Independent report

AI Adoption Plan: Creative Industries

Published 8 June 2026

A report by Sally Davies, AI Champion for the Creative Industries sector.

Summary

The UK’s creative industries are one of our greatest national strengths. They are world-leading, economically significant and central to the government’s Industrial Strategy as a growth-driving sector.

In 2024, the creative industries contributed nearly £146 billion in gross value added (GVA), almost 6% of the UK total. Between 2010 and 2024 their GVA grew at more than twice the rate of the wider economy. In 2023 they accounted for nearly 13% of all UK services exports. This is a world-class national asset, with significant potential to drive growth, exports, innovation and high-quality employment.

The sector has always been quick to explore new technologies. That is reflected in current levels of artificial intelligence (AI) adoption. AI use is higher in the creative industries than across the economy as a whole: 51% of creative businesses report using AI, compared with 33% of all businesses. As global markets and technologies adjust at an unprecedented pace, acting now is vital. By confidently adopting these tools, we can ensure our sector remains globally competitive.

Across the sector, businesses and creators are experimenting with AI tools and processes. However, adoption is uneven. Larger businesses are moving faster than smaller firms, and most organisations are using AI first for operational tasks before applying to creative workflows. This is understandable. AI presents real opportunities, but it also raises particular questions for a sector where trust, rights, reputation and creative integrity are central to value.

I have prepared this plan as the AI Champion for the Creative Industries. Its purpose is to support a practical, trusted and sector-specific approach to AI adoption.

The approach I propose is augmentation first. AI should support human creativity, not displace it. It should help creative workers spend more time on the creative aspects of their jobs and roles. It should help businesses compete, innovate and grow. It should give smaller firms and independent creators access to new tools and It should be adopted in ways that maintain trust, respect creative labour and support high quality jobs.

This plan does not promote AI for its own sake, nor does it seek to replace human creativity, craft and judgement that makes the UK’s creative industries world-leading. Its purpose is to help creative businesses, freelancers and institutions use AI responsibly where it strengthens their work, improves productivity, supports growth and creates new opportunities.

Taken together, the recommendations in this plan can help make the UK the global home of trusted createch and reinforce our leadership in creative innovation. Used ethically, AI can help grow a dynamic creative economy in which technology strengthens human creativity, more firms scale into createch businesses, and the next generation of high quality creative jobs is created in the UK.

This plan proposes 8 practical steps:

1. Establish an augmentation first approach to AI adoption
2. Continue to develop a responsible AI framework for the creative industries
3. Raise practical AI knowledge and confidence
4. Develop trusted guidance, standards and tools for AI adoption
5. Support skills, leadership and workforce transition
6. Reduce cost barriers to responsible adoption
7. Expand nationwide AI infrastructure through testbeds, anchor institutions and regional support
8. Review, evaluate and update the plan as technologies evolve

Existing adoption landscape

The creative industries have always embraced new technologies. Music, film, design, games and the wider creative economy were early adopters of digital tools, using them to reach new audiences, improve production and develop new business models.

The sector has also driven innovation beyond its own boundaries. Games technologies, simulation, virtual production and immersive tools are now used in aerospace, healthcare, education and other sectors. This shows the creative industries long standing ability to act as both early adopters and practical innovators.

AI is the next wave of this change. It is increasingly embedded across creative industries and is reshaping how content is developed, produced, distributed and experienced. In games, AI has been a part of development for decades, including through symbolic AI and machine learning for non-player character behaviour and procedural generation.

Across the wider sector, businesses are engaging carefully with emerging AI technologies. Many recognise that AI may shape the medium-term viability of their business models, while also being alert to the risks of adopting tools too quickly, or without enough confidence.

Adoption varies across subsectors. The IT, software and computer services sub-sector is a major with 60% of businesses using AI. This compares with 53% in design and designer fashion, 44% in film, TV, video and photography, and 22% in music, performing and visual arts.

