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The Art in Artificial Intelligence

Impact of Generative AI on Canada’s Creative Sector Workers

MARCH 2026

Report - English

Rapport - Français

Authors

Viet Vu

Viet Vu
Manager, Economic Research

Mahtab Laghaei

Mahtab Laghaei
Policy Analyst

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The Art in Artificial Intelligence: Impact of Generative AI on Canada's Creative Sector Workers is part of the portfolio of work by the Future Skills Centre, which is funded by the Government of Canada’s Future Skills Program.

The opinions and interpretations in this publication are those of the author(s) and do not necessarily reflect those of the Government of Canada.

Contributors

  • Catherine Amburgey
    Zaynab Choudhry
    Suzanne Bowness, Codeword Communications
    Marie-Pierre Lavoie Translation Inc.

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Executive Summary

Generative artificial intelligence (AI) is reshaping Canada’s creative sector, introducing both productivity-enhancing opportunities and significant risks to employment, compensation, and intellectual property protections. The creative sector, which encompasses artistic production, media, design, marketing, and cultural heritage activities, plays a vital economic and cultural role in Canadian society, contributing an estimated $65 billion to Canada’s GDP and employing roughly 690,000 workers.

While creative workers have historically adapted to technological change, generative AI presents a unique disruption. Unlike digital tools that augment creative production, generative AI enables non-creatives to generate creative outputs with minimal training, potentially weakening demand for professional creative labour. At the same time, concerns around unauthorized use of creative products to train generative AI models, and copyright infringement, have intensified tensions.

This report examines the implications of generative AI on workers in the creative sector through an occupational and task-based analytical framework previously applied across Canada’s labour market. The findings show that overall AI exposure in the creative sector closely mirrors the broader workforce; however, creative occupations exhibit relatively high levels of complementarity with the technology, suggesting AI tools will often assist rather than replace workers.

Task-level analysis reveals more nuanced dynamics. Within the creative sector, generative AI use is concentrated in tasks with low error consequences, such as editing, generating graphics, and drafting creative content, while physical, managerial, and high-stakes informational tasks are less affected by generative AI tools. This pattern highlights a key disruption channel: even imperfect AI outputs may be widely adopted when the consequences of errors are limited, and cost savings are substantial. As a result, “good enough” AI-generated work may reduce demand for entry-level and freelance creative labour, potentially weakening talent pipelines.

Qualitative evidence indicates widespread ambivalence among creative professionals. Many people report using generative AI for ideation and minor editing, while expressing strong concerns about intellectual property rights, worker displacement, and erosion of creative authenticity. Meanwhile, workers outside the sector increasingly use these tools to generate creative outputs, bypassing professional creatives and reshaping market demand.

To support responsible generative AI adoption, we recommend that creative professionals adopt workflow-integrated AI tools, pursue coordinated intellectual property protection strategies, and emphasize the authenticity and distinctive value that their work provides. Organizations using generative AI for creative purposes should strengthen copyright safeguards, carefully assess quality-cost trade-offs, and account for reputational risks associated with AI-generated content.

Ultimately, generative AI’s impact on Canada’s creative sector will depend less on direct occupational replacement and more on shifts in demand driven by widespread non-professional use of AI creative tools. Ensuring positive outcomes will require policy interventions addressing intellectual property, labour market transition, and skills development, alongside adaptive regulatory approaches that balance innovation with worker protection.

Introduction

The creative sector (sometimes called the cultural industries) encompasses workers and organizations that contribute to the production and dissemination of culture, defined as “creative, artistic activity and the goods and services produced by it, and the preservation of heritage.”1 The creative sector includes purely artistic endeavours (such as theatre productions), as well as creative inputs into business processes (such as creative outputs involved in marketing, communications, and public affairs). Collectively, the sector contributes not just to our economy, but also to preserving and advancing Canadian heritage, culture, and even democracy.

Workers in the sector are no strangers to emerging technologies. For decades, the creative sector has been shaped by advancements in information and communication technologies. From new audiences resulting from advances in communication technologies (including radio, television, internet, smartphones, social media and streaming services), to new digital tools to generate creative outputs (including Photoshop and Canva), creative workers have had to learn to incorporate new technologies while protecting their livelihood.

However, the introduction of generative artificial intelligence (AI) technology could introduce a fundamentally different type of challenge to creative workers — one that threatens copyright and intellectual property rights to creative works, and undermines the role creative workers play in the production of creative and cultural products.

