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Every major technology transition creates a number of assumptions about where the market is headed. Assumptions are often indicative, but they tend to underestimate the extent to which organizations can adapt new technologies to their circumstances. Artificial intelligence follows a similar trajectory.
Many current discussions of enterprise AI envision a future in which employees interact with business systems through a common interface. The details vary depending on the forecast, but the goal often seems similar: a conversational system that becomes the primary way people access information, perform tasks, and interact with software.
The history of enterprise technology suggests a more complex conclusion. Organizations rarely embrace new opportunities uniformly because different parts of the business operate under different constraints. The finance team, which is responsible for reporting accuracy, controls, and approvals, approaches technology differently from the analytics team that examines operational data. Both groups have different requirements from a customer service organization focused on response times and case resolution. Even when there is broad agreement that technology is valuable, the path to adoption varies across functions.
The shift to cloud software has followed this pattern—some organizations have moved aggressively, while others have spent years in hybrid environments. Different departments often modernize on different timelines, reflecting the priorities of the business itself rather than any industry consensus about the right pace of adoption.
There is no one-size-fits-all AI
Artificial intelligence has accelerated many aspects of technology development, but it has not changed this fundamental dynamic. Organizations still evaluate new opportunities through the lens of existing processes, responsibilities, and operational requirements.
For some workers, the most useful AI capabilities may be the least visible. A financial manager closing the books is often less interested in a new interface than in shortening the reporting period. An operations manager dealing with inventory issues typically focuses on identifying problems earlier and resolving them more quickly. In these situations, the value of AI comes from reducing the amount of effort required to complete the task at hand.
At the same time, another group of users increasingly wants direct interaction with artificial intelligence systems. Analysts, planners, and operations teams often benefit from the ability to explore information conversationally, compare scenarios, and explore questions that don’t fit neatly into pre-defined reports. For these users, the interface itself becomes valuable because it provides a more flexible way to work with business data.
A customer service representative handling high-volume inquiries has different requirements than a financial analyst examining trends in transaction costs. One benefits from information that automatically appears in an existing process, while another may benefit from the freedom to ask additional questions, explore alternative explanations, and move more dynamically through the data.
Many organizations are discovering that both patterns exist simultaneously, reflecting the broader reality of how business evolves. Operational complexity accumulates, systems proliferate, and processes fragment. Information is distributed across applications, reports, spreadsheets, and workflows, and employees spend more and more time finding information before they can act on it.
Much of the value generated by enterprise software over the past few decades has come from reducing this fragmentation. Bringing financial data, operations, inventory, customer information, planning and reporting into one common system has created a more complete picture of how the business is performing.
AI begins to solve the related problem. Once information is available in connected systems, employees still need to find, interpret, and apply it. Reporting cycles take time. Routine questions require research. Managers often spend considerable effort gathering information before making a decision. As organizations grow, these activities become increasingly expensive because they require the attention of people who often lack expertise.
The promise of AI is to reduce the effort required to transform information into action.
At Dura Software, AI-enabled workflows help automate parts of the income statement that previously required manual preparation each reporting period. Sloan Session, CFO of Dura Software, described the arrangement in practical terms: “Agents drive attraction. People drive judgment and personal connection.”
This observation covers an important aspect of current AI adoption. Most organizations do not attempt to make judgments about business processes. They seek to reduce the time spent collecting, organizing, and preparing information so that experienced workers can focus on decisions that require expertise.
A similar pattern emerged at S&B Filters. Employees previously collected order information from multiple systems within minutes of customer interaction. By connecting AI to transactional data, the company reduced this process to seconds and ultimately extended this capability directly to customers through self-service.
Don’t forget about management
In both cases, the benefit comes from reducing the friction associated with finding and using information rather than introducing a new interface. As information becomes easier to access, questions about access itself become more important. Permissions, approval structures and security policies exist because businesses need mechanisms to control access to information and manage risk. These demands don’t go away when employees start interacting with data through AI systems. If anything, they’re becoming more important because AI can make it easier to access information.
Barry Carter, CEO of S&B Filters, clearly described the principle. If a user cannot access specific information within NetSuite, that user should not be able to access the same information through an AI assistant. The statement sounds obvious. Implementing it consistently across systems, workflows, and models requires more discipline than the statement itself.
Lauren Polasek, a former NetSuite administrator and Texas NetSuite User Group board member, recently made a related point. The connection technology is often the easier part. Organizations still need to determine which tools should be used, who should have access to them, and how governance should evolve as adoption expands.
This is one reason why predictions of a unified AI interface are difficult to reconcile with how businesses actually operate. The requirements of a financial institution that closes the books are different from those of a customer service team that handles thousands of interactions every day. Some AI capabilities will be embedded directly into work processes, where employees may barely notice them. Others will provide more direct access to operational information through conversational systems. Many businesses will use both approaches because the core business is different.
Let AI be your way
This perspective has shaped how we think about AI at NetSuite. Some customers want AI embedded directly into their operational workflows. Others want the ability to connect NetSuite data to external models and assistants so they can interact with business data through tools that are already part of their daily work. Increasingly, organizations require both.
Our support for NetSuite AI Connector Service and Model Context Protocol (MCP) was designed with this reality in mind. The goal is to enable organizations to securely integrate business data into the workflows and systems that make sense for them, while continuing to benefit from the AI capabilities built right into NetSuite.
The history of enterprise software shows that adoption rarely happens in a straight line. As organizations adopt artificial intelligence, business leaders must define the business objective and the workflows involved so that they can align the solution with the reality of the business.
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