and compete on a forward basis. Examining historic trends will always be important, but smart business is about looking forward. Relying on descriptive analytics alone is forever playing from behind.
Descriptive and diagnostic intelligence explain what happened in the past. Enterprise AI materially enhances an organization’s ability to understand both.
Predictive intelligence is the ability to forecast outcomes based on input variables. Enterprise AI computes the probability of potential outcomes with high accuracy.
Progressive intelligence is the ability to recommend actions and business decisions. Enterprise AI can automate recommendations with or without human guidance.
Enterprise AI instantaneously executes statistical processes in parallel — which would otherwise consume hours of human labor — to build predictive models. On a macro scale, predictive AI insight gives media and entertainment providers a head start on the market, calling attention to emerging trends in consumer demand or shifting economic conditions that will impact the bottom line.
Predictive insight is likewise essential to daily decision-making. AI absorbs vast quantities of data and conducts what-if analyses in real time. What if average subscriber household income changes by 1%? Or 10%? Enterprise AI solutions determine the probability of potential outcomes based on any combination of variables. A content team can evaluate interest in a new title; a finance team can measure the operational implications of a sudden change in cash flow; a distribution team can predict demand per streaming platform.
Just as important as knowing what could happen is deciding what to do about it. Prescriptive AI generates recommendations based on what it’s learned from predictive, descriptive, and diagnostic analyses – for instance, notifying the retention team when a subscriber (or entire customer segment) meets the threshold for “high risk of cancellation” and suggesting a promotional offer with the greatest probability of success for each individual or group.
Finally, automated AI decision-making enables workers to reallocate time to higher value tasks. Conditional logic allows users to set rules to automatically execute prescriptive recommendations or require prior approval. The most advanced solutions offer fine-grained control via role-based permissions settings.
Enterprise AI is not intended to displace human decision-making, but rather to empower businesses with the insight to optimize it.
Enterprise AI Use Case: Churn Mitigation
Preventing churn is top-of-mind for media and entertainment providers, and rightfully so. OTT churn reached a record high of 41% in the United States in 2020. 23% of online gaming subscribers plan to cancel their subscriptions within one year. Streaming music providers are battling for customer loyalty, with Spotify reporting half the churn rate of its closest competitor Apple Music.
Churn is a permanent fixture of digital streaming, but enterprise AI solutions mitigate the impact of customer churn on multiple fronts.
Churn Mitigation Strategies
- Churn prevention
- Subscriber engagement
- Acquisition and winback
Enterprise AI solutions improve the predictive models that providers rely on to prevent churn in the first place. AI technology is capable of parsing multiple data sets (e.g. behavioral, demographic, payment) to detect churn earlier – possibly before customers themselves have made the decision to cancel service. Customer segments can then be formulated by churn risk (high, medium, low) to scale retention efforts. Prescriptive insight surfaces recommended actions to reach customers at the most impactful moment with the most impactful message, statistically shown to minimize loss.
Upstream, enterprise AI enriches the customer engagement strategies that head off cancellations to begin with. It’s widely known what frustrates customers the most, but understanding the individual drivers of customer loyalty requires more specific insight. Enterprise AI algorithmically calculates which offers, promotions, and content are most likely to generate a positive response from each subscriber, improving their experience and thus reducing the likelihood they’ll consider cancelling at all.
While churn prevention gets the most attention, customer acquisition and winback are commonly overlooked but highly effective churn mitigation tactics.
The so-called “churn learnings” generated by AI analysis answer important strategic questions. What drives customers to leave or stay on a platform? Who are the most loyal customers? Which tactics increase free trial conversions? Churn learnings can and should inform acquisition target criteria to reduce customer acquisition cost (CAC) and maximize customer lifetime value (CLV).
As customers churn through platforms, acquisition is not limited to net-new subscribers. Churned customers represent potential resubscription revenue; in one survey, 75% of subscribers on a popular streaming platform said they would resubscribe if a new show caught their interest.
Peak OTT churn (U.S.)
Winback is only becoming more integral to broader acquisition strategy. Enterprise AI can extract insight from backlogs of cancelled subscriber data, which in turn can be utilized to recapture the interest of “lost” subscribers. When a new title is released, for example, AI can distinguish which customers previously engaged with the same series, game, artist, or actor, and recommend a well-timed reminder that their favorite pastime is making a comeback.