Many creative workers have raised concerns and sought redress for the perceived threats posed by AI, with high-profile examples like the Hollywood screenwriters strike that challenged the use of AI in the film industry,2 and numerous lawsuits by creators and media companies against major AI companies claiming widespread theft of intellectual property.3 In Canada, a copyright suit filed by Canadian news publishers against OpenAI is proceeding through the courts.4

The relationship between the creative sector and generative AI deserves a comprehensive, multidisciplinary treatment that we are ill-equipped to provide here. Instead, we aim to contribute to this conversation through a specific analytical lens: assessing how the technology is likely to impact Canada’s creative sector workers. Building on our previous work exploring AI’s impacts on jobs and skills across Canada’s labour market,5 this study employs unique methods and data sources to identify occupational contexts where creative sector workers are most likely to encounter generative AI, as well as identifying tasks that are at the highest risk of impact. We hope to guide responsible adoption by identifying what these changes mean to Canadian creatives, and by providing recommendations to sector stakeholders.

Sector Background

The creative sector is notoriously hard to define, given the diverse contexts of “creative” work. Many creative workers are employed directly with creative agencies, others work in-house within larger non-creative firms, while many others work as freelancers or solo entrepreneurs. While the intrinsic value of creative sector outputs is hard to evaluate, given the social and cultural benefits, a recent study found the sector contributes $65 billion to Canada’s GDP.6 For the purpose of this study, we apply an industry standard definition that captures any worker who reports working in a creative industry, resulting in an estimated 690,000 workers in 2021.7

The diversity of the creative sector is also reflected in generative AI adoption trends, which fall at both ends of the spectrum. Our research found that firms within the information and culture industries had the second highest rates of AI adoption in 2024, whereas firms within the art, entertainment and recreation sector were among the lowest AI adopters.8 As the discussion in the next section reveals, narratives around generative AI adoption are as polarized as these adoption trends.

Sector generative AI adoption discussions

Within the creative sector, the perceptions and narratives about generative AI technology differ significantly between those who directly produce creative work and those who are not directly involved in production. In fact, the strongest voices advocating for the potential of generative AI in the sector have often come from outside the sector.9 To ensure that creative sector voices are included, the evidence in this section combines insights from both a literature review and an informal engagement with multiple Canadian creative professionals.

Those encouraging the adoption of generative AI in the creative industry stress the continued importance of creative professionals in the diffusion process.10 Generative AI can support the work of creatives as a tool for more efficient prototyping and ideation, and inspire creatives to consider directions they would not otherwise. Human judgement and emotional depth remain critical for determining the final output. On the other hand, many stress that, if companies rely solely on generative AI for content creation, output quality will diminish and result in widespread wage reduction and job losses.11 This reflects a common view among creative professionals that generative AI cannot replace human creativity.12 However, some creative professionals note that their voices have been absent from conversations on how generative AI should be deployed in the sector.13

A significant level of distrust is directed at the practices AI companies use to develop large language models (LLMs), not at generative AI itself. An Adobe survey of young digital content creators found that 69 per cent were concerned that their work would be used without authorization to train generative AI tools.14 In another survey, 96 per cent agreed that they had not given permission for generative AI technologies to train on their art.15 There is a growing body of copyright-related litigation related to generative AI, with plaintiffs ranging from visual artists to open source software advocates to large media companies, image repositories and music producers.16 Fears around unauthorized use have led some artists to advise others against posting their work online, at the expense of emerging artists' visibility and ability to attract new customers.17 Other creatives shared that they now have to defend their work against accusations that it uses AI.

Yet, these fears have not prevented use of these tools in creative work. Adobe’s survey of young digital content creators shows that 86 per cent have adopted generative AI tools, often for minor editing, alternations, as well as upscaling. Another survey shows almost 49 per cent of creative professionals use generative AI tools daily, while 34 per cent use them occasionally for specific tasks.18

Even when creative professionals are not directly using generative AI in their work, many may still be exposed to it. Qualitative research by the Institute for the Future of Work shows that some creatives report being hired to oversee and evaluate creative content produced by generative AI rather than contracted to make their own work.19 This trend may have an impact on how creatives value their own competencies. Such reduction in active involvement by creatives in their work has also raised fears around diminishing pipelines for emerging talent to hone their crafts. Some work has pointed out preliminary signals that freelance contracts have been marginally reduced since the introduction of generative AI technologies, though more comprehensive analysis is needed.20

As such, many creative industry professionals we engaged agreed that the creative industry is at risk of job displacement due to existing issues of underemployment in the sector. To solve such issues, many saw government intervention as one of the few paths available to ensure the transition to generative AI adoption is not too disruptive to the livelihoods of creatives.

