Even now, ERP implementations can make or break CIOs
The more important strategic signal is that ERP is becoming a platform choice for analytics and AI, not only transaction processing. That shifts portfolio logic. Leaders should resist treating embedded AI as a free bonus attached to a cloud migration and instead ask where vendor-native capabilities are sufficient, where third-party tools are justified, and where proprietary workflows create competitive differentiation worth building internally. Those choices affect lock-in, integration complexity, and benefits realization.
For many organizations, the real constraint is foundational: fragmented processes, weak master data, and legacy customization. AI in ERP will underperform if these issues remain unresolved. CIOs should sequence investment accordingly, with explicit gates for data quality, process standardization, controls, and model oversight before scaling autonomous or semi-autonomous workflows.
Change management is therefore a governance topic, not a training afterthought. Leaders should define where human override is mandatory, how exception handling works, and which workforce groups need new AI judgment skills. Practical next questions include:
- Which ERP processes are standardized enough for AI-assisted automation today?
- Who owns data quality and policy decisions across finance and operations?
- What benefits, control metrics, and adoption thresholds will justify further rollout?
In my experience working with CIOs, ERP implementations represent the biggest technology commitment a CIO makes. Typically, CIOs devote more energy to their ERP investments than to any other part of their job.
If a #CIOChat regular was missing from our weekly social media discussions, an ERP implementation was often to blame. That CIO’s absence could last 6 to 12 months.
Simply put, the complexity of ERP implementations can age a CIO quickly — and that’s if an implementation is successful. When it isn’t, CIOs can be asked to find the exit.
Given the high stakes, ERP news matters to CIOs, especially as the platforms add agentic AI capabilities, a push that’s also changing the requirements for success with ERP. The technology remains an integrated suite of applications designed to standardize operations, automate tasks and provide consolidated data across finance, human resources and supply chain management. Done well, an ERP should:
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Centralize data management
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Automate business processes
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Drive business efficiency
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Create business flexibility
Dresner Advisory Services’ latest survey of more than 100 companies shows ERP utilization is growing. Today, more than 70% of companies surveyed have implemented ERP software.
The research also shows that organizations continue to rely on ERP capabilities for strategic decision-making. Almost 26% of respondents utilize ERP capabilities for business intelligence, analytics, planning and performance management strategies. Around 13% of respondents lean on ERP vendors for their AI strategy, deploying AI components as they become available and forgoing investment into additional third-party products. We believe this percentage will grow as ERP vendors introduce more agentic capabilities into their ERP products.
Finance supports ERP in the cloud
Interestingly, finance — because of data sensitivity — for many years dragged its feet on migrating to the cloud. Finance organizations now prefer cloud ERP deployment.
John Van Decker, an analyst at Dresner Advisory Services, said, “ERP is evolving into a comprehensive operational and analytical platform that organizations leverage in digital transformation — for example, by building agentic AI into it.”
Unfortunately, legacy ERP implementations are not extensible enough to support the incorporation of new business intelligence, analytics and AI capabilities. Furthermore, today’s data and analytics technologies require adoption of a cloud version of ERP to take full advantage of new capabilities, which will require a new, holistic ERP strategy.
This fact explains why, even with ERP vendors adding exciting agentic AI capabilities, finance and operations organizations lag in AI adoption. Van Decker argued there is value in “shifting ERP from a passive system of record into an active system of intelligence, driven by autonomous agents, conversational interfaces and predictive analytics.”
Making ERP a comprehensive system
ERP vendors are pushing to make ERP a comprehensive operational and analytical platform that streamlines and automates business operations for financial management, billing, purchasing, human capital management, supply chain, CRM and manufacturing. To achieve this, CIOs and CFOs need to fix disjointed implementations, manual operations, poor visibility and scaling challenges.
Vendors are turning to AI to help achieve this goal. Customers are hoping AI will help to:
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Automate routine, high-volume tasks such as invoice matching, data entry and purchase order approvals.
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Build complex forecasts by combining historical trends with real-time inputs to predict inventory needs, supply chain bottlenecks or cash flow fluctuations.
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Enable natural language queries so employees can pull standard reports without navigating complex menus or transaction codes, making data more accessible.
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Detect operational exceptions through real-time anomaly detection — supporting risk mitigation, fraud tracking and predictive maintenance on heavy machinery.
Change management front and center
To succeed, change management must be front and center when integrating AI into ERP processes. Change management goes beyond using AI for basic system transactions to building user trust, judgment and AI literacy.
For this to work, CIOs need to ensure workers are trained to override poor AI recommendations, addressing change saturation caused by automated deployments and establishing clear data trust. Agentic capabilities shift AI from a passive “chat assistant” into an active co-worker that understands intent and drives workflows to completion.
To function this way, agentic AI must break complex objectives down into smaller, actionable steps, then use external tools to complete those tasks within the ERP system. This step makes AI well-suited to business process workflows that require multi-step reasoning, autonomous tool use across systems and adaptive problem solving, but adopting it will require CIOs to manage change across people, process, technology and data.
Parting words
ERP, without question, isn’t losing steam or being replaced by vibe coding, where natural language is used to write code. As vendors add AI capabilities, fixing underlying data and process problems becomes critical. Embedded and third-party agents depend on creating a foundation that works. This requires CIOs to prioritize data maturity since AI maturity can’t outpace it. Once this foundation is in place, organizations can adopt agents embedded in their ERP system or build their own, especially in areas where doing so creates a distinct competitive advantage. Now is the time to fix the ERP foundational issues, and this requires leadership and investment.
How has AI shaped your ERP strategy? Let us know at [email protected].
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