How AI Is democratising creativity, according to Canva India’s Chandrika Deb

“Democratising creativity” means making creative work accessible to people who may not have specialist training, large teams, or expensive tools. Canva’s Chandrika Deb says people already have inherent creativity, but often struggle to turn ideas into finished work. AI helps close that gap.

For example, a small business owner may know what a product should be but struggle to picture its design. AI can help generate an early output, overcome an ideation block, and make it easier to test and improve ideas. The person still decides what is useful and relevant.

This changes creativity from a skill reserved for specialists into a process more people can join. Founders, marketers, product staff, and salespeople can begin creative work earlier, while designers and other specialists remain important for craft, cultural nuance, audiences, and brand consistency.

Canva presents AI as part of a shared creative process. Chandrika Deb calls it “a collaborator, a partner and an enabler.” This framing matters because AI is not treated as an independent replacement for people. It helps users move from an idea to an output, while people decide what they want to make and why.

The mechanism is practical. AI can help someone overcome a mental block, generate an initial video concept, or create a visual when a team lacks time or resources. Users can then assess the result, refine it, and iterate. The quality of the final work depends on the direction and judgement applied around the generated output.

Canva’s role is therefore broader than providing a design tool. Its next phase aims to connect scattered AI platforms and workflows. AI may widen participation, but specialists still protect craft, cultural nuance, audience fit, and brand consistency.

The clearest figure is Canva’s survey finding: 97% of the marketers surveyed said they use AI in some form. That suggests AI has moved beyond experimentation for most respondents. However, adoption alone does not guarantee strong creative work. Users still need to judge whether a generated result is the best possible response to a prompt.

The article links this usage to everyday creative tasks. AI can help marketers generate ideas, produce content, and tailor communication across more audience segments, formats, and languages. Canva AI is given as an example of a tool that can generate multilingual content while accounting for cultural nuances.

The source does not provide a separate adoption statistic for major companies. It does show that AI is changing business workflows more broadly. Canva sees its current decade as a period of bringing scattered AI platforms and workflows together so businesses can work more efficiently.

AI can widen participation in creative work. Historically, design teams produced much of a company’s creative output. With easier tools, people in other roles can begin shaping ideas before specialist designers become involved. This changes not only who contributes, but also when they enter the process.

A founder might sketch a campaign direction, a marketer might test a message, or a salesperson might create an early customer-facing asset. AI lowers the need for every first step to pass through a specialist team. It also helps small businesses move from an idea to a visual output more quickly and iterate on it.

Designers are not removed from the process. Chandrika says specialists remain essential for craft, cultural nuance, and audience understanding. Creative-operations experts also help keep the work on brand. Leaders must mentor early-career staff so broader AI use remains efficient, thoughtful, and responsible.

Human judgement matters because generation is not the same as good communication. Chandrika Deb says people must ask whether an output is the best result their prompt could produce. Intent clarifies the purpose, while point of view gives the work direction. These qualities help people choose, reject, and improve AI-generated options.

Cultural understanding and audience knowledge are equally important. AI can create multilingual content and scale personalisation, but the result must fit the people receiving it. A message can be technically accurate yet miss local meaning, cultural nuance, or the audience’s real needs. Specialists help evaluate those risks and maintain quality.

The article presents this human role as increasingly important, not less. AI expands participation and speeds up iteration, but leaders must mentor early talent in responsible use. Designers, cultural experts, audience specialists, and creative-operations professionals continue to shape whether generated work is relevant and on brand.

AI-powered personalisation becomes noise when it creates more content without creating more value. Chandrika Deb says audiences must see a benefit, or personalisation stops being useful communication. The issue is not how much content a brand can produce. It is whether the content fits the audience’s needs and situation.

AI can help brands scale personalisation across more audience segments, formats, and languages. Canva AI, for example, can generate multilingual content. This makes it possible to tailor communication beyond a limited number of groups. Yet language variation alone does not guarantee relevance. Cultural nuance and audience understanding still shape whether a message works.

The current challenge is therefore judgement, not production capacity. Brands must decide what information matters to each audience and how to express it appropriately. If technology produces many versions that offer no clear benefit, the result is clutter. Human intent and audience knowledge keep scaled communication meaningful.

Generative AI refers to systems that produce new content in response to a user’s instructions, commonly called prompts. Depending on the system, the output can include text, images, video, audio, or other creative material. The prompt gives the system a goal, description, or set of constraints to follow.

In the article’s examples, AI can help overcome an ideation barrier, generate a video concept, or help a small business owner visualise a product. The system turns the user’s direction into an initial output. The user then reviews it, decides whether it fits the intended audience, and iterates. This is why prompt quality alone is not enough.

The article stresses that human judgement and intent remain central. Generative AI can speed production and reduce barriers, but it does not decide what is relevant, culturally appropriate, or on brand. Those decisions remain with people, including creators, specialists, marketers, and business teams.