Other studies advocate for efforts in the creative industries to ensure workers are prepared to harness generative AI as a tool. A World Bank discussion paper advises creative directors and upper management to reskill their creative teams.21 Proponents in Canada’s tech sector call for upskilling programs that focus on training creative workers on “prompt engineering” (a skill to effectively submit input prompts to generative AI tools) to understand how to iteratively develop outputs.22 Many such proponents also argued that generative AI tools can “democratize” the sector by allowing more people to pursue creative endeavours, though with considerable controversy.23

Written sources, as well as the creative industry professionals engaged for this study, generally agreed that national policies were required to address generative AI’s impact in the creative field, both to strengthen copyright and intellectual protection regime for artists in the training and deployment of AI tools, and its impact on jobs in an industry that faces higher levels of underemployment.24 However, some argued for adaptive regulation (where regulation is intentionally designed to be updated iteratively) to address emerging issues in order to ensure that governance efforts do not stifle innovation.25

Creative Sector Occupational AI Exposure

With the sector-specific context about AI adoption in mind, this section provides two lenses for analysis: the AI exposure of workers in the creative sector and likely impact based on job tasks, and the actual use of generative AI tools for creative tasks. This analysis informs the identification of common use cases for tasks associated with creative professionals, and considerations for responsible AI deployment and use to support productivity-enhancing outcomes for the sector.

Previous research has examined the degree to which occupations in the Canadian economy are exposed to generative AI, and how well AI-use may complement work.2627 Exposure to AI reflects the likelihood that a worker encounters an AI system when they work, where high-exposure occupations are those most likely to interact with AI systems in day-to-day work. Complementarity to AI measures the nature of a worker’s interaction with AI. An occupation with high complementarity to AI means that a worker’s interaction with AI could be assistive to their job tasks, whereas low complementarity suggests an AI system is more likely to replace parts of their tasks. We estimate these two values for each occupation in the creative sector (with employment data coming from the 2021 long-form census). This largely follows the approach introduced in previous work.28

29
Table 1

Breakdown of employment in the Creative sector by AI exposure & complementarity

Exposure to AI Complementarity to AI Number of employees Employment share (percentage) Employment share (percentage of the overall Canadian workforce)
High Exposure High Complementarity 173,675 25 27
Low Complementarity 248,945 36 29
Low Exposure High Complementarity 114,515 17 14
Low Complementarity 148,645 22 29

Figure 1, which presents the distribution of workers within the creative sector across the four quadrants, fairly closely resembles that of the general Canadian workforce. This stands in contrast to other sectors that have been examined in the past using this framework, including Canada’s public and financial sectors.30 31 While the creative sector has a slightly higher share of workers in the high-exposure, low-complementarity quadrant (36 per cent compared to 29 per cent for the general workforce), this distribution reflects the fact that many jobs in the creative sector have a manual aspect to their work (tasks that require physical interaction with the world), limiting the scope of exposure for generative AI technology to impact them.

Such distribution shows the variation of work that exists in the creative industry. For some occupations (e.g. journalists in the HE-HC quadrant), AI, while prevalent, may present opportunities to augment and enhance their work, while for others (e.g. web developers and programmers in the HE-LC quadrant), AI may mean a reorientation of work, away from coding of websites, to focus on the design aspect of their work. This means that, for creative occupations, using AI technology is more likely to assist many work tasks, though creative sector professionals will not be immune to negative impact from AI diffusion. However, such a channel of impact is not due to how AI is used in a creative occupations’ context, but due to usage by non-creative individuals to perform creative tasks, something we explore in the following section.

Task exposure to AI in the creative sector

Reproduced from a previous Dais study, “Banking on AI: Generative AI Adoption in Canada’s Financial Sector”

The occupational analysis provides broad patterns of likely AI exposure and complementarity. However, it provides little insight into where AI could be applied, or how such exposure maps to actual creative task-level AI usage, which is critical in identifying high potential areas to deploy generative AI. To supplement our occupational analysis, we introduce a new analytical method and a conceptual framework that links automation exposure to actual usage of generative AI systems, anchored in a conceptual framework centred around the idea of task-specific error consequence.

