Established in 1981, Care Corner is a non-profit organisation providing social and health care services to build hope and promote well-being for those in need. With more than 48 service points across Singapore, the organisation provides a range of services for children, youth, families, and seniors.
Last year, Care Corner’s roughly 600 staff and over 7,000 volunteers supported over 28,000 beneficiaries and engaged close to 37,000 individuals through outreach, providing a holistic continuum of care to the marginalised across their life stages and major transitions.
The organisation, always looking for ways to better support their beneficiaries and do meaningful work, is now exploring the potential for AI to enhance their operations. In this, they were assisted by AI.dea, Singtel’s AI business transformation programme designed to help Singapore SMEs move beyond pilots and achieve real, scalable AI adoption.
SMEhorizon speaks with Christian Chao, CEO, Care Corner, on their journey as a social services organisation implementing AI, what it brings to those they support, and what other organisations and SMEs can learn from their experience.
Meanwhile, Wilson Tan, Senior Director, Cyber Consulting, Education and Professional, Singtel Singapore also weighs in on the current state of AI adoption amongst Singapore SMEs, and how barriers to its uptake can be overcome.
Challenges in effective AI adoption
While providing social and healthcare services is meaningful work, it is not an easy sector to operate in. “Resources,” explains Chao, “are precious and the work is intense.”
These constraints, familiar to SMEs across all industries, contribute to a limited adoption of AI amongst SMEs that is currently still uneven and largely at an early stage. Tan cites recent government reporting that found 71.5% of firms had not adopted AI and only 3.8% were integrating it into core processes,
“This shows that many businesses are still experimenting rather than scaling,” he explains. “While there are encouraging signs that SME adoption is improving, with some reports citing a rise to 14.5% in 2024, suggesting momentum is building even if the majority have yet to make AI part of their everyday operations.”
“This is creating a widening competitive gap, as SMEs increasingly compete against larger enterprises already using AI at scale to improve productivity and customer experience.”
Despite the danger of being left behind, why are SMEs so hesitant? “The biggest challenge is not access to tools but turning tools into business value,” continues Tan.
“Many SMEs face data quality issues, integration complexity, legacy systems, skill shortages, and uncertainty about governance and risk, all of which slow down progress once the initial excitement of a pilot fades.
“Cost is also a real concern, especially for smaller firms that need clear ROI before committing to broader deployment.”
These were also the challenges faced by Care Corner as it embarked on its AI implementation journey. Chao recalls issues with data foundations, AI literacy and the difficulty of driving meaningful transformation within an organisation.
“Data in our sector is often fragmented across systems, with different standards and limited interoperability,” he explains. “Building the underlying data and process foundations has taken patience.”
Issues with AI literacy went beyond just hiring AI specialists. For effective AI implementation, “we need AI literacy across the entire organisation, from leadership using AI to aid strategic decision-making to frontline staff using AI tools in day-to-day tasks. Without that, adoption remains shallow,” he says.
Finally, there was the daunting task of the implementation itself. “Embedding AI into operations requires redesigning workflows, retraining staff, and building trust through the process of change,” says Chao.
“And as much as we want to drive adoption of AI, we do not want to encourage overreliance on it, leading to cognitive off-loading, because professional judgement is critical in social work.”
Tackling obstacles to meaningful change
The AI.dea programme from Singtel, recently launched in partnership with SIM Academy, aims to address the pain points faced by SMEs and other organisations like Care Corner by providing structured guidance, hands-on learning, and governance support. Tan emphasises that SMEs should begin with a focused use case that solves a real business pain point, rather than trying to transform everything at once.
“The most successful AI adoption approach typically starts with a defined pilot supported by good data preparation, clear success metrics, and a plan for operational handover, so the solution can be sustained after launch,” he explains.
“Businesses also benefit from working with partners that can support process redesign, governance, and change management alongside the technology itself.”
AI.dea is designed to support SMEs in this journey by turning the theoretical “roadmaps” for AI into actual business results for SMBs through a structured three-phase approach.
“In phase one, ‘Discover & Strategise’, SMEs identify where AI can create the most value, align use cases to business priorities, and consider governance, risk, and security requirements early in the process,” continues Tan.
“In phase two, ‘Plan & Evaluate’ SMEs receive hands-on guidance in evaluating AI use cases, assessing costs and requirements, developing adoption plans, and making informed decisions on Proof-of-Concepts and implementation approaches.
“Finally, in phase three, ‘Implement, Test & Measure’, the focus is on operational readiness including deployment preparation, performance monitoring, accountability, and scaling considerations,” he concludes.
Eligible SMEs can receive up to 90% funding support through SkillsFuture Singapore, helping to lower barriers to adoption.
Planning strategically for successful implementation
Chao concurs with Tan on the need for strategic clarity, and thorough preparation before implementing AI. “Without clarity on purpose, role, value, and measures of success, everything downstream, like what tools we should use and how we should implement AI, becomes fragmented,” he explains. “We had to slow down before we could speed up.”
