AI adoption is no longer a question of if, but how fast and how well. Most organisations are exploring AI in some form, but they’re moving at very different speeds.
The ones seeing the most value share a few traits: cross-functional collaboration, strong leadership sponsorship, and tight alignment between business and tech. That’s how they sharpen focus, deploy critical skills where it matters, and accelerate from idea to outcome.
But the gap between ambition and execution is real. As one executive put it, “We’ve seen digital natives do in 24 hours what takes our industry six months.” The risks of getting it wrong are just as real; think of Zillow’s USD 500M loss from overreliance on flawed AI models.
When done right, AI benefits every part of the organisation; not just data teams.
“Our AI-powered screening for insurance agents fast-tracks candidate selection by analysing resumes and applications to pinpoint top talent.” – HR Leader
“Conversational AI delivers 24/7 customer engagement, instantly resolving queries, easing team workload, and boosting CX.” – CX Leader
“AI transforms work by streamlining workflows and optimising transport routes, making operations faster and smarter.” – Operations Leader
“Using AI to streamline our sales pipeline has cut down the time it takes to qualify leads, enabling our team to focus on closing more deals with greater precision.” – Sales Leader
“We’re unlocking data value: AI agents personalise customer support at scale, while AI-driven network optimisation ensures seamless IT operations.” – Data Science Leader
In the short term, most businesses are focusing on operational efficiency, but the real wins will be in longer-term innovation and financial value.
For tech teams, this means delivering robust, scalable AI systems while supporting responsible experimentation by business teams – all in a fast-moving, high-stakes environment.
However, that’s not easy.
• High Costs. AI requires substantial upfront and operational spend. Without measurable outcomes, it’s hard to justify scaling.
• Security & Governance Risks. AI heightens exposure to bias, misuse, and compliance gaps. Most organisations lack mature guardrails to manage this.
• Regulatory Uncertainty. Shifting global policies make it difficult to design AI systems that are both future-proof and compliant.
• Skills Shortage. There’s a growing gap in AI and data expertise. Without the right talent, even promising use cases falter.
• Data Challenges. AI needs vast, high-quality data, but many organisations struggle with silos, poor lineage, and inconsistent standards.
Yet the toughest obstacles aren’t technical.
• Limited AI Fluency at the Top. Many leaders lack a practical understanding of AI’s capabilities and constraints, slowing decisions and making cross-functional alignment difficult.
• No Clear Ownership or Strategy. Without clear ownership, AI efforts remain scattered across IT, innovation, and business teams, leading to fragmentation, misalignment, and stalled progress.
• Unclear ROI and Benefits. AI’s value isn’t always immediate or financial. Without clear metrics for success, it’s hard to prioritise initiatives or secure sustained investment.
• Short-Term Pressure. The push for quick wins and fast ROI often comes at the expense of long-term thinking and foundational investments in AI capabilities.
• Rigid Business Models. AI demands adaptability in processes, structures, and mindsets. But legacy workflows, technical debt, and organisational silos frequently stand in the way.
• Change Management is an Afterthought. Many AI efforts are tech-first, people-later. Without early engagement and capability building, adoption struggles to gain traction.
Bridging the Innovation-AI Gap: The Power of Ecosystems
Bridging this gap between AI ambitions and success requires more than technology; it needs a coordinated ecosystem of vendors, enterprises, startups, investors, and regulators working together to turn innovation into real-world impact.
Public-private partnerships are key. In Singapore, initiatives like IMDA’s Spark and Accreditation programmes tackle this head-on by spotting high-potential startups, rigorously validating solutions, and opening doors to enterprise and government procurement. This approach de-risks adoption and speeds impact.
• For Enterprises. It means quicker access to trusted, local solutions that meet strict performance and compliance standards.
• For Startups. It unlocks scale, credibility, and funding.
• For the Economy. It creates a future-ready digital ecosystem where innovation moves beyond the lab to drive national competitiveness and growth.
The latest shift in AI takes us beyond data analysis and content generation. With agentic AI we are now seeing systems that can plan, reason, act autonomously, and adapt based on outcomes. This shift marks a practical turning point for operational execution and strategic agility.
