From playground to production: How to jump-start your content transformation with generative AI

A person showing AI technology that connects information to various systems.

Image credit: Adobe Stock/ 418studio.

As a leader in Adobe’s Enterprise business, I meet regularly with CMOs, CIOs and other senior executives at companies across industries. The conversations invariably turn to generative AI and two big questions: “how can it help my business,” and “when will I see the returns on my investment?” Nearly all organizations are experimenting with generative AI. Many leaders say they’re running as many as several hundred concurrent pilots, yet few have been able to deliver on AI’s promise for their business.

A recent Adobe Digital Trends study found that only about a quarter of senior executives feel that their company has been successful in connecting generative AI to their larger goals for customer experience and digital transformation. Nearly half report that doing so is a work in progress (45 percent), and roughly a third haven’t even rolled up their shirt sleeves and gotten to work on this opportunity.

With that said, marketing leaders are still very enthusiastic about what generative AI can mean for their teams and the challenges it will help them solve. Often, the first place they look at is content.

Content personalization requires generative AI operationalization

Every CMO wants to expand and scale their personalization efforts but are finding that having the right breadth and depth of content is a blocker. Additionally, global businesses need to run marketing efforts across multiple markets, but regional teams don’t have the resources and budgets to localize content. At the same time, there’s an ever-growing need to refresh content frequently to stay relevant on crowded channels such as social and paid media, with some recommending biweekly or even weekly updates.

Marketing teams — our own and our customers’ — routinely tell us that demand for content is growing exponentially, and their teams and tools simply cannot keep up. Creative teams, agencies, and vendors are siloed, limited in bandwidth and resources. This makes content delivery at the necessary speed, scale, and budget levels virtually impossible to achieve.

We believe that generative AI’s impact on content creation will be transformational, enabling productivity improvements of 10–100 times or more — for some workflows. These staggering lifts are real and can translate into higher performing campaigns, faster time to market, and lower costs. The challenge, but also the opportunity for those ready to seize it, is operationalizing generative AI for content across the enterprise.

Five strategies for transforming generative AI from being experimental to having real-world value

Businesses today need to modernize the way they think about content and take a holistic approach.
Below are five strategies to get started:

  1. Supercharge your creative teams.
    Generative AI can help creatives ideate and complete tasks such as image editing more quickly, accelerating productivity, and freeing up capacity for new projects. To maximize their impact, look for AI models and solutions that integrate into the tools that creative teams use every day, reducing steps rather than adding to them. Adobe Firefly, for example, is integrated directly into industry standard tools like Adobe Photoshop, eliminating the need to switch between applications.

  2. Empower marketers to create and remix content.
    For the most part, marketers today do not create media content, they develop campaign briefs and then turn things over to creative teams to deliver. With AI-first creative tools like Adobe Express, a substantial portion of the content marketers need can become self-service. Existing assets can be easily repurposed for “last-mile edits,” such as when regional marketing teams tailor offers and adjust content for their markets.

  3. Automate highly manual and repetitive work.
    Organizations spend vast sums of money producing asset variations and editing content in post-production. A single campaign, for example, may require thousands of assets to be produced with minor variations in size for different channels or in imagery for different audiences or products. That’s why we created Firefly Services, a collection of generative and creative APIs to automate routine image creation and editing tasks, which can be embedded in any workflow.

  4. Stay on brand.
    Ultimately, for generative AI to be deployed at scale and help organizations differentiate themselves, it needs to generate content consistent with your brand. When evaluating generative AI content solutions, consider those that allow you to customize the generative models to your brand’s unique style and voice.

  5. Choose technology that is designed to be safe for business.
    To move beyond experimentation, business need to have the confidence that their AI solutions will not open them to legal or security risks. Be intentional about selecting generative AI solutions designed to create content that is safe for use in commercial settings (i.e., it does not violate third party copyrights). Businesses must also ensure their data is protected and will not be used to train other companies’ AI models.

Brands that are putting these strategies into use are already seeing pronounced benefits — including my own company, Adobe, as well as Pepsi, Unilever, Pfizer, and IBM. With the demand for content expected to rise five-fold during the next couple of years, organizations will be well served to move generative AI out of the playground and into production, today.