Market View
Navigating the AI transformation journey: How global service providers are reimagining value creation
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Produced by:
Khanh Tran
Head of Finance Services at Vistra
About Khanh Tran
Khanh Tran currently serves as the Head of Finance Services at Vistra, where he leads comprehensive finance services with IT-enhanced solutions in the Vietnam market. He delivers a full suite of financial services including accounting, tax compliance, and business process outsourcing (BPO), specialising in applying advanced technologies like RPA, Microsoft Power Automate, and VBA for Excel. With extensive experience as an auditor in the financial services industry, Khanh holds multiple certifications including CPA, CIMA Advanced Diploma MA, CPTA, and MFin.
How are you currently identifying where AI can add the most value in your service delivery, and what has your journey towards AI implementation looked like so far?
Our approach begins with understanding genuine client needs. We identified that clients were particularly struggling with accounts payable processes and wanted to move beyond human resource dependency, which led us to implement RPA solutions for invoice processing.
Currently, we use RPA, which requires establishing specific rules that robots follow exactly. However, AI represents the next evolutionary step. With advanced AI, we won't need rigid rules as the system will analyse data patterns and automatically perform tasks without explicit programming.
The accounts payable process was our starting point because it was the most challenging workflow clients wanted to improve. As we gain more experience with automation technologies, we're exploring how AI can enhance other aspects of our end-to-end accounting services, from bookkeeping to reporting.
After automation, we deliver better services with greater efficiency. Clients no longer see our headcount scaling proportionally with their business growth.
What measurable benefits have you seen from your automation investments, and what metrics do you anticipate using to evaluate future AI implementations?
The benefits have been substantial for both our organisation and clients. Before implementing RPA and Intelligent Document Processing solutions, our team was constantly under pressure as transaction volume increases required additional staff.
After automation, we deliver better services with greater efficiency. Clients no longer see our headcount scaling proportionally with their business growth. Previously, when a retail client opened a new store, we might request one or two additional staff. Now, we only request personnel for tasks robots cannot handle.
For future AI implementations, the key performance indicator will be transaction processing capacity—measuring how many transactions AI can handle in a specific timeframe, such as processing 100 invoices per minute. This focus on volume and speed will help quantify efficiency gains and demonstrate value through enhanced service delivery.
How has automation changed your commercial model, and how do you see AI further transforming how you deliver value to clients?
Our commercial model has undergone significant transformation. The traditional FTE-based model created a challenging trade-off—while increasing headcount raised revenue, it risked client dissatisfaction when businesses expanded.
With automation, we've shifted away from the FTE-based model towards value-based pricing. We now base commercial arrangements on factors like client revenue and operational units rather than headcount. This allows for balanced negotiations where we justify fee increases based on efficiency and value provided.
Looking ahead, AI will enable us to serve larger clients previously beyond our capacity. Our largest client already benefits from RPA, while smaller clients have yet to experience the same technological enhancement. With AI, we can scale services to accommodate substantial clients without proportional team growth, while delivering better outcomes for all clients.
With automation, we've shifted away from the FTE-based model towards value-based pricing. We now base commercial arrangements on factors like client revenue and operational units rather than headcount.
With AI, I envision systems automatically generating comprehensive reports from raw data, saving time while enhancing insight quality and depth.
What additional services do you envision developing as AI capabilities mature, and how might your current service portfolio evolve?
We currently provide end-to-end accounting services in Vietnam. While accounts payable has been our primary automation focus, I see significant opportunities to enhance management reporting services through AI.
Our reporting process remains largely manual, with monthly data extraction and report compilation using client templates. With AI, I envision systems automatically generating comprehensive reports from raw data, saving time while enhancing insight quality and depth.
Beyond reporting, AI could transform how we handle account reconciliation and financial analysis. While clients typically manage accounts receivable through point-of-sale integration, numerous areas within the record-to-report cycle could benefit from AI enhancement.
Our service portfolio will likely shift from processing services to analytical and advisory capabilities, as AI handles routine tasks while team members focus on higher-value activities requiring human judgment.
What organisational changes do you anticipate making to support effective AI implementation, particularly regarding data management?
Implementing AI across our service offerings requires fundamental changes to our structure and processes. Our current delivery model is designed around human workflows, which must be redesigned to optimise interaction between automated systems and human oversight.
Our data infrastructure needs significant investment. We currently use simple storage solutions like SharePoint and OneDrive, alongside client systems. This fragmented approach won't support sophisticated AI implementations requiring robust, centralised data repositories.
For the future, we should develop a comprehensive in-house data warehouse. AI systems require large volumes of high-quality data, making a centralised, well-structured repository essential for successful implementation.
The changes extend beyond technology—our team composition will evolve as well. While automation may reduce the need for certain processing roles, we'll need to develop capabilities in data science, AI development, and solution architecture to fully capitalise on AI's potential.
About Vistra
Vistra is a leading global provider of corporate, fiduciary and administrative services, helping businesses navigate complex international regulations and operations. With a significant presence across major financial hubs worldwide, the company offers comprehensive solutions in corporate services, alternative investments, private client services, and finance services. Vistra empowers clients to focus on their core business activities while its expert teams handle critical administrative functions, compliance matters, and financial operations using advanced technology solutions including RPA and automation tools.
About Enate
Enate is the leading SaaS solution for business services. Enate orchestrates work from start to finish, giving clients the visibility and control needed to deliver better services. From email management and data analysis to intelligent document processing, Enate also offers a host of touch-button AI features designed to slash the time spent on manual work. Trusted by global service teams, Enate ensures smooth, consistent operations that help clients perform at their best.