Finance is undergoing a quiet but profound transformation. Once focused primarily on compliance and retrospective reporting, it is now expected to guide real-time decisions, model uncertainty, and act as a strategic nerve centre for the business. This evolution is being accelerated by new forms of AI.
GenAI is already reshaping how finance teams operate, automating time-consuming tasks like reporting, analysis, and documentation; translating spreadsheets into plain-language insights; and preparing stakeholder updates. From board reports to risk disclosures, GenAI improves speed, clarity, and consistency, freeing up teams to focus on interpretation and strategic input.
Agentic AI takes this further by introducing autonomous decision-making. It monitors systems in real time, flags anomalies, simulates scenarios, and takes targeted action. Acting like a digital financial analyst, it enables Finance to stay ahead of risk, optimise outcomes, and respond dynamically as situations unfold.
Together, GenAI and Agentic AI are pushing Finance from the sidelines to the centre, embedded across teams, influencing performance, and enabling faster, smarter, and more proactive decision-making.
Why Involving CFOs Is Critical Now
One of the biggest challenges organisations face with AI isn’t adoption; it’s accountability. Despite rising investment, many still struggle to define the value of AI initiatives and measure whether that value is actually realised.
That’s a missed opportunity. Finance brings the discipline and strategic lens needed to align AI initiatives with real business value. But the CFO’s role shouldn’t stop at oversight. With tools like Agentic AI, Finance can actively model ROI scenarios in real time, monitor performance signals as they emerge, and steer capital dynamically towards what’s working.
This enables a shift from static business cases to continuous, evidence-based optimisation. CFOs can help the business move faster, ensuring AI delivers measurable outcomes. When Finance leads on AI, it turns experimentation into execution, and innovation into impact.
Taking Finance a Step Ahead with Agentic AI
That shift from oversight to impact is exactly where Agentic AI comes in. It equips Finance not just to track performance, but to shape it, enabling intelligent systems that monitor, plan, and act continuously. Here’s how.
- Cash Flow Optimisation. Agentic AI tracks cash inflows and outflows across the business. When it detects a potential surplus or shortfall, it recommends steps such as rescheduling payments, accelerating collections, or reallocating funds. This keeps the organisation financially agile.
- Scenario Simulation. Whether it’s a revenue drop, raw material price spike, or new regulatory cost, Agentic AI can instantly model the impact and suggest mitigating actions. This enables better, faster decision-making under uncertainty.
- Expense Monitoring. Agentic AI watches spending in real time. If it detects duplicate invoices, unapproved vendor charges, or unexpected cost spikes, it flags them immediately and recommends next steps. This reduces waste and strengthens controls.
- Automated Close and Reconciliation. Month-end processes often strain teams. Agentic AI helps reconcile transactions, review journal entries, and highlight discrepancies, making the close faster and more accurate.
- Live KPI Tracking. Agentic AI keeps an eye on financial metrics like margins, liquidity, and burn rate. When something crosses a threshold, it sends alerts and proposes adjustments so teams can respond quickly.
- Investor and Board Preparation. Agentic AI supports leadership by analysing previous board interactions, market sentiment, and current performance. It anticipates likely questions and ensures messaging is aligned and strategic.
- Cross-Functional Planning. When marketing spends more or HR increases hiring, Agentic AI models the financial impact in real time. This helps keep forecasts aligned with real business activity and ensures coordination across teams.
How GenAI and Agentic AI Elevate Finance’s Expanding Mandate
As Finance takes on broader responsibilities within the enterprise, GenAI and Agentic AI offer targeted support across three critical fronts, transforming Finance into a strategic partner embedded across the organisation.
1. Investor & ROI Gatekeeper: Turning Data into Capital Strategy
Finance plays a central role in guiding where the business places its bets. GenAI accelerates financial modelling, investment case analysis, and performance reporting, helping teams move faster with greater clarity. Agentic AI enhances this by continuously scanning market signals, simulating return scenarios, and triggering early alerts when projections veer off course. Together, they enable sharper capital allocation, stronger investor narratives, and faster decision cycles.
2. Risk & Compliance Steward: Navigating AI’s New Risk Landscape
As AI adoption grows, so do regulatory expectations. GenAI streamlines documentation, audit preparation, and policy reviews, while surfacing potential compliance gaps. Agentic AI takes on active monitoring, detecting anomalies in transactions, tracking adherence to evolving standards, and escalating risk signals in real time. These capabilities give Finance a proactive stance on governance, helping the business stay ahead of both financial and algorithmic risks.
3. AI Champion & Business Leader: Driving Adoption in Finance
Finance teams are well-placed to lead by example. By embedding GenAI into reporting, forecasting, and planning workflows, they demonstrate AI’s practical value. Agentic AI automates close processes, responds to budget deviations, and adapts forecasts dynamically. This not only boosts Finance’s efficiency but also positions the function as a credible champion of AI transformation across the business.
Building a Future-Ready Finance Function
Beyond task-level improvements, the combined power of GenAI and Agentic AI unlocks new strategic capabilities for Finance:
- Capital Allocation with Confidence. AI helps prioritise investments not just by raw numbers but through scenario modelling and real-time feedback. Finance teams can compare expected ROI across initiatives and confidently direct capital where it matters most.
- Strategic Risk Management. AI tools track internal data alongside external signals – policy changes, market movements, supply chain disruptions – to flag emerging threats early. This allows finance leaders to plan and adapt proactively.
- Workforce and Headcount Planning. When business demands shift, Agentic AI recommends resource reallocations, hiring, training, or restructuring. GenAI then builds clear business cases and headcount proposals to support leadership decisions.
