Coté

Original: Enterprise AI is still stuck at experimentation – Valiantys and Glean think they know why by diginomica. Summarized by AI on June 3, 2026.

Most enterprise AI pilots stall, and the diagnosis from Nathan Chantrenne, Chief AI Officer at Valiantys, is that the field measures the wrong things and fragments its tooling.

The dominant success metric - “employees save four hours a week” - tells you nothing, because nobody knows what those hours become; they might just mean more coffee. The partnership being pitched is Valiantys, an Atlassian-centric consultancy, joining with Glean, an enterprise AI platform valued at $7.2 billion on roughly $300 million ARR as of May 2026.

Maturity varies widely, and the brakes are predictable. In Europe, governance and security are the primary obstacle; in North America, less so.

Tech-savvy firms have the opposite problem: they over-experiment.

Companies test 15 different technologies and let every team pick its own, which collapses at scale because there’s no unified strategy for compliance, security, or cost. Each platform ships its own AI tooling and data layer, and none reaches well into another vendor’s stack - so technical silos mirror organizational ones, and the cross-functional teams meant to bridge them get treated as a burden rather than an authority.

Glean’s role is the connective tissue. Its permissions-aware Knowledge Graph¹ is a semantic layer that links data across applications - so the system knows a Salesforce opportunity ties to a ServiceNow case and a Jira ticket.

Valiantys, which began moving beyond pure Atlassian work about 18 months ago (anchored by the July 2024 Contegix acquisition for North American expansion), is taking this to market first in its native strengths: software development lifecycle modernization and enterprise service management, with an eventual ~50 use cases in view.

The KPIs Chantrenne cares about are behavioral, not cosmetic. For development, the headline is idea-to-production time, dropping from weeks or months to two weeks or, in extreme cases, two days.

For service management, the entry metric is ticket deflection² - 50 to 70% of a typical 1,000-monthly-ticket desk is repeatable level-one work that can be automated.

The harder, more revealing layer is agent productivity on level-two tickets and how many knowledge base articles agents contribute - both signals of whether the work itself is actually changing.

The recurring lesson is that change management, not technology, is the binding constraint. “You can have the best possible technical solution out there. If you’re unable to bring the people with you… people will do everything that they can for the project to fail."

The prescription for firms that have spent heavily without returns: define real business KPIs, centralize governance, bring people along, and narrow from 15 technologies to two or three - then go nearly all-in and run with it.

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🤖 Enterprise AI is still stuck at experimentation – Valiantys and Glean think they know why - Enterprise AI stalls because firms track meaningless productivity metrics and scatter across too many tools; the Valiantys-Glean bet is that a cross-platform knowledge graph plus behavioral KPIs and centralized governance is what moves pilots into production. Change management, not tech, is the real bottleneck.