Smart On-Ramps for Agentic AI
Technology providers are rapidly maturing their agentic AI offerings, increasingly packaging them as pre-built agents designed for quick deployment. These often require minimal customisation and target common enterprise needs – onboarding assistants, IT helpdesk agents, internal knowledge copilots, or policy compliance checkers – integrated with existing platforms like Microsoft 365, Salesforce, or ServiceNow.
For example, a bank might deploy a templated underwriting agent to pre-screen loan applications, while a university could roll out a student support bot that flags at-risk learners and nudges them toward action. These plug-and-play pilots let organisations move fast, with lower risk and clearer ROI.
Templated agents won’t suit every context, particularly where rules are complex or data is fragmented. But for many, they offer a smart on-ramp: a focused, contained pilot that delivers value, builds momentum, and lays the groundwork to scale Agentic AI more broadly.
Here are 10 such opportunities – five cross-industry and five sector-specific – ideal for launching agentic AI in your organisation. Each addresses a real-world pain point, with measurable impact and momentum for broader change.
Horizontal Use Cases
1. Employee Onboarding & Integration Assistant
An AI agent that guides new hires through their critical first weeks and months by answering FAQs about company policies, automating paperwork, scheduling introductory meetings, and sending personalised reminders to complete mandatory training, all integrated with HRIS, LMS, and calendaring systems. This can help reduce the administrative load on HR teams by handling repetitive onboarding tasks, potentially freeing up significant time, while also improving new hire satisfaction and accelerating time-to-productivity by providing employees with better support and engagement from day one.
Consideration. Begin with a specific department or a targeted hiring wave. Prioritise roles with high turnover or complex onboarding needs. Ensure HR data is clean and accessible, and policy documents are up to date.
2. Automated Meeting Follow-ups & Action Tracking
With permission, AI agents can listen to virtual meetings, identify key discussion points, summarise decisions, extract and assign action items with deadlines, and proactively follow up via email or collaboration platforms like Slack or Teams to help ensure tasks are completed. By integrating with meeting platforms, project management tools, and email, this can reduce the burden of manual note-taking and follow-up, potentially saving team members 1-2 hours per week, while also improving execution rates and accountability to make meetings more action-focused.
Consideration. Deploy with a small, cross-functional team that has frequent meetings. Clearly communicate the agent’s role and data privacy protocols to ensure user comfort and compliance.
3. Intelligent Procurement Assistant
An agent that interprets internal requests, initiates purchase orders, compares vendor options against predefined criteria, flags potential compliance issues based on policies and spending limits, and manages approval workflows, integrating with ERP systems, vendor databases, and internal policy documents. This can help accelerate procurement cycles, reduce manual errors, and lower the risk of non-compliant spending, potentially freeing procurement specialists to focus more on strategic sourcing rather than transactional tasks.
Consideration. Begin with a specific category of low-to-medium value purchases (e.g., office supplies, standard software licenses). Define clear, rule-based policies for the agent to follow.
4. Enhanced Sales/Outreach Research Agent
Given a target account, citizen segment, or potential beneficiary profile, this agent autonomously gathers and synthesises insights from CRM data, public financial records, social media, news feeds, and industry reports. It then generates tailored talking points, personalised outreach messages, and intelligent discovery questions for human operators. This can provide representatives with deeper insights, potentially improving their preparation and boosting early-stage conversion rates, while reducing manual research time significantly and allowing teams to focus more on building relationships.
Consideration. Train the agent on a specific sales vertical or a targeted public outreach campaign. Ensure robust data privacy compliance when accessing and synthesising public information.
5. Proactive Internal IT Helpdesk Agent
This agent enables employees to describe technical issues in natural language through familiar platforms like Slack, Teams, or internal portals. It can intelligently troubleshoot problems, guide users through self-service solutions from a knowledge base, or escalate more complex issues to the appropriate IT specialist, often pre-filling support tickets with relevant diagnostic information. This approach can lead to faster issue resolution, reduce the number of common support tickets, and improve employee satisfaction with IT services, while freeing IT staff to focus on more complex problems and strategic initiatives.
Consideration. Start with a well-documented set of frequently asked questions (FAQs) or common Tier 1 IT issues (e.g., password resets, VPN connection problems). Ensure a clear escalation path to human support.
