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24/06/2026 7:33 pm
TL;DR: Gartner says enterprise product teams using AI tools are seeing a 35% productivity gain and a two to three year time-to-market advantage, which helps explain why Kong is positioning AI as part of its internal operating model. The harder question for identity teams is whether speed gains are outpacing the governance needed to control AI-enabled workflows.
NHIMG editorial — based on content published by Kong: Gartner Recognizes Kong as a Progressive AI Adopter
By the numbers:
- Companies using AI tools for product management are reaping a significant 35% productivity gain.
Questions worth separating out
Q: How should security teams govern AI-assisted product workflows?
A: Security teams should map the entire AI-assisted workflow, from prompt creation to prototype output to downstream system changes, and attach identity controls at each step.
Q: Why do AI-driven development cycles create identity governance risk?
A: AI-driven development cycles create risk because they increase the number of temporary accounts, ephemeral permissions, and fast-moving changes that do not fit periodic review models.
Q: What do organisations get wrong about AI productivity in product teams?
A: Organisations often treat AI productivity as a pure engineering gain and ignore the control changes it requires.
Practitioner guidance
- Inventory AI-assisted product workflows List every workflow where prompts, generated output, or AI tools can create changes in code, design, or configuration.
- Separate human approval from machine execution Require a clear control point before AI-generated output can move from experimentation into shared systems.
- Review transient access as a lifecycle problem Treat short-lived prototype environments, sandbox tokens, and temporary API access as lifecycle-managed identity assets.
What's in the full analysis
Kong's full blog post covers the operational detail this post intentionally leaves for the source:
- How Kong describes its internal vibe-coding workflow and the specific prototyping tools involved.
- The exact Gartner case study framing behind the reported productivity and delivery advantage.
- Examples of how Kong says AI is being used across product organisation workflows.
- The vendor's own rationale for why internal AI adoption matters to customer trust and platform selection.
👉 Read Kong’s blog post on Gartner’s AI productivity findings and internal AI adoption →
AI productivity gains and the governance gap for IAM teams?
Explore further
25/06/2026 4:54 am
AI-driven product velocity creates an identity governance lag. Gartner’s finding is not just a productivity story. It signals that teams are adopting AI faster than they are redesigning the controls that govern access, change approval, and auditability. The practitioner conclusion is that delivery acceleration now has an identity management cost attached to it.
A few things that frame the scale:
- Only 52% of companies can track and audit the data their AI agents access, according to AI Agents: The New Attack Surface report.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including unauthorised access, sensitive data sharing, and credential exposure.
A question worth separating out:
Q: Who should own governance for prompt-to-prototype workflows?
A: Ownership should sit jointly with product leadership, IAM, and security operations because prompt-to-prototype workflows affect both delivery and control. Product teams define the workflow, but identity teams must define the approval gates, account lifecycle rules, and audit requirements. Without shared ownership, AI adoption outpaces accountability.
👉 Read our full editorial: Kong’s AI productivity claim raises new IAM governance questions
