Part of:Generative AI's impact on industry sectors
How generative AI is changing creative work
As generative AI accelerates concepting and production, creative teams are evolving, requiring new skills, governance models and stronger human insight to guide meaningful work.
While AI can't replace human imagination, it can amplify creative work. Generative tools such as ChatGPT, DALL-E and Midjourney produce text, visuals, audio and video at scale. They can rapidly transform how businesses build and deliver creative content. Users can create marketing campaigns, product prototypes and brand stories in minutes.
As these tools scale, success in creative industries increasingly depends on talent coupled with effective human and AI collaboration. For example, Warner Bros. used generative AI to create thousands of augmented reality variations for The Flash marketing campaign, demonstrating the potential of human-AI collaboration at scale.
This type of collaboration unlocks unprecedented speed and scale. Campaigns that once relied on a few assets can now include hundreds of hyperpersonalized variations. This lets brands test directions earlier and content teams experiment more freely.
"This shift means moving from 'think, then make' to 'think while making,'" said Matt Silliman, senior vice president and head of production at ad agency Trade School. Teams can prototype earlier, test more frequently and eliminate weak ideas faster, he added, reshaping both the pace of production and the creative mindset itself.
Generative AI is used to create text, summaries, translations and code, among other content types.
How Gen AI is reshaping workflows
In the past, creative work often followed a set approach: people came up with ideas, created a design and collaborated to improve it. Campaigns, media projects and prototypes relied on skilled professionals working together over extended periods. As such, creativity was time-consuming, people-intensive and tied to individual expertise.
Today, generative AI is redefining how creative work begins and evolves. It's transforming the earliest stages of projects, so ideas take shape faster and in new ways. Adobe's recent survey as part of its "Creators' Toolkit Report" found that 86% of creative professionals have integrated generative AI tools into their workflows and are seeing improvements in their output.
This acceleration is visible in practice. Alex Weishaupl, managing director at consulting firm Protiviti, said designers are seeing a four-to-fivefold increase in speed from idea generation and concept development to the final result. Teams are using AI to quickly assemble inspiration boards and prototypes, letting them explore creative directions early in the process.
The nature of creativity is also evolving. The daunting blank page moment at the beginning of a project is less intimidating when AI provides a starting point. Creative professionals are increasingly seen as guiding, curating and refining ideas rather than producing every initial concept.
Industry leaders are observing this change firsthand. Narine Galstian, chief marketing officer at SADA, a consulting firm that specializes in cloud services, explained that generative AI makes the first draft faster and easier to produce. It's pushing creative teams to spend more time on conceptual verification and late-stage refinement, she explained.
Restructuring creative teams
As workflows evolve, generative AI is redefining creative teams, reshaping roles, responsibilities and collaboration models. Designers double as prompt engineers, writers guide AI-generated content and creative directors orchestrate human-AI collaboration across projects. These hybrid roles blend traditional skills with technical expertise to drive faster and more scalable workflows.
Many businesses are building dedicated AI operations units to oversee model testing, data governance, style consistency and quality control. Cross-functional collaboration is increasing, with creative teams working in parallel with data scientists, AI researchers and engineers.
Nishant Jeyanth, practice director at research firm Everest Group, explained that AI is shaping the content lifecycle -- from generating first drafts to automating tasks, such as resizing, cropping and tagging across formats. However, overreliance on these tools can lead to a lack of human touch, loss of cultural context and aesthetic fatigue, he said.
Silliman echoed this concern, emphasizing that creative teams should create from a human truth first and only bring AI into the process once intent and emotional stakes are defined.
The best creative talent now knows how to orchestrate AI like an instrument, not treat it like a vending machine.
Matt SillimanSVP and head of production at Trade School
This shift brings both benefits and challenges for talent development. Many tasks that could build skills are now partially or fully automated. As a result, team leaders must find new ways to help less experienced team members develop judgment, craftsmanship and core abilities while preparing them to work alongside AI.
Evolving skills requirements
Fluency with AI tools and the ability to design effective prompts are becoming as fundamental as traditional copywriting and design skills. At the same time, teams are learning to orchestrate workflows that integrate AI seamlessly, balancing automation with human oversight.
