One interface isn't enough for enterprise AI
Our take
The relentless march of technological progress invariably spawns predictions about the future, often overlooking the nuanced reality of organizational adoption. This article, presented by Oracle NetSuite, offers a crucial corrective to the prevalent assumption that enterprise AI will coalesce around a single, universal interface. It’s a welcome dose of realism, particularly when considering how past transitions – like the shift to cloud software – unfolded, with varied adoption rates across departments reflecting their unique operational needs. As explored in The enterprise AI challenge nobody solves with code generation alone, simply generating code isn't enough; integrating it into existing, often complex, enterprise workflows presents a significant hurdle. Similarly, the innovative approaches being explored by companies like Character.AI, as detailed in Character.AI enters the microdrama arena with its own productions, but there’s a twist, demonstrate the diverse applications and interfaces AI can take, further challenging the notion of a singular solution.
The core argument is compelling: the expectation of a unified AI interface fundamentally misunderstands how businesses operate. Different departments—finance, analytics, customer service—have distinct priorities and constraints, leading to varied approaches to technology adoption. A finance team prioritizing accuracy and controls will likely embrace AI in ways that subtly improve existing processes, while an analytics team might actively seek conversational interfaces to explore data more freely. This isn’t a matter of one approach being “better” than the other; it’s a reflection of the diverse needs within an organization. The article rightly points out that AI’s initial value often lies in reducing the friction of existing workflows – shortening reporting cycles, speeding up data retrieval – rather than completely overhauling how work is done. The examples of Dura Software and S&B Filters powerfully illustrate this point: AI augmenting human judgment and expertise, rather than replacing it entirely. This resonates with a broader shift in thinking about AI, moving beyond the hype of automation and towards a more pragmatic focus on augmentation and efficiency.
The piece’s emphasis on governance is particularly noteworthy. As AI facilitates easier access to information, the need for robust access controls, approval structures, and security policies only intensifies. It’s not enough for a user to be able to access information through NetSuite; they shouldn't gain access through an AI assistant if they lack the necessary permissions within the core system. This seemingly obvious point highlights a critical challenge for organizations navigating the AI landscape – ensuring that governance frameworks evolve alongside technological advancements. The difficulties in connecting technology, as noted by Lauren Polasek, are often less about the technology itself and more about defining roles, access levels, and ongoing governance, a challenge that requires considerable discipline. Ultimately, the article champions a flexible approach, acknowledging that different departments will leverage AI in different ways, depending on their unique requirements.
NetSuite’s response, through the AI Connector Service and support for Model Context Protocol (MCP), aligns perfectly with this perspective. By enabling organizations to connect AI to their existing workflows and systems, NetSuite is empowering them to have AI their way – a far more practical and sustainable approach than forcing a one-size-fits-all solution. The long history of enterprise software teaches us that technology adoption is rarely linear; it's an iterative process of adaptation and refinement. As we move forward, the question becomes: how will organizations balance the allure of seamless, conversational AI experiences with the practical realities of fragmented systems, diverse user needs, and the critical imperative of robust governance?
Presented by Oracle NetSuite
Every major technology transition produces a set of assumptions about where the market is headed. The assumptions are often directionally correct, but they tend to underestimate the degree to which organizations adapt new technologies to their own circumstances. AI is following a similar trajectory.
Many current discussions about enterprise AI assume a future in which employees interact with business systems through a common interface. The details vary depending on the prediction, but the destination often looks similar: a conversational system that becomes the primary way people access information, complete tasks, and interact with software.
The history of enterprise technology suggests a more complicated outcome. Organizations rarely adopt new capabilities uniformly because different parts of the business operate under different constraints. A finance team responsible for reporting accuracy, controls, and approvals approaches technology differently than an analytics group exploring operational data. Both groups have different requirements than a customer service organization focused on response times and case resolution. Even when there is broad agreement that a technology is valuable, the path to adoption tends to vary across functions.
The shift to cloud software followed this pattern — some organizations moved aggressively while others spent years operating hybrid environments. Different departments often modernized on different timelines, reflecting the priorities of the work itself rather than any industry consensus about the correct pace of adoption.
