Article by Giovanni Forero, Konica Minolta Australia's senior intelligent information management (IIM) consultant
The next wave of automation is being shaped by agentic AI and the orchestration of multiple AI models across systems, data sources, and business environments.
These capabilities can support the automation of routine tasks and more complex end-to-end processes, often alongside technologies across such as robotic process automation (RPA) and large language models (LLMs). As this approach gains traction, organisations will need to prepare for changes in how work is coordinated, how information is managed, and how regulatory obligations are met. As organisations meet the new wave of automation, information governance will play an important role in helping organisations adopt these technologies in a way that is effective, accountable, sustainable and well managed.
Automation readiness depends on the quality, accuracy and integrity of the information environment into which these technologies are introduced. For many organisations across all sectors, the question remains the same: it’s not about when automation will be adopted, but whether the organisation is ready to support it. Information sits at the centre of this shift. Every operational process relies on how information is captured, organised, and accessed. If information is inconsistent or difficult to locate, automation inherits those problems and makes them more visible. That is why organisations must first examine must first examine how information and data is maintained, organised, and transported across teams and systems, and whether those pathways are structured enough for intelligent tools to use reliably, according to Konica Minolta Australia.
Giovanni Forero, Senior Intelligent Information Management (IIM) Consultant, Konica Minolta Australia, said, “Every organisation operates through the exchange of information. Company records, production data, supplier documentation, financial reports, internal knowledge, and correspondence all form part of that environment. Agentic AI and the orchestration of multiple AI models, automating technologies and environments do not change this dynamic; however, it increases the need for understanding on how information flows through the organisation, how it is captured, stored, indexed, classified, how long it is kept, and how it is accessed. The organisations that prepare well are the ones that recognise information quality is the foundation on which automation must sit.”
Aligning technology with an information framework
Many organisations have trialled automation in isolated parts of its workflows; however, these efforts often take place without a broader plan. Without alignment between technology and the organisation’s information framework, automated tools improve specific tasks while leaving systemic issues unaddressed, or worse, increasing the risk and exposure of the organisation. A strategic approach around governance ensures automation not only introduces efficiencies but strengthens the underlying environment by adhering to information governance rather than bypassing it.
Organisations in Australia are also facing the impact of talent losses and skills gaps following long-serving staff, whose knowledge has guided processes for decades, retiring or changing industries. As this happens, organisations risk losing context, history, and decision-making insight that cannot be easily reconstructed from formal documents alone. A well-defined information governance and a thorough strategy for knowledge transfer that involves AI codifying and categorising dark data and tacit knowledge, in combination with Retrieval-Augmented Generation (RAG) to enhance the output and context of LLMs, can be an extremely beneficial strategy to reuse knowledge that is traditionally lost to an organisation.
Giovanni Forero said, “Much of an organisation’s valuable knowledge resides within individuals or scattered in informal files that are difficult to locate or interpret. When the key individuals holding this information leave, the organisation risks losing more than just documents; it loses critical context and expertise. AI, RAG and a succession plan can help recover this material by identifying links between information, interpreting meaning, and making content searchable and usable. However, to gain this benefit, organisations need to manage the information lifecycle from ingestion to disposition, ensuring it can be processed reliably.”
Guidance concerning the use of AI is still developing. However, privacy obligations, safety considerations, and reporting standards are already well-established in many industries. Agentic AI and the orchestration of multiple agents automating cross-functional technologies and environments will eventually fall under more specific regulatory scrutiny; however, current legislation, such as the Privacy Act 1988 and ISO 27001 and ISO 42001 frameworks, provide constraints on how information is secured and managed. Organisations that have prioritised robust information governance will be well-placed to be compliant with future standards.
Agentic AI systems can review large volumes of data for irregularities, flag potential issues, and support decision-making when human oversight is required. This is particularly relevant in settings where early identification of risk is critical, such as monitoring patterns in student behaviour or identifying production anomalies in manufacturing environments.
Preparing for agentic AI and orchestration
Preparing for agentic AI and orchestration also requires cultural alignment, with clear communication about responsibilities, access, and oversight across the whole organisation. In other words, a proper information management framework. When people understand how information should be handled and why consistency matters, the introduction of agentic AI becomes more reliable and predictable.
Giovanni Forero said, “Agentic AI and the orchestration of multiple agents automating cross-functional technologies and environments is beginning to operate in areas that were previously handled entirely by humans. That shift requires careful planning. Organisations need controlled testing environments, detailed evaluation of high-risk scenarios, and transparency around how automated decisions are produced. Human oversight or a human in the loop (HITL) is essential. It is not about replacing judgement but ensuring that AI agents follow defined boundaries and produce results that can be accounted for.”
Long-term preparation requires ongoing assessment rather than a single project. As agentic AI capabilities expand, organisations must routinely review their information structures, update governance practices, and refine the way information is shared across teams. This continuous approach helps prevent outdated processes and data from undermining emerging technologies.
The next wave of automation driven by agentic AI and the orchestration of multiple AI agents across technologies and environments will reward organisations that approach information management with intent and discipline, placing the discipline at the centre of the information ecosystem. When information is reliable and well-governed, intelligent systems become more effective and people can work with greater confidence. This combination of structured information and responsible AI initiatives will set the direction for how Australian organisations progress successfully in the years ahead.