One factor that has often been overlooked when it comes to task exposure to automation is the consideration of quality differentials between tasks performed by humans as compared to tasks performed by machines, and how relevant those quality differentials are in the decision to automate a task. Some tasks may have large quality differentials between work performed by AI and humans, but may be used in instances where those quality differentials matter less (e.g. route navigation for taxis, where different routing marginally impacts the time it takes to reach their destination). In other tasks, despite having a low-quality differential between AI doing that work and humans doing that work, those small quality differences can have drastic consequences (e.g. prescribing and dispensing medication where getting exact amount and medication is essential).

To improve our ability to reveal links between automation exposure, and real AI usage, we introduce a measure on the suitability for tasks to be performed by AI. We distinguish between different automation suitability of each task by understanding the relative size of the consequence of errors, or providing lower quality goods in performing work tasks. We broadly call this a task’s error consequence and ranks work tasks (using the Detailed Work Activities(DWA) construct in O*NET, a US-based occupational taxonomy) on tasks that have the highest consequence of making errors, to tasks that have the lowest consequence of making errors.

We use data on actual AI tool usage,from Microsoft’s CoPilot32 and Anthropic’s Claude,33 from all users of the tools. The usage is grouped at different levels, with Claude’s usage being more granular at the DWA level, while CoPilot’s usage at Intermediate Work Activities (IWA), which is one level higher than DWAs.

We then calculate these concepts for tasks associated with core occupations in the creative sector, defined as occupations that relate to core outputs of workers in the sector. For the creative sector industry, this includes occupations such as designers, painters, writers, and video editors. This allows us to discuss AI usage for core business needs, as opposed to ancillary usages of the technology within the sector.

Taken together, this analysis allows us to better understand how well automation exposure maps to the suitability of automating technology in performing tasks, to actual AI usage, where such insights will allow better strategic decisions about where to deploy AI to be made. In a forthcoming paper, we specifically assess the validity of this analytical frame as a way to understand the variation in AI adoption across the task and occupational space.

Table 2

Top 5 and bottom 5 Detailed Work Activities (DWAs) by real generative AI usages (across all users, not just creative)

DWA description
Top tasks Edit written materials
Write material for artistic or entertainment purposes
Collaborate with others to determine design specification or details
Program equipment to perform production tasks
Create computer-generated graphics or animation
Bottom tasks Apply finishes to artwork, crafts, or display
Coordinate construction or installation activities
Select staff, team members, or performers
Repair textiles or apparel
Build models, patterns, or templates

Table 2 presents the most- and least-common creative DWAs (tasks) the generative AI systems are used for. The most commonly performed tasks using generative AI tools involve core creative sector tasks that can be done digitally such as editing and writing materials, or programming and creating computer-generated graphics. Conversely, the list of bottom five tasks suggests that generative AI tools are less commonly used for certain conceptual design tasks (building models or patterns), physical tasks (applying finishes to art and repairing textiles), or managerial tasks (coordinating construction and selecting staff, teams or performers). Figure 2 plots each task (DWA) along two continuums - exposure to AI, and the level of error consequence attached to each task.

As can be seen in Figure 2, most tasks associated with core creative sector occupations have middle to low consequences of error, with a relatively high spread across the exposure spectrum (mirroring the exposure pattern seen for creative sector occupations). However, tasks associated with the creative sector tend to have a lower risk of consequences resulting from errors, and may therefore face a greater risk of being performed using generative AI.

Table 3 confirms this observation by examining a number of illustrative tasks associated with core creative occupations. Creative tasks with lower error consequence such as “designing layouts for print publication” are associated with high Claude usage, while tasks with higher error consequence, such as “Gathering Information for News Stories” have much lower Claude usage.

Table 3

Illustrative Tasks in Core Creative Occupations

Task Occupational context Automation exposure (percentile) Error consequence(percentile) Current Claude AI usage(percentile)
Review art or design materials Art directors 37 12 39
Design layouts for print publications Graphic designers 62 24 89
Create musical compositions, arrangements or scores Music directors and composers 31 25 75
Gather information for news stories Journalists 74 63 26
Write informational material Technical writers 55 35 94

Overlaying this framework allows us to focus on an important channel of disruption faced by creative sector workers. Even if generative AI technologies are imperfect, they may still be adopted widely if the consequences arising from small mistakes created by these tools are tolerable.