- Policy Testing and Simulation. Agentic AI models the impact of policy changes, such as altering bonus structures or shifting to hybrid work, on morale, retention, and costs. GenAI produces change briefs and simulation reports to guide leadership through these decisions.
- Intelligent Communication. GenAI strengthens Finance’s voice by transforming analysis into compelling narratives. Whether crafting strategy memos or investor updates, it ensures messaging is sharp, consistent, and aligned with business goals.
Letting Finance Lead with Insight and Intention
The goal of AI in finance isn’t to replace people. It’s to elevate their impact.
GenAI takes care of routine documentation and reporting. Agentic AI senses risks and opportunities and acts in real time. Together, they create space for finance professionals to focus on what really matters – insight, strategy, and leadership. This shift goes beyond efficiency. It’s about reimagining the role of finance in a fast-moving world. AI doesn’t remove the human element. It enhances it.
Finance is no longer just the record-keeper. With AI as a partner, it becomes a navigator, guiding the business with clarity, care, and conviction.
Unlike search engines, LLMs generate direct answers without links, which can create an impression of deeper understanding. LLMs do not comprehend content; they predict the most statistically probable words based on their training data. They do not “know” the information—they generate likely responses that fit the prompt.
Additionally, LLMs are limited to the knowledge within their training data and cannot access real-time or comprehensive information. Retrieval Augmented Generation (RAG) addresses this limitation by integrating LLMs with external, current data sources.
Click here to download RAG & LLMs as a PDF.
What is RAG?
RAG allows LLMs to draw on knowledge sources, such as company documents, databases, or regulatory content, that fall outside their original training data. By referencing this information in real time, LLMs can generate more accurate, relevant, and context-aware responses.
Why RAG Matters for Business Leaders
LLMs generate responses based solely on information contained within their training data. Each user query is addressed using this fixed dataset, which may be limited or outdated.
RAG enhances LLM capabilities by enabling them to access external, up-to-date knowledge bases beyond their original training scope. While not infallible, RAG makes responses more accurate and contextually relevant.
Beyond internal data, RAG can harness internet-based information for real-time market intelligence, customer sentiment analysis, regulatory updates, supply chain risk monitoring, and talent insights, amplifying AI’s business impact.
Getting Started with RAG
Organisations can unlock RAG’s potential through Custom GPTs; tailored LLM instances enhanced with external data sources. This enables specialised responses grounded in specific databases or documents.
Key use cases include:
• Analytics & Intelligence. Combine internal reports and market data for richer insights.
• Executive Briefings. Automate strategy summaries from live data feeds.
• Customer & Partner Support. Deliver instant, precise answers using internal knowledge.
• Compliance & Risk. Query regulatory documents to mitigate risks.
• Training & Onboarding. Streamline new hire familiarisation with company policies.
Ecosystm Opinion
RAG enhances LLMs but has inherent limitations. Its effectiveness depends on the quality and organisation of external data; poorly maintained sources can lead to inaccurate outputs. Additionally, LLMs do not inherently manage privacy or security concerns, so measures such as role-based access controls and compliance audits are necessary to protect sensitive information.
For organisations, adopting RAG involves balancing innovation with governance. Effective implementation requires integrating RAG’s capabilities with structured data management and security practices to support reliable, compliant, and efficient AI-driven outcomes.
AI can no longer be treated as a side experiment; it is often embedded in core decisions, customer experiences, operations, and innovation. And as adoption accelerates, so does regulatory scrutiny. Around the world, governments are moving quickly to set rules on how AI can be used, what risks must be controlled, and who is held accountable when harm occurs.
This shift makes Responsible AI a strategic imperative – not just a compliance checkbox. It’s about reducing reputational risk, protecting customers and IP, and earning the trust needed to scale AI responsibly. Embedding transparency, fairness, and accountability into AI systems isn’t just ethical, it’s smart business.
Understanding the regulatory landscape is a key part of that responsibility. As frameworks evolve, organisations must stay ahead of the rules shaping AI and ensure leadership is asking the right questions.
EU AI Act: Setting the Standard for Responsible AI
The EU AI Act is the world’s first comprehensive legislative framework for AI. It introduces a risk-based classification system: minimal, limited, high, and unacceptable. High-risk applications, including those used in HR, healthcare, finance, law enforcement, and critical infrastructure, must comply with strict requirements around transparency, data governance, ongoing monitoring, and human oversight. Generative AI models above certain thresholds are also subject to obligations such as disclosing training data sources and ensuring content integrity.
Although an EU regulation, the Act has global relevance. Organisations outside the EU may fall within its scope if their AI systems impact EU citizens or markets. And just as the GDPR became a de facto global standard for data protection, the EU AI Act is expected to create a ripple effect, shaping how other countries approach AI regulation. It sets a clear precedent for embedding safety, accountability, and human-centric principles into AI governance. As a result, it is one of the most closely tracked developments by compliance teams, risk officers, and AI governance leads worldwide.
However, as AI governance firms up worldwide, Asia Pacific organisations must look beyond Europe. From Washington to Beijing, several regulatory frameworks are rapidly influencing global norms. Whether organisations are building, deploying, or partnering on AI, these five are shaping the rules of the game.
AI Regulations Asia Pacific Organisations Must Track
1. United States: Setting the Tone for Global AI Risk Management
The U.S. Executive Order on AI (2023) signals a major policy shift in federal oversight. It mandates agencies to establish AI safety standards, governance protocols, and risk assessment practices, with an emphasis on fairness, explainability, and security, especially in sensitive domains like healthcare, employment, and finance. Central to this effort is the NIST AI Risk Management Framework (AI RMF), quickly emerging as a global touchstone.
