We are joined by Anand Naidu, a leading development expert with profound proficiency across both frontend and backend technologies. His deep insights into modern coding languages and enterprise systems give him a unique perspective on the profound shifts happening in business software. Today, we’ll delve into the practical implications of AI becoming the core of ERP systems, exploring how this transformation moves beyond simple automation to create intelligent, governed, and intent-driven business environments. We’ll discuss how natural language interfaces are changing daily workflows, the security measures underpinning these powerful tools, and the tangible impact of agentic AI on complex processes like financial reconciliation and compliance, particularly within the context of the UAE’s evolving regulatory landscape.
NetSuite Next positions AI as the operational core of its ERP, shifting from navigation-heavy processes to intent-driven interactions. How does this practically change the day-to-day experience for a finance manager, and what metrics can they use to measure the efficiency gains from this new model?
For a finance manager, this shift is monumental. Instead of spending the first hour of their day navigating through a dozen different screens and running canned reports, they can now simply ask, “What are my top five overdue accounts in the Northern Emirates, and what’s their payment history?” The system doesn’t just return a static list; it delivers an interactive dashboard, visualizes the data, and perhaps even suggests a collections workflow. It feels less like operating a machine and more like having a conversation with an incredibly competent analyst. The day-to-day becomes less about data retrieval and more about strategic decision-making based on the insights served up. To measure the gains, they can track metrics like a reduction in the time-to-close for month-end reporting, a decrease in days sales outstanding (DSO) due to proactive alerts, and a measurable drop in the hours spent on manual data reconciliation and analysis.
The “Ask Oracle” assistant provides context-aware answers using natural language. How does the system ensure these powerful queries remain secure and compliant with existing user permissions, and can you share a step-by-step example of how it provides explainable reasoning behind a recommendation?
Security and governance are the absolute bedrock of this technology. It’s not a loose AI model layered on top; it’s deeply integrated into the core data model. Every query a user makes through “Ask Oracle” is fundamentally filtered through their existing user profile. So, if a junior accountant who doesn’t have permission to view executive salaries asks about payroll expenses, the system will only return data within their authorized scope, just as it would if they were clicking through the traditional interface. It simply won’t see or process the restricted data for that user. For an explainable recommendation, imagine a user asks, “Which vendor should I prioritize payment for this week?” The system might recommend “Vendor A.” It would then explain its reasoning step-by-step: “1. Vendor A has an outstanding invoice of $50,000 due in two days, which carries a 5% late fee penalty. 2. Your current cash flow forecast shows sufficient funds. 3. Prioritizing this payment avoids a projected $2,500 penalty, positively impacting your P&L.” Each point would be a hyperlink, allowing the user to drill down into the invoice, the cash flow report, and the vendor contract terms, making the AI’s logic completely transparent and auditable.
Agentic workflows can now proactively handle complex tasks like vendor selection and payment reconciliation. Can you walk us through how a user decides between approving key decisions and allowing autonomous action? Please provide an anecdote on how this automation has impacted a team’s productivity.
The beauty of this system is the balance it strikes between automation and human oversight. A user can configure the thresholds for autonomy based on risk and materiality. For instance, in procurement, a manager might set a rule that any new vendor for a critical component must be proposed by the AI but requires manual approval. However, for routine office supply purchases under $1,000 from a pre-approved list of vendors, they can empower the agentic workflow to act autonomously, selecting the best option based on real-time price and availability. It’s a sliding scale of trust. I recall a finance team that was perpetually bogged down by payment reconciliation. They would spend the last week of every month manually matching hundreds of invoice line items to bank statements. By implementing an agentic workflow, they automated over 85% of this process. The system flagged only the true exceptions for human review. This didn’t just speed up their close cycle; it completely changed the team’s morale. They went from feeling like data-entry clerks to financial analysts, spending their newfound time investigating the exceptions rather than searching for them.
A key feature is turning unstructured data from documents like contracts or invoices into actionable ERP workflows. Can you detail the process of how a PDF invoice is validated and processed automatically, and explain how this specifically improves audit readiness for businesses in the UAE?
This is a game-changer for reducing manual work. When a PDF invoice arrives, say, in an email inbox, the system’s large language models first perform optical character recognition (OCR) to “read” the document. It then intelligently identifies and extracts key fields—vendor name, invoice number, date, line items, quantities, and amounts. The real magic happens next: it validates this extracted information against the existing data in the ERP. It checks if the vendor exists, if there’s a corresponding purchase order, and if the quantities and prices match the PO. If everything aligns, it can automatically create a bill in the system, ready for the payment run. For audit readiness in the UAE, this is critical. It creates a digital, time-stamped, and verifiable trail for every single transaction. An auditor can instantly see the original PDF, the system’s data extraction, the PO it was matched against, and the subsequent payment record, all linked together. This eliminates the risk of manual entry errors and provides an airtight, easily accessible audit trail, which is invaluable in a regulated environment.
With the UAE’s e-invoicing mandate approaching, how does the new solution help businesses prepare for the 2026 pilot and the 2027 rollout? Could you also explain how the AI Connector Service allows for integration with external models while maintaining strict data governance?
The new solution directly addresses the UAE’s e-invoicing mandate head-on by providing a purpose-built module. It’s designed to handle the specific formats and protocols required by the Federal Tax Authority, ensuring that businesses can generate and process compliant e-invoices right out of the box. This is crucial for preparing for the July 1, 2026 pilot because it removes the technical guesswork and development burden from the businesses themselves. It turns a major compliance hurdle into a manageable, integrated process. The AI Connector Service is particularly brilliant because it recognizes that some companies may have invested in specialized external AI models. This service acts as a secure gateway. It allows a company to send a specific, controlled dataset to an external model—for example, a custom logistics optimization algorithm—and receive an insight back, all without ever exposing their entire ERP database. It uses open standards to communicate but enforces NetSuite’s own data permissions and governance rules, ensuring that sensitive financial or customer data never leaves the secure environment. It provides the best of both worlds: flexibility and control.
What is your forecast for the adoption of AI-native ERP systems in the UAE over the next five years?
My forecast is for an extremely rapid and widespread adoption. The UAE market is characterized by a unique blend of ambition, digital-first thinking, and a fast-paced regulatory environment. Businesses here aren’t just looking to keep up; they are looking to leapfrog. AI-native ERP isn’t a “nice-to-have” innovation; it’s a competitive necessity that directly addresses the core challenges of growth, efficiency, and compliance. Given the upcoming e-invoicing mandate and the increasing complexity of regional supply chains, I predict that within the next five years, the majority of forward-thinking mid-market and enterprise companies in the UAE will either have adopted or be in the advanced stages of implementing an AI-centric ERP. The conversation will shift from “Should we use AI in our ERP?” to “How can we leverage agentic AI to create new business models?” It will become the standard for how successful businesses operate.
