How AI Will Transform the TV Industry, According to ChatGPT: The Full Response

Written By Remi Brand

With generative AI technology already integrating into workflows throughout the TV production process, what better (or meta) way to explore how AI could transform the TV sector over the coming years than to enlist the help of algorithmically-powered and increasingly popular AI tool ChatGPT? That’s exactly what Gritty Talent marketing manager Remi said before turning to the large language model (LLM) to help him investigate AI’s potential to revolutionise TV production in the coming years. 

As you can see from the video, the goal was to get ChatGPT to provide a prediction of how the integration and influence of AI will transform the TV industry every year over the next five years. To give the LLM some parameters, it was asked to focus on the following areas:

  • Evolving production processes

  • How employment will be affected 

  • How roles and jobs will evolve; 

  • The content itself; 

  • Audience engagement; 

  • How data and metrics will be used; 

  • Potential issues

  • Potential policy and regulation

ChatGPT was also fed a blog recently published by Gritty Talent about how AI is already being used, what skills are required to work with it and the sorts of tools that are being utilised. This gave the tool some contextual information about where AI currently sits within the TV industry, that could be used to inform its response.

After that, it was just a case of letting ChatGPT run free and generate its timeline of predictions, which you can read in full below:

Year 1: 2024

  • Evolving Production Processes: AI continues to penetrate various stages of TV production, from ideation to post-production. GenAI tools like Runway Machine Learning and Microsoft Azure AI Video Indexer gain traction, enhancing efficiency in content creation and analysis.

  • Impact on Employment: Existing roles in TV production see augmentation rather than replacement. Professionals equipped with AI skills find themselves more competitive in the job market. Companies invest in training programs and workshops to upskill their workforce.

  • Content Evolution: AI-driven content generation becomes more commonplace, leading to innovative formats and storytelling techniques. Deep fake technology sees increased utilisation, sparking debates around authenticity and ethics in content creation.

  • Audience Engagement: Viewers experience more personalised content recommendations and interactive experiences driven by AI algorithms. Dynamic content formats, such as AI-generated scenes and translated speech, cater to diverse audiences, enhancing engagement.

  • Data and Metrics Utilisation: AI-powered analytics tools enable broadcasters to extract deeper insights from viewer behaviour and preferences. Data-driven decision-making becomes integral to content strategy, leading to more targeted programming and advertising.

  • Potential Issues: Concerns around bias and misinformation in AI-generated content emerge. Industry stakeholders collaborate to develop ethical guidelines and standards for AI usage in content creation. Regulatory bodies start monitoring AI-driven content for compliance with ethical standards.

  • Policy and Regulation: Industry bodies and government agencies collaborate to establish frameworks for responsible AI usage in TV production. Guidelines for transparency, accountability, and bias mitigation are introduced to ensure ethical AI practices across the industry.

Year 2: 2025

  • Evolving Production Processes: AI adoption accelerates, with virtual production tools like Unreal Engine becoming mainstream. AI-driven automation streamlines repetitive tasks, allowing creatives to focus on more innovative aspects of content creation.

  • Impact on Employment: Demand for AI-specialised roles like AI Product Monitoring Officers and Bias Mediators increases. Traditional roles undergo transformation, requiring professionals to acquire AI fluency and adapt to new workflows.

  • Content Evolution: AI-generated content gains acceptance, enabling producers to experiment with novel formats and genres. Collaborations between human creatives and AI algorithms result in hybrid content that pushes the boundaries of creativity.

  • Audience Engagement: AI-powered recommendation systems deliver hyper-personalised content experiences across multiple platforms. Interactive storytelling experiences driven by AI algorithms become popular, blurring the lines between traditional TV and gaming.

  • Data and Metrics Utilisation: Advanced AI analytics platforms offer real-time audience feedback, enabling content creators to iterate and optimise content in response to viewer preferences. Predictive analytics algorithms forecast trends and inform strategic content planning.

  • Potential Issues: Concerns over AI-generated deep fakes escalate, prompting industry-wide discussions on content authenticity and digital rights management. Collaborative efforts focus on developing AI tools for content verification and authenticity verification.

  • Policy and Regulation: Regulatory frameworks evolve to address emerging challenges in AI-driven content creation. Legislation mandates transparency and disclosure requirements for AI-generated content, ensuring consumer protection and ethical standards compliance.

Year 3: 2026

  • Evolving Production Processes: AI-driven innovation continues to transform TV production workflows, with AI-enabled tools becoming indispensable for content creation, editing, and distribution. Virtual production techniques powered by AI redefine the boundaries of visual storytelling.

  • Impact on Employment: Upskilling initiatives and educational programs focus on integrating AI into TV production curricula. Hybrid roles that combine creative expertise with AI fluency become prevalent, driving demand for interdisciplinary talent.

  • Content Evolution: AI-driven content personalization reaches new heights, catering to niche audience segments with tailored experiences. Cross-platform integration enables seamless content distribution and engagement across traditional and digital channels.

