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.
A lot has been written and spoken about DeepSeek since the release of their R1 model in January. Soon after, Alibaba, Mistral AI, and Ai2 released their own updated models, and we have seen Manus AI being touted as the next big thing to follow.
DeepSeek’s lower-cost approach to creating its model – using reinforcement learning, the mixture-of-experts architecture, multi-token prediction, group relative policy optimisation, and other innovations – has driven down the cost of LLM development. These methods are likely to be adopted by other models and are already being used today.
While the cost of AI is a challenge, it’s not the biggest for most organisations. In fact, few GenAI initiatives fail solely due to cost.
The reality is that many hurdles still stand in the way of organisations’ GenAI initiatives, which need to be addressed before even considering the business case – and the cost – of the GenAI model.
Real Barriers to GenAI
• Data. The lifeblood of any AI model is the data it’s fed. Clean, well-managed data yields great results, while dirty, incomplete data leads to poor outcomes. Even with RAG, the quality of input data dictates the quality of results. Many organisations I work with are still discovering what data they have – let alone cleaning and classifying it. Only a handful in Australia can confidently say their data is fully managed, governed, and AI-ready. This doesn’t mean GenAI initiatives must wait for perfect data, but it does explain why Agentic AI is set to boom – focusing on single applications and defined datasets.
• Infrastructure. Not every business can or will move data to the public cloud – many still require on-premises infrastructure optimised for AI. Some companies are building their own environments, but this often adds significant complexity. To address this, system manufacturers are offering easy-to-manage, pre-built private cloud AI solutions that reduce the effort of in-house AI infrastructure development. However, adoption will take time, and some solutions will need to be scaled down in cost and capacity to be viable for smaller enterprises in Asia Pacific.
• Process Change. AI algorithms are designed to improve business outcomes – whether by increasing profitability, reducing customer churn, streamlining processes, cutting costs, or enhancing insights. However, once an algorithm is implemented, changes will be required. These can range from minor contact centre adjustments to major warehouse overhauls. Change is challenging – especially when pre-coded ERP or CRM processes need modification, which can take years. Companies like ServiceNow and SS&C Blue Prism are simplifying AI-driven process changes, but these updates still require documentation and training.
• AI Skills. While IT teams are actively upskilling in data, analytics, development, security, and governance, AI opportunities are often identified by business units outside of IT. Organisations must improve their “AI Quotient” – a core understanding of AI’s benefits, opportunities, and best applications. Broad upskilling across leadership and the wider business will accelerate AI adoption and increase the success rate of AI pilots, ensuring the right people guide investments from the start.
• AI Governance. Trust is the key to long-term AI adoption and success. Being able to use AI to do the “right things” for customers, employees, and the organisation will ultimately drive the success of GenAI initiatives. Many AI pilots fail due to user distrust – whether in the quality of the initial data or in AI-driven outcomes they perceive as unethical for certain stakeholders. For example, an AI model that pushes customers toward higher-priced products or services, regardless of their actual needs, may yield short-term financial gains but will ultimately lose to ethical competitors who prioritise customer trust and satisfaction. Some AI providers, like IBM and Microsoft, are prioritising AI ethics by offering tools and platforms that embed ethical principles into AI operations, ensuring long-term success for customers who adopt responsible AI practices.
GenAI and Agentic AI initiatives are far from becoming standard business practice. Given the current economic and political uncertainty, many organisations will limit unbudgeted spending until markets stabilise. However, technology and business leaders should proactively address the key barriers slowing AI adoption within their organisations. As more AI platforms adopt the innovations that helped DeepSeek reduce model development costs, the economic hurdles to GenAI will become easier to overcome.