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As the video game industry reacts to a global surge in demand, they’re also looking for ways to enhance their capabilities, increase their productivity, streamline their organizations and become more efficient in how they operate. The advent of generative artificial intelligence (GenAI) solutions presents an opportunity for video game companies to transform how they function, from development to operation to distribution to community, as well as, from their front office to their back-office functions.
Let’s explore possibilities for innovation and reinvention with GenAI, including our recommendations on how to begin your journey.
Video game revenue is projected to reach $312 billion by 2027
Market trends create a clear case for change
The video game industry has grown at a remarkable rate in recent years, driven largely by online and mobile gaming (which reached approximately $185 billion this year) and the COVID-19 pandemic during which gaming sales surged over 30%. This trend is not expected to subside, as total video game revenue is projected to rise from $263 billion in 2023 to $312 billion in 2027. Industry players have made significant investments to capitalize on this growing demand, which included growing headcount partnered with an increase in tech & tools licensing and the desire to build more complex immersive gaming experiences, which resulted in ballooning production costs. Now industry leadership is looking to become more operationally efficient. With the days of bespoke solutions gone and the maturity of numerous technologies that have been contextualized for the games industry use-case, there’s an opportunity to leverage common tech and structures that are repeatable to achieve greater efficiency.
As an added strain, the organic industry expansion and post-pandemic remote work environment resulted in a far more complicated value chain. Companies are expected to make transformative changes while continuing to manage increasingly complex monetization strategies, launch products across a variety of platforms and reign in development timelines. They’re also being forced to compete with an ever-growing set of competitors vying for entertainment time. This creates the desire to accelerate development processes and release content faster to retain gamers and not lose out to other entertainment providers. This use-case creates a meaningful benefit for GenAI solutions across the value chain.
In essence, they’re being asked to do more with less, and GenAI solutions can power companies to do just that. A workforce empowered with GenAI can more effectively manage an intricate web of value streams, manage timelines and budgets, and mitigate potential risks more efficiently than teams armed with the software of the last decade.
Applying GenAI across the video game value chain
Given the state of the video game industry, now is the time to transform workflows by investing in GenAI solutions. Use cases for value chain transformation vary in complexity, scalability, and area of focus, and gaming companies should first identify where they can drive the most value.
For example, in the development segment, GenAI can be used as a valuable tool to assist the executive producer. It can provide an AI framework to serve as the Project Management Office (PMO) for game development by analyzing how changes in project scope affect game development timelines. Furthermore, GenAI solutions can serve as bots to simulate gameplay, detecting gameplay bugs and analyzing the monetization potential. Moreover, GenAI can be utilized for personalized gameplay and map generation. For instance, a key player is developing an AI solution that takes prompts and generates outputs such as detailed scripts, dialogue trees and quests. This empowers game designers to develop games more efficiently.
GenAI can be used in partnership with live operations data for loyalty and rewards. Furthermore, GenAI is a solution for content moderation, including text, audio, in-game actions, user-generated Content (UGC) and other trust and safety services. Moreover, as the game industry seeks to leverage ads within games, pre-roll, post-roll, and campaigns, GenAI offers a solution for dynamic contextualized ad placement.
For studio operations back-office technology, there are significant use cases for GenAI in domains such as Financial Planning & Analysis (FP&A), HRIS, IT, customer support, legal, and risk and ancillary services. These use cases represent only a fraction of the potential investments in this field. Given the vast array of opportunities, industry players should embark on their own GenAI journeys.
How to start the GenAI journey
As game companies consider how to kick-start their own GenAI transformations, it’s important to take a pragmatic approach to increase the value and ROI that can be achieved. PwC has supported clients across the industry in this process and has the capabilities to help organizations launch and manage their specific circumstances – in part built upon our own $1B investment to expand and scale AI capabilities to deliver human-led and tech-powered solutions. Though there are many steps to consider, we can simplify it into five core phases of work:
- Define GenAI strategy - Define the vision, objectives and North Star for the GenAI program in the context of overall company and functional strategies, and align on strategy, success criteria, roadmap, timing and other elements needed. Depending on the given organization’s technical maturity, this may include educating teams on the ''art-of-the-possible” for GenAI (e.g., definitions, examples of applications across industries, lessons learned from others, case studies with proven ROI, upcoming challenges/risks, etc.).
- Create trust in GenAI - Establishing security policies, governance, and responsible AI practices is critical to building trust and managing the risks associated with GenAI. To achieve relevant, reliable results governance should be an ongoing effort, which should be carefully monitored and adapted as regulations change, risks emerge and technologies evolve.
- Ideate and prioritize use cases - Work with relevant stakeholders and team members to generate and prioritize a list of potential use cases. It’s important to distinguish the definition of use cases as “patterns” that can scale rather than narrow, specific applications. Using GenAI in isolated instances can create limited value. As noted earlier, not all use cases will be a good fit for GenAI. Organizations should prioritize use cases which can deliver across three key dimensions:
- Value creation - Delivers impact aligned with targeted success criteria
- Reusability - Results in a solution pattern that can be easily modified to deliver against other use cases
- GenAI applicability - Is a good fit for GenAI based on available data, desired output and general business requirements
- Design solutions - Work with stakeholders across the business to identify key requirements and develop initial solution designs for priority use cases. These solutions should integrate with existing systems, include a “human-in-the-loop” where necessary, and adhere to responsible AI policies. Consider our Responsible AI framework which includes a suite of customizable frameworks, tools and processes designed to help clients harness the power of AI in an ethical and responsible manner.
- Build, scale and operate solutions - This step entails establishing a secure GenAI environment and building a technical team (or an AI Factory) capable of leading the end-to-end delivery of solutions. Over time, the capacity of the GenAI factory should scale efficiently, so that use case outcomes cost less, have increased impact and accelerate over time.
With contributions from
Ryan Pennock, Rachel Roschelle, Ross Parket, David Wang, Matthew Malenky
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