Most firms begin by using AI in non-core functions before moving into creative workflows. This includes routine office automation, cost forecasting, pricing, information synthesis and strategic decision support. For micro-businesses and small and medium-sized enterprises (SMEs), this back office experimentation often acts as an informal form of risk management where formal governance is not yet in place.^{[footnote 1]}

Decision-making becomes more complex when AI use is visible to clients or the public. Client facing and public facing uses require stronger quality assurance, legitimacy and reputational trust. Commissioned work may also be subject to restrictions or prohibitions. Some clients encourage AI enabled efficiency, while others restrict or prohibit AI use altogether.

This means adoption is not simply a question of whether a tool works. It is also a question of whether its use is contractually permitted, ethically acceptable and commercially safe.

Adoption is more constrained in subsectors where generative AI is seen as overlapping with core creative tasks. In animation for example, AI is often used for generating early-stage visual concepts, but newer tools support processes such as upscaling of images and textures. However, many firms maintain a clear boundary against using experimental outputs in final, client facing work because of intellectual property (IP) and ethical concerns.

Case study - Revolution Software

For the 2024 remaster of Revolution Software’s 1996 game Broken Sword, studio co-founder Charles Cecil explained that AI could help make the graphics work for modern audiences.

To update the visuals from 480p to 4k resolution, the studio used AI to upscale existing data, including character sprites. The main character alone had several thousand sprites, giving the generative adversarial network enough training data to build on.

The tool was not successful in generating heads or hands correctly, so the developers used Ai only where it worked well and then manually refined the frames. This is a useful example of AI supporting, rather than replacing, skilled creative judgement.

Adoption by business size

Adoption varies significantly by business size. Larger businesses adopt AI at much higher rates. Enterprises with 250 or more employees have adoption rates of 44%, while micro-businesses sit around 24%. Evidence suggests micro-businesses are approximately 18 months behind large firms in terms of AI adoption.

SMEs often struggle to dedicate time and resources to market scanning, tool evaluation, workflow redesign and oversight. Larger organisations are more likely to have internal governance frameworks, phased implementation processes, approval mechanisms and explicit risk categories.

The result is an uneven landscape. Some parts of the sector are integrating AI as a practical production tool. Others experience AI mainly as an external market force that is reshaping commissioning, pricing and bargaining power across creative labour markets.

There is also a risk unequal access to finance could worsen existing disparities, including firms led by women and ethnic minority founders. The evidence base on this specific link is still limited but the risk is significant enough to require further investigation.

Key challenges

Barriers to adoption 

The main barrier to AI in the creative industries is not lack of interest. It is a lack of capacity, confidence and infrastructure, particularly among microbusinesses, small firms and freelancers.

Concerns around IP, data security, client acceptability and reputational risk are still slowing wider deployment, especially in client facing creative work.

Client expectations are a major factor. Some clients actively encourage AI use to improve efficiency. Others prohibit or tightly restrict it. This often confines AI adoption to lower-visibility functions. In the screen sector, UK adoption can therefore be understood as an iceberg, much of the activity is experimental or behind-the-scenes, rather than visible to the public.

Case study - Aardman Animation

Aardman Animation has been researching AI and generative technology for several years. The studio uses directed AI tools such as Copycat to automate standardised post-production processes. This helps identify and fix problematic frames across sequences, rather than requiring animation to be reshot.

Generative AI is also being explored, but only by specific teams with the necessary expertise.

Aardman’s approach is guided by a simple question: does this give the creative parts of our jobs more time? The studio uses AI cautiously and in a way it considers creatively safe. It is also aware of the financial pressures facing the industry and sees AI as one way to improve efficiency and reduce production costs without replacing jobs.

The reception among staff has been mixed. Aardman has responded by encouraging open conversation and feedback. By appointing internal AI champions and promoting debate, the studio is building confidence organically.

Copycat now completes 70 to 80% of work in its target process, reducing repetitive manual work for VFX staff and creating significant time savings in post-production. The company also sees AI literacy as increasingly important, as clients expect studios to understand and engage with the technology while maintaining their values.