Though designed as domestic policy, the Order’s influence is global. It sets a high bar for what constitutes responsible AI and is already shaping procurement norms and international expectations. For Asia Pacific organisations, early alignment isn’t just about accessing the U.S. market; it’s about maintaining credibility and competitiveness in a global AI landscape that is rapidly converging around these standards.
Why it matters to Asia Pacific organisations
- Global Supply Chains Depend on It. U.S.-linked firms must meet stringent AI safety and procurement standards to stay viable. Falling short could mean loss of market and partnership access.
- NIST Is the New Global Benchmark. Aligning with AI RMF enables consistent risk management and builds confidence with global regulators and clients.
- Explainability Is Essential. AI systems must provide auditable, transparent decisions to satisfy legal and market expectations.
- Security Isn’t Optional. Preventing misuse and securing models is a non-negotiable baseline for participation in global AI ecosystems.
2. China: Leading with Strict GenAI Regulation
China’s 2023 Generative AI Measures impose clear rules on public-facing GenAI services. Providers must align content with “core socialist values,” prevent harmful bias, and ensure outputs are traceable and verifiable. Additionally, algorithms must be registered with regulators, with re-approval required for significant changes. These measures embed accountability and auditability into AI development and signal a new standard for regulatory oversight.
For Asia Pacific organisations, this is more than compliance with local laws; it’s a harbinger of global trends. As major economies adopt similar rules, embracing traceability, algorithmic governance, and content controls now offers a competitive edge. It also demonstrates a commitment to trustworthy AI, positioning firms as serious players in the future global AI market.
Why it matters to Asia Pacific organisations
- Regulatory Access and Avoiding Risk. Operating in or reaching Chinese users means strict content and traceability compliance is mandatory.
- Global Trend Toward Algorithm Governance. Requirements like algorithm registration are becoming regional norms and early adoption builds readiness.
- Transparency and Documentation. Rules align with global moves toward auditability and explainability.
- Content and Data Localisation. Businesses must invest in moderation and rethink infrastructure to comply with China’s standards.
3. Singapore: A Practical Model for Responsible AI
Singapore’s Model AI Governance Framework, developed by IMDA and PDPC, offers a pragmatic and principles-led path to ethical AI. Centred on transparency, human oversight, robustness, fairness, and explainability, the framework is accompanied by a detailed implementation toolkit, including use-case templates and risk-based guidance. It’s a practical playbook for firms looking to embed responsibility into their AI systems from the start.
For Asia Pacific organisations, Singapore’s approach serves as both a local standard and a launchpad for global alignment. Adopting it enables responsible innovation, prepares teams for tighter compliance regimes, and builds trust with stakeholders at home and abroad. It’s a smart move for firms seeking to lead responsibly in the region’s growing AI economy.
Why it matters to Asia Pacific organisations
- Regionally Rooted, Globally Relevant. Widely adopted across Southeast Asia, the framework suits industries from finance to logistics.
- Actionable Tools for Teams. Templates and checklists make responsible AI real and repeatable at scale.
- Future Compliance-Ready. Even if voluntary now, it positions firms to meet tomorrow’s regulations with ease.
- Trust as a Strategic Asset. Emphasising fairness and oversight boosts buy-in from regulators, partners, and users.
- Global Standards Alignment. Harmonises with the NIST RMF and G7 guidance, easing cross-border operations.
4. OECD & G7: The Foundations of Global AI Trust
The OECD AI Principles, adopted by over 40 countries, and the G7 Hiroshima Process establish a high-level consensus on what trustworthy AI should look like. They champion values such as transparency, accountability, robustness, and human-centricity. The G7 further introduced voluntary codes for foundation model developers, encouraging practices like documenting limitations, continuous risk testing, and setting up incident reporting channels.
For Asia Pacific organisations, these frameworks are early indicators of where global regulation is heading. Aligning now sends a strong signal of governance maturity, supports safer AI deployment, and strengthens relationships with investors and international partners. They also help firms build scalable practices that can evolve alongside regulatory expectations.
Why it matters to Asia Pacific organisations
- Blueprint for Trustworthy AI. Principles translate to real-world safeguards like explainability and continuous testing.
- Regulatory Foreshadowing. Many Asia Pacific countries cite these frameworks in shaping their own AI policies.
- Investor and Partner Signal. Compliance demonstrates maturity to stakeholders, aiding capital access and deals.
- Safety Protocols for Scale. G7 recommendations help prevent AI failures and harmful outcomes.
- Enabler of Cross-Border Collaboration. Global standards support smoother AI export, adoption, and partnership.
5. Japan: Balancing Innovation and Governance
Japan’s AI governance, guided by its 2022 strategy and active role in the G7 Hiroshima Process, follows a soft law approach that encourages voluntary adoption of ethical principles. The focus is on human-centric, transparent, and safe AI, allowing companies to experiment within defined ethical boundaries without heavy-handed mandates.
For Asia Pacific organisations, Japan offers a compelling governance model that supports responsible innovation. By following its approach, firms can scale AI while staying aligned with international norms and anticipating formal regulations. It’s a flexible yet credible roadmap for building internal AI governance today.
Why it matters to Asia Pacific organisations
- Room to Innovate with Guardrails. Voluntary guidelines support agile experimentation without losing ethical direction.
- Emphasis on Human-Centred AI. Design principles prioritise user rights and build long-term trust.