Enterprise AI Use Case: Personalized Customer Experience (CX)
Media and entertainment providers have long embraced personalization. In our age of customer empowerment, organizations must develop high-precision, multi-touchpoint personalization strategies to remain relevant. As executives internalize this new reality, enterprise AI once again emerges as a crucial tool.
“Companies that fail to show customers they know them and their buying preferences risk losing business to competitors who are more attuned to what their customers want.”
Personalized content recommendation is the most prevalent AI application with respect to customer experience, but AI insight can be leveraged for a range of use cases.
AI Use Cases: CX
- Pricing models
- Ad delivery
- Interactive content
- Immersive entertainment
- Licensed product offers
Currently, CX is personalized to varying degrees based on limited subscriber insight such as broad segmentation, customer health scores, or historic behavioral data. The effect is an oversimplified, reductive view of a customer’s wants and needs. Enterprise AI solutions synthesize unlimited customer data sources to generate a real-time, comprehensive understanding of their wants and needs, including:
- Authentication, device, location, users on account
- Payment data, credit card expiration, billing location
- Search history, session frequency, duration
- Content preferences by genre and granular metadata
- Quality of experience: streaming speed, network, etc.
- Off-platform activities and interests
- Research, third-party, and public data
Consider the freemium model popular with VOD platforms. Most providers offer standard free trial duration, upgrade pricing, and content to every customer. Content recommendations may be personalized based on basic demographic data provided at signup and behavioral data gathered during the trial period. CX (and conversions) stand to be far more optimized than that. Adjusting introductory pricing in real time based on credit card expiration date and home WiFi network speed to optimize CSAT and CLV is not a far-fetched concept.
Enterprise AI Use Case: Content Optimization
The streaming wars aren’t just raising the stakes for content creators; they are driving up the cost of media production and licensing. In total, media and entertainment companies spent over $120 billion on original content in 2019. (Disney alone accounted for a quarter of that spending across its studies, networks and SVOD services).
What’s that content worth? Neither streaming giants nor their competitors can sustain a competitive advantage by investing in content alone. To maximize return, providers must continuously monitor and capitalize on its real-time value.
The value of every asset in a content portfolio continuously fluctuates based on changing consumer demand and a host of other market forces. Content valuation is a crucial strategic practice in the heavily saturated content marketplace.
Enterprise AI affords content owners the data intelligence required to valuate content, understand its potential, and realize ROI.
At the pre-production stage, predictive AI models can project audience demand and revenue potential. Creative decisions, production financing, and promotional budgets can be adjusted accordingly to expand profit margin and reduce risk. Distribution teams can maximize licensing terms by forecasting demand by platform, audience segment, release date, and other criteria to maximize licensing terms.
Once content is released, enterprise AI solutions boost discoverability. By making content metadata highly structured and easily searchable (rather than depending on titles or summaries), AI can identify and tag items of interest to potential viewers and advertisers: plot devices, themes, locations, product placement, etc. Mapping content metadata to customer and market data reveals more precise placement and promotional opportunities.
Yearly original content spend (U.S.)
For existing content, AI can identify the root cause of asset performance. If revenue falls short, providers know why. Inaccurate demographic targets? Promotional timing? Device incompatibility? For unreleased or archived assets, enterprise AI solutions monitor trends that reveal uncaptured value. An archived asset could thematically align to a current event of interest to a large audience segment, for instance.
Without enterprise AI technology, precisely measuring and seizing the full value of a content portfolio is all but impossible.
Enterprise AI Use Case: Distribution Strategy
Media and entertainment revenue is highly fragmented. Collecting and managing data for every streaming service, mobile app, console, and partner platform demands innovative new strategies.
The multi-layered contracts and self-reporting models that govern distribution agreements put expedient insight out of reach for content licensors. Miscalculating any aspect of distribution revenue activity, even by a small error percentage, can lead to contractual disputes, subscriber loss, and revenue leakage.
Enterprise AI brings transparency, control, and insight to distribution revenue workflows, particularly the back office domains comparatively underserved by enterprise technology (legal, finance, accounting).