Recent ‘Business Insights and Conditions Survey’ data shows that the most commonly cited factor preventing or delaying adoption is lack of trust in AI product safety or transparency, This is reported by 10.1% of creative industry businesses, compared with 5.7% of all businesses.

Other barriers include skills, expertise and knowledge; integration and scalability challenges; lack of identified use cases; concerns about job security; and uncertainty about government regulation. This is reflective across the economy. 

Cost is another barrier. Enterprise-grade tools can carry high subscription and integration costs, there are a significant barrier for independent creators. Microbusinesses and SMEs also face high opportunity costs: to research the market, test tools and assess risks all this can outweigh their available capacity.

There is also a mismatch between expectations and reality on productivity. Some AI use cases deliver clear gains. In many workflows, however, the benefits are harder to prove. Prompting, iteration, error correction and quality control can absorb significant time. Organisations may struggle to distinguish genuine efficiency from displaced effort.

Sustainability should also form part of responsible adoption. The growing use energy-intensive AI models creates challenges for sectors that are also trying to make production more sustainable. Responsible adoption should encourage proportionate tool use and better understanding of environmental impacts, particularly where high compute models are involved.

Skills and the labour market 

Skills are one of the most important conditions for responsible AI adoption. The sector does not only need technical specialists. It needs practical AI capability across leadership, production, creative, commercial and operational roles.

A lack of training and investment remains a major barrier to innovation. There is a recognised funding gap, especially during early ideation and exploratory stages. Employers identify machine learning and AI as important digital skills gaps. The sector also needs more people who can combine technical knowledge with other creative, commercial and strategic expertise.

At present, many organisations rely on informal learning. This is understandable given the pace of change, but it is not enough for long-term responsible adoption. Informal learning can support experimentation, but it cannot provide the consistent understanding needed to manage workforce risks. More formal, practical and sector-specific training is needed, including provision designed for freelancers, microbusinesses and SMEs.

Case study - Framestore

Framestore is a visual effects and animation studio working across film, TV, advertising and immersive media. It is led by a chief technology officer with experience in emerging technologies and AI, and employs creative technologists who are used to experimentation and research and development (R&D).

Framestore focuses on using directed AI. This means using machine learning systems to solve specific and predictable workflow problems. Its directed AI focuses on repetitive tasks, such as rotoscoping, so that workers can spend more time on creative and higher-value work.

To address skills gaps, Framestore has introduced targeted training and resources built internally by AI experts. These include video courses and self-service modules designed to embed practical AI knowledge across teams.

Framestore reports that this approach has delivered:

• seven-figure cumulative value per project for directed AI initiatives that have reached full-scale deployment
• break-even within 2 months for early adoption projects
• more workforce time shifted from repetitive tasks to higher-value creative work across  multiple departments

Workforce impacts 

The labour market impacts of AI need careful attention. Many creative workers are concerned that generative AI may overlap with human tasks. Even where full worker replacement is not expected, there is uncertainty regarding career pathways and the future value of existing skills.

AI implementation often follows a staged path, starting with back-end and operational tasks before moving into outward-facing creative workflows. This can be sensible, however it should not obscure the potential impacts of AI on the skills pipeline.

Some generative AI tools can perform tasks traditionally assigned to junior or entry-level workers. If those tasks are automated without a plan for training and progression, established routes into the creative industries could be weakened.

As AI Champion, my objective is to promote trust in augmentative tools that support human creativity. That means encouraging adoption where AI helps creative workers spend more time on higher-value activity, while ensuring that skills, job quality and entry routes are built into responsible adoption.

Opportunities

If shaped responsibly, AI adoption can support 5 major opportunities for the creative industries.

First, AI can improve productivity and competitiveness. It can reduce repetitive tasks, support production planning, improve post-production, assist with localisation, accelerate research and help firms respond to tighter budgets and delivery timelines. Used well, it can reduce the risk of ambitious creative projects, free up more time for creative development and support investment in new creative IP.