- G7-Driven Interoperability. As a G7 leader, Japan’s standards help companies align with broader international norms.
- Transparency and Safety Matter. Promoting explainability and security sets firms apart in global markets.
- Blueprint for Internal Governance. Useful for creating internal policies that are regulation-ready.
Why This Matters: Beyond Compliance
The global regulatory patchwork is quickly evolving into a complex landscape of overlapping expectations. For multinational companies, this creates three clear implications:
- Compliance is no longer optional. With enforcement kicking in (especially under the EU AI Act), failure to comply could mean fines, blocked products, or reputational damage.
- Enterprise AI needs guardrails. Businesses must build not just AI products, but AI governance, covering model explainability, data quality, access control, bias mitigation, and audit readiness.
- Trust drives adoption. As AI systems touch more customer and employee experiences, being able to explain and defend AI decisions becomes essential for maintaining stakeholder trust.
AI regulation is not a brake on innovation; it’s the foundation for sustainable, scalable growth. For forward-thinking businesses, aligning with emerging standards today will not only reduce risk but also increase competitive advantage tomorrow. The organisations that win in the AI age will be the ones who combine speed with responsibility, and governance with ambition.
Over the past year of moderating AI roundtables, I’ve had a front-row seat to how the conversation has evolved. Early discussions often centred on identifying promising use cases and grappling with the foundational work, particularly around data readiness. More recently, attention has shifted to emerging capabilities like Agentic AI and what they mean for enterprise workflows. The pace of change has been rapid, but one theme has remained consistent throughout: ROI.
What’s changed is the depth and nuance of that conversation. As AI moves from pilot projects to core business functions, the question is no longer just if it delivers value, but how to measure it in a way that captures its true impact. Traditional ROI frameworks, focused on immediate, measurable returns, are proving inadequate when applied to AI initiatives that reshape processes, unlock new capabilities, and require long-term investment.
To navigate this complexity, organisations need a more grounded, forward-looking approach that considers not only direct gains but also enablement, scalability, and strategic relevance. Getting this right is key to both validating today’s investments and setting the stage for meaningful, sustained transformation.
Here is a summary of the key thoughts around AI ROI from multiple conversations across the Asia Pacific region.
1. Redefining ROI Beyond Short-Term Wins
A common mistake when adopting AI is using traditional ROI models that expect quick, obvious wins like cutting costs or boosting revenue right away. But AI works differently. Its real value often shows up slowly, through better decision-making, greater agility, and preparing the organisation to compete long-term.
AI projects need big upfront investments in things like improving data quality, upgrading infrastructure, and managing change. These costs are clear from the start, while the bigger benefits, like smarter predictions, faster processes, and a stronger competitive edge, usually take years to really pay off and aren’t easy to measure the usual way.
Ecosystm research finds that 60% of organisations in Asia Pacific expect to see AI ROI over two to five years, not immediately.
The most successful AI adopters get this and have started changing how they measure ROI. They look beyond just money and track things like explainability (which builds trust and helps with regulations), compliance improvements, how AI helps employees work better, and how it sparks new products or business models. These less obvious benefits are actually key to building strong, AI-ready organisations that can keep innovating and growing over time.
2. Linking AI to High-Impact KPIs: Problem First, Not Tech First
Successful AI initiatives always start with a clearly defined business problem or opportunity; not the technology itself. When a precise pain point is identified upfront, AI shifts from a vague concept to a powerful solution.
An industrial firm in Asia Pacific reduced production lead time by 40% by applying AI to optimise inspection and scheduling. This result was concrete, measurable, and directly tied to business goals.
This problem-first approach ensures every AI use case links to high-impact KPIs – whether reducing downtime, improving product quality, or boosting customer satisfaction. While this short-to-medium-term focus on results might seem at odds with the long-term ROI perspective, the two are complementary. Early wins secure executive buy-in and funding, giving AI initiatives the runway needed to mature and scale for sustained strategic impact.
Together, these perspectives build a foundation for scalable AI value that balances immediate relevance with future resilience.
3. Tracking ROI Across the Lifecycle
A costly misconception is treating pilot projects as the final success marker. While pilots validate concepts, true ROI only begins once AI is integrated into operations, scaled organisation-wide, and sustained over time.
Ecosystm research reveals that only about 32% of organisations rigorously track AI outcomes with defined success metrics; most rely on ad-hoc or incomplete measures.
To capture real value, ROI must be measured across the full AI lifecycle. This includes infrastructure upgrades needed for scaling, ongoing model maintenance (retraining and tuning), strict data governance to ensure quality and compliance, and operational support to monitor and optimise deployed AI systems.
A lifecycle perspective acknowledges the real value – and hidden costs – emerge beyond pilots, ensuring organisations understand the total cost of ownership and sustained benefits.
4. Strengthening the Foundations: Talent, Data, and Strategy
AI success hinges on strong foundations, not just models. Many projects fail due to gaps in skills, data quality, or strategic focus – directly blocking positive ROI and wasting resources.
Top organisations invest early in three pillars:
- Data Infrastructure. Reliable, scalable data pipelines and quality controls are vital. Poor data leads to delays, errors, higher costs, and compliance risks, hurting ROI.
- Skilled Talent. Cross-functional teams combining technical and domain expertise speed deployment, improve quality, reduce errors, and drive ongoing innovation – boosting ROI.
- Strategic Roadmap. Clear alignment with business goals ensures resources focus on high-impact projects, secures executive support, fosters collaboration, and enables measurable outcomes through KPIs.