In this crucial stage, the AI.dea programme proved significantly helpful, giving them the space and structure they needed for their implementation to succeed.
“The space was important because we were juggling many priorities and AI risked becoming another item on a long list. The programme created room for our team to step back, think strategically, and ask the fundamental questions about purpose, role, value, and success measures,” says Chao.
“The structure of the programme helped us move from having ideas to prioritising them. It is not enough to generate use cases for implementation; we must decide which ones are worth pursuing. So instead of simply running a series of experiments, we identified a meaningful lever for social impact.
“The programme also gave us a structure for working through key implementation considerations such as guardrails, cybersecurity, and data protection. These are often overlooked in the early enthusiasm of implementation, but absolutely critical in a sector that handles sensitive client data.”
Putting the systems in place
When identifying which of Care Corner’s pain points to address, three areas stood out. “The first is in case management and administration,” says Chao. “Caseloads are heavy and case documentation is necessary but time-consuming.
“The second opportunity is in capability development. Supervisors carry significant training loads, and it takes time for practitioners to hone their skills. The third opportunity lies in meeting the community’s needs, which are increasingly multi-dimensional and complex.”
Chao is clear about the goals the company has for implementing AI. “The first is to reduce administrative burden and let practitioners spend more time on direct care. The second is to strengthen the capabilities of our people, particularly newer practitioners, for whom readiness takes time to build.
“The third is to grow our reach and impact in ways that reinforce human relationships at the heart of our work.”
To this end, Care Corner has intentionally introduced AI in low-risk, high-gain areas where it can augment practitioners’ work without replacing their professional judgement and exercise of care.
“One such area is case documentation,” explains Chao. “We launched Scribe, an AI-enabled transcription and summarisation tool in 2025 to help social workers focus more fully on the client during a session without the pressure of taking detailed notes.
“We were the first social service agency to implement this organisation-wide, and it has helped our social workers reduce the time spent on case documentation by at least 50%.
“We are also exploring AI-powered simulations and role-play for staff training. This can complement learning in our sector, which is otherwise apprenticeship-based and constrained by the capacity of senior practitioners.
“Additionally, we are looking at using AI to help with client triage and risk assessment, personalised service recommendations, and aggregating data that currently sits in silos across the organisation.
Overall, shares Chao, these implementations promise a 20% to 25% reduction in “time-to-readiness” for staff, a similar reduction in supervisors’ training load, freeing senior practitioners for more complex work, more consistent quality of service across teams, and, lastly, expanded reach and ability to serve more people in the community.
Yet despite these gains, Chao remains clear about the limits of AI in his organisation. “What we have not done — and will not do — is fully automate sensitive processes,” he emphasises.
“Complex situations that require human empathy remain the “kungfu” of our social workers. AI is a partner to augment that expertise, not replace it.”
Disciplined introduction of AI
Chao notes that while Care Corner is a non-profit, the organisation shares many things in common with SMEs. “We have lean teams, limited in-house tech capability, real pressure on ROI, and a workforce that joined for a mission, not to wrestle with new systems,” he explains.
From their experience, he shares five pointers with other organisations:
- Start with the purpose, not the tool. Be clear on why you are adopting AI, what role it should play, where it will create value, and how you will measure success. If the “why” is not clear, everything downstream becomes fragmented.
- Start small and learn fast. To avoid expensive failures, anchor on a few use cases that will genuinely move the needle for users and staff. Run bounded experiments in lower-risk areas such as improving productivity and easing manual documentation, before tackling higher-stakes use cases. Pilots that teach you something are worth more than ambitious plans that never ship.
- Focus on augmenting, not automating. Particularly in sectors built on professional judgement and human relationships, AI is most valuable as a partner to your people, not a replacement for them. Design for a human-in-the-loop. Build in safeguards. Be honest about what AI cannot yet do.
- Invest in capabilities, not just tools. We cannot afford to chase after every new tool, and tools will change. What matters ultimately is if your team can understand, question, and apply AI meaningfully. That means building AI literacy across roles — leadership, supervisors, and frontline staff.
- Normalise not having all the answers. AI is a space where everyone is learning in real time. Organisations that keep pace with the rapid developments are not those that perfectly predict what’s next but those that build up the confidence to adapt continuously.
Tan echoes many of these pointers when advising SMEs on what to look for in the tools and partners they use. “SMEs should look for tools that are reliable, explainable, and easy to integrate into existing workflows, rather than tools that only look impressive in a demo,” he shares
“They should also assess whether the AI partner understands the business context, can help define outcomes, and support them throughout the entire adoption journey.
“In practice, the best partners are those who can combine AI capability with implementation experience, governance awareness, and a clear roadmap for scaling.
Sharing from Singtel’s own AI transformation, Tan explains that “what began as pockets of experimentation has evolved into a more structured effort focused on strengthening our technology foundations, governance frameworks, and workforce capabilities to scale AI effectively across the organisation.”
“We are taking the lessons from our own experience and sharing with SMBs to give them much-needed support to compete in the digital economy.”
