August 25, 2026
Healthcare is no stranger to innovation, new technologies and digital transformations. Like previous technological shifts, AI in healthcare is currently navigating a familiar lifecycle as it moves past the initial swell of high expectations and into the slow, steady work of long-term transformation.
However, AI carries a fundamental difference that sets it apart from past transformations in that it is entirely dependent on strong, well-managed data within integrated systems. While multi-million dollar investments in Electronic Medical Records (EMRs) and digital platforms have laid crucial groundwork, significant friction and fragmentation remain in storing, accessing, using and securing data at scale.
Moving promising AI initiatives beyond isolated proofs-of-concept into scalable, clinical-grade tools requires a baseline that many healthcare organisations have yet to establish. In the second instalment of our Healthcare AI series, we examine the data and technology foundations required to scale AI safely. If you missed it, catch up on Blog 1: Strategy & Governance.
In our recent research report, AI by design in healthcare, Equal Experts gathered insights from more than 100 digital leaders across Australia’s healthcare ecosystem. While enthusiasm for clinical and administrative AI is unmistakable, our findings revealed significant operational bottlenecks in data management and legacy tech:
For many digital leaders, the core challenge is moving from simply collecting data in disparate silos to actively engineering clean, reliable data pipelines that feed modern, cloud-based AI models safely. Legacy platforms also emerged as a primary blocker to AI innovation, as many of the existing applications that healthcare organisations rely on for day-to-day operations were simply not designed to support modern, cloud-based AI solutions.
When faced with legacy tech stacks, sensitive patient data and strict compliance requirements, healthcare leaders often assume their challenges are entirely unique. However, other heavily regulated, high-stakes sectors, such as banking, superannuation and national government services, have also faced many of these data pipeline and infrastructure hurdles over the past decade.
By looking outside health, executives can learn how to modernise legacy estates safely, build reliable data pipelines and achieve operational ROI, while consistently putting patients and clinicians at the heart of technology. This includes approaches, tools and partnering.
Drawing on Equal Experts’ AI in healthcare research and our deep engineering track record across regulated industries, we have structured a four-part blueprint to help health systems build AI-ready technology foundations.
Before investing in complex or clinical-focused AI projects, healthcare leaders must first understand their baseline architecture, cloud foundations and data capabilities.
An Equal Experts Data and Technology Health Check systematically evaluates an organisation’s infrastructure against its strategic objectives, uncovering hidden blockers before they derail project momentum:
Armed with these insights, digital leaders can identify quick wins, streamline cloud spend and establish a clear execution roadmap.
Impact in practice: Optimising cloud and data infrastructure can lead to immediate ROI. Equal Experts delivered a health check for a global media business and sped up data ingestion time from 24 hours to under 2 hours. Consolidating platforms and adding automation to analytics and reporting has driven $3 million in savings and improved data quality by 20%.
Generative AI and advanced machine learning models are only as good as the data feeding them. Healthcare organisations must move away from piecemeal, ad-hoc data approaches and invest in enterprise-grade data platforms with modern automated pipelines. Building reliable data pipelines requires robust engineering to guarantee data quality, privacy and reliability before it becomes useful for an AI model. In some cases, getting the foundations in place can also mean big steps in moving all into the cloud.
Impact in practice: Equal Experts helped HMRC turn vast, unstructured data into a powerful, scalable platform that drives fraud prevention, customer insights, and smarter decision-making for UK government services.
Similarly, working with a global financial organisation based in Australia, Equal Experts helped to untangle complex, monolithic data pipelines within a security ecosystem. The result was an extensible, highly repeatable data platform that embedded security and data quality directly into the engineering framework.
Legacy systems do not have to be a permanent barrier to AI. In fact, AI itself can be used to accelerate modernisation of outdated technology. By combining evolutionary architecture, automated engineering practices, proven modern tools, and AI-driven workflows, organisations can modernise ageing software without high-risk “big bang” platform replacements. Modernising legacy codebases and automating data workflows allows legacy systems to expose APIs and streaming data feeds, enabling integration with contemporary cloud AI tools.
Impact in practice: In a recent 3-week experiment with a financial services giant, Equal Experts demonstrated the potential of AI for modernisation and how it can help teams understand legacy systems faster, prototype sooner and re-evaluate previously deferred work. The team was able to deliver 50% of the modernised change of address final product in just 3.5 days.
Healthcare has seen many digital tools struggle to gain frontline adoption, especially when systems are considered too slow, clunky or interrupt clinical workflows. Success depends on using AI responsibly and where it will add real value, not just because AI is the exciting new technology. Key to this is understanding where plain old automation and human-led approaches remain most relevant and appropriate. Where AI is identified as a value-adding opportunity, long-term adoption depends on seamlessly embedding AI into clinical systems and workflows, such as within EMRs, imaging platforms or clinical documentation tools.
Impact in practice: Equal Experts worked with Médecins Sans Frontières (MSF) to use AI-driven data insights to support better care and support during malaria outbreaks. Built with a simple dashboard view, the Malaria Anticipation Project (MAP) tool helped clinical staff know when to increase stocks of blood and medical supplies, and recruit additional staff and adjust rotas with more confidence. Similarly, community health teams can implement vital preventative and education programmes at the right time, including distribution of preventative medication. By providing MSF staff with easy access to transmission rate predictions, they are able to make informed choices about where and when to target vital resources.
AI success in healthcare will be defined by the organisations that build the strongest foundations across data, infrastructure and technology, as well as those that embrace learnings from other industries. As systems become more interconnected, healthcare providers with clean data pipelines, modern architectures and frictionless clinical workflows will thrive.
Is your organisation struggling with data silos, legacy infrastructure or moving AI pilots to production? Download the full AI by design in healthcare research report to explore more insights or contact the Equal Experts Australia team today to discuss how we can support your organisation.
Andy Canning is the Chief Technology Officer and Managing Director for Equal Experts APAC. With a technology career that spans the globe, Andy has been at the forefront of innovation for more than 30 years and is on a mission to revolutionise the business landscape through the transformative power of AI. Connect with Andy on LinkedIn.
All business models have their pros and cons. But, when you consider the type of problems we help our clients to solve at Equal Experts, it’s worth thinking about the level of experience and the best consultancy approach to solve them.
If you’d like to find out more about working with us – get in touch. We’d love to hear from you.