  • Audience Engagement: AI-driven chatbots and virtual assistants enhance viewer interaction and engagement, providing personalised content recommendations and real-time feedback. Immersive experiences powered by augmented reality and virtual reality redefine viewer engagement paradigms.

  • Data and Metrics Utilisation: AI-powered predictive analytics tools revolutionise content strategy and audience targeting, enabling broadcasters to anticipate viewer preferences and market trends. Data-driven content optimization becomes standard practice across the industry.

  • Potential Issues: Ethical concerns surrounding AI bias and data privacy intensify, prompting industry stakeholders to prioritise transparency and accountability in AI usage. Regulatory bodies introduce stricter guidelines for AI-driven content creation and algorithmic decision-making.

  • Policy and Regulation: Governments enact legislation to regulate AI usage in the media industry, mandating ethical AI standards and accountability mechanisms. Regulatory bodies collaborate with industry stakeholders to establish certification programs for AI ethics and compliance.

Year 4: 2027

  • Evolving Production Processes: AI becomes deeply integrated into every aspect of TV production, from content creation to distribution. AI-driven tools automate complex tasks, enabling creatives to focus on innovation and storytelling.

  • Impact on Employment: The job market adapts to the AI-driven paradigm, creating new opportunities for AI-specialised roles and interdisciplinary professionals. Lifelong learning and continuous upskilling become essential for career advancement in the AI-powered media landscape.

  • Content Evolution: AI-generated content evolves beyond novelty, becoming a mainstream feature of TV programming. Collaborative AI-human creativity platforms empower creators to explore new narrative possibilities and audience engagement strategies.

  • Audience Engagement: AI-driven immersive experiences and interactive storytelling formats redefine viewer engagement, blurring the boundaries between passive consumption and active participation. AI-powered content personalization enhances viewer satisfaction and loyalty.

  • Data and Metrics Utilisation: AI-powered predictive analytics algorithms enable broadcasters to anticipate market trends and viewer preferences with unprecedented accuracy. Data-driven content optimization becomes a cornerstone of content strategy and audience engagement.

  • Potential Issues: Challenges related to AI bias, transparency, and accountability persist, necessitating ongoing dialogue and collaboration among industry stakeholders. Innovations in AI verification and authentication technologies mitigate risks associated with deep fakes and misinformation.

  • Policy and Regulation: Regulatory frameworks evolve to address emerging challenges in AI governance and ethics, emphasising transparency, accountability, and consumer protection. International collaboration leads to harmonised standards for AI usage in the media industry.

Year 5: 2028

  • Evolving Production Processes: AI-driven innovation reaches maturity, revolutionising TV production workflows and creative processes. AI-powered virtual production techniques enable unprecedented levels of realism and immersion in storytelling.

  • Impact on Employment: AI becomes a ubiquitous tool in TV production, augmenting human creativity and efficiency across all roles and disciplines. Upskilling initiatives and AI education programs ensure a skilled workforce capable of leveraging AI technologies effectively.

  • Content Evolution: AI-generated content becomes indistinguishable from human-created content, blurring the boundaries between AI and human creativity. Collaborative AI-human storytelling platforms empower creators to push the boundaries of narrative innovation.

  • Audience Engagement: AI-driven personalised experiences redefine viewer engagement, fostering deeper connections between audiences and content. Interactive storytelling formats powered by AI algorithms enable unprecedented levels of viewer participation and immersion.

  • Data and Metrics Utilisation: AI-powered analytics platforms provide actionable insights into viewer behaviour and preferences, informing strategic content decisions and audience engagement strategies. Data-driven content optimization becomes integral to maximising audience reach and impact.

  • Potential Issues: Ethical considerations remain paramount as AI continues to shape the media landscape, with ongoing efforts to address bias, transparency, and accountability in AI-driven content creation. Collaboration between industry, academia, and policymakers ensures responsible AI governance.

  • Policy and Regulation: Robust regulatory frameworks govern AI usage in the media industry, safeguarding ethical standards

Pretty impressive stuff, right? Sure, some parameters were put in place to focus ChatGPT’s response, but what we got back is a conceivable prediction that really addresses some of the big questions around upskilling, AI-powered production, data and regulations. You can check out our highlights in our video above covering the experiment, if you haven’t already. 

Can you see something like this happening in the near future? Will the industry begin to upskill talent this year? Will the lines between reality and broadcast be blurred beyond recognition? Can regulations and policy catch up in time to keep AI under control? Let us know your thoughts on any and all social media platforms!

Remi Brand

Remi Brand is a journalism-trained digital marketer, with an undying passion for music and culture. He joined Gritty Talent on their mission to help the media landscape transform to one that is truly inclusive.

Previous

AI’s not going to take your telly job… but you’ll need to up-skill to keep it!
Next

Want to be an inclusive leader? Start with how you communicate