Strengthening these fundamentals turns AI investments into consistent growth and competitive advantage.
5. Navigating Tool Complexity: Toward Integrated AI Lifecycle Management
One of the biggest challenges in measuring AI ROI is tool fragmentation. The AI lifecycle spans multiple stages – data preparation, model development, deployment, monitoring, and impact tracking – and organisations often rely on different tools for each. MLOps platforms track model performance, BI tools measure KPIs, and governance tools ensure compliance, but these systems rarely connect seamlessly.
This disconnect creates blind spots. Metrics sit in silos, handoffs across teams become inefficient, and linking model performance to business outcomes over time becomes manual and error prone. As AI becomes more embedded in core operations, the need for integration is becoming clear.
To close this gap, organisations are adopting unified AI lifecycle management platforms. These solutions provide a centralised view of model health, usage, and business impact, enriched with governance and collaboration features. By aligning technical and business metrics, they enable faster iteration, responsible scaling, and clearer ROI across the lifecycle.
Final Thoughts: The Cost of Inaction
Measuring AI ROI isn’t just about proving cost savings; it’s a shift in how organisations think about value. AI delivers long-term gains through better decision-making, improved compliance, more empowered employees, and the capacity to innovate continuously.
Yet too often, the cost of doing nothing is overlooked. Failing to invest in AI leads to slower adaptation, inefficient processes, and lost competitive ground. Traditional ROI models, built for short-term, linear investments, don’t account for the strategic upside of early adoption or the risks of falling behind.
That’s why leading organisations are reframing the ROI conversation. They’re looking beyond isolated productivity metrics to focus on lasting outcomes: scalable governance, adaptable talent, and future-ready business models. In a fast-evolving environment, inaction carries its own cost – one that may not appear in today’s spreadsheet but will shape tomorrow’s performance.
The financial services sector stands at a pivotal moment. Shaped by shifting customer expectations, fintech disruption, and rising demands for security and compliance, the industry is undergoing deep, ongoing transformation. From personalised digital engagement to AI-driven decisions and streamlined operations, BFSI is being fundamentally reshaped.
To thrive in this intelligent, interconnected future, financial organisations must embrace new strategies that turn challenges into opportunities.
Click here to download “Future Forward: Reimagining Financial Services” as a PDF.
Scaling for Impact
CreditAccess Grameen, a leading microfinance institution in India, struggled to scale its operations to meet the rising demand for microloans. Its manual processes were inefficient, causing delays and hindering its ability to serve an expanding customer base.
To overcome this, CreditAccess Grameen digitised its operations, automating processes to handle over 80,000 loans per day, streamlining loan approvals and improving operational efficiency.
This transformation significantly reduced loan processing times, from seven to ten days down to a more efficient, timely process. It also enhanced customer satisfaction, empowered financial independence, and strengthened CreditAccess Grameen’s position as a leader in financial inclusion, driving economic growth in rural India.
Seamless Operations, Improved Reporting
After merging three separate funds, Aware Super, one of Australia’s largest superannuation funds, faced fragmented operations, inconsistent documentation, and poor visibility into workflows. These inefficiencies hampered the organisation’s ability to optimise operations, ensure compliance, and deliver a seamless member experience.
To overcome this, Aware Super implemented a business process management suite to standardise and automate key processes, providing a unified platform for continuous improvement.
The transformation streamlined operations across all funds, improving reporting accuracy, reducing waste, and boosting procurement efficiency. The creation of a Centre of Excellence fostered a culture of ongoing process improvement and regulatory compliance, elevating Aware Super’s process maturity and solidifying its leadership in the financial services sector.
Empowering Employees and Improving Operations
The Norinchukin Bank, a major financial institution serving Japan’s agriculture, forestry, and fisheries sectors, struggled with outdated, paper-based processes and disconnected systems. Manual approvals and repetitive data entry were hindering operations and frustrating staff.
The digital team implemented a low-code platform that quickly automated approvals, integrated siloed systems, and streamlined processes into a single, efficient workflow.
The results were striking: approval times dropped, development cycles halved, and implementation costs fell by 30% compared to legacy upgrades. Employees gained real-time visibility over requests, cutting errors and speeding decisions. Crucially, the shift sparked a wave of digital adoption, with teams across the bank now embracing automation to drive further efficiency.
Eliminating Handoffs, Elevating Experience
Axis Bank, one of India’s largest private sector banks, struggled with slow, manual corporate onboarding processes, which hindered efficiency and customer satisfaction. The bank sought to streamline this process to keep up with growing demand for faster, digital services.
The bank implemented a robust API management solution, automating document handling and onboarding tasks, enabling a fully digital and seamless corporate client experience.
This transformation reduced corporate onboarding time by over 50%, eliminated manual handoffs, and enabled real-time monitoring of API performance, resulting in faster service delivery. As a result, Axis Bank saw a significant increase in customer satisfaction, a surge in API traffic, and a deeper, more loyal corporate client base.
Taming Latency, Unleashing Bandwidth
WebSpace, renowned for its in-store payment systems, faced challenges as it expanded to wholesalers. The migration to a new architecture required low-latency cloud connectivity, but its legacy network, relying on hardware routers, caused performance slowdowns, complexity, and high costs.
WebSpace adopted a cloud-based routing solution, replacing physical routers with a virtual, automated system for multicloud connectivity, enabling on-demand configuration changes from a central control point.
With the new solution, WebSpace achieved faster cloud connectivity, reducing latency and increasing bandwidth. The modern, agile network reduced management costs and complexity, while usage-based billing ensured that WebSpace only paid for the resources it used, supporting its strategic expansion and enhancing overall efficiency.
