Enterprise resource planning (ERP) suites have long underpinned global business operations. Their purpose has been to codify best practices, optimize business processes, improve data management and support informed decision-making. Agentic AI, however, creates an opportunity to rethink how an ERP implementation supports core business capabilities.
Embedding agentic AI within ERP systems opens the door to meaningful business transformation by enabling new, more efficient operating models. Agentic AI can reshape how organizations work — improving agility, efficiency and responsiveness to customer needs.
For organizations already invested in monolithic ERP and integrated financial suites, prepackaged agentic capabilities provide a practical and accessible entry point. In fact, for many enterprises, ERP will be the earliest and most practical place to apply agentic AI to real business problems. In an agentic organization, small, multidisciplinary teams will oversee “agent factories” that manage entire processes from end to end. Crucially, agentic AI will transform, rather than replace, most ERP systems.
“In what was once a sleepy applications space, ERP is being reimagined with agentic AI capabilities,” is how John Van Decker, a distinguished analyst at Dresner Advisory Services, explained the change. He said agentic AI’s ability to prioritize tasks and interact with applications across finance, supply chain and HR ecosystems — and to continuously improve through feedback and learning — transforms ERP “from a set of software tools into an intelligent workforce.”
The shift: From passive tools to proactive agents
Since the rise of the modern enterprise, people have managed core business processes across finance, supply chain and manufacturing. Management theorist Gary Hamel has observed that many of the processes still in use today were originally designed during the early Industrial Revolution. Now, many tasks that humans have performed for more than a century — and which were later embedded within ERP implementations — are shifting to agentic AI. Here, agents will increasingly take on work across finance and planning, supply chain management and procurement, manufacturing, HR and customer service.
Agentic AI offers the potential to evolve from passive to proactive management, enabling independent multi-step tasks and decisions. Autonomous “digital workers” will manage and execute sophisticated, end-to-end business processes. Van Decker calls out the following areas of impact:
-
Finance and planning. Agents will automate functions including invoice processing, expense reporting, financial statement generation and compliance verification. Additionally, they will independently detect anomalies and provide liquidity forecasts.
-
Supply chain management and procurement. Agents will monitor inventory levels, renegotiate supplier contracts in real time in response to commodity price fluctuations, adjust production schedules to accommodate material delays and optimize logistics networks.
-
Manufacturing. Agents will enhance operational efficiency through real-time monitoring, predictive maintenance scheduling, automated quality assurance powered by computer vision, and adaptive reconfiguration of factory floor processes to address disruptions.
-
HR. Agents will streamline recruiting processes, manage onboarding documentation, address routine payroll inquiries and respond to employee benefits questions, thereby allowing HR professionals to focus on strategic initiatives.
-
Customer service. Agents will manage Level 1 support interactions, perform sentiment analysis, access order histories, deliver personalized communications and escalate complex issues to human representatives with comprehensive context.
2026 strategy: Priorities for CIOs and CFOs
Given these opportunities, what actions should CIOs — and the CFOs they support — take? The following priorities should be added to the CIO agenda for 2026:
-
Develop a clear business plan for adopting agentic AI, including its implications for the current ERP environment, operating model, and financial controls.
-
Engage ERP vendors early to understand their agentic AI roadmaps, timelines for adoption and the operational, security and financial risks these capabilities may introduce.
-
Define how humans and agentic systems will work together, establishing explicit “human-in-the-loop” roles, decision rights and escalation paths. AI is powerful — but not infallible.
-
Establish a comprehensive risk mitigation framework that addresses security threats (e.g., prompt injection), ethical risks (such as biased decisions), infrastructure and data quality issues, implementation challenges and organizational barriers, including weak governance or unrealistic expectations.
-
Address foundational readiness gaps, including immature tools, inconsistent data quality, unclear processes and the difficulty of measuring ROI from agentic AI initiatives.
-
Embed governance, transparency, security and vendor lock-in considerations into every agentic AI initiative from the outset, rather than treating them as afterthoughts.
-
Plan for the specialized talent required to design, deploy and manage agentic AI systems, recognizing that effective implementation depends on scarce and evolving expertise.
-
Build a proactive communication and change management strategy to address cultural resistance, employee concerns about job impact and leadership caution around risk and cost. Clear intent and thoughtful rollout will be critical to adoption at scale.
To optimize or transform: That is the question
ERP should continue to be a core element of every CIO’s 2026 agenda. For leading organizations, the central challenge will be deciding whether to deploy agents to optimize existing business processes or to pursue game-changing business transformation. For this reason, AI-savvy CIOs will work to strike the right balance between incremental process improvement and cost-cutting and bold reinvention. Our data shows that organizations with mature, industrialized data and process foundations are already taking a broader, more transformative approach to agentic AI.