Second, AI can support the growth of createch businesses. Createch being a business focused on delivering new creative technologies. The UK has existing strengths in creative content, games, design, software, visual effects, immersive technology and cultural assets. Responsible AI adoption can help more firms combine these strengths, develop new products and services and scale internationally.

Third, AI can improve access to capability for smaller firms. Shared tools, testbeds, guidance, training and anchor institution models can help SMEs, microbusinesses and freelancers access capabilities that would otherwise sit mainly inside larger organisations. If designed well, this can spread opportunity across the UK, including through regional creative clusters.

Fourth, AI can support higher-quality creative work by removing avoidable manual burdens and creating more time for creative judgement, iteration and audience engagement. The value of AI should not be measured only in speed or cost reduction. It should also be judged by whether it helps creative workers produce better work, reach audiences more effectively and expand consumer choice.

Fifth, the UK can build international leadership in trusted creative AI. A distinctive UK approach could combine innovation with creative protections and public confidence. This would strengthen the UK’s reputation as a world leader in the creative industries, attract investment and support long-term economic growth.

By accelerating our adoption of these tools, there is the potential to unlock an inspiring future for the sector. We can look towards a landscape where independent creatives have the ability to match production capabilities of global studios. We can give our workforces the freedom to focus entirely on what they do best. It’s about giving our creators the time, bandwidth and backing to thrive.

Recommendations

AI gives the industries a major opportunity to benefit from emerging technologies. But adoption will only succeed if it is trusted, practical and grounded in the realities of creative work.

The recommendations below set out a roadmap for a creative sector that can use AI responsibly and confidently. They put creative businesses, freelancers, workers and rightsholders at the centre. They are designed to support growth, strengthen human creativity, widen access to new tools and ensure that the UK remains a world leader in the creative industries.

Recommendation 1: establish an augmentation-first approach to AI adoption

Action: government and industry should establish an augmentation-first approach to AI adoption in the creative industries.

Many creatives are concerned that AI may replace their craft. That concern must be taken seriously. The value of the UK’s creative industries comes from human creativity, judgement and originality. AI should therefore be adopted to enhance human creativity, productivity and opportunity, not to displace the people and creative content that make the sector world-leading.

As AI Champion, I would encourage government and industry to publish an augmentation-first statement for the creative industries and use it as the foundation for my engagement and advocacy. This should support human creative control, encourage best practice before major AI deployment, and link adoption to skills and transition support. To ensure it reflects the realities on the ground, this must be designed with creative workers and trade unions to ensure it acts as a clear pro-worker statement.

Recommendation 2: continue to develop a responsible AI framework for the creative industries

Action: government should continue to develop the responsible AI framework needed to give creative businesses, creators, rightsholders, clients and audiences confidence in AI.

Responsible adoption depends on trust. Businesses will not adopt AI at scale if they cannot understand how tools have been trained, whether rights have been respected, whether outputs can be used commercially, whether likenesses are protected, and when audiences or clients should be told AI has been used.

The creative industries are particularly exposed to unresolved questions around copyright and AI. These issues do not need to be fully resolved before adoption can happen, but the direction of travel must be clear enough to support confidence.

Government should therefore continue to create conditions for long-term adoption by progressing its work on copyright and AI. This includes the measures announced in its 18 March 2026 Report and Impact Assessment on Copyright and AI:

• launching a consultation on digital replicas to address harms where a person’s likeness is replicated without permission, while protecting legitimate innovation.
• establishing a taskforce on AI labelling to propose best practice so consumers understand whether content has been made using AI.
• publishing a review of mechanisms available for creators to control their works online, including standards, technical solutions and best practice on input transparency.
• launching a working group on independent and smaller creative organisations to explore whether government should support their ability to license content.

