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Valiantys releases new data report revealing gap between AI ambition and enterprise readiness. Survey of 400 global technology leaders finds 98% are running an AI initiative, but only 3% consider their organizations fully ready for AI. BOSTON, Oct. 15, 2026 (GLOBE NEWSWIRE) - Global consulting firm Valiantys, in partnership with Atlassian, today released new Omdia research revealing that AI adoption is moving faster than organizations' ability to scale it effectively. Fragmented systems, data, and workflows can erode business value: siloed AI initiatives may accelerate individual tasks while simply moving bottlenecks elsewhere. Unlocking meaningful gains requires an end-to-end view of the value chain, spanning service delivery, software development, and portfolio decision-making. The report, Why Enterprises Won Cloud Adoption but Risk Losing AI, surveyed 400 global technology leaders about the barriers preventing organizations from scaling AI and delivering successful technology transformation. The findings reveal a significant readiness gap: while 98% of respondents have at least one AI initiative underway, only 3% consider their organizations fully ready for AI. At the same time, just 28% report that more than half of their technology transformation initiatives deliver the expected outcomes. "Enterprises have moved quickly to adopt AI, but adoption alone is not transformation," said Israel Forst, CEO of Valiantys. "The organizations that create lasting value will be those that connect AI to the fundamentals: trusted data, modern systems, clear governance and an operating model that can turn strategy into execution." Organizations combining cloud maturity with AI ambition are 2.4 times more likely to succeed, underscoring the importance of strong technology and operating foundations. Cloud Migration Has Not Eliminated Fragmentation Seventy-four percent of businesses adopted cloud through partial modernization, using either a hybrid approach or a pure lift-and-shift migration. Only 11% now describe their organizations as predominantly or fully cloud-based. That incomplete modernization continues to create barriers to transformation. Forty-four percent of respondents identify data quality and accessibility as their single biggest transformation barrier, while 39% cite legacy systems and technical debt as a leading challenge. Fragmentation also extends into employees' day-to-day work. Fifty-six percent of organizations say the average employee relies on seven or more tools each week, while only 27% agree their technology environment is largely integrated with minimal fragmentation. "Roughly three-quarters of organizations moved to the cloud tactically, and in doing so they relocated technology fragmentation rather than removing it," said Adam Holtby, principal analyst at Omdia. "AI is now exposing many of those unresolved issues." AI Adoption Is Outpacing AI Readiness Although AI activity is nearly universal, most organizations have yet to move from experimentation to scale. Thirty-five percent remain in pilot or proof-of-concept phases, while only 7% have reached a scaling phase and just 2% describe AI as mature and integrated across the organization. "AI is creating enormous opportunity for enterprises, but realizing that potential requires more than adopting new technology," said Itai Njanji, Global Head Partner Solutions Success at Atlassian. "Organizations need connected teams, trusted data and ways of working that make it easier to turn AI investment into measurable outcomes. This research gives leaders a clearer view of where the gaps are today and where they need to focus to move from experimentation to impact." The research identifies five foundations organizations need to strengthen to move securely from pilot to production: data, infrastructure, measurement, governance and skills. The findings also expose a gap between AI activity and the ability to demonstrate business value. Fifty-two percent of technology leaders say formal return on investment is how they would ideally measure AI value, yet only 21% currently use formal ROI calculations. Enterprises Are Prioritizing Cloud and AI Together Organizations increasingly recognize that AI and cloud strategy cannot be separated. One-third of respondents name cloud optimization with AI integration as their leading strategic focus for the next two to three years, ahead of operational excellence at 18% and innovation at 15%. Most also expect outside expertise to play a role. Sixty-four percent prefer a collaborative transformation model in which internal teams work with external experts, while just 2% want to keep transformation fully internal. Respondents rank industry expertise, proven experience, cloud and AI skills and knowledge transfer among the most important capabilities they seek from transformation partners. The full report, Why Enterprises Won Cloud Adoption but Risk Losing AI, provides an executive roadmap for assessing readiness, optimizing cloud environments, strengthening governance, measuring value and scaling AI across the business. About Omdia Omdia is a market-leading data, research and consulting business serving technology companies, digital service providers and enterprise decision makers. Through its global analyst base, Omdia offers analysis and strategic insight across IT, telecoms and media. Omdia is part of Informa TechTarget. For more information, visit https://www.omdia.com. About Valiantys Valiantys helps organizations transform how work gets done across the enterprise, leading with transformation consulting, enterprise service management and engineering productivity. By combining deep process expertise with modern collaboration, automation and value stream practices, Valiantys enables business, IT and product teams to design connected, efficient and human-centered ways of working. With a strong focus on the modern workforce, the company helps customers reimagine service delivery beyond IT, streamline cross-functional workflows, and improve flow and throughput for engineering teams. For more information, visit https://valiantys.com. About Atlassian Atlassian unleashes the potential of every team. A recognized leader in software development, work management, and enterprise service management software, Atlassian enables enterprises to connect their business and technology teams with an AI-powered system of work that unlocks productivity at scale. Atlassian's collaboration software powers over 85% of the Fortune 500 and 350,000+ customers worldwide - including NASA, Rivian, Deutsche Bank, United Airlines, and Bosch - who rely on its solutions to drive work forward. Legal Disclaimer: EIN Presswire provides this news content "as is" without warranty of any kind. Africa Finance Today do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.

Valiantys-Glean partnership bets that cross-platform knowledge graphs and behavioral KPIs are what move Enterprise AI past pilots. 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 Graph[1] 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 deflection[2] - 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. Links. 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. * Knowledge Graph - a data structure representing entities (people, documents, tickets, accounts) and the relationships between them, allowing software to traverse connections rather than treat each record in isolation. "Permissions-aware" means it respects each user's existing access rights when surfacing linked data. | * Ticket deflection - resolving a support request automatically (via self-service, bots, or automation) so it never reaches a human agent.