AI in the SDLC: Insights from our recent Seedcloud session
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Generating code is getting faster. Governing what gets built needs to keep pace. As agent-based workflows move from experimentation to deployment across software development lifecycles, the real challenge for enterprise technology teams is less about adopting new tools, and more about maintaining discipline to manage what they produce. We recently hosted a portfolio session with Seedcloud to discuss what the latest AI tooling shift actually means for teams building complex and high-stakes enterprise technology. A few themes stood out:
- Strong foundations unlock the most value: well-structured code, composable architectures, robust testing, and clear documentation are critical to capitalise on agentic tools
- Productivity gains are real but targeted: scaffolding, tests, and routine functionality builds accelerate well; complex architecture and business logic still need experienced product and engineering oversight
- Disciplined adoption is essential: gated workflows, review processes, and strong security postures ensure that faster code production doesn't outpace quality and governance.
The opportunity is significant. All product and engineering organisations are adopting AI tooling. The ones that do this intentionality, with discipline and governance, and at pace, will be those that build meaningful competitive advantage.
AI in the SDLC: Insights from our recent Seedcloud session
Generating code is getting faster. Governing what gets built needs to keep pace. As agent-based workflows move from experimentation to deployment across software development lifecycles, the real challenge for enterprise technology teams is less about adopting new tools, and more about maintaining discipline to manage what they produce. We recently hosted a portfolio session with Seedcloud to discuss what the latest AI tooling shift actually means for teams building complex and high-stakes enterprise technology. A few themes stood out:
- Strong foundations unlock the most value: well-structured code, composable architectures, robust testing, and clear documentation are critical to capitalise on agentic tools
- Productivity gains are real but targeted: scaffolding, tests, and routine functionality builds accelerate well; complex architecture and business logic still need experienced product and engineering oversight
- Disciplined adoption is essential: gated workflows, review processes, and strong security postures ensure that faster code production doesn't outpace quality and governance.
The opportunity is significant. All product and engineering organisations are adopting AI tooling. The ones that do this intentionality, with discipline and governance, and at pace, will be those that build meaningful competitive advantage.