The Creative Content Exchange could also support responsible AI adoption if it develops as a trusted marketplace for digitised cultural and creative assets. It could help content owners commercialise their assets while giving data users easier access to high quality, lawful and trusted material. This could support the next wave of creative innovation and help develop high-value AI models.

As AI Champion, I would use my engagement with the sector to ensure that the practical concerns of creators, freelancers, SMEs and creative businesses are fed into this wider responsible AI framework.

Recommendation 3: raise practical AI knowledge and confidence

Action: government and industry should celebrate responsible AI use, raise practical AI knowledge and support peer learning.

Creative businesses should not be expected to adopt tools they do not trust or understand. Equally, responsible AI use should not be treated as anti-creative. The sector needs a more mature conversation, one that recognises risks, shows practical benefits, and makes responsible adoption visible.

As AI Champion, I will support this through 3 practical initiatives:

• Abbey Road AI salons: I will host breakfast roundtables with leaders and practitioners from the UK creative sector to share examples of AI use, including tools, impacts and lessons learned. These will be practical discussions focused on real adoption journeys.
• Roadshows: I will run quarterly showcases on how AI is reshaping the creative industries ethically and augmenting human creativity. These should take place across the UK and be linked to regional creative clusters, universities, cultural institutions and local businesses.
• Inside the Creative Stack: I will develop a short-form film series showing how leading creatives use technology in their creative process. This will help demystify AI and show that responsible technology use can sit within authentic creative practice.

I will share learning from these activities with government and industry to build the evidence base and support practical case studies for the AI Toolkit.

Government and industry should support a national programme of knowledge building, including:

• AI demonstrators: curated examples of safe and effective AI adoption that reduce perceived risk of AI across the sector and support knowledge-sharing.
• Peer learning networks: forums for organisations to exchange lessons, show what works, and collaborate.

As AI Champion, I would promote these examples through my salons and roadshows, and help ensure that learning reaches smaller firms, freelancers and regional hubs.

Recommendation 4: develop trusted guidance, standards and tools for AI adoption

Action: government and industry should develop business friendly guidance, standards and tools to help creative organisations adopt AI responsibly and confidently

AI adoption is currently fragmented. Many creative businesses do not know which tools to trust, what questions to ask suppliers, how to assess productivity gains, how to manage client expectations or how to put proportionate safeguards in place.

The sector needs clear, practical and trusted guidance that helps organisations make decisions about real creative and commercial workflows. Creators should be able to adopt AI with confidence where they choose to do so. There should also be a more consistent approach to implementation, while recognising that requirements differ by subsector, business size and use case.

Guidance and tools should help businesses and freelancers answer practical questions, including:

• Is this tool appropriate for this use case?
• What rights, data, confidentiality or sustainability issues arise?
• What questions should we ask suppliers?
• How do we assess whether AI is delivering genuine productivity or quality gains?
• How should we manage client expectations and disclosure?
• What level of human oversight is needed?
• When should AI not be used?

I recommend government and industry consider the following:

• AI toolkit: government and industry should develop a single, practical AI toolkit for freelancers, SMEs, microbusinesses and larger organisations. It should include guidance on AI use, procurement, template policies and case studies. The toolkit should reduce search costs, improve confidence and help businesses avoid unsafe or inefficient experimentation.
• Practical adoption standards: government and industry should explore light touch adoption standards or good practice principles for responsible AI use in the creative industries. This could form part of the augmentation-first approach and create a shared baseline for responsible practice.
• Trusted tool and support directories: government and industry should consider a trusted directory or signposting service to help businesses find relevant tools, guidance, training, testbeds, R&D labs, funding and support. In my role as AI Champion, I will consider what role Abbey Road Studios can play in convening such a list.

Recommendation 5: support skills, leadership and workforce transition

Action: government and industry should create practical AI capability programmes for creative leaders, workers and freelancers

A lack of skills and internal capacity is one of the main barriers to AI adoption. The creative sector needs to build the right capabilities for long term adoption and growth. This means upskilling both leaders and workers so businesses can adapt to emerging AI workflows.