"The best creative talent now knows how to orchestrate AI like an instrument, not treat it like a vending machine," Trade School's Silliman said.
Data literacy is another emerging core requirement. Creative professionals need to understand how data sets shape outputs, recognize bias and curate inputs to align with brand voice and ethical standards.
Collaboration across disciplines is also expanding, with creative teams working closely with data scientists, engineers and governance leaders. As a result, hybrid skill sets that blend creativity with technical awareness are increasingly in demand.
Human sentiment always gets the final vote.
Matt SillimanSVP and head of production at Trade School
What remains uniquely human
Even as generative AI reshapes workflows and accelerates production, the essence of creativity still relies on distinct human capabilities that machines can't replicate. Emotional intelligence, empathy, humor and sensitivity to lived experience are still beyond the reach of machines. Human judgment ensures creative work resonates authentically with audiences. Cultural and contextual awareness is equally important; AI often struggles to navigate nuance across diverse communities and shifting social norms.
Ethical and strategic judgment also remains important. Leaders must decide when and how AI should be deployed, weighing risks regarding bias, intellectual property and authenticity. As SADA's Galstian observed, "No one has a definitive answer on how to handle copyright and attribution, making leadership oversight essential." Intellectual property, authenticity and responsible deployment still require human decision-making, especially as regulators and courts work through emerging gray areas.
Certain aspects of creativity should never be automated, Silliman said. AI can't feel embarrassment, heartbreak, jealousy, love, humor or pride, and those are the ingredients that separate content from creative work. "Taste, insight, lived experience, personal story and emotional risk can never be automated." Silliman said. "Human sentiment always gets the final vote."
Finally, original vision and intuition continue to set humans apart. While AI can remix existing patterns, breakthrough ideas often come from human leaps of imagination. Humans define the creative vision and guiding principles, while AI provides the speed and scale to bring ideas to life.
Organizational challenges and governance
As generative AI becomes embedded in creative workflows, organizations face new structural, ethical and governance challenges. Leaders must establish clear policies for deploying AI that ensure consistency in style, quality and ethical standards across teams.
AI governance frameworks are essential to ensure content aligns with brand, legal and ethical standards. Silliman highlighted three standards his team is working toward: transparent disclosure of AI use, legal and ethical clearance at every step and guardrails to protect brand integrity.
Yet intellectual property remains unsettled, as courts and regulators determine how copyright applies to AI-generated content and training data. Indemnities from major AI providers offer some protection, Protiviti's Weishaupl said, but uncertainty will persist until legislation or court decisions provide clarity.
Quality control adds another layer of complexity. With multiple tools and models in use, outputs can vary, making review, approval and refinement processes critical to maintaining standards.
Cultural adoption is equally important. Some creatives might feel threatened or deskilled by AI, which makes leadership communication, training and reassurance essential to building trust and confidence.
Budgets and operations are shifting as well. Licensing models, tool costs, AI oversight and training all affect how resources are allocated. Companies that treat AI as a holistic transformation rather than an efficiency upgrade are more likely to realize both creative and strategic benefits.
The future of creative work
Future creative ecosystems are likely to be hybrid, where AI handles scale and iteration while humans provide originality, emotional resonance and ethical oversight.
According to a McKinsey report, more than 12 million U.S. workers might need to transition to new roles by 2030 as AI reshapes the creative and knowledge-based workforce. For organizations, this means rethinking not only workflows but also the foundations of talent development and governance. Teams must become fluent in AI while grounding their work in human-centered skills such as empathy, storytelling and strategic awareness.
Generative tools will also expand creative possibilities. Marco Santos, Global CEO at digital transformation consultancy GFT Technologies, said designers are already bypassing hours of manual inspiration-gathering by prompting AI to generate interface concepts in seconds. These tools don't replace the designer, but they provide more raw material to shape into something new.
Silliman believes co-creativity will eventually be measured more like music production credits, where contribution matters and both human and machine roles are recognized.
Ultimately, the future of creative work will be defined less by whether AI can generate ideas and more by how humans and machines collaborate to make those ideas meaningful. Creativity will become more orchestrated, more strategic and more deeply tied to values that remain uniquely human.
Kinza Yasar is a technical writer for Informa TechTarget's AI and Emerging Tech group and has a background in computer networking.