In the case of tasks most relevant for creative sector occupations, such a measure focuses on whether imperfections in a piece of writing or artwork may create negative business consequences. For the vast majority of work, such imperfections likely only create minor costs or inconvenience to the business. Exceptions include where the output of the creative professional directly has to do with the accuracy of information contained in the work (such as tasks associated with journalists and news writers that we profiled in table 2).

In other words, the risk from generative AI to workers in the creative sector may not come from the fact that these technologies are truly capable of matching creative workers skill one-to-one, but for the work to be “good enough” and for the individual penalty of “good enough” work reaching the market to be sufficiently small, that companies consider adopting these technologies instead of hiring or contracting a creative professional. Such a reduction in demand may lead to job losses for the sector, and fewer “low-stakes” work to train junior designers and creatives. Recent empirical evidence for illustrators have provided early evidence for such dynamics.34

Guidance for Responsible AI Adoption in Creative Firms and Work

Reflecting the important delineation in the previous section between AI exposure of creative sector jobs, and broad-based usages of AI tools for creative tasks, this section offers recommendations for responsible AI adoption both for creative industries and for all users of AI tools for creative purposes.

For creative industries

Importantly, our focus is on those directly belonging to the creative industry who create (either primarily or derivatively) digital products (e.g. music, writing, graphics). This focus may not be relevant to those who only produce analog works that have to be created physically (e.g. sculptures).

Recommendations:

  • Articulate unique value and authenticity to their work: One potential consequence of wide generative AI technology is in both a higher quality differential for clients to consider employing the services of creative professionals, and assurances that the work creative professionals put forward is genuine, and made by the artist (as opposed to generative AI tools). In practice, this could mean submitting “proof-of-work”, or relying on “authentic imperfection” to show work has been conducted by humans. Some have also suggested that some creative sector professionals will have to manage client expectations to incorporate AI tools in their work (as a cost savings measure).
  • Identify and use generative AI tools that integrate into a creative person’s workflow: Many off-the-shelf generative AI products are not designed for integration into creative workflows. As a result, it is important to identify generative AI tools that are specifically designed to help creative professionals. These products often focus on speeding up or optimizing specific work tasks, as opposed to fully replacing it, and are often designed by companies that create other digital tools to assist creative professionals, or by those within the industry.
  • Approach intellectual property protection from both a technical and social approach: One of the major threats experienced by the creative sector when it comes to the introduction of generative AI is the potential for protected intellectual property to be used without license or attribution. Preventing unauthorized uses of a creative’s work must have both technical and social consideration, from placing visible and invisible watermarks on digital images, to joining sector-wide intellectual property (IP) protection efforts. There is wide support for such coordination schemes, where one survey shows 84 per cent of artists surveyed would sign up for licensing mechanisms that pay artists whenever their work is used by AI.35

For creative purposes

A separate set of recommendations is geared to users of AI tools for creative purposes outside of the creative sector (e.g. a financial company hiring designers for their advertising work).

Recommendations:

  • Ensure robust copyright and intellectual property considerations: Many generative AI tools that are available on the market have a well-documented history of generating copyright-protected content (likely reflecting the existence of such copyright-protected content in its training data). Robust investments must be made to ensure copyrighted work does not end up in final product that can open up companies to legal liabilities.
  • Be clear-minded about the quality-cost trade-off: Despite rapid improvements, the quality of creative work that is produced by generative AI tools are still of inferior quality (both writing and design). While the cost savings can be great, it’s important to consider that for high-profile usage, the subsequent cost of fixing lower quality goods may exceed the cost savings initially provided by generative AI tools.
  • Consider the reputational cost of obvious AI artifacts: Even when efforts are made to minimize the visible signs of AI generation, recognizable artifacts of AI-produced content can remain. This is particularly true for visual material produced by generative AI tools, which can be difficult to meaningfully edit after creation. In these cases, companies should consider the reputational cost of publishing work that has been wholly or substantially created by generative AI.

Conclusions

The creative sector has always been sensitive to advancements in technology, from the ability to record sound on vinyl discs, to rapid transitions in how people create and consume creative content from the rise of social media to streaming. Often, creative sector workers have been able to leverage these technologies to explore new mediums, and exercise their creativity. Crucially, however, past advances did not introduce capabilities for producing creative work without creative workers, as generative AI has. These new technologies enable those who have not been trained in creative fields to generate outputs, sometimes mimicking styles developed by creative professionals and artists.

