John Ludeke, Chief Brand Officer at Dr. Squatch, shares the brand's strategy of prioritizing immersive experiences over traditional advertising. By partnering with popular streamer IShowSpeed, the Unilever-owned company aims to inspire Gen Z consumers to generate their own content, fostering authentic engagement and brand loyalty.

The creator economy is projected to reach nearly $500 billion next year, yet many brands still struggle to connect effectively with creators. Despite the booming market size, the true value lies in how well creators fit with brand identity rather than just their follower counts.

Algorithm-driven tools hold the promise of democratizing knowledge access and boosting creativity. However, research shows a hidden downside: these tools can restrict creative potential by undervaluing expertise. The root cause lies in their design. Algorithms typically prioritize popular and relevant information, reinforcing users' existing knowledge rather than encouraging new exploration. This creates 'ideation bubbles'—clusters of similar ideas leading to homogeneity in thinking.

Our research introduced an alternative approach, modifying traditional algorithms to emphasize diversity and uncommon ideas. In experiments involving sustainability challenges, users of this exploration-focused algorithm produced notably more creative solutions. Experts, in particular, benefited, outperforming novices by leveraging diverse insights and combining knowledge across fields—a process we call recombinant innovation.

The organizational impact is significant: exploration-based algorithms help experts break free from conventional thinking, generating a broader range of novel ideas. Businesses should treat algorithm design as a strategic choice, matching exploration tools to innovation tasks while maintaining exploitation tools for efficiency. Encouraging experts to critically engage with algorithm-generated content and continuously auditing idea diversity can further combat ideation bubbles. Ultimately, expertise remains essential, but its value transforms with AI, shifting toward the ability to synthesize and innovate from diverse information pools.

This shift calls for investing in expertise and configuring AI tools to unlock creative potential, positioning organizations to harness AI-driven innovation effectively.

Many companies believe they truly understand their customers through research, behavior tracking, and feedback collection. However, products often fail to resonate with real life because initial insights get diluted during handoffs between teams. Insights are gathered in one place, interpreted elsewhere, then adjusted under different constraints before reaching production, losing their original nuance. This gap is especially evident in how products are described versus designed. For example, furniture made to support real human use often gets simplified into broad, less meaningful descriptions like "stylish and functional." A common issue is translating specific insights into generalized language to ensure clarity and alignment, but in doing so, the true value of the design is lost.

A better approach is to lead with the design story itself and then reveal who it serves, offering clarity and trust without reducing the insight. Companies that protect original insights by involving designers at every product development stage—from problem definition to manufacturing—maintain the essence of the product. Small features that may seem minor, like a built-in grab bar or storage shelf, are crucial and survive only when those who understand their importance influence tradeoffs. The details that remove friction and bring delight are what create lasting loyalty to products. These qualities thrive when the original insight remains central throughout development.

Ben Wintner, CEO of Michael Graves Design, emphasizes that the products which build loyalty over time are those that stay true to genuine insight and real user needs.

You've probably noticed it in everyday brands—hand-drawn typography, rough scanned textures, and imperfect designs aimed at evoking a sense of the past or craftsmanship. Cleaning product labels might echo a simpler era with hand-painted signs and retro illustrations, while new bakeries often opt for identities that feel purposely "imperfect." This handmade aesthetic has long been a topic of discussion, but recently it feels increasingly forced. For instance, Panera Bread's polished swash typography recalls local hand-painted signs but contrasts with its mass-produced branding. St. Regis luxury properties now include hand-drawn elements, which conflict with their polished image. Claude’s branding tries to appear as the more "human AI" through hand-drawn illustrations.

This trend stems largely from designers' unease with AI technology, pushing them to reject anything digital. Similar to past design fads like skeuomorphism or the 90s grunge style, this movement reflects a tension with technology rather than genuine brand needs.

While genuine hand-crafted design has value, overuse of this aesthetic dilutes its impact and fails to differentiate brands authentically in an AI-driven world.

To avoid pitfalls, designers should root handmade elements in meaningful project context, rethink what it means to be "human," collaborate with AI through iterative dialogue, and embrace brand complexity with varied logos and tones. The goal is to create distinction rather than blend in, ensuring brands convey authentic human qualities beyond surface trends.

Adobe has launched a powerful new plug-in for ChatGPT, integrating over 70 of its creative and productivity tools—including Photoshop, Premiere, Express, Acrobat, Firefly, Illustrator, and InDesign—into one seamless experience inside the chat platform. Users simply describe their desired output in natural language, and ChatGPT orchestrates the Adobe tools behind the scenes to generate photos, videos, social media content, and PDFs.

What sets this plug-in apart is the delightful unpredictability ChatGPT brings when interpreting requests. As Forest Key, Adobe’s VP of Firefly and Agentic AI, explains, ChatGPT’s unique reasoning and memory of previous interactions allow it to combine Adobe’s tools in innovative ways, often producing surprising and magical results.

Available globally on ChatGPT’s web, mobile, Work, and Codex apps, the plug-in consolidates Adobe’s previously separate ChatGPT apps into one central install. Users can easily restyle photos, edit videos, customize templates, or turn spreadsheets into creative assets simply by typing “@Adobe” and describing their goals. All outputs are fully editable, with options to refine designs within the chat or export to Adobe’s full suite for detailed tweaks.

This non-deterministic design represents a major shift in software interaction, attracting new users who may not have engaged with Adobe’s traditional apps. With free guest mode access and tiered features unlocked by signing in with an Adobe ID, Adobe is positioning this tool as a compelling competitor to platforms like Canva, pushing creativity and AI integration further than ever before.

AI tools are commonly accessed through text boxes, but Oleksii Hrzhehorzhevskyi envisions a fresh way to engage with AI. He discusses developing a new kind of AI assistant that steps outside traditional interfaces and shares insights on how designers can adapt and innovate as AI evolves rapidly.

Some AI influencers worry that the EU AI Act's uncertain regulations could disrupt their profitable ventures. Meanwhile, others are embracing the situation by integrating AI transparency as a core part of their creative approach.

Runway spent weeks tackling a persistent bug where AI-generated avatars drifted off-center in real-time video generation. Instead of fixing it through backend patches, they created a front-end feature to automatically re-center user images, ensuring stable video output. Ryan Phillips, head of enterprise product at Runway ML, shared insights at VB Transform 2026 on how their real-time generative video technology evolved, highlighting the importance of cross-team evaluation processes and innovative development techniques like distillation and adversarial post-training to optimize performance. Runway’s approach emphasizes turning model limitations into user-friendly features, rigorous infrastructure debugging, and embracing “failure hell” to drive breakthroughs. This new mindset allows creatives to shift from designing single assets to creating dynamic worlds generated in real-time by AI models.

As AI transforms the landscape of product design, it offers designers more independence but also reveals vulnerabilities that come with increased autonomy. Andy Budd explores both optimistic and cautious perspectives on what it means for designers to operate with less oversight.