Jellyfish vs Next 15

Brandtech's Rebecca Sykes on AI Implementation Challenges

In a podcast interview, Rebecca Sykes, partner at The Brandtech Group and head of its emerging technology practice, argues that generative AI can work with messy, siloed data, contrary to conventional wisdom. She emphasizes that brand-specific data, even if disorganized, prevents AI outputs from becoming generic. Sykes highlights that many AI pilots fail at scale because companies only automate isolated steps without re-engineering the entire workflow, using the 'frog in a sock' analogy. She advocates for building new operating models around AI capabilities, focusing on people, processes, and new roles like AI governance. The episode also discusses Brandtech's new global data and AI practice, Jellyfish's 'share of model' tool, and the importance of video in AI brand recommendations.

• Rebecca Sykes is a partner at The Brandtech Group and leads its emerging technology practice.
• Sykes argues that generative AI can work with messy, siloed data, not just pristine data.
• She uses the 'frog in a sock' analogy to illustrate how existing operating models hinder AI transformation.

Black History Month: Culture Chats, with Global Chief People Officer, Annabelle Walters

Annabelle Walters, Global Chief People Officer at Jellyfish, joined The Digital Voice™ Black History Month Culture Chats for a candid discussion, unpacking what it truly means for black women to own their space in executive leadership, whilst navigating the nuanced reality of vulnerability at the top.

Harvard Study: AI Does Not Truly Boost Developer Productivity

A study by Harvard researchers Fiona Chen and James Stratton, based on data from the engineering analytics platform Jellyfish covering 300 million work outputs from over 700,000 employees at 718 companies (January 2021 to March 2026), finds that while AI coding tools increase code output, these gains do not translate into actual productivity improvements. Reactive AI assistants led to a 12% increase in lines of code and a 9% increase in commits, but only the commit effect was statistically significant. Semi-autonomous AI agents showed larger increases (30% more code lines, 20% more commits, 23% more pull requests), but the review bottleneck negated these gains. The average time to review a pull request rose by 49%, the share of pull requests requiring changes nearly doubled, and comments per pull request increased by 35%. The proportion of engineers doing code reviews rose by 14%. Qualitative interviews with 21 professionals confirmed that AI speeds up routine tasks but requires more manual checking for complex tasks.

• Harvard researchers analyzed 300 million work outputs from Jellyfish, covering 718 companies and over 700,000 employees from January 2021 to March 2026.
• Reactive AI assistants increased code lines by 12%, commits by 9%, and pull requests by 5%, but only the commit effect was statistically significant.
• Semi-autonomous AI agents increased code lines by 30%, commits by 20%, and pull requests by 23%.