About
Deepashree has a straightforward belief: the future of design won't be shaped by people waiting for a seat at Google. It'll be shaped by people bold enough to build that culture wherever they already are.She works at the crossroads of AI, product strategy, and innovation and has done so across some very different environments. From global organizations like Cisco and Globant to building a design team from scratch, she's seen what it takes to make design matter at scale and from the ground up. At Apptware Design Studio, she grew the team from 1 to 12 and helped define what AI-first product thinking actually looks like in practice, not just in theory.Outside of industry work, she runs DesignHive a community asking harder questions about AI after the hype dies down: what collaboration really looks like now, and what designers are actually supposed to do in a world moving this fast. She also guest lectures and sits on juries at MIT World Peace University, staying close to the next generation of designers, technologists, and builders.She's not just thinking about where design is going. She's been helping push it there.
Talk details
Shipped & Imperfect
About this talk
This session follows a personal and operational journey rather than a polished 'AI success story.' I begin with the emotional reality of leading through uncertainty while AI rapidly reshapes design expectations. The core problem was simple but painfully real: our team created strong client work, but most case studies never got published because the workflow between design, writing, development, approvals, and publishing was too fragmented.Instead of treating AI as a trend experiment, I used it on a real organizational bottleneck. I stopped designing case studies in Figma and began building them directly in HTML with Claude as a collaborator, then integrating them into a Next.js workflow. This reduced handoff friction and helped us ship faster.But the talk is equally about the trade-offs. AI accelerated execution, research synthesis, and structuring, but it also flattened creative nuance and made outputs feel repetitive. I'll share where AI genuinely helped, where it failed.
Key takeaway
- Why AI works best when applied to real operational problems, not experimental side projects
- How reducing workflow friction can unlock visibility for design teams and their work
- The difference between AI-generated output and human creative judgement
- Practical insights into using AI for research synthesis, content structuring, and faster publishing workflows
- Why leadership today is less about certainty and more about experimenting, learning publicly, and shipping imperfectly
- A grounded framework for thinking about AI as a collaborator rather than a replacement