AI Use Cases: Distribution
- Agreement management
- Distributor audit
- Revenue recognition
- Licensing negotiation
- Scenario planning
- Predictive forecast
Simply normalizing the unstructured data within non-standardized reports and agreement documents provides the means to instantly search, analyze, and compare partnership terms – drastically reducing the number of hours required to execute simple recurring tasks. Enterprise AI solutions further validate the accuracy of self-reported distributor data by detecting anomalies. Inconsistencies in remittance payments require providers to expend resources regardless of outcome. If no issue is found, hours of labor yield no monetary gain. Worse, organizations may not have the capacity to investigate and reconcile the discrepancy. Revenue leakage remains invisible.
Enterprise AI data analysis extends to data stores located outside in-house accounting systems. Solutions that readily integrate with customer databases introduce more powerful predictive insight into the future revenue potential of distribution channels. AI systems trained to autonomously correlate changes in subscriber signups by platform give content owners powerful negational leverage.
With better data in hand, content owners can effectively adjust rates, revenue sharing agreements, and contract terms in their favor.
Enterprise AI Use Case: Anomaly Detection
An untold number of business-critical threats and opportunities go undetected every day. Media and entertainment revenue models are especially vulnerable to hidden anomalies, since data is dispersed across distributors, payment platforms, streaming devices, subscriber management systems, and content delivery networks (to name a few).
Enterprise AI excels at pattern detection to locate anomalous signals hidden in complex data sets.
AI Anomaly Detection
- Late payments
- Distributor reports
- Subscriber counts
- Reported revenue
- Cash flow
While business intelligence tools without AI capabilities may be able to detect some anomalies, there are real limitations: delayed error detection, lack of visibility into root cause, and access to fewer data sets. Enterprise AI can drastically reduce the risk of missing subtle data anomalies that have an outsized impact on business.
Case Study: A Million-Dollar Miss
Within 24 hours of implementation, the Revedia AI platform discovered an error on a popular streaming service that had gone unnoticed: 15,000 subscribers were signed up for free trials with no defined trial period expiration date. The revenue impact of this anomaly totaled over $1 million
The Best Enterprise AI: What to Look for in a Solution
Investing in an enterprise AI solution is a major business decision. Though the technology is fairly straightforward, the purchasing decision is complex. Multiple stakeholders, confusing product categories, and highly technical features deter even seasoned business executives.
What’s more, commercial AI is less established in media and entertainment than in other sectors. Analyst rankings, peer reviews, and other resources normally consulted in the buying process are not readily available. As a result, concerns commonly arise:
- What features do I need?
- Is it future proof?
- When should I invest?
- Is this solution proven?
- What’s the risk?
In this section of the guide, we’ll review key questions for business leaders to consider when evaluating an enterprise AI solution.
Define the Business Case
The wide-ranging enterprise AI use cases in media and entertainment may trigger analysis paralysis. With so many potential applications of AI technology, it can be difficult to clarify the need.
Depending on your role, you may be looking to enterprise AI as a potential solution to multiple challenges. Isolate the most important one (or two) business needs and scale from there.
Clarify the specific outcomes you seek to achieve with AI; these will be used as decision criteria when evaluating vendor solutions. The five most common outcomes sought by media and entertainment leaders considering enterprise AI investment are:
- Reduce churn/increase retention
- Maximize distribution revenue
- Optimize content portfolio
- Stabilize financial operations
- Efficiently manage data
A well-defined business case enables buyers to scope requirements, quantify the effectiveness, and communicate the benefits of potential investment to stakeholders.
Buy, Don’t Build
The inclination to develop proprietary AI systems is understandable. Driven by the availability of open-source components, lack of industry-specific solutions, and need for specialized functionality, some organizations take on the cost and risk of developing in-house AI solutions. In rare instances, there is a strong justification for the build-versus-buy decision: a radically transformative, highly specific initiative that is simply not achievable with commercially available technology.
In the other 99% of cases, commercial AI solutions offer strategic advantages in the buy-versus-build debate:
- Lower cost
Hiring (or diverting the attention of) data scientists and engineers to develop proprietary AI is a multimillion-dollar proposition. Even integrating open source resources into the existing IT milieu consumes resources, often involving multiple vendors and racking up indirect labor costs. The months required to develop basic machine learning algorithms can further delay implementation.
Gartner predicts that through 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them.