The promise of AI agents – intelligent programs or systems that autonomously perform tasks on behalf of people or systems – is enormous. These systems will augment and replace human workers, offering intelligence far beyond the simple RPA (Robotic Process Automation) bots that have become commonplace in recent years.
RPA and AI Agents both automate tasks but differ in scope, flexibility, and intelligence:
7 Lessons for AI Agents: Insights from RPA Deployments
However, in many ways, RPA and AI agents are similar – they both address similar challenges, albeit with different levels of automation and complexity. RPA adoption has shown that uncontrolled deployment leads to chaos, requiring a balance of governance, standardisation, and ongoing monitoring. The same principles apply to AI agent management, but with greater complexity due to AI’s dynamic and learning-based nature.
By learning from RPA’s mistakes, organisations can ensure AI agents deliver sustainable value, remain secure, and operate efficiently within a governed and well-managed environment.
#1 Controlling Sprawl with Centralised Governance
A key lesson from RPA adoption is that many organisations deployed RPA bots without a clear strategy, resulting in uncontrolled sprawl, duplicate bots, and fragmented automation efforts. This lack of oversight led to the rise of shadow IT practices, where business units created their own bots without proper IT involvement, further complicating the automation landscape and reducing overall effectiveness.
Application to AI Agents:
- Establish centralised governance early, ensuring alignment between IT and business units.
- Implement AI agent registries to track deployments, functions, and ownership.
- Enforce consistent policies for AI deployment, access, and version control.
#2 Standardising Development and Deployment
Bot development varied across teams, with different toolsets being used by different departments. This often led to poorly documented scripts, inconsistent programming standards, and difficulties in maintaining bots. Additionally, rework and inefficiencies arose as teams developed redundant bots, further complicating the automation process and reducing overall effectiveness.
Application to AI Agents:
- Standardise frameworks for AI agent development (e.g., predefined APIs, templates, and design patterns).
- Use shared models and foundational capabilities instead of building AI agents from scratch for each use case.
- Implement code repositories and CI/CD pipelines for AI agents to ensure consistency and controlled updates.
#3 Balancing Citizen Development with IT Control
Business users, or citizen developers, created RPA bots without adhering to IT best practices, resulting in security risks, inefficiencies, and technical debt. As a result, IT teams faced challenges in tracking and supporting business-driven automation efforts, leading to a lack of oversight and increased complexity in maintaining these bots.
Application to AI Agents:
- Empower business users to build and customise AI agents but within controlled environments (e.g., low-code/no-code platforms with governance layers).
- Implement AI sandboxes where experimentation is allowed but requires approval before production deployment.
- Establish clear roles and responsibilities between IT, AI governance teams, and business users.
#4 Proactive Monitoring and Maintenance
Organisations often underestimated the effort required to maintain RPA bots, resulting in failures when process changes, system updates, or API modifications occurred. As a result, bots frequently stopped working without warning, disrupting business processes and leading to unanticipated downtime and inefficiencies. This lack of ongoing maintenance and adaptation to evolving systems contributed to significant operational disruptions.
Application to AI Agents:
- Implement continuous monitoring and logging for AI agent activities and outputs.
- Develop automated retraining and feedback loops for AI models to prevent performance degradation.
- Create AI observability dashboards to track usage, drift, errors, and security incidents.
#5 Security, Compliance, and Ethical Considerations
Insufficient security measures led to data leaks and access control issues, with bots operating under overly permissive settings. Also, a lack of proactive compliance planning resulted in serious regulatory concerns, particularly within industries subject to stringent oversight, highlighting the critical need for integrating security and compliance considerations from the outset of automation deployments.
Application to AI Agents:
- Enforce role-based access control (RBAC) and least privilege access to ensure secure and controlled usage.
- Integrate explainability and auditability features to comply with regulations like GDPR and emerging AI legislation.
- Develop an AI ethics framework to address bias, ensure decision-making transparency, and uphold accountability.
#6 Cost Management and ROI Measurement
Initial excitement led to unchecked RPA investments, but many organisations struggled to measure the ROI of bots. As a result, some RPA bots became cost centres, with high maintenance costs outweighing the benefits they initially provided. This lack of clear ROI often hindered organisations from realising the full potential of their automation efforts.
Application to AI Agents:
- Define success metrics for AI agents upfront, tracking impact on productivity, cost savings, and user experience.
- Use AI workload optimisation tools to manage computing costs and avoid overconsumption of resources.
- Regularly review AI agents’ utility and retire underperforming ones to avoid AI bloat.
#7 Human Oversight and Hybrid Workflows
The assumption that bots could fully replace humans led to failures in situations where exceptions, judgment, or complex decision-making were necessary. Bots struggled to handle scenarios that required nuanced thinking or flexibility, often leading to errors or inefficiencies. The most successful implementations, however, blended human and bot collaboration, leveraging the strengths of both to optimise processes and ensure that tasks were handled effectively and accurately.
Application to AI Agents:
- Integrate AI agents into human-in-the-loop (HITL) systems, allowing humans to provide oversight and validate critical decisions.
- Establish AI escalation paths for situations where agents encounter ambiguity or ethical concerns.
- Design AI agents to augment human capabilities, rather than fully replace roles.
The lessons learned from RPA’s journey provide valuable insights for navigating the complexities of AI agent deployment. By addressing governance, standardisation, and ethical considerations, organisations
can shift from reactive problem-solving to a more strategic approach, ensuring AI tools deliver value while operating within a responsible, secure, and efficient framework.