Government and industry should support the sector to identify, develop and embed the skills needed to use AI responsibly. Future workers should also enter the sector with the practical AI literacy they need. Working in lockstep with trade unions and guilds to ensure that workers are given a formal seat when designing training and upskilling programmes, we can ensure that AI adoption elevates job quality and protects established entry routes into the sector.

I recommend that government and industry should:

• Make creative businesses visible in AI skills programmes: the creative industries should be properly represented in national AI skills initiatives like the AI Skills Boost Programme. Training and upskilling in AI technologies should reflect the needs of the sector from independent creators to larger firms.
• Create practical AI learning: government and industry should create and promote courses designed around real creative use cases and teach businesses how to use AI effectively and responsibly. The industry should make use of existing units which seek to boost AI literacy for individuals working in leadership roles.
• Support leaders to manage change: AI upskilling and strategy must be implemented across the career ladder. Creative leaders need help to understand how AI may affect teams, skills, workflows and business models, so they can introduce it in a way that builds trust. The Help to Grow: Management Scheme is an example of an existing initiative for small business leaders wanting to grow AI in their business.
• Develop hybrid creative-technical roles: the sector should invest in people who can bridge creative practice and technology. This could mean supporting the development of pathways for these hybrid roles and industry exchange programmes to upskill teaching professionals.

Recommendation 6: reduce cost barriers to responsible adoption

Action: government should explore targeted financial support to help smaller creative businesses and freelancers adopt AI responsibly.

Access to finance is a material barrier. Current funding often focuses on R&D and new technology creation rather than adoption and transition costs. SMEs, microbusinesses and freelancers are less able to experiment with AI or implement tools after initial testing because adoption carries real costs.

These costs include subscriptions, compute, cloud access, AI tokens, integration, legal advice, staff training, workflow redesign, quality assurance, cyber security and the opportunity cost of time spent evaluating tools. Without support, responsible AI adoption may become a privilege mainly available to larger businesses.

I therefore support financial assistance that goes beyond the traditional innovation funding and helps smaller businesses explore and adopt AI responsibly. Support should be visible, transparent, targeted, evidence-led and evaluated for value for money.

Government should consider:

• Pilot funding or pilot access: funding should help SMEs and freelancers test new technologies, build capacity and increase confidence. It could also support AI developers by creating opportunities to test models and ideas with creative businesses.
• Adoption grants: government should consider grants for businesses and individuals that want to adopt AI but lack the financial resources to do so. Applicants should be able to explain the need, intended use and expected benefits,
• Support for experimentation and transition costs: funding should cover not only tools, but also the wider transition costs of responsible adoption. This would be especially valuable to freelancers, SMEs and microbusinesses.
• Advice vouchers: government should consider smaller vouchers for legal, technical or commercial advice. Many businesses need help understanding contracts, IP, data protection, procurement and workflow change, not just money for software.

Recommendation 7: expand nationwide AI infrastructure, through testbeds, anchor institutions and regional support

Action: government and industry should develop nationwide support infrastructure that gives SMEs and freelancers safe access to AI resources, expertise and adoption support

For many businesses, the challenge is not simply finding an AI tool. It is safely testing whether that tool works in a specific creative workflow. This requires infrastructure, technical expertise, data, legal advice, time and evaluation support.

AI infrastructure costs, including cloud access, compute, data access, AI tokens, specialist software, and technical support, can be prohibitive for smaller creative businesses. The goal should be to reduce adoption risk and help smaller firms access expertise that would otherwise be out of reach.

AI adoption should not be concentrated among larger firms or in a small number of locations. The creative industries are shaped by place, identity, networks, talent and cultural context. Adoption support must therefore be locally accessible and connected to existing creative clusters.

