Technological transitions generate expectations about where the market is heading; these assumptions are often directionally correct but underestimate how organizations adapt new technologies to their specific circumstances. The same dynamic applies to enterprise artificial intelligence (AI).
Much current discussion envisions a future in which employees access business systems through a single conversational interface that becomes the primary way to retrieve information, complete tasks, and interact with software. Enterprise technology history suggests a more nuanced outcome. Different business units operate under different constraints and priorities, so adoption rarely happens uniformly.
A finance team responsible for reporting accuracy, controls and approvals approaches technology differently than an analytics group or a customer service organization handling thousands of daily interactions. During the shift to cloud software, some departments modernized quickly while others stayed in hybrid environments for years; the same fragmented adoption pattern is appearing with AI.
There is no one-size-fits-all AI
AI has accelerated many technological developments but has not erased this underlying dynamic. Organizations evaluate new capabilities through the lens of existing processes, responsibilities, and operational requirements.
For many employees the most valuable AI features will be the invisible ones. A finance manager closing the books cares less about a new interface and more about shortening reporting cycles. An operations leader wants earlier detection and faster resolution of inventory issues. In these cases AI creates value by reducing the effort required to perform existing tasks.
At the same time, other users—analysts, planners, and operational teams—increasingly benefit from the ability to explore information conversationally, compare scenarios, and investigate ad hoc questions. For these users the interface itself is valuable because it enables a more flexible way to work with business data.
A customer service representative handling a high volume of inquiries has very different needs from a financial analyst researching expense trends: one benefits from information surfaced automatically within a workflow, the other from the freedom to ask follow-ups and probe data dynamically.
Many organizations find both patterns exist simultaneously. Operational complexity accumulates, systems multiply, and processes fragment. Information gets distributed across applications, reports, spreadsheets, and workflows, and employees spend growing amounts of time simply locating the information they need before they can act.
Much of enterprise software’s historical value came from reducing that fragmentation—bringing financials, operations, inventory, customer information, planning, and reporting into a connected system. AI is beginning to address the next problem: once information is present in connected systems, employees still need to find, interpret, and apply it. Reporting cycles and routine investigations consume time, and managers expend significant effort assembling information to make decisions. AI’s promise is to reduce the effort required to move from information to action.
Practical examples: Dura Software and S&B Filters
At Dura Software, AI-connected workflows automate portions of revenue reporting that previously required manual preparation each reporting cycle. Sloan Session, CFO at Dura Software, summarized the arrangement: “The agents handle the pull. The humans handle the judgment and the personal touch.” This encapsulates a key aspect of current AI adoption: organizations are not trying to remove human judgment from business processes, but to reduce time spent gathering and preparing information so experienced employees can focus on expert decisions.
A similar pattern occurred at S&B Filters. Employees used to spend several minutes during customer interactions collecting backorder information from multiple systems. By connecting AI to operational data, the company reduced that process to seconds and then extended the capability directly to customers via self-service. Berry Carter, CEO of S&B Filters, stated the governance principle clearly: if a user cannot access specific information in NetSuite, that user should not gain access to the same information through an AI assistant.
Don’t neglect governance and access control
In both examples the benefit derived from reducing the friction of finding and using information, not merely from introducing a new interface. As data becomes easier to access, questions about who can access what become more important. Permissions, approval structures, and security policies exist so businesses can control access to information and manage risk. Those requirements do not vanish with the arrival of AI; if anything, they grow in importance because AI can make information more readily available.
Lauren Polasek, a former NetSuite administrator and board member of the Texas NetSuite User Group, noted that connecting technologies is often the easier part. Organizations still need to decide which tools to use, who should have access, and how governance should evolve as adoption expands.
Implications: parallel approaches will coexist
This reality helps explain why predictions of a single AI interface are hard to reconcile with actual enterprise operations. The needs of a finance team closing the books differ from those of a customer service unit managing thousands of interactions. Some AI capabilities will be embedded directly into processes where users scarcely notice them; others will offer conversational access to operational data. Most businesses will adopt both approaches because the underlying work varies.
NetSuite’s approach and product support
That perspective has shaped NetSuite’s approach to AI. Some customers want AI embedded in operational workflows; others want to connect NetSuite data to external models and assistants to use tools already part of daily work. Increasingly, organizations want both.
The NetSuite AI Connector Service and NetSuite’s support for Model Context Protocol (MCP) are designed with that reality in mind. The goal is to allow organizations to securely connect business information to the workflows and systems that make sense for them while continuing to benefit from AI capabilities built directly into NetSuite.
Conclusion
Enterprise software history shows that adoption rarely follows a straight line. As organizations adopt AI, leaders should identify business objectives and the workflows involved so they can match solutions to the reality of work. At the same time, access controls, approvals, and security policies must be reinforced: AI will bring multiple interfaces into the enterprise, but governance and controls remain essential.
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