There’s no one-size-fits-all AI
AI has accelerated many aspects of technology development, but it has not changed this underlying dynamic. Organizations still evaluate new capabilities through the lens of existing processes, responsibilities, and operational requirements.
For some employees, the most useful AI capabilities may be the least visible ones. A finance manager closing the books is often less interested in a new interface than in shortening a reporting cycle. An operations leader dealing with inventory issues is usually focused 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 existing work.
At the same time, another group of users increasingly wants direct interaction with AI systems. Analysts, planners, and operational teams often benefit from the ability to explore information conversationally, compare scenarios, and investigate questions that do not fit neatly into predefined reports. For these users, the interface itself becomes valuable because it provides a more flexible way to work with business information.
A customer service representative handling a high volume of inquiries has different requirements than a financial analyst investigating a trend in operating expenses. One benefits from information appearing automatically within an existing process while the other may benefit from the freedom to ask follow-up questions, explore alternative explanations, and move through data more dynamically.
Many organizations are discovering that both patterns exist simultaneously, which reflects a broader reality about how businesses evolve. Operational complexity accumulates gradually, systems multiply, and processes become fragmented. Information becomes distributed across applications, reports, spreadsheets, and workflows and employees spend increasing amounts of time locating information before they can begin acting on it.
Much of the value created by enterprise software over the last several decades came from reducing that fragmentation. Bringing financials, operations, inventory, customer information, planning, and reporting into a common system created a more complete picture of how the business was operating.
AI is beginning to address a related problem. Once information exists within connected systems, employees still need to find it, interpret it, and apply it. Reporting cycles consume time. Routine questions require investigation. Managers often spend considerable effort assembling information before they can make decisions. As organizations grow, these activities become increasingly expensive because they consume attention from people whose expertise is often in short supply.
AI's promise is to reduce the effort required to move from information to action.
At Dura Software, AI-connected workflows are helping automate portions of revenue reporting that previously required manual preparation during each reporting cycle. Sloan Session, CFO at Dura Software, described the arrangement in practical terms: “The agents handle the pull. The humans handle the judgment and the personal touch.”
That observation captures an important aspect of current AI adoption. Most organizations are not attempting to remove judgment from business processes. They are trying to reduce the amount of time spent gathering, organizing, and preparing information so that experienced employees can focus on the decisions that require expertise.
A similar pattern emerged at S&B Filters. Employees previously spent 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 eventually extended the capability directly to customers through self-service.
Don’t forget about governance
In both cases, the benefit comes from reducing the friction associated with finding and using information rather than introducing a new interface. The moment information becomes easier to access, questions about access itself become more important. Permissions, approval structures, and security policies exist because businesses need mechanisms for controlling access to information and managing risk. Those requirements do not disappear when employees begin interacting with data through AI systems. If anything, they become more important because AI can make information easier to access.
Berry Carter, CEO of S&B Filters, described the principle clearly. If a user cannot access specific information within NetSuite, that user should not gain access to the same information through an AI assistant. The statement sounds obvious. Implementing it consistently across systems, workflows, and models requires considerably more discipline than the statement itself suggests.
Lauren Polasek, former NetSuite administrator and board member of the Texas NetSuite User Group, recently made a related point. Connecting 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 predictions about a single AI interface are difficult to reconcile with how enterprises actually operate. The requirements of a finance organization closing the books are different from those of a customer service team handling thousands of interactions each day. Some AI capabilities will be embedded directly into business processes where employees may barely notice them. Others will provide more direct access to operational information through conversational systems. Many businesses will end up using both approaches because the underlying work is different.
Have AI your way
That perspective has shaped how we think about AI at NetSuite. Some customers want AI embedded directly within operational workflows. Others want the ability to connect NetSuite data to external models and assistants so they can interact with business information through tools that are already part of their daily work. Increasingly, organizations are asking for both.
The NetSuite AI Connector Service and our support for Model Context Protocol (MCP) were designed with that reality in mind. The goal is to allow organizations to connect business information securely to the workflows and systems that make sense for them while continuing to benefit from AI capabilities built directly into NetSuite.
The history of enterprise software suggests that adoption rarely follows a straight line. As organizations adopt AI, business leaders should identify the business objective and the workflows involved so they can match the solution to the reality of the work.
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