Operations leaders are on the front lines of the AI revolution. They see the transformative potential of AI and are actively driving its adoption to streamline processes, boost efficiency, and unlock new levels of performance. The value is clear: AI is no longer a futuristic concept, but a present-day necessity.
Over the past two years, Ecosystm’s research – including surveys and deep dives with business and tech leaders has confirmed this: AI is the dominant theme.
Here are some insights for Operations Leaders from our research.
Click here to download “AI Stakeholders: The Operations Perspective” as a PDF
From Streamlined Workflows to Smarter Decisions
AI is already making a tangible difference in operations. A significant 60% of operations leaders are currently leveraging AI for intelligent document processing, freeing up valuable time and resources. But this is just the beginning. The vision extends far beyond, with plans to expand AI’s reach into crucial areas like workflow analysis, fraud detection, and streamlining risk and compliance processes. Imagine AI optimising transportation routes in real-time, predicting equipment maintenance needs before they arise, or automating complex scheduling tasks. This is the operational reality AI is creating.
Real-World Impact, Real-World Examples
The impact of AI is not just theoretical. Operations leaders are witnessing firsthand how AI is driving tangible improvements. “With AI-powered vision and sensors, we’ve boosted efficiency, accuracy, and safety in our manufacturing processes,” shares one leader. Others highlight the security benefits: “From fraud detection to claims processing, AI is safeguarding our transactions and improving trust in our services.” Even complex logistical challenges are being conquered: “Our AI-driven logistics solution has cut costs, saved time, and turned complex operations into seamless processes.” These real-world examples showcase the power of AI to deliver concrete results across diverse operational functions.
Operations Takes a Seat at the AI Strategy Table (But Faces Challenges)
With 54% of organisations prioritising cost savings from AI, operations leaders are rightfully taking a seat at the AI strategy table, shaping use cases and driving adoption. A remarkable 56% of operations leaders are actively involved in defining high-value AI applications. However, a disconnect exists. Despite their influence on AI strategy, only a small fraction (7%) of operations leaders have direct data governance responsibilities. This lack of control over the very fuel that powers AI – data – creates a significant hurdle.
Further challenges include data access across siloed systems, limiting the ability to gain a holistic view, difficulty in identifying and prioritising the most impactful AI use cases, and persistent skills shortages. These barriers, while significant, are not deterring operations leaders.
The Future is AI-Driven
Despite these challenges, operations leaders are doubling down on AI. A striking 7 out of 10 plan to prioritise AI investments in 2025, driven by the pursuit of greater cost savings. And the biggest data effort on the horizon? Identifying and prioritising better use cases for AI. This focus on practical applications demonstrates a clear understanding: the future of operations is inextricably linked to the power of AI. By addressing the challenges they face and focusing on strategic implementation, operations leaders are poised to unlock the full potential of AI and transform their organisations.
The Asia Pacific region is rapidly emerging as a global economic powerhouse, with AI playing a key role in driving this growth. The AI market in the region is projected to reach USD 244B by 2025, and organisations must adapt and scale AI effectively to thrive. The question is no longer whether to adopt AI, but how to do so responsibly and effectively for long-term success.
The APAC AI Outlook 2025 highlights how Asia Pacific enterprises are moving beyond experimentation to maximise the impact of their AI investments.
Here are 5 key trends that will impact the AI landscape in 2025.
Click here to download “The Future of AI-Powered Business: 5 Trends to Watch” as a PDF.
1. Strategic AI Deployment
AI is no longer a buzzword, but Asia Pacific’s transformation engine. It’s reshaping industries and fuelling growth. Initially, high costs and complex ROI pushed leaders toward quick wins. Now, the game has changed. As AI adoption matures, the focus is shifting from short-term gains to long-term, innovation-driven strategies.
GenAI is is at the heart of this shift, moving beyond the periphery to power core business functions and deliver competitive advantage.
Organisations are rethinking AI investments, looking beyond pure financials to consider the impact on jobs, governance, and data readiness. The AI journey is about balancing ambition with practicality.
2. Optimising AI: Tailored Open-Source Models
Smaller, open-source, and specialised AI models will gain momentum as organisations seek efficiency, flexibility, and sustainability in their AI strategies.
Unlike LLMs, which require high computational power, smaller, task-specific models offer comparable performance while being more resource-efficient. This makes them ideal for organisations working with proprietary data or limited computational resources.
Beyond cost and performance, these models are more energy-efficient, addressing growing concerns about AI’s environmental impact.
3. Centralised Tools for Responsible Innovation
Navigating the increasingly complex AI landscape demands unified management and governance. Organisations will prioritise centralised frameworks to tame the chaos of diverse AI solutions, ensuring compliance (think EU AI Act) while boosting transparency and security.
Automated AI lifecycle management tools will streamline oversight, providing real-time tracking of model performance, usage, and issues like drift.
By using flexible developer toolkits and vendor-agnostic strategies, organisations can accelerate innovation while maintaining adaptability, as the technology evolves.
4. Supercharging Workflows With Agentic AI
Organisations will embrace Agentic AI to automate complex workflows and drive business value. Traditional automation tools struggle with real-world dynamism, but AI-powered agents offer a flexible solution. They empower autonomous task execution, intelligent decision-making, and adaptability to changing circumstances.
These agents, often using GenAI, understand complex instructions and learn from experience. They collaborate with humans, boosting efficiency, and adapt to disruptions, unlike rigid traditional automation.