Industry-Specific Use Cases
6. Intelligent Insurance Claims Triage (Insurance)
This agent reviews incoming insurance claims by processing unstructured data such as claim descriptions, photos, and documents. It automatically cross-references policy coverage, identifies missing information, and assigns priority or flags potential fraud based on predefined rules and learned patterns. This can speed up initial claims processing, reduce the manual workload for claims adjusters, and improve the early detection of suspicious claims, helping to lower fraud risk and deliver a faster, more efficient customer experience during a critical time.
Consideration. Focus on a specific, high-volume, and relatively standardized claim type (e.g., minor motor vehicle damage, simple property claims). Ensure robust data integration with policy management and fraud detection systems.
7. Automated Credit Underwriting Assistant (Banking)
An AI agent that pre-screens loan applications by gathering and analysing data from internal banking systems, external credit bureaus, and public records. It identifies key risk factors, generates preliminary credit scores, and prepares initial decision recommendations for human loan officers to review and approve. This can significantly shorten loan processing times, improve consistency in risk assessments, and allow human underwriters to concentrate on more complex cases and customer interactions.
Consideration. Apply this agent to a specific, well-defined loan product (e.g., unsecured personal loans, small business loans) with clear underwriting criteria. Strict human-in-the-loop oversight for final decisions is paramount.
8. Clinical Trial Workflow Coordinator (Healthcare)
This agent monitors clinical trial timelines, tracks participant progress, flags potential non-compliance or protocol deviations, and coordinates tasks and communication between research teams, labs, and regulatory bodies. Integrated with Electronic Health Records (EHRs), trial management systems, and regulatory databases, it helps reduce delays in complex clinical workflows, improves adherence to strict protocols and regulations, and enhances data quality, potentially speeding up drug development and patient access to new treatments.
Consideration. Focus on a single phase of a trial or specific documentation compliance checkpoints within an ongoing study. Ensure secure and compliant access to sensitive patient and trial data.
9. Predictive Maintenance Scheduler (Manufacturing)
By continuously analysing real-time IoT sensor data from machinery, this agent uses predictive analytics to anticipate potential equipment failures. It then schedules maintenance at optimal times, taking into account production schedules, spare part availability, and technician workloads, and automatically assigns tasks. This approach can significantly boost machine uptime and overall equipment effectiveness by reducing unplanned downtime, optimize technician efficiency, and extend asset lifespan, resulting in notable cost savings.
Consideration. Implement for a critical, high-value machine or a specific production line where downtime is extremely costly. Requires reliable and high-fidelity IoT sensor data.
10. Personalised Student Success Advisor (Higher Education)
This agent analyses student performance data such as grades, attendance, and LMS activity to identify those at risk of struggling or dropping out. It then proactively nudges students about upcoming deadlines, recommends personalised learning resources, and connects them with tutoring services or academic advisors. This support can improve retention rates, contribute to better academic outcomes, and enhance the overall student experience by providing timely, tailored assistance.
Consideration. Start with a specific cohort (e.g., first-year students, transfer students) or focus on a particular set of foundational courses. Ensure ethical data usage and transparent communication with students about the agent’s role.
Pilot Success Framework: Getting Started Today
As we have seen in the considerations above, starting with a high-impact, relatively low-risk use case is the recommended approach for beginning an agentic AI journey. This focuses on strategic, measured steps rather than a massive initial overhaul. When selecting a first pilot, organisations should identify projects with clear boundaries – specific data sources, explicit goals, and well-defined actions – avoiding overly ambitious or ambiguous initiatives.
A good pilot tackles a specific pain point and delivers measurable benefits, whether through time savings, fewer errors, or improved user satisfaction. Choosing scenarios with limited stakeholder risk and minimal disruption allows for learning and iteration without significant operational impact.
Executing a pilot effectively under these guidelines can generate momentum, earn stakeholder support, and lay the groundwork for scaling AI-driven transformation throughout the organisation. The future of autonomous operations begins with such focused pilots.
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.
Consider the sheer volume of information flowing through today’s financial systems: every QR payment, e-KYC onboarding, credit card swipe, and cross-border transfer captures a data point. With digital banking and Open Banking, financial institutions are sitting on a goldmine of insights. But this isn’t just about data collection; it’s about converting that data into strategic advantage in a fast-moving, customer-driven landscape.
With digital banks gaining traction and regulators around the world pushing bold reforms, the industry is entering a new phase of financial innovation powered by data and accelerated by AI.