This study finds that creative sector workers’ exposure to AI is comparable to the broader Canadian workforce, and that creative professionals are more likely to be in occupations with high levels of complementarity to AI (potential for assistance). The analysis of usage of CoPilot and Claude finds that creative workers and non-creatives use these generative AI tools in different ways. Creative professionals often incorporate AI into their workflow for minor tasks (such as touching up an illustration, upscaling a video, or using an LLM as a writing feedback tool). These uses of the technology are assistive, where the creative professional retains core contribution in a creative process. By contrast, non-creative users more commonly directly generate creative work, often from scratch. This pattern of usage cuts the central role creative professionals play in making creative work.

Envisioning a positive future where generative AI technologies are used responsibly to benefit creative professionals must involve clear standards that govern the use of these tools for creative outputs outside of the creative industry. Such a world will ensure that artificial intelligence can contribute to creating art, without devaluing artists themselves.

1

Stephanie Fieldings and Nisa Malli, A Portrait of Creative Entrepreneurship and the Creative Economy in Canada,” Brookfield Institute for Innovation and Entrepreneurship, 2020, https://dais.ca/reports/a-portrait-of-creative-entrepreneurship-and-the-creative-economy-in-canada/. 

2

Dani Anguiano and Lois Beckett, “How Hollywood Writers Triumphed over AI – and Why It Matters,” The Guardian, October 1, 2023, https://www.theguardian.com/culture/2023/oct/01/hollywood-writers-strike-artificial-intelligence.

3

Hayleigh Bosher, “Two AI Copyright Cases, Two Very Different Outcomes – Here’s Why.” The Conversation, December 2025, https://theconversation.com/two-ai-copyright-cases-two-very-different-outcomes-heres-why-270229.

4

Karadeglija, Anja. “Lawsuit by Canadian News Publishers against OpenAI Gets Green Light to Proceed in Ontario,” CBC, November 27, 2025, https://www.cbc.ca/news/canada/toronto/openai-new-publishers-lawsuit-ontario-9.6995520.

5

Vivian Li  and Graham Dobbs, Right Brain, Left Brain, AI Brain: AI’s Impact on Jobs and Skill Demand in Canada’s Workforce, The Dais, 2025, https://dais.ca/reports/right-brain-left-brain-ai-brain. 

6

Business Data Labs, “Artworks: The Economic and Social Dividends from Canada’s Arts and Culture Sector,” Canadian Chamber of Commerce, 2025, https://businessdatalab.ca/wp-content/uploads/2025/10/BDL_Artworks_Report_October2025_FINAL.pdf.

7

Estimates derived from census data. Creative sectors were defined to be workers operating in the following industry codes: 5111, 5121, 5122, 5151, 5152, 5414, 5418, 7111, 7113, 7114, 7115, 7131, 7139.

8

Statistics Canada, Survey of Digital Technology and Internet Use, 2023, 2024, https://www150.statcan.gc.ca/n1/daily-quotidien/240917/dq240917c-eng.htm.

9

Baptiste Caramiaux, Kate Crawford, Q. Vera Liao, Gonzalo Ramos and Jenny Williams, “Generative AI and Creative Work: Narratives, Values, and Impacts,” arXiv, 2025, https://arxiv.org/html/2502.03940v1.

10

Minos Bantourakis and Francesco Venturini, “The Impact of GenAI on the Creative Industries, and the Ethics and Governance We Must Put in Place,” World Economic Forum, January 21, 2025, https://www.weforum.org/stories/2025/01/the-impact-of-genai-on-the-creative-industries/.

11

Joseph Amankwah-Amoah, Samar Abdalla, Emmanuel Mogaji, Amany Elbanna, and Yogesh K. Dwivedi, “The Impending Disruption of Creative Industries by Generative AI: Opportunities, Challenges, and Research Agenda,” International Journal of Information Management 79 (2024), https://www.sciencedirect.com/science/article/abs/pii/S0268401224000070.  

12

Ted Chiang, “Why A.I. Isn’t Going to Make Art,” The New Yorker, August 31, 2024, https://www.newyorker.com/culture/the-weekend-essay/why-ai-isnt-going-to-make-art.

13

Baptiste Caramiaux et al, “Generative AI and Creative Work. 

14

Adobe, Creators’ Toolkit Report, 2025.

