Antony Anand, Head of Digital – Asia for Crown Information Management, discusses how large language models are shifting corporate information management practices across the Asia Pacific region.
Across the Asia Pacific (APAC) region, artificial intelligence (AI) has moved firmly into senior discussion.
Government programmes such as Singapore’s Smart Nation, India’s growing public digital infrastructure, and regulatory reforms in Australia all show how quickly adoption is gathering pace across sectors.
Amidst this momentum, one question still goes largely unaddressed: are organisations ready for AI to work with their information?
Tools built to work with large collections of written material, such as large language models (LLMs), now promise intelligent search, clearer summaries, compliance support, and even assistance with everyday decision-making. Their capabilities, however, depend entirely on the quality, structure, accessibility, and governance of enterprise information.
For many APAC organisations, the reality is more complex. Decades of paper archives, siloed shared drives, fragmented enterprise content management (ECM) systems, and inconsistent metadata frameworks create an environment where AI cannot operate safely or effectively.
Before AI can support businesses, an information foundation must be properly structured and supported by clear governance.
THE APAC LEGACY REALITY
Unlike many Western markets that underwent earlier waves of digitisation, the APAC region is uniquely complex, with many organisations in Asia still managing a hybrid environment.
Physical records remain stored across multiple facilities, retention rules vary from one country to another, and essential documents often sit buried in email inboxes or shared drives rather than in structured systems.
Large numbers of scanned files exist with no structured metadata, limiting how easily they can be searched. Alongside this, many line-of-business systems operate in isolation, each holding their own individual data. This creates a fragmented picture that is challenging to manage and even harder to rely on with confidence.
In regulated sectors such as banking, financial services, and insurance (BFSI), healthcare, manufacturing, and the public sector, compliance adds another layer of complexity. Meanwhile, data privacy laws across Singapore, India, Australia, and Association of Southeast Asian Nations (ASEAN) markets mean that AI must operate within strict governance boundaries. These conditions make it difficult for LLMs to work reliably.
However, the organisations that treat digitisation and governance as core business priorities are those that will introduce these tools safely and with lasting confidence.
CONTEXT MATTERS
LLMs excel at interpreting language and hold the ability to summarise documents, extract insights, draft responses, and assist in compliance review, even when source files are unstructured.
However, what they lack is an understanding of organisations’ retention policies, access rights hierarchies, or document lifecycle controls.
If connected blindly to unstructured information, LLMs introduce real risk. Sensitive material can be revealed to unauthorised users, results may be produced from outdated or duplicated files, and organisations can unintentionally create compliance exposure across different jurisdictions.
This is where governance is crucial. Compliance plays a growing role in determining how confidently organisations in APAC can introduce these tools. Businesses that maintain controlled environments for their data, keep clear audit trails, and operate secure access rules are in a far better position to use AI safely and with confidence.
These tools are not designed to work without guidance or structure, they need context, rules, and controls around them. In the APAC region, this means they must operate on top of a well organised information management framework rather than being allowed to work around it.
IN PRACTICE WITH THE HR FUNCTION
HR, a universal function across every organisation, exemplifies how these systems behave in real settings. Indeed, LLMs have the potential to summarise employee policies, draft offer letters from agreed templates, locate onboarding documents and respond to routine internal questions.
However, LLM benefits depend entirely on how the information is structured. Without clear document categories and role-based access controls, risks quickly emerge. An AI system might surface confidential compensation details to unauthorised managers, reference outdated policy versions, or expose sensitive personal data governed by local privacy laws.
When HR files are properly digitised, classified, and governed, with metadata aligned to employee lifecycle stages and retention policies, these tools support teams and help them respond quicker without compromising compliance.
THE DIGITISATION IMPERATIVE
Before AI agents can support internal processes, the processes themselves need to be digitised.
Across Asia there is still considerable opportunity to bring long‑standing records into a usable form. For instance, many organisations are beginning to convert older paper archives into digital files that can be searched. They are also beginning to create clearer labelling frameworks that reflect regulatory requirements and adopt structured content management systems to store material in one place.
This work goes beyond reducing the volume of paper held in storage; it’s about creating a structured base of knowledge that supports quicker decisions, regulatory transparency, cross-border collaboration, and genuine readiness for the AI tools organisations are planning to use.
Those that avoid this stage often discover that new systems amplify problems rather than resolve them because the information underneath has not been prepared to the standard required.
FROM ECM TO AGENTIC WORKFLOWS
As organisations strengthen their digital foundations, the next step is enabling intelligent workflows. This includes the use of tools like agentic AI, where AI systems can take defined actions under governance controls.
These systems can perform tasks like identifying HR records that are due for retention review, flagging financial documents that are close to regulatory deadlines, and trigger retrieval or disposal activity in line with internal policy.
Such examples build on well governed ECM systems that hold documents in one place and apply consistent rules to how they are managed. The key is that governance remains at the centre, with the technology working within it rather than replacing it.
ORGANISATIONAL READINESS VS AI LITERACY
Whilst much of the AI conversation focuses on systems, platforms, and data readiness, there is a more fundamental issue that is often missed: does the workforce know how to use these tools responsibly?
Literacy in this area does not mean every employee must train as a data scientist. However, it does mean that staff need to recognise what these tools can and cannot do, know how to use them securely, and how to interpret results without over-reliance.
Without this literacy, even the most advanced AI systems will struggle to deliver impact. Tools may be available, but adoption remains superficial. Outputs may be generated, but trust remains low. And governance frameworks may exist, but employees may not fully understand their responsibility within them.
The APAC organisations that make literacy and capability building as important as technical deployment, and prepare both their information systems and their people, are those that will lead the next phase of progress across the region.
THE ROAD AHEAD FOR APAC
APAC is one of the fastest-growing digital economies in the world, shaped by both emerging markets and established hubs.
However, sustainable AI adoption requires a structured and disciplined approach. That begins with reviewing and organising legacy information, digitising key processes, adopting governed content systems, and maintaining ongoing attention to data quality and access rules.
LLMs are redefining how businesses interact with information, but their success in APAC will rest on the strength of the information practices that support them. This, alongside clear governance and well-structured content ecosystems, are now core to how organisations prepare for the next phase of digital progress.
BIO:
Antony Anand, Head of Digital – Asia for Crown Information Management (Crown), is driving the evolution from traditional record management to AI-native, software-as-a-service (SaaS)-led information ecosystems.
His work sits at the intersection of ECM, digitisation, workflow automation, AI agents, and secure information governance, helping large enterprises modernise how they capture, process, protect, and extract value from information.
Over the years, Anand has built and scaled multiple digital solution pillars across Asia, focusing on both product development and building sustainable digital revenue models. He works closely with country leaders, sales teams, and technology partners to grow SaaS subscriptions, create consulting-led engagements, and position Crown as a long-term digital transformation partner.