Agentic workflows are key to redefining work, enabling agility and innovation.
5. From Productivity to People
The focus of AI conversations will shift from simply boosting productivity to using AI for human-centric innovation that transforms both employee roles and customer experiences.
For employees, AI will handle routine tasks, enabling them to focus on creativity and innovation. Education and training will be crucial for a smooth transition to AI-powered workflows.
For customers, AI is evolving to offer more empathetic, personalised interactions by understanding individual emotions, motivations, and preferences. Organisations are recognising the need for transparent, explainable AI to build trust, tailor solutions, and deepen engagement.
Hit or miss AI experiments have leaders demanding results. In this breakneck AI landscape, strategy and realism are your survival tools. A pragmatic approach? High-impact, achievable goals. Know your capabilities, prioritise manageable projects, and stay flexible. The AI winners will be those who champion human-AI collaboration, bake in ethics, and never stop researching.
Cybersecurity is essential to every organisation’s resilience, yet it often fails to resonate with business leaders focused on growth, innovation, and customer satisfaction. The challenge lies in connecting cybersecurity with these strategic goals. To bridge this gap, it is important to shift from a purely technical view of cybersecurity to one that aligns directly with business objectives.
Here are 5 impactful strategies to make cybersecurity relevant and valuable at the executive level.
1. Elevate Cybersecurity as a Pillar of Business Continuity
Cybersecurity is not just a defensive strategy; it is a proactive investment in business continuity and success. Leaders who see cybersecurity as foundational to business continuity protect more than just digital assets – they safeguard brand reputation, customer trust, and operational resilience. By framing cybersecurity as essential to keeping the business running smoothly, leaders can shift the focus from reactive problem-solving to proactive resilience planning.
For example, rather than viewing cybersecurity incidents as isolated IT issues, organisations should see them as risks that could disrupt critical business functions, halt operations, and destroy customer loyalty. By integrating cybersecurity into continuity planning, executives can ensure that security aligns with growth and operational stability, reinforcing the organisation’s ability to adapt and thrive in a constantly evolving threat landscape.
2. Translate Cyber Risks into Business-Relevant Insights
To make cybersecurity resonate with business leaders, technical risks need to be expressed in terms that directly impact the organisation’s strategic goals. Executives are more likely to respond to cybersecurity concerns when they understand the financial, reputational, or operational impacts of cyber threats. Reframing cybersecurity risks into clear, business-oriented language that highlights potential disruptions, regulatory implications, and costs helps leadership see cybersecurity as part of broader risk management.
For instance, rather than discussing a “data breach vulnerability”, frame it as a “threat to customer trust and a potential multi-million-dollar regulatory liability”. This approach contextualises cyber risks in terms of real-world consequences, helping leadership to recognise that cybersecurity investments are risk mitigations that protect revenue, brand equity, and shareholder value.
3. Build Cybersecurity into the DNA of Innovation and Product Development
Cybersecurity must be a foundational element in the innovation process, not an afterthought. When security is integrated from the early stages of product development – known as “shifting left” – organisations can reduce vulnerabilities, build customer trust, and avoid costly fixes post-launch. This approach helps businesses to innovate with confidence, knowing that new products and services meet both customer expectations and regulatory requirements.
By embedding security in every phase of the development lifecycle, leaders demonstrate that cybersecurity is essential to sustainable innovation. This shift also empowers product teams to create solutions that are both user-friendly and secure, balancing customer experience with risk management. When security is seen as an enabler rather than an obstacle to innovation, it becomes a powerful differentiator that supports growth.
4. Foster a Culture of Shared Responsibility and Continuous Learning
The most robust cybersecurity strategies extend beyond the IT department, involving everyone in the organisation. Creating a culture where cybersecurity is everyone’s responsibility ensures that each employee – from the front lines to the boardroom – understands their role in protecting the organisation. This culture is built through continuous education, regular simulations, and immersive training that makes cybersecurity practical and engaging.
Awareness initiatives, such as cyber escape rooms and live demonstrations of common attacks, can be powerful tools to engage employees. Instead of passive training, these methods make cybersecurity tangible, showing employees how their actions impact the organisation’s security posture. By treating cybersecurity as an organisation-wide effort, leaders build a proactive culture that treats security not as an obligation but as an integral part of the business mission.
5. Leverage Industry Partnerships and Regulatory Compliance for a Competitive Edge
As regulations around cybersecurity tighten, especially for critical sectors like finance and infrastructure, compliance is becoming a competitive advantage. By proactively meeting and exceeding regulatory standards, organisations can position themselves as trusted, compliant partners for clients and customers. Additionally, building partnerships across the public and private sectors offers access to shared knowledge, best practices, and support systems that strengthen organisational security.
Leaders who engage with regulatory requirements and industry partnerships not only stay ahead of compliance but also benefit from a network of resources that can enhance their cybersecurity strategies. Proactive compliance, combined with strategic partnerships, strengthens organisational resilience and builds market trust. In doing so, cybersecurity becomes more than a safeguard; it’s an asset that supports brand credibility, customer loyalty, and competitive differentiation.
Conclusion
For cybersecurity to be truly effective, it must be woven into the fabric of an organisation’s mission and strategy. By reframing cybersecurity as a foundational aspect of business continuity, expressing cyber risks in business language, embedding security in innovation, building a culture of shared responsibility, and leveraging compliance as an advantage, leaders can transform cybersecurity from a technical concern to a strategic asset. In an age where digital threats are increasingly complex, aligning cybersecurity with business priorities is essential for sustainable growth, customer trust, and long-term resilience.
