Singapore Enterprise AI and Automation Summit 2027

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

Explore how technology is set to transform Singapore in 2027 and beyond, with enterprise AI and automation driving smarter operations, faster innovation and new models of growth.

• How Singapore’s innovation agenda is shaping the next wave of enterprise AI adoption across industries and functions.
• What leaders need to do now to turn automation, data and AI into practical business outcomes at scale.
• Where the biggest opportunities and challenges will emerge as organisations balance speed, trust and readiness for what comes next.

This keynote will explore how international enterprises navigate AI innovation and adoption across countries, and what it takes to align resources, communication and workforce readiness so the rollout works in practice.

• What resource decisions matter most when scaling AI across borders, from infrastructure and talent to legal and operational support.
• How to communicate change clearly across regions, so leaders and teams understand what is changing, why it matters and how success will be measured.
• Why workforce readiness is just as important as technology readiness when AI adoption needs to succeed in multiple countries at once.

This panel looks at how enterprises can build practical AI governance frameworks that keep pace with regulatory change while still allowing teams to innovate and deliver value.

• How organisations can translate changing privacy and AI requirements into governance practices that are clear, workable and scalable.
• What balance to strike between central oversight and business-unit flexibility so innovation does not stall under excessive control.
• How leaders can prepare for ongoing regulatory change by strengthening data handling, documentation, accountability and risk review.

Learn insights on building customer trust in AI by making transparency, accountability and clear value central to every step of the rollout.

• How to identify the biggest trust barriers, from privacy concerns and uncertainty to fear of poor outcomes or hidden automation.
• What communication and experience design choices help customers understand when AI was being used and what it means for them.
• How governance, human oversight and clear escalation paths make the AI feel safer and more reliable in practice.

A hands-on, interactive session working through a real enterprise AI scenario as a room. Details announced soon.

Learn how simplifying a crowded AI tool landscape, reduces duplication and confusion, and creates clearer ways of working that made teams faster and more effective.

• How organisations can identify overlapping platforms, duplicated workflows and hidden waste across its AI and automation stack.
• The practical steps needed to rationalise tools, streamline ownership and reduce confusion for business and technology teams.
• What metrics and operating changes prove the simplification is working, from lower costs to better adoption and clearer accountability.

Hear how one organisation tackled unclear AI ownership, inconsistent controls and rising compliance pressure to make enterprise AI safer and more scalable.

• How the team identified the governance gaps blocking AI from moving beyond pilots.
• What practical controls, standards and workflows were introduced to keep AI deployment moving without slowing innovation.
• The lessons learned for other organisations trying to build trust in AI while managing regulation, privacy and internal risk expectations.

This panel will explore how Singapore enterprises can track whether AI programs are actually working, measure success against business goals and stay grounded when excitement starts moving faster than results.

• How to define the right success measures for AI, from productivity and cost savings to customer outcomes, risk reduction and adoption.
• What leaders should track to know whether a program is creating value, and when early enthusiasm is outpacing real-world impact.
• How to connect AI metrics to business goals so teams are not optimising for experimentation alone.

Small-group, discussion-based sessions where you'll work through real enterprise AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

Hear insights on how to ensure your workforce understands new AI programs, builds confidence through clear communication and training, and stays up to speed as the technology kept evolving.

• How to explain new AI programs in plain language so employees understood what was changing, why it mattered and how it would affect their roles.
• What training approaches work best to lift confidence, build practical skills and make AI feel useful rather than intimidating.
• How to keep learning continuous, so upskilling does not stop after launch but keeps pace with new capabilities and expectations.

Learn how international enterprises based in Singapore can navigate AI rollouts across different countries and teams, with a focus on workforce readiness, resourcing and the AI literacy gaps that can slow adoption.

• How to coordinate AI rollout across regions without losing local relevance, consistency or control.
• What resourcing challenges emerge when teams in different markets have different levels of maturity, support and access to expertise.
• What leaders need to prioritise to build a rollout model that is scalable, practical and sustainable across borders.

This think tank will examine how organisations can align AI priorities, governance and execution across teams, and how to make responsibility for each program clear from the start.

• How to define ownership for AI programs so business, technology, data and risk leaders know who is accountable at each stage.
• What governance model helps avoid duplication, competing priorities and shadow AI initiatives across the enterprise.
• How leadership teams can set shared standards for data, security, compliance and value tracking without slowing delivery.
• What practical decision rights should sit with central teams versus business units when AI programs move from idea to implementation.

