Topic starter
24/06/2026 9:11 pm
TL;DR: Enterprise GenAI adoption is accelerating, with Gartner cited in Kong's survey finding more than 80% of enterprises will have deployed GenAI applications or used GenAI APIs by 2026, while 72% of respondents expect LLM spending to rise in the next year. The governance gap is that budget growth is outrunning security and compliance readiness, not just model choice.
Editorial analysis by NHI Mgmt Group, based on content published by Kong: “# 72% Say Enterprise GenAI Spending Going Up in 2025, Study Finds”.
By the numbers:
- 72% of respondents say they anticipate an increase in their organisation’s LLM spending in the year ahead.
- 37% say they are currently spending more than $250,000 USD a year on LLMs.
- 73% are already spending over $50,000 annually on LLMs.
Key questions
Q: How should enterprises govern unauthorized GenAI use across business teams and data platforms?
A: Enterprises should treat unauthorized GenAI use as a governance and data control problem, not just a policy issue.
Q: Why do GenAI applications create new security and legal risk for organisations?
A: GenAI systems can process sensitive inputs, generate unsafe outputs, and make decisions with too much autonomy.
Q: What breaks when AI agent data access is not tied to identity governance?
A: What breaks is accountability.
Practitioner guidance
- Map GenAI entry points Identify every approved and unofficial path into GenAI, including APIs, plugins, embedded assistants, and developer workflows, then assign ownership for each path.
- Bind access to identity and data policy Require authenticated access, scoped entitlements, and data-classification rules for each GenAI integration so usage can be tied back to a governed identity.
- Review token and API credential scope Check whether GenAI services are consuming shared tokens or broad API keys that outlive the workflow they support, then narrow the access scope to the minimum needed.
Bottom line: Enterprise GenAI spending is accelerating faster than the governance structures needed to control it.
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25/06/2026 6:15 am
Enterprise GenAI growth is forcing API governance to become the primary control surface. The article’s spending data matters because most enterprise GenAI use is mediated through APIs, tokens, and application integrations rather than standalone prompts. That shifts the governance problem from user experience to access control, logging, and data-path enforcement. Practitioners should treat GenAI as a governed service plane, not as a separate innovation sandbox.
A few things that frame the scale:
- According to Gartner, worldwide spending on generative AI is set to reach $644 billion in 2025, a nearly 77% year-over-year increase.
A question worth separating out:
Q: How do teams decide whether to prioritise GenAI adoption or governance first?
A: Teams should prioritise governance first when GenAI will touch sensitive data, customer workflows, or developer tooling. Adoption without policy, logging, and scoped access creates rework later and raises the cost of remediation. If the use case is narrow and experimental, governance can be lighter, but it still needs to exist before production rollout.
👉 Read our full editorial: Enterprise genai spending is rising faster than governance