Date & time
17:00
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Engineering decisions are now evaluated using financial logic that most engineering workflows were never built to produce evidence for. CFOs want capex and opex split out by project, the impact of AI spend quantified, and tax auditors want hours tied to specific pieces of technical work.
These requests used to be annual, but agile delivery and tighter compliance rules mean that they’re needed quarterly or more regularly, creating more time-consuming work for engineering leaders. The stakes are high: get the evidence-capture wrong and a legitimate tax claim or budget defense falls apart under lengthy audits.
The good news is that AI can unlock the data that already exists in your organization by pulling audit evidence directly from tools like Jira , GitHub, or Slack.
This panel brings together engineering and finance leaders to walk through the practical mechanics of closing the evidence gap: what to tag, when to tag it, and what needs to be agreed upfront. It will help you to avoid cobbling evidence together last-minute, trying to remember what work was done two years ago, and pulling your team away from critical build time.
Key takeaways:
- Close the gap between a quarterly or annual budget cycle and engineering costs that move week to week
- Have R&D tax and audit evidence ready before it’s asked for, by capturing qualifying work as it happens
- Efficiently classify capex and opex