The State of Enterprise Resource Planning
By: Sam Gupta - Guest Contributor
What CFOs Need to Know as AI Reshapes Business Models
Enterprise Resource Planning (ERP) wasn’t designed for intelligent agents, dynamic
workflows, or machine-driven transactions. Yet, those are now becoming the default operating model for modern businesses.
As AI capabilities mature, most business models will change materially. This shift is likely to drive the largest disruption CFOs will experience in their careers. Everything is in scope. Web traffic patterns. Interaction models. Transactional workflows.
These changes will fundamentally alter how transactions are created, processed, and governed and, by extension, the role of ERP. ERP systems will be forced to adapt to new transactional boundaries, new interaction layers, and new integration models. At the same time, emerging architectural standards, the redistribution of capabilities across software categories, and rapid advances in technical infrastructure will reshape the ERP landscape itself.
There is a very real possibility that ERP, as a category, either disappears entirely or evolves so far that it no longer fits its traditional definition. Before examining what comes next, it’s worth stepping back and revisiting a more basic question: why did ERP exist as a category in the first place?
The Traditional View of ERP
The answer is cross-functional consolidation. Companies adopted ERP systems to consolidate interdepartmental processes. Operating in silos allowed expenses to grow faster than revenue, driven by duplicated effort and inflated SG&A created by disconnected workflows.
But effective consolidation required far more than shared visibility. It depended on proactive business rules, embedded cross-functional controls, and database-level constraints that enforced data integrity. Without these guardrails, organizations are left managing excessive variances, often with an army of accountants reconciling what the system should have prevented in the first place.
This architectural approach imposed a long-term mindset. Implementations demanded specialized skills, significant business-process rewiring, and acceptance of rigid design trade-offs, including limited historical data retention, constrained data retrieval, and inflexibility. These realities made ERP the most feared category of enterprise software, sending chills down CFOs’ spines at the mere thought of an implementation.
While successive waves of SaaS, microservices, and data platforms promised faster, easier alternatives, their outcomes rarely aligned with ERP’s core purpose: enforcing discipline across the enterprise.
The ERP Landscape Entering 2026
AI promises to redefine the core identity of ERP, but whether it ultimately succeeds remains an open question. It is still early. Yet the direction of change is impossible to ignore. These shifts will not only reshape ERP but also influence business models and entire industries.
Multiple forces are at play, but the most decisive is where capital is flowing. Investment priorities tend to follow what can be sold quickly, scaled easily, and exited predictably within compressed time horizons. Those preferences are further amplified by today’s macroeconomic headwinds and ongoing geopolitical uncertainty.
Against that backdrop, software investor priorities are becoming increasingly clear, directly impacting the state of ERP and influencing purchase, implementation, adoption cycles for ERP buyer CFOs:
● SaaS reset user expectations. The modern business-application experience is largely shaped by SaaS-like user experiences. But in most cases, that experience was achieved by stripping away underlying constraints. Ironically, this constraint-light design is exactly what users now expect. It has become a foundational assumption for many AI-native systems.
○ Implications for CFOs: While this delivers speed and usability, it creates downstream chaos for CFOs for reconciliation, integration, and consolidation. So, with the new systems, CFOs can enjoy ease of use, faster implementation, and even shorter-term siloed adoption, but expect data and integrity issues as with most preliminary systems such as manual processes, excel, or lighter accounting systems, likes of QuickBooks.
● Minimal business-process change. Newer platforms promise little to no process re-engineering, lowering friction in implementation and adoption. From a sales perspective, this is powerful. It avoids the hard work of building cross-functional consensus. But structurally, it’s similar to building a house on a weak foundation.
○ Implications for CFOs: Implementation may be easier, but realizing sustainable financial benefits becomes challenging.
● Plug-and-play integration. Simplified integration shortens sales cycles, particularly when CFOs have a limited appetite for multi-year integration programs. The trade-off is architectural. True plug-and-play integration requires flattened data models and relaxed constraints; precisely the opposite of what traditional ERP architecture enforced to maintain control and integrity.
○ Implications for CFOs: While these integrations might appear appealing on paper, be suitable for simpler use cases, and may even come across as cost-effective, the challenges would remain with complex use cases, layered integration workflows, and data integrity with cross-functional use cases.
● Historical data migration. In legacy ERP environments, historical data migration was often impractical due to deeply layered dependencies and rigid hierarchies. By removing those constraints, newer platforms can ingest history more easily and position themselves as an ERP, FP&A, and data warehouse in a single box.
○ Implications for CFOs: While this allows bringing any form of structured or unstructured datasets in one “box,” newer problems might surface with unnecessary data storage, expensive storage costs, and data integrity issues.
● Vibe coding. Generative AI can produce code at a pace that once required billions of dollars and decades of vendor investment. This allows AI-native vendors to appear functionally comparable to established ERP platforms almost overnight. Even end users with limited IT maturity, experience, and investment can do that as well.
○ Implications for CFOs: Expect, over bloated technical, process, and data backlogs with uncontrollable maintenance nightmare posing financial risks.
● Conversational interfaces. Traditional UI-driven interactions are rapidly giving way to conversational interfaces that require minimal training—even for legacy workloads. Users can query systems directly, without help from consultants. This shift extends well beyond internal systems. Sales models, customer engagement, and web traffic itself are likely to change, with LLMs increasingly mediating discovery and interaction.
○ Implications for CFOs: The conversational interfaces would hide the underlying data integrity issues driving more downstream reconciliation efforts and costs.
Navigating the New World of AI-Native Business Models and ERP
While it’s still early to predict exactly when these shifts will materially affect your business model, the pace of AI change leaves little room for complacency. No one knows how quickly industry structures will evolve, but given how quickly AI capabilities are advancing, many traditional models may quickly become irrelevant. We see the most successful CFOs are taking a deliberate, structured approach that includes:
● Build a deep understanding of the AI landscape. Go beyond surface-level awareness. Understand how AI is reshaping competitors’ and industry business models, and develop a better view of AI technologies; their capabilities, constraints, and failure modes.
● Develop cohesive transformation strategies, business cases, and governance models. Avoid treating AI as a series of siloed experiments. Don’t ignore existing systems or software categories, Don’t deploy AI within isolated functions without a cross-departmental view of the enterprise-wide impact. Ask your team, Does this align with corporate objectives?
● Audit, align, and rewire core processes, architecture, and data models. Start with a rigorous audit of current-state processes and data flows. Identify where agentic capabilities can realistically drive financial outcomes.
● Decide what to replace, build, or buy. Once the target operating model is defined, assess where AI should augment products, services, or internal processes. The right approach will vary by use case—some capabilities warrant internal development, while others are better sourced through commercial, off-the-shelf solutions.
● Recognize the risks of AI-native architectures. Don’t be seduced by polished demos or modern aesthetics. Avoid replacing systems simply because newer platforms appear more advanced. Understand the native constraints and trade-offs of AI-native architectures and evaluate them rigorously against business objectives before making procurement decisions.
Conclusion
While the promise of AI-native technologies is compelling, their constraints are rarely discussed. These technologies have the potential to reshape business models, processes, and transactional flows in ways that will directly influence the future state of ERP.
There has rarely been a better time for CFOs to capitalize on these opportunities and rethink how value is created. But success depends on organizational readiness, architectural discipline, and the strategies used to turn AI capability into scalable, sustainable, and controllable financial outcomes.
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