Scaling creativity in the age of AI

Storytelling is core to humanity’s DNA, stemming from our impulse to express ideals, warnings, hopes, and experiences. Technology has always been woven through the medium and the distribution: from early humans’ innovation of natural pigments and charcoals for cave paintings to literal representation by the camera.

The landscape of storytelling continues to shift under our feet. Social and streaming platforms have multiplied, audiences have fragmented, and our demand for fresh, unique media is insatiable. A recent McKinsey podcast cites that we are watching upwards of 12 hours of video content daily, often on multiple devices and multiple platforms.

All this content is expensive to produce: With a baseline budget of $150M, a Hollywood feature runs $1M per minute of finished film; prestige streaming content is in the hundreds of thousands per minute. And since consumers want to engage with authentic, original material, every company is now effectively a media company. That means we all face the same pressure: more content, with the same time and budget constraints.

There is no longer a question whether to use AI for content; the math doesn’t work any other way. What leaders need to focus on now is how to adapt responsibly, protect brand integrity, uplift team creativity, and build customer trust.

A few things worth holding onto as this era accelerates:

• AI amplifies what’s already there, both good and bad. Weak strategy stays weak.
• Responsible adoption means knowing what’s in your tools and models. Provenance and transparency are the foundation, not the finish line.
• Scale without taste is just noise. Investing in your team’s judgment is what makes more content matter.
• Fundamentals of great storytelling have not changed. Regardless of format or channel, what makes audiences lean in are still characters, arc, ingenuity, and surprise.

The permanent sprint

Creative teams are trapped on the endless hamster wheel of production, and it’s not slowing down. According to Adobe research, content demand will grow 5x over the next two years. Social content shelf life is now measured in hours, not weeks. Keeping fresh work in the pipeline is a permanent sprint, requiring teams to rethink how creative production functions.

The first move is freeing creative teams by having AI absorb the repetitive work so they have space for the strategic creative decisions that require human ingenuity. In a recent study from Adobe, 94% of creatives report that AI helps them produce content faster, saving an average of 17 hours per week. That recovered time is not a productivity metric; it is renewed creative capacity.

As a use case, Nestlé offers a useful blueprint. Its teams operate across 180 countries with a portfolio of iconic brands including Nescafé, KitKat, and Purina. Using Adobe Firefly Custom Models embedded in existing content workflows allows teams to generate assets in a brand-informed style without disrupting creative flow. At Nestlé, workflow cycle times dropped 50%. “With Firefly Custom Models, we can react at the speed of culture. It’s the closest thing we’ve had to magic.” says Wael Jabi, global strategic comms lead for KitKat.

As we move into the agentic era, the possibilities expand further. Adobe’s Creative Agent thinks in systems, not tasks, orchestrating across workflows, apps, and processes to close the gap between idea and execution, and get teams out of the production cycles that consume their productivity.

Build for your brand, not every brand

A company’s brand is how the world recognizes and connects with them. And it’s more than a collection of assets—it is dynamic, subjective, and expressed in thousands of micro-decisions made every day by the people who know it best. As production scales, keeping everything tuned to the brand gets more challenging. Off-the-shelf AI cannot replicate the level of nuance creative teams bring to content, and there’s a real cost to getting it wrong; diluting a brand in market with almost-right output is not an acceptable option. Customer trust is fragile.

Starting with a bespoke AI model built with Adobe Firefly Foundry addresses this directly. Firefly Foundry starts with a commercially safe base model and trains further on a company’s IP, making it possible to produce content that genuinely reflects the team’s vision.

And to ensure that Firefly Foundry models truly represent the creatives at the helm, Adobe has partnered with film studios like Wonder Studios, Promise.ai, and B5 Studios, and the “big three” talent agencies CAA, UTA, and WME to deeply understand what it means (and what it takes) to build an IP-immersive model that keeps creatives at the center as these film studios and talent agencies scale their visions. These brand ecosystems can accelerate nearly every phase of the production process, from ideation and storyboarding to production and promotion, all while preserving artistry and authorship. And to power the next generation of creativity and content, Adobe has recently announced a strategic partnership with NVIDIA, delivering best-in-class creative control along with enterprise-grade, commercially safe content at scale.

Generic AI gives teams a starting point. But a model trained on a brand’s own IP gets them to the finish line, while still leaving room for the creative calls that matter most.

When agents become the audience

AI is not only reshaping how we create; it is reshaping how customers find and engage with brands entirely. According to Adobe Digital Insights, AI-powered shopping has surged 4,700%. Agentic web traffic is up 7,851% year over year. Yet, most businesses still have significant gaps in AI-led brand visibility. If content is invisible to AI agents, then a brand is invisible to customers.

Major League Baseball is ahead of this curve. Using Adobe LLM Optimizer, the league monitors how its content surfaces across AI interfaces and makes real-time adjustments to maintain visibility. As fans search for tickets, stats, or game-day experiences, the league ensures its brand shows up wherever that search is happening. And with Adobe’s recent acquisition of Semrush, brand visibility goes even further.

The agentic web created an entirely new content surface that did not exist two years ago, and this exponential proliferation of content illustrates precisely why scaled, on-brand content production has become a strategic imperative. A well-built agentic foundation offers full visibility into (and control over) every piece of content, from production to performance.

How to prepare for AI integration

Here are a few steps to get started:

Audit before automation. Content supply chains usually include duplicated processes, unclear ownership, and assets living in many different places. Before AI can accelerate anything, develop a clear map of how content moves through the organization today: who creates it, who approves it, where it lives, and where it breaks down. AI applied to a broken process just breaks it faster.

Walk through workflows. Resist the urge to overhaul everything at once. Start with production tasks that are high-volume, low-stakes, and well-defined: asset resizing, localization, and background generation. Use those wins to build internal confidence before expanding into more complex creative territory.

Build responsible governance from the start. Governance added as an afterthought becomes a bottleneck. Building it in from the beginning creates a competitive advantage that lets teams move fast with confidence. And this means clear policies on model training, content provenance, human review thresholds, and communicating AI use to customers. The brands that earn lasting trust will treat transparency as a feature, not a footnote.

This content was produced by Adobe. It was not written by MIT Technology Review’s editorial staff.