In this interview, e27 speaks with Ian about AIBYML SG’s approach to designing and deploying custom AI systems for enterprise environments. As organisations move beyond experimentation toward operational AI adoption, consulting partners increasingly play a role in bridging technology capability with governance, cost discipline, and measurable business outcomes.
This conversation forms part of e27’s broader AI Pulse coverage, which examines how organisations across the region are building, deploying, and governing AI in real-world settings.
Custom AI systems for operational workflows
e27: Briefly describe what your organization does, and where AI plays a meaningful role in your work or offering.
Ian: AIBYML SG is an AI consulting and solution firm focused on designing, building and deploying custom AI systems tailored to real operational needs, from AI-native assistants and workflow automation to intelligent customer engagement and analytics.
Transforming workflows with end-to-end AI systems
e27: What is one concrete way AI is currently creating value within your organisation or for your users or customers?
Ian: One concrete way AI is creating value for our clients is through end-to-end process transformation powered by custom AI systems, rather than standalone tools. At AIBYML SG, we begin by working closely with clients to analyse their existing “as-is” workflows – identifying operational bottlenecks, manual handoffs, and hidden cost drivers.
leverage human intelligence and LLM AI
From there, we design “to-be” processes where AI is embedded in a targeted and measurable way.
A key part of our approach is selecting the most cost-effective model and architecture for each use case pairing it with clear performance metrics, investment estimates and usage projections. This allows clients to evaluate AI initiatives like any other business project. This discipline is what turns AI from an experiment into a scalable capability with positive ROI.
Also read: AI Pulse Exclusive: How Asia AI Association is advancing human-centred AI across the region
Balancing advanced models with operational sustainability
e27: What was a key decision or trade-off you had to make when adopting, building, or scaling AI?
Ian: One recurring trade-off we help clients navigate is between using the most advanced AI models available and building solutions that are more economically and operationally sustainable at scale with open source foundation models. In many early discussions, stakeholders are understandably thrilled by the latest models because of their impressive capabilities. However, in production environments, model performance is only one part of the equation – cost, latency, reliability, data governance, and integration complexity.
Another important trade-off is between speed and organisation’s readiness. Moving quickly with a proof of concept demonstrates value, while scaling too fast without process redesign, user training, and clear benefits often lead to underutilised systems.
One thing we learned is that sustainable AI adoption requires balancing technical ambition with operational maturity. The right decision is rarely about maximising model capability – it is about maximising long-term business impact.
Adoption momentum and operational uncertainties
e27: Looking back, what has worked better than expected, and what proved more challenging than anticipated?
Ian: Looking back, what has worked better than expected is how rapidly our AI engineering team pivot suitable AI models embedded directly into their workflow design and tied to clear business outcomes. When we redesign the “to-be” process properly and define practical metrics — such as turnaround time, cost per case, or productivity uplift — adoption tends to accelerate.
What proved more challenging was managing uncertainty across cost, governance, and technology evolution. Clients understandably want clarity on short- and long-term AI investment, from model usage and infrastructure to maintenance and scaling. In practice, variable demand patterns and shifting pricing models make precise forecasting difficult, requiring scenario-based planning rather than fixed projections.
We also regularly navigate trade-offs between cost-efficiency and data governance. Stronger controls — private deployments, access management, auditability — reduce risk but increase operational overhead. At the same time, fast-moving advances in AI models make interoperability critical. Designing modular architectures adds upfront complexity, but protects long-term flexibility.
AI exposing broken processes
e27: What is one lesson about applying AI in real-world settings that leaders or founders often underestimate?
Ian: One lesson from our client engagements is that AI does not fix broken processes – it exposes them. In many engagements, we are initially asked to “add AI” to improve speed and reduce cost. However, once we analyse the existing workflow, we often discover redundancy, inconsistent data, unclear objective and undocumented exceptions. If AI is layered on these inefficiencies, it only automates complexity rather than solving it.
Another challenge is that stakeholders start by asking for a single AI feature. But once users test a prototype in their real workflow, they quickly form an “I know it when I see it” understanding and they start uncovering latent needs they couldn’t articulate upfront. Prototyping is powerful precisely because it reveals these hidden requirements through hands-on use.
Treating AI as structured transformation
e27: Based on your experience, what is one practical recommendation you would give to organisations that are just starting to explore or scale AI?
Ian: “AI doesn’t fail because models aren’t smart enough — it fails because organisations aren’t ready enough. Teams that treat AI as a structured transformation, with clear metrics, cost discipline, and room for iteration, are the ones that turn experimentation into lasting ROI.”
From AI features to operating models
e27: Over the next 12 months, how do you expect your organisation’s use of AI, or the role of AI in your industry, to evolve?
Ian: As AI tools become more powerful and no-code platforms lower the barrier to building applications, our strategic focus has shifted from “building AI features” to enabling sustainable enterprise capability. Basic chatbots and workflows will continue to be commoditised. Our long-term relevance depends on helping clients solve the harder problems around governance, integration, data, cost control, and operating ownership.
As part of our next strategic initiative, we will be working with regional enterprises that have experimented with multiple off-the-shelf AI tools but now face rising usage costs, inconsistent outputs, and growing compliance concerns. Our approach is to redesign their AI architecture to be model-agnostic, introduce structured cost monitoring and governance controls, and embed AI more deeply into core workflows.
Our key pivot is towards becoming an “AI operating model” partner — combining process redesign, modular architecture, and ongoing optimisation. The takeaway is simple: in the AI era, tools will keep changing, but organisations will always need partners who can turn fast-moving technology into reliable, governable, and scalable business capability.
Also read: AI Pulse Exclusive: How GenAI Fund is accelerating enterprise AI adoption across Southeast Asia
Operationalising AI beyond experimentation
This conversation highlights a growing shift from experimenting with AI tools to building sustainable operational capability. As enterprises face rising costs, governance considerations, and integration complexity, the focus increasingly turns toward process redesign, architecture flexibility, and measurable business outcomes. Organisations that successfully operationalise AI may find that long-term advantage lies less in the models themselves and more in how effectively they embed AI into everyday workflows.
For more interviews, analysis, and real-world perspectives on how organisations across the region are applying AI in practice, explore more stories here.
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Featured Image Credit: AIBYML SG
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