Ecosystm gathered insights and identified key challenges from senior banking leaders during a series of roundtables we moderated across Asia Pacific. The conversations revealed a clear picture of where momentum is building – and where obstacles continue to slow progress. From these discussions, several key themes emerged that highlight both opportunities and ongoing barriers in the Banking sector.
1. AI is Leading to End-to-End Transformation
Banks are moving beyond generic digital offerings to deliver hyper-personalised, data-driven experiences that build loyalty and drive engagement. AI is driving this shift by helping institutions anticipate customer needs through real-time analysis of behavioural, transactional, and demographic data. From pre-approved credit offers and contextual investment nudges to app interfaces that adapt to individual financial habits, personalisation is becoming a core strategy, not just a feature. This is a huge departure from reactive service models, positioning data as a long-term strategic asset.
But the impact of AI isn’t limited to customer-facing experiences. It’s also driving innovation deep within the banking stack, from fraud detection and SME loan processing to intelligent chatbots that scale customer support. On the infrastructure side, banks are investing in agile, AI-ready platforms to support automation, model training, and advanced analytics at scale. These shifts are redefining how banks operate, make decisions, and deliver value. Institutions that integrate AI across both front-end journeys and back-end processes are setting a new benchmark for agility, efficiency, and competitiveness in a fast-changing financial landscape.
2. Regulatory Shifts are Redrawing the Competitive Landscape
Regulators are moving quickly in Asia Pacific by introducing frameworks for Open Banking, real-time payments, and even AI-specific standards like Singapore’s AI Verify. But the challenge for banks isn’t just keeping up with evolving external mandates. Internally, many are navigating a complicated mix of overlapping policies, built up over years of compliance with local, regional, and global rules. This often slows down innovation and makes it harder to implement AI and automation consistently across the organisation.
As banks double down on AI, it is clear that governance can’t be an afterthought. Many are still dealing with fragmented ownership of AI systems, inconsistent oversight, and unclear rules around things like model fairness and explainability. The more progressive ones are starting to fix this by setting up centralised governance frameworks, investing in risk-based controls, and putting processes in place to monitor things like bias and model drift from day one. They are not just trying to stay compliant; they are preparing for what’s coming next. In this landscape, the ability to manage regulatory complexity with speed and clarity, both internally and externally, is quickly becoming a competitive edge.
3. Success Depends on Strategy, Not Just Tech
While enthusiasm for AI is high, sustainable success hinges on a clear, aligned strategy that connects technology to business outcomes. Many banks struggle with fragmented initiatives because they lack a unified roadmap that prioritises high-impact use cases. Without clear goals, AI projects often fail to deliver meaningful value, becoming isolated pilots with limited scalability.
To avoid this, banks need to develop robust return-on-investment (ROI) models tailored to their context — measuring benefits like faster credit decisioning, reduced fraud losses, or increased cross-selling effectiveness. These models must consider not only the upfront costs of infrastructure and talent, but also ongoing expenses such as model retraining, governance, and integration with existing systems.
Ethical AI governance is another essential pillar. With growing regulatory scrutiny and public concern about opaque “black box” models, banks must embed transparency, fairness, and accountability into their AI frameworks from the outset. This goes beyond compliance; strong governance builds trust and is key to responsible, long-term use of AI in sensitive, high-stakes financial environments.
4. Legacy Challenges Still Hold Banks Back
Despite strong momentum, many banks face foundational barriers that hinder effective AI deployment. Chief among these is data fragmentation. Core customer, transaction, compliance, and risk data are often scattered across legacy systems and third-party platforms, making it difficult to access the integrated, high-quality data that AI models require.
This limits the development of comprehensive solutions and makes AI implementations slower, costlier, and less effective. Instead of waiting for full system replacements, banks need to invest in integration layers and modern data platforms that unify data sources and make them AI-ready. These platforms can connect siloed systems – such as CRM, payments, and core banking – to deliver a consolidated view, which is crucial for accurate credit scoring, personalised offers, and effective risk management.
Banks must also address talent gaps. The shortage of in-house AI expertise means many institutions rely on external consultants, which increases costs and reduces knowledge transfer. Without building internal capabilities and adjusting existing processes to accommodate AI, even sophisticated models may end up underused or misapplied.
