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11/06/2026 11:15 pm
TL;DR: A panel at Enterprise Ready Conference said about 80% of companies using agentic coding tools see net negative value, highlighting a gap between AI productivity promises and enterprise implementation realities, according to WorkOS's ERC 2025 recap. The real issue is not model capability alone, but whether teams can define, measure, and govern productivity outcomes well enough to capture value.
NHIMG editorial — based on content published by WorkOS: The Productivity Paradox: When AI Tools Make Things Worse Before They Make Them Better
Questions worth separating out
Q: How should security teams measure whether AI-assisted workflows are actually helping?
A: Track outcomes, not just output.
Q: When does AI-assisted productivity become a governance risk?
A: It becomes a governance risk when teams scale access before they can define quality, accountability, and acceptable use.
Q: What do organisations get wrong about AI coding tools?
A: They often treat prompting skill as the main issue when the real problem is product fit, workflow design, and control placement.
Practitioner guidance
- Define productivity outcomes before expanding AI-assisted workflows Tie tool adoption to specific outcomes such as cycle time, review quality, defect rates, or downstream rework.
- Create enablement patterns for high-value users Document prompt formats, task boundaries, review expectations, and escalation paths for the teams most likely to produce value.
- Rebalance metrics away from raw output volume Add measures that capture whether AI-assisted work is usable, maintainable, and trusted in production.
What's in the full article
WorkOS's full recap covers the panel's operational detail this post intentionally leaves for the source:
- The panel's live debate on how enterprise teams should define productivity when AI tools change the shape of delivery.
- The contrasting monetisation views behind generous free tiers, hard paywalls, and ad-supported developer tooling.
- The audience discussion on quality versus velocity, including how teams think about AI-generated code and downstream review.
- The practical market framing behind why some tools create advocates while others create tire-kickers.
👉 Read WorkOS's recap of the Enterprise Ready Conference productivity panel →
AI coding tools and enterprise productivity: what teams are missing?
Explore further
12/06/2026 7:52 am
Productivity is becoming an identity governance problem, not just a tooling problem. When enterprises cannot define what productive output looks like, they create room for AI-assisted systems to optimise the wrong thing. That failure mode affects human workflows today and will matter even more as delegated machine actions expand across development, operations, and access decisions. The practitioner conclusion is simple: measure what the identity-linked workflow is supposed to achieve, not just how much it produces.
A few things that frame the scale:
- 43% of security professionals are concerned about AI systems learning and reproducing sensitive information patterns from codebases, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap.
A question worth separating out:
Q: How can enterprises keep humans accountable when AI speeds up execution?
A: Keep a clear human approval or review point wherever AI output can affect production, access, or customer experience. The goal is not to slow everything down, but to preserve a named owner for quality, exceptions, and escalation before the work is committed.
👉 Read our full editorial: AI coding tools create net negative value for most enterprises