Maintenance is another frequently underestimated cost factor. Sustaining a highly functional AI system requires sizeable technical and support staff and ongoing training. As AI capabilities evolve, innovating and upgrading existing systems involves a long-term commitment to research and development.
By partnering with companies that have already invested heavily (and continue to invest) in bringing robust AI capabilities to market, media and entertainment leaders can focus on turning AI-driven intelligence into superior decisions, products, and customer experiences.
Look Past Point Solutions
Given the hype around AI, point solution vendors are hurrying to incorporate AI capabilities into existing platforms. Businesses can certainly realize incremental value from these feature enhancements, which should not be discredited as part of an overall AI strategy.
To realize the full value of artificial intelligence, however, media and entertainment organizations must look past siloed AI capabilities toward end-to-end platforms capable of operationalizing intelligence enterprise-wide.
Putting AI to work on centrally available, integrated data sets from disparate sources yields the meaningful returns that differentiate AI-powered insight from common business intelligence.
An AI-capable point solution, for example, may offer predictive forecasting. But analysis is limited to data contained within that system. Enterprise AI can aggregate the input of multiple point solutions to deliver deeper findings and disseminate insight back to those systems and stakeholders throughout the organization.
Of in-house AI efforts produce erroneous results
Integrated enterprise AI also facilitates long-term data management efficiencies. By nature, a centralized source of truth eliminates redundancies, reduces errors, and expedites the ingest and analysis of exponentially growing real-time data streams.
Aggregating data does not require stakeholder to relinquish control. Rather, it enables the AI solution to recognize trends and produce usable insights wherever they emerge, for all business functions.
Take a Vertical Approach
Most AI offerings available today are not built to spec for the media and entertainment sector’s unique challenges. These “horizontal” solutions serve the basic needs of enterprises across industries, but ultimately limit the value that AI can deliver.
The Rise of Specialized AI
Fortunately, the market is shifting in favor of verticalized AI. By 2023, 85% of enterprise AI solutions will focus on domain-specific or industry-specific needs.
Niche solutions have concrete benefits for media and entertainment organizations. Verticalized AI systems are trained to detect, analyze, and address the most pressing issues on industry executives’ agendas.
- Subscriber turnover
- Content engagement
- Streaming distribution
- Licensing and royalty fees
- Customer experience
Correctly designing and training vertically specialized AI systems requires deep domain expertise. The difference between a horizontal and vertical solution is palpable to users, from the architectural level to the software interface.
Of commercial AI will be specialized by 2023
Machine learning algorithms trained with industry learn to better detect common patterns. Interfaces built by experts with extensive knowledge of media and entertainment workflows are inherently more intuitive to users. Data normalization, customer segmentation, scenario modeling, and a host of other processes are primed to accommodate the unique and ever-shifting dynamics of the industry.
Media and entertainment moves fast, and verticalized solutions can keep pace. When the market evolves, verticalized solution vendors will invest in product innovations to evolve along with it.
Enterprise AI is a long game. Beginning with a proof-of-concept enables organizations to set benchmarks that ROI will ultimately be measured against, such as:
- Ease of implementation
- Data ingest speed
- Algorithmic accuracy
- Infrastructural readiness
- Vendor performance
A scaled approach empowers leaders to focus on validating use cases and building internal capacity to ensure continuity in uptake across the company.
The Bottom Line
Enterprise AI replaces the human tasks, duties, and activities that AI is best suited to perform. Humans can then focus on what they do best: thinking more creatively, making decisions, determining what data AI should even consider.
This insight helps define the capacity and constraints of enterprise AI in media and entertainment. AI can digest, sort, segment, and analyze tremendous amounts of information. It can identify gradual and sudden shifts in the business and produce insights independent of human instruction. However, it remains the domain of humans to decide what actions to take and how to turn that information into power.
Applied to media and entertainment, enterprise AI can monitor and report on audiences with depth and detail that was impossible even a decade ago. Companies that deploy AI will not only survive streaming wars, cord-cutting, and whatever other disruption lies ahead; they will see the changes coming sooner, move from insight to action faster, and ultimately turn information into power and profit.
“In today’s media landscape, context is king. It’s one thing to apply AI algorithms to problems, but what the market needs are real-time behavioral analysis and prescriptive recommendations that make a measurable difference to providers seeking customer engagement at scale.”