What the latest Gartner® predictions could mean for life sciences organisations in Australia and New Zealand
Life sciences organisations are moving beyond the first wave of AI experimentation.
Generative AI has already demonstrated how technology can assist with searching, summarising and creating information. Agentic AI takes this further. Instead of simply responding to a request, AI agents can support multi-step workflows by planning activities, coordinating tasks and taking defined actions within appropriate controls.
For pharmaceutical, biotechnology and other regulated life sciences organisations, this creates significant opportunities. It also raises an important question: how do you introduce greater intelligence and autonomy without compromising compliance, data integrity or scientific oversight?
The Gartner® report Predicts 2026: Agentic AI Reshapes Competitive Advantage in Life Sciences examines how rapidly this landscape is changing and what life sciences organisations should be preparing for now.
AI adoption is changing the life sciences landscape
According to Gartner, by 2028 more than 70% of healthcare payers, providers and consumers will adopt AI, putting pressure on traditional pharmaceutical commercial models and the technologies supporting them. Gartner also predicts that by 2028, 30% of life sciences organisations will implement new software testing frameworks for AI-augmented solutions, helping optimise key processes by 20%.
For organisations across Australia and New Zealand, the implications extend well beyond adopting another piece of software.
Laboratories and life sciences businesses need to consider whether their data, systems, validation processes and people are prepared for an environment in which AI becomes increasingly embedded within everyday operations.
From AI experimentation to operational AI
One of the biggest changes with Agentic AI is the transition from AI that provides information to AI that can participate in workflows.
Within a laboratory environment, this could mean intelligent systems helping to evaluate laboratory context, prioritise work, identify exceptions and coordinate activities while scientists retain oversight of critical decisions.
This is particularly important in regulated environments.
The objective should not be unrestricted automation. It should be controlled intelligence built around trusted data, defined processes, traceability and appropriate human approval.
That is also why organisations need to think about their underlying laboratory informatics architecture before attempting to scale AI.
Three priorities for life sciences organisations
1. Build integrity and traceability into AI-enabled processes
As AI becomes involved in regulated processes, organisations need to be able to understand and validate how technology is being used.
Gartner highlights the need to prepare regulatory submissions for AI consumption through semantic layers and machine-interpretable clinical rationale, alongside pragmatic GxP validation strategies for GenAI and Agentic AI.
For laboratories, this reinforces the importance of structured data, auditability, governance and clearly defined human oversight.
2. Treat AI literacy as an organisational capability
AI cannot remain an isolated technology initiative managed solely by IT.
Scientists, laboratory managers, quality teams and business leaders need to understand what AI can do, where its limitations lie and how it should be governed within their operating environment.
Developing this capability across the organisation will become increasingly important as AI moves deeper into R&D, quality, manufacturing and laboratory operations.
3. Establish the right digital foundation
Agentic AI depends on context.
If laboratory information remains fragmented across disconnected systems, spreadsheets and manual processes, AI has a much harder job understanding what is happening across an operation.
Connecting LIMS, ELN, LES, SDMS, instruments and scientific data provides a stronger foundation for intelligent workflows.
This is where laboratory informatics is evolving from simply recording what happened towards helping laboratories determine what should happen next.
Where LabVantage CORTEX™ fits
LabVantage CORTEX™ represents this next stage of laboratory informatics.
Rather than treating AI as a separate tool sitting outside the laboratory environment, LabVantage CORTEX™ brings Agentic AI capabilities into the broader LabVantage ecosystem.
This creates opportunities for intelligent laboratory execution across areas such as sample management, worksheet generation, stability studies, exception-based review and other complex laboratory workflows.
The goal is not to remove scientists from decision-making.
It is to reduce unnecessary manual work, surface relevant information sooner and allow intelligent agents to support defined activities while maintaining Human-in-the-Loop control where decisions require scientific or regulatory accountability.
Preparing laboratories in Australia and New Zealand
For organisations across Australia and New Zealand, the immediate priority does not need to be implementing autonomous laboratory operations overnight.
A more practical starting point is understanding whether the organisation is ready.
That means asking questions such as:
- Is our laboratory data structured, accessible and trustworthy?
- Are our major laboratory systems connected?
- Where are scientists still spending significant time on repetitive administrative work?
- Which workflows could benefit from intelligent orchestration?
- How would AI-enabled processes be validated and governed?
- Where must human approval remain part of the workflow?
These questions help shift the AI conversation away from technology for its own sake and towards measurable laboratory outcomes.
The next competitive advantage may be orchestration
Agentic AI is likely to change more than individual laboratory tasks. It has the potential to change how work moves through the entire life sciences value chain.
The organisations best positioned to benefit will be those that combine AI capabilities with connected data, robust governance, scientific expertise and systems designed to evolve.
For laboratories, that means preparing the digital foundation today for a future where software does more than record laboratory activity. It can increasingly help laboratories understand context, coordinate work and determine the next appropriate action.
Explore the Gartner® Report
Read Predicts 2026: Agentic AI Reshapes Competitive Advantage in Life Sciences to explore Gartner’s predictions and recommendations in more detail.