As AI Champion, I support the distribution of growth and innovation across the UK so that regional creative economies can participate in, and shape AI adoption. I encourage government and large firms at the intersection of creativity and technology to identify existing offers, improve signposting, develop partnerships and ensure private infrastructure complements public infrastructure.

This requires action across 3 key areas:

1. Practical testbeds: government and industry should move beyond theoretical R&D and support practical, everyday AI adoption.
   • Existing programmes should explicitly support AI adoption in creative workflows, not only R&D and new technology development.
   • Subsector-specific test environments should support use cases across film, music, fashion, games and other creative industries.
   • Testbeds should generate case studies and learning resources that show what works, what does not, and what safeguards are needed.
2. Anchor institutions: large creative firms, studios, broadcasters, universities, and technology companies can help smaller firms access expertise and infrastructure.
   • Anchor institutions should provide practical access to compute, software tools, technical environments, mentoring and workshops.
   • Private and public infrastructure should be better aligned so existing offers are easier to find and use.
3. Regional growth and distributed innovation: regional creative economies must be able to participate in and shape AI adoption.
   • Regional hubs should host hands-on demonstrations and training designed around local business needs and subsector strengths.
   • Support should build on existing creative clusters, universities, local authorities  and industry networks.
   • Local hubs should connect to national testbeds, R&D programmes and peer learning networks.
   • Evidence from across the UK should inform national policy, so policy reflects regional realities as well as the experiences of larger, better-connected firms.

Recommendation 8: review, evaluate and update the plan as technologies evolve

Action: government and industry should treat the AI adoption plan as a live programme that is updated as technology, evidence and market practice evolve.

AI technologies are changing quickly. Tools that are experimental today may become standard within a year. Risks, legal frameworks and client expectations may change. Creative practice will continue to adapt alongside emerging technology.

This plan should therefore be reviewed and updated regularly. This should include:

• Continuous evidence gathering: government and industry should continue to build evidence on adoption by subsector, firm size, region and workforce type. Evidence should also cover productivity gains, implementation costs, workforce impacts, client expectations, public confidence, sustainability impacts and barriers faced by underrepresented founders and workers.
• Evaluation of interventions: toolkits, training, grants, testbeds and other interventions should be evaluated. Evaluation should assess whether support reaches SMEs, freelancers and microbusinesses; improves confidence and capability; changes adoption behaviour; produces measurable benefits; and represents value for money.
• Regular updates to guidance: the AI toolkit and associated case studies should be updated regularly. Outdated AI guidance can quickly become misleading.

As AI Champion for the Creative Industries, I will use salons, roadshows and sector engagement to gather feedback and feed it back into government. This will help ensure the plan remains practical, current and grounded in the needs of creative businesses, freelancers, workers and rightsholders.

Conclusion

The UK creative industries are already adopting AI. The question is no longer whether AI will affect the sector, but how adoption is shaped.

The opportunity is significant. AI can help creative businesses reduce repetitive work, improve workflows, reach audiences, develop new products and compete internationally. It can support the growth of creative businesses and strengthen the UK’s global leadership in creativity and technology. By accelerating our AI adoption with the right frameworks and support, our creators can secure a lasting international advantage.

But if adoption is opaque, extractive, poorly governed or concentrated among larger firms, it could undermine trust, disrupt entry routes and widen existing inequalities across the creative industries.

That is why the UK needs a practical, trusted and augmentation-first adoption plan for the creative industries.

As AI Champion, my role is to convene, listen, challenge, champion responsible use and build confidence across the sector. I want to support a creative economy where technology strengthens human creativity, more firms can scale, freelancers and small businesses are not left behind, and the UK becomes the global home of trusted createch.

AI adoption in the creative industries should not be something that happens to the sector. It should be something the sector shapes.

1. Creative UK (2026), CreativeUK AI & Emerging Technology Adoption in the Cultural and Creative Industries Report
   
   Angus Finney, Brian Tarran, Rishi Coupland (2025), CoSTAR, AI in the Screen Sector: Perspectives and Paths Forward ↩