Sat.Jul 26, 2014 - Fri.Aug 01, 2014

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Balancing Innovation via Organizational Ambidexterity – Part 3

Integrative Innovation

This is part three of a three-parts article co-written with innovation-3 ‘s Frank Mattes. The first part highlighted that radical and incremental innovation build on two different innovation set-ups (exploration and exploitation, respect. The second part showed in a sample of seven leading firms that ambidexterity is used in two main types – contextual and structural ambidexterity.

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Driving Corporate Innovation: Design Thinking vs. Customer Development

Steve Blank

Startups are not smaller versions of large companies, but interestingly we see that companies are not larger versions of startups. I’ve been spending some time with large companies that are interested in using Lean methods. One of the conundrums is why does innovation take so long to happen in corporations? Previously Hank Chesbrough and I have written about some of the strategic issues that impede innovation inside large corporations here and here.

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GE’s Chief Marketing Officer on Opening up to Innovation

Innovation Leader

Beth Comstock talks about how GE pursues speed and growth through partnerships: investing in startups, crowdsourcing, & inviting inventors to use patents…

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User Experience Research at Scale

Boxes and Arrows

'An important part of any user experience department should be a consistent outreach effort to users both familiar and unfamiliar. Yet, it is hard to both establish and sustain a continued voice amongst the business of our schedules. Recruiting, screening, and scheduling daily or weekly one-on-one walkthroughs can be daunting for someone in a small department having more than just user research responsibilities, and the investment of time eventually outweighs the returns as both the number of pa

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.