15

DACS, Artificial Intelligence and Artists’ Work: A Survey of Artists on AI, 2024, https://cdn.dacs.org.uk/uploads/documents/News/Artificial-Intelligence-and-Artists-Work-DACS.pdf.

16

Theresa M. Weisenberger, Diana C Milton and Harrison A Enright, “Case Tracker: Artificial Intelligence, Copyrights and Class Actions.” BakerHostetler, n.d. Accessed November 27, 2025, https://www.bakerlaw.com/services/artificial-intelligence-ai/case-tracker-artificial-intelligence-copyrights-and-class-actions/. 

17

Emma Cownie, “AI and Art: What You Need to Know,” Medium (blog), April 7, 2024, https://emmafcownie.medium.com/ai-and-art-what-you-need-to-know-a1fd76311b8b.

18

Joanna Birleson, “Beyond Adoption: The State of AI in Creative Work 2026,” Envato (blog), November 17, 2025, https://author.envato.com/hub/beyond-adoption-the-state-of-ai-in-creative-work-2026/. 

19

Michael Katell,  Mhairi Aitken, Kester Brewin et al, “Creative Industries and GenAI: Good Work Impacts on a Sector in Rapid Transition,” Institute for the Future of Work, June 5, 2025, https://doi.org/10.5281/zenodo.15535400.

20

Xiang Hui and Oren Reshef, Is Generative AI a Job Killer? Evidence from the Freelance Market, Brookings Institute, 2025, https://www.brookings.edu/articles/is-generative-ai-a-job-killer-evidence-from-the-freelance-market/ 

21

World Bank Group, Jobs in the Orange Economy: Impact of Disruptive Technologies, Discussion Note, no. 7 2023, https://thedocs.worldbank.org/en/doc/83524c72d2551661f645a89526934e55-0460012023/original/S4YE-DiscussionNote-JobsinCreativeEconomy-ImpactofDisruptiveTech-Final-Nov.pdf?deliveryName=FCP_1_DM202199. 

22

Aidan Tracey, “Reimagine Canada’s Creative Future,” Build Canada, April 11, 2025, https://www.buildcanada.com/memos/creative-future.

23

Maja Bak Herrie, Nicholas Rene Maleve, Lotte Philipsen and Asker Bryld Staunæs, “Democratization and Generative AI Image Creation: Aesthetics, Citizenship, and Practices.” AI & Society 40 (2025): 3495–3507, https://link.springer.com/article/10.1007/s00146-024-02102-y 

24

Lin Kyi, Amruta Mahuli, M. Six Silberman, Reuben Binns, Jun Zhao and Asia J. Beiga, “Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond,” arXiv, 2025,  https://arxiv.org/html/2501.11457v1.

25

Thomas Paris, “AI Is Reshaping the Creative Economy,” Knowledge@HEC, January 29, 2025, https://www.hec.edu/en/dare/innovation-entrepreneurship/ai-reshaping-creative-economy. 

26

Li and Dobbs. Right Brain, Left Brain, AI Brain, The Dais.

27

Graham Dobbs, Vivian Li, Viet Vu, and Andre Cote, Adoption Ready? The AI Exposure of Jobs and Skills in Canada’s Public Sector Workforce, The Dais, 2025, https://dais.ca/reports/adoption-ready-the-ai-exposure-of-jobs-and-skills-in-canadas-public-sector-workforce/. 

28

Ibid.

29

Due to rounding, figures may not add up to 100%

30

Ibid.

31

Viet Vu, and Mahtab Laghaei, “Banking on AI: Generative AI Adoption in Canada’s Financial Sector,” The Dais, 2026.

32

Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, and Siddharth Suri, “Working with AI: Measuring the Applicability of Generative AI to Occupations,” arXiv, 2025, https://arxiv.org/pdf/2507.07935.

33

Kunal Handa, Alex Tamkin, Miles McCain et al, “Which Economic Tasks Are Performed with AI? Evidence from Millions of Claude Conversations,” arXiv, 2025, https://arxiv.org/pdf/2503.04761. 

34

Sueyoul Kim, Ginger Zhe Jin and Eungik Lee, “Does Generative AI Crowd Out Human Creators? Evidence from Pixiv” NBER Working Paper, 2026, https://www.nber.org/papers/w34733.

35

DACS, Artificial Intelligence and Artists’ Work, 2024, https://cdn.dacs.org.uk/uploads/documents/News/Artificial-Intelligence-and-Artists-Work-DACS.pdf.