Unwind with your peers for a couple of drinks on us!

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

Explore how technology is set to transform Singapore in 2027 and beyond, with enterprise AI and automation driving smarter operations, faster innovation and new models of growth.

• How Singapore’s innovation agenda is shaping the next wave of enterprise AI adoption across industries and functions.
• What leaders need to do now to turn automation, data and AI into practical business outcomes at scale.
• Where the biggest opportunities and challenges will emerge as organisations balance speed, trust and readiness for what comes next.

This keynote will explore how international enterprises navigate AI innovation and adoption across countries, and what it takes to align resources, communication and workforce readiness so the rollout works in practice.

• What resource decisions matter most when scaling AI across borders, from infrastructure and talent to legal and operational support.
• How to communicate change clearly across regions, so leaders and teams understand what is changing, why it matters and how success will be measured.
• Why workforce readiness is just as important as technology readiness when AI adoption needs to succeed in multiple countries at once.

This panel looks at how enterprises can build practical AI governance frameworks that keep pace with regulatory change while still allowing teams to innovate and deliver value.

• How organisations can translate changing privacy and AI requirements into governance practices that are clear, workable and scalable.
• What balance to strike between central oversight and business-unit flexibility so innovation does not stall under excessive control.
• How leaders can prepare for ongoing regulatory change by strengthening data handling, documentation, accountability and risk review.

Learn insights on building customer trust in AI by making transparency, accountability and clear value central to every step of the rollout.

• How to identify the biggest trust barriers, from privacy concerns and uncertainty to fear of poor outcomes or hidden automation.
• What communication and experience design choices help customers understand when AI was being used and what it means for them.
• How governance, human oversight and clear escalation paths make the AI feel safer and more reliable in practice.

A hands-on, interactive session working through a real enterprise AI scenario as a room. Details announced soon.

Learn how simplifying a crowded AI tool landscape, reduces duplication and confusion, and creates clearer ways of working that made teams faster and more effective.

• How organisations can identify overlapping platforms, duplicated workflows and hidden waste across its AI and automation stack.
• The practical steps needed to rationalise tools, streamline ownership and reduce confusion for business and technology teams.
• What metrics and operating changes prove the simplification is working, from lower costs to better adoption and clearer accountability.

Hear how one organisation tackled unclear AI ownership, inconsistent controls and rising compliance pressure to make enterprise AI safer and more scalable.

• How the team identified the governance gaps blocking AI from moving beyond pilots.
• What practical controls, standards and workflows were introduced to keep AI deployment moving without slowing innovation.
• The lessons learned for other organisations trying to build trust in AI while managing regulation, privacy and internal risk expectations.

This panel will explore how Singapore enterprises can track whether AI programs are actually working, measure success against business goals and stay grounded when excitement starts moving faster than results.

• How to define the right success measures for AI, from productivity and cost savings to customer outcomes, risk reduction and adoption.
• What leaders should track to know whether a program is creating value, and when early enthusiasm is outpacing real-world impact.
• How to connect AI metrics to business goals so teams are not optimising for experimentation alone.

Small-group, discussion-based sessions where you'll work through real enterprise AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

Hear insights on how to ensure your workforce understands new AI programs, builds confidence through clear communication and training, and stays up to speed as the technology kept evolving.

• How to explain new AI programs in plain language so employees understood what was changing, why it mattered and how it would affect their roles.
• What training approaches work best to lift confidence, build practical skills and make AI feel useful rather than intimidating.
• How to keep learning continuous, so upskilling does not stop after launch but keeps pace with new capabilities and expectations.

Learn how international enterprises based in Singapore can navigate AI rollouts across different countries and teams, with a focus on workforce readiness, resourcing and the AI literacy gaps that can slow adoption.

• How to coordinate AI rollout across regions without losing local relevance, consistency or control.
• What resourcing challenges emerge when teams in different markets have different levels of maturity, support and access to expertise.
• What leaders need to prioritise to build a rollout model that is scalable, practical and sustainable across borders.

This think tank will examine how organisations can align AI priorities, governance and execution across teams, and how to make responsibility for each program clear from the start.

• How to define ownership for AI programs so business, technology, data and risk leaders know who is accountable at each stage.
• What governance model helps avoid duplication, competing priorities and shadow AI initiatives across the enterprise.
• How leadership teams can set shared standards for data, security, compliance and value tracking without slowing delivery.
• What practical decision rights should sit with central teams versus business units when AI programs move from idea to implementation.

Unwind with your peers for a couple of drinks on us!