5. Collaboration and Capability Building are Key Enablers
AI transformation isn’t just a technology project – it’s an organisation-wide shift that requires new capabilities, ways of working, and strategic partnerships. Success depends on more than just hiring data scientists. Relationship managers, credit officers, compliance teams, and frontline staff all need to be trained to understand and act on AI-driven insights. Processes such as loan approvals, fraud escalations, and customer engagement must be redesigned to integrate AI outputs seamlessly.
To drive continuous innovation, banks should establish internal Centres of Excellence for AI. These hubs can lead experimentation with high-value use cases like predictive credit scoring or real-time fraud detection, while ensuring that learnings are shared across business units. They also help avoid duplication and promote strategic alignment.
Partnerships with fintechs, technology providers, and academic institutions play a vital role as well. These collaborations offer access to cutting-edge tools, niche expertise, and locally relevant AI models that reflect the regulatory, cultural, and linguistic contexts banks operate in. In a fast-moving and increasingly competitive space, this combination of internal capability building and external collaboration gives banks the agility and foresight to lead.
AI is not just reshaping how businesses operate — it’s redefining the CFO’s role at the centre of value creation, risk management, and operational leadership.
As stewards of capital, CFOs must cut through the hype and ensure AI investments deliver measurable business returns. As guardians of risk and compliance, they must shield their organisations from new threats — from algorithmic bias to data privacy breaches with heavy financial and reputational costs. And as leaders of their function, CFOs now have a generational opportunity to modernise finance, champion AI adoption, and build teams ready for an AI-powered future.
LEAD WITH RIGOUR. SAFEGUARD WITH VIGILANCE. CHAMPION WITH VISION.
That’s the CFO playbook for AI success.
Click here to download “AI Stakeholders: The Finance Perspective” as a PDF.
1. Investor & ROI Gatekeeper: Ensuring AI Delivers Value
CFOs must scrutinise AI investments with the same discipline as any major capital allocation.
- Demand Clear Business Cases. Every AI initiative should articulate the problem solved, expected gains (cost, efficiency, accuracy), and specific KPIs.
- Prioritise Tangible ROI. Focus on AI projects that show measurable impact. Start with high-return, lower-risk use cases before scaling.
- Assess Total Cost of Ownership (TCO). Go beyond upfront costs – factor in integration, maintenance, training, and ongoing AI model management.
Only 37% of Asia Pacific organisations invest in FinOps to cut costs, boost efficiency, and strengthen financial governance over tech spend.
2. Risk & Compliance Steward: Navigating AI’s New Risk Landscape
AI brings significant regulatory, compliance, and reputational risks that CFOs must manage – in partnership with peers across the business.
- Champion Data Quality & Governance. Enforce rigorous data standards and collaborate with IT, risk, and business teams to ensure accuracy, integrity, and compliance across the enterprise.
- Ensure Data Accessibility. Break down silos with CIOs and CDOs and invest in shared infrastructure that AI initiatives depend on – from data lakes to robust APIs.
- Address Bias & Safeguard Privacy. Monitor AI models to detect bias, especially in sensitive processes, while ensuring compliance.
- Protect Security & Prevent Breaches. Strengthen defences around financial and personal data to avoid costly security incidents and regulatory penalties.
3. AI Champion & Business Leader: Driving Adoption in Finance
Beyond gatekeeping, CFOs must actively champion AI to transform finance operations and build future-ready teams.
- Identify High-Impact Use Cases. Work with teams to apply AI where it solves real pain points – from automating accounts payable to improving forecasting and fraud detection.
- Build AI Literacy. Help finance teams see AI as an augmentation tool, not a threat. Invest in upskilling while identifying gaps – from data management to AI model oversight.
- Set AI Governance Frameworks. Define accountability, roles, and control mechanisms to ensure responsible AI use across finance.
- Stay Ahead of the Curve. Monitor emerging tech that can streamline finance and bring in expert partners to fast-track AI adoption and results.
CFOs: From Gatekeepers to Growth Drivers
AI is not just a tech shift – it’s a CFO mandate. To lead, CFOs must embrace three roles: Investor, ensuring every AI bet delivers real ROI; Risk Guardian, protecting data integrity and compliance in a world of new risks; and AI Champion, embedding AI into finance teams to boost speed, accuracy, and insight.
This is how finance moves from record-keeping to value creation. With focused leadership and smart collaboration, CFOs can turn AI from buzzword to business impact.
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.
