Will Okta disclose quantifiable AI agent identity revenue or customer metrics by Q2 FY2027 earnings?

The question asks about disclosure, not revenue magnitude. With three earnings calls (Q4 FY2026, Q1 FY2027, Q2 FY2027) in the observation window, management has multiple opportunities to disclose. Auth0 for AI Agents is approaching GA in FY2026, creating a pathway to at least minimal revenue. The one financial services customer win already referenced 'significant ACV uplift' — management may be building toward quantification. However, the committee found products are pre-revenue with only 2 of 345 dedicated roles, and 100+ customers are 'engaged' not paying. The strict resolution criteria requiring quantified metrics (not qualitative statements) makes this harder than it appears. Management may choose to delay quantification until numbers are impressive enough to validate the $80B TAM narrative.

The resolution criteria are strict — qualitative statements like 'strong interest' or 'growing pipeline' do not qualify. Management must disclose specific dollar amounts, percentages, or ARR contributions. The operational evidence is thin: only 2 dedicated 'Okta for AI Agents' roles out of 345 positions, products are pre-revenue, and 100+ engaged customers are not yet paying. The insider selling pattern (100% of new compensation) while describing a 'generational' opportunity creates a say/do disconnect that suggests management is not betting their own capital on this timeline. Even if Auth0 for AI Agents reaches GA, the time from GA to disclosable revenue metrics is typically 2-4 quarters for enterprise SaaS products.

Large enterprise software companies under narrative pressure sometimes manufacture metrics that technically qualify as quantified. The resolution criteria would accept customer adoption metrics 'with revenue implications' — e.g., 'X hundred AI agent customers generating Y in ARR.' Auth0 for AI Agents reaching GA creates a technical pathway. However, the committee's finding that AI agent identity is the widest narrative-reality gap sub-dimension suggests management would be exposing vulnerability by disclosing tiny numbers. The 100+ engaged customers representing $200M+ existing ARR could convert to paying, but the conversion timeline is uncertain. The unresolved debate about disclosure incentives is directly material: management may delay quantification until metrics are impressive enough to maintain the narrative.

Three earnings calls provide three shots at disclosure, and management has elevated AI agents to '#1 priority,' which will generate analyst pressure to quantify progress. Auth0 for AI Agents approaching GA means some revenue could materialize by Q1-Q2 FY2027. Even early-stage metrics like 'X customers generating Y ARR from AI agent products' would satisfy the resolution criteria. However, the operational evidence contradicts the narrative: only 2 dedicated roles, pre-revenue status, and the insider selling pattern suggests management is not confident in near-term execution. The committee's HIGH confidence on NARRATIVE_REALITY_GAP = DIVERGING signals this remains aspiration, not reality.

Enterprise SaaS companies typically don't disclose revenue from products that launched less than 6 months ago unless it's material. With GA expected during FY2026 (ending January 2027), the product may only be GA for a few months before Q2 FY2027 earnings in August-September 2026. Companies usually wait until a metric is impressive before disclosing — early numbers that are small could undermine the $80B TAM narrative rather than support it. Okta's management has been making bold qualitative claims; switching to quantification when numbers are tiny would create a vulnerability that short sellers and skeptical analysts could exploit. More likely to continue qualitative momentum language through the observation window.

Wall Street analysts will increasingly press for AI monetization metrics, especially as competitors like Microsoft and CyberArk disclose their own AI identity capabilities. Three earnings calls give analysts 3 opportunities to extract numbers. Management may provide 'color' that technically qualifies — for example, 'AI agent identity contributed X basis points to net retention rate' or 'we have Y paying AI agent customers with average ACV of Z.' But strict reading of resolution criteria requires dollar amounts, percentages, or ARR. Management's established track record of narrative-ahead-of-reality (Myth Meter E2 finding) suggests continued qualitative framing. The one financial services customer mentioned 'significant ACV uplift' without quantification — this pattern of almost-but-not-quite disclosure may persist.

Pre-revenue products rarely generate disclosable metrics within 6-8 months. Auth0 for AI Agents GA timing is the key swing factor — if it reaches GA early in FY2026, some revenue could materialize by Q2 FY2027. Three earnings calls provide multiple disclosure windows. But only 2 dedicated roles and 100+ engaged-not-paying customers suggest the pipeline is thin. Resolution criteria require quantification, which management has avoided so far.

The operational evidence is the strongest signal: only 2 dedicated roles out of 345 positions, products pre-revenue, 100% insider selling of new compensation. Management elevated AI to #1 priority but investment doesn't match. The widest narrative-reality gap sub-dimension per committee analysis. Unlikely to have quantifiable metrics by September 2026 given the starting position of pre-revenue with minimal dedicated headcount.

Management has three chances and strong incentive to validate the AI narrative, especially given analyst scrutiny. Even small metrics could qualify under resolution criteria. But the strict quantification requirement combined with pre-revenue status and thin operational investment makes disclosure unlikely. The committee's unresolved debate on disclosure incentives is directly relevant — management may delay until numbers are meaningful rather than risk exposing the narrative-reality gap.