Enterprise organizations have spent nearly two decades migrating to the cloud. They have invested billions, retired data centers, and reorganized IT teams around cloud-first mandates. Yet, according to a new study by NTT DATA, only 14% of enterprises have reached the highest level of cloud maturity, the cloud-evolved state, where cloud delivers measurable, compounding business value.
That number should stop every ERP and IT leader in their tracks. As AI moves from pilot to production and embedded AI begins reshaping how finance, supply chain, and procurement teams operate, the state of an organization’s cloud foundation has become a strategic constraint.
The Gap Between AI Ambition and Cloud Reality
The NTT DATA report, titled Cloud-led innovation in the era of AI: The new rules for driving value with cloud, draws on a survey of 2,300+ senior decision-makers across 33 countries and 13 industries. The findings highlight that cloud is now the execution layer for AI, and most enterprises are not ready for the role it needs to play.
The numbers tell a pointed story:
- 99% of organizations say AI is increasing their demand for cloud investment.
- 84% report that their cloud spending remained flat over the past year.
- 88% say current cloud investments are actively putting AI, cloud-native, and modernization initiatives at risk.
- 49% are fully satisfied with cloud’s role in driving innovation.
Moreover, Chief AI Officers (CAIOs) are 22% more likely than CIOs or CTOs to recognize that AI demands greater cloud investment. This signals that organizational silos between AI strategy and cloud infrastructure are themselves part of the dysfunction.
Analysis
What This Means for ERP Insiders
Pressure-test your cloud and AI strategies against each other. If your AI roadmap assumes capabilities that your current cloud architecture cannot support, you have a sequencing problem, not a technology problem. Resolve it at the planning stage.
Legacy Systems and Data Silos: The Hidden Blockers
For ERP professionals, the report’s findings on application modernization will resonate sharply. Half of all organizations surveyed acknowledge that legacy applications and data platforms are actively holding back cloud-related innovation, and this finding is especially critical when considered in the context of AI initiatives.
As Melissa Itoh, Principal Solution Architect for SAP BTP at NTT DATA Business Solutions, noted in a recent interview with SAPinsider, “Many organizations apply the same technical, lift-and-shift mindset to data as they do to ERP. That may get them compliant, but it often does not set them up for analytics or AI.”
Large language models, agentic AI systems, and real-time analytics all require clean, accessible, unified data. When transactional data lives in siloed legacy systems, when master data is fragmented across bolt-on solutions, or when integrations between the ERP system and adjacent platforms rely on brittle, custom-built connectors, AI cannot function as designed.
The NTT DATA report is direct on this point: “Many organizations manage complex application estates that are difficult to refactor, integrate, or retire.” Thus, skills gaps in cloud-native development, automation, and DevOps compound the challenge, slowing modernization just as AI is placing new architectural demands on enterprise systems.
For organizations still running on legacy or hybrid systems with significant custom code, this creates a clear and present risk. Workflows that could be automated through an AI agent or other AI capabilities are blocked upstream by data quality and architecture issues that no amount of prompt engineering will solve.
Analysis
What This Means for ERP Insiders
Audit your data estate before scaling AI. Identify where master data, transactional records, and integration pipelines create AI readiness gaps. For example, if an SAP S/4HANA migration is a forcing function for this work, then use it.
What Platform-Led Modernization Means for ERP Teams
Two of NTT DATA’s imperatives for cloud value creation carry particular weight for the SAP ecosystem.
First, there is the call for a platform-led approach. Organizations expect a threefold increase in the use of fully managed cloud platforms over the next 12–18 months. For example, this is a direct argument for accelerating SAP Cloud ERP adoption for organizations on SAP systems.
The second imperative is to align cloud and AI strategy simultaneously. The report finds that organizations developing these strategies in isolation consistently underperform. For ERP leaders, this means cloud transformation roadmaps can no longer be scoped purely around finance or logistics process redesign. They must explicitly account for AI readiness, including clean core compliance, a robust integration architecture, and data models designed for real-time AI inference, not just batch reporting.
Security rounds out the picture. As cloud environments grow more distributed and AI introduces new autonomous decision paths, 68% of cloud leaders report high confidence in security, versus only 36% of others. The gap between leaders and laggards on security hygiene is substantial, and ERP systems, as repositories of an organization’s most sensitive financial and operational data, sit at the center of that risk.
Analysis
What This Means for ERP Insiders
Reframe cloud KPIs around business outcomes. Technical metrics such as uptime, migration velocity, and ticket volume no longer capture the cloud’s strategic value. Tie cloud investment directly to AI-driven business outcomes such as cycle time reduction, exception automation rates, and forecast accuracy.
The NTT DATA report presents a mirror for organizations on their cloud journey. AI is accelerating faster than cloud maturity. For the 86% of organizations that have not yet reached cloud-evolved status, the question is not whether to close that gap, but how quickly they can afford not to.
