The voice of reason: natural language interfaces to transform lab informatics

From asking Siri for a weather forecast, to Alexa playing a specific song, or Google Gemini summarising notes, conversational technology has become second nature at home and at work.

For scientists, this trend towards natural language interfaces in lab informatics technology is set to completely change the way research is conducted, recorded and reported. That said, today’s scientific software often requires navigating multiple menus, complex interfaces, or even coding in SQL or Python just to capture, find or analyse data — an approach that requires training, specialised skills and experience.

Integrating natural language, be it voice or chat, into such tools is set to make lab informatics technology more usable, interactive, accessible and efficient.

Why voice?

In everyday life, voice interaction with technology can save time and reduce friction. The same applies in scientific research.

Stanford University research found that voice is three times faster than typing for text entry, while a McKinsey study identified that employees spend almost 20% of their work time searching for information or navigating software. For researchers, that translates directly to less time navigating systems and more time doing science. Voice-driven interfaces also reduce cognitive load, making complex tools more approachable and intuitive.

The real power of voice lies not only in efficiency but also in inclusivity. Scientists at different levels of technical expertise can immediately access sophisticated tools without weeks of training. A new researcher can describe their intent in plain language, rather than the menu- and mouse-driven interfaces of traditional lab software, becoming productive much more quickly. More experienced scientists can also benefit from focusing on higher-level insights instead of system navigation.

The rise of voice in the lab: three stages of transformation

1. Moving beyond the mouse

Instead of learning a complex sequence of menu clicks, scientists will simply say out loud what they need. This removes a significant barrier to adoption by allowing researchers to work in their own words.

2. The emergence of virtual scientific assistants

Voice interaction allows lab informatics platforms to take on more of the set-up and coordination of experiments. Scientists will be able to describe what they want to do and have the system configure the experiment, link instruments and prepare data collection without manual intervention.

3. The arrival of agentic AI

At this point, the system doesn’t just execute commands but can autonomously plan and carry out multi-step workflows. Instead of directing every action, scientists will be able to set objectives and let the assistant integrate data, instruments and analysis techniques to advance research goals.

Voice as the co-scientist

According to a recent Pistoia Alliance survey, the electronic lab notebook (ELN) is the most widely used technology in the lab, with adoption in Europe, the Americas and APAC rising to 81% in 2025, from 66% in 2024. However, traditional ELNs are passive recorders of scientific data, driven largely by mouse clicks and drop-down menus. A new generation of AILNs (AI lab notebooks) is entering the lab, tools that combine natural language interaction with AI-driven assistance. Rather than navigating menus, scientists can describe what they want to achieve, and the AILN interprets, executes and records the process. Complex queries, experiment set-up and even instrument control can be controlled with spoken prompts or typed instructions.

But an AILN is more than simply a voice-activated ELN — it can actively participate in research by retrieving relevant information, assembling multi-step experimental designs, analysing results and suggesting logical next steps. This represents a fundamental change in the role of software in the lab: no longer a passive system of record, but an active collaborator that extends the scientist’s capability.

By moving from passive record-keeping to active participation, the AILN lowers the barrier to adoption, shortens the time from idea to execution, and frees scientists to focus on insight rather than interfaces.

Voice is emerging as the new scientific UI. It removes friction, accelerates research and helps scientists concentrate on discovery, and as lab software evolves and integrates AI capabilities at the core rather than as an add-on, voice will move from a convenience to the foundation of how scientists interact with technology in the lab.

*As Chief Growth Officer for Strategic Partnerships at Sapio Sciences, Andrew Wyatt is responsible for growing the company’s international operations. With his deep understanding of the life sciences industry and proven ability to navigate the complexities of scaling operations, Andrew has consistently built organisations that delight customers while maximising shareholder value.

Top image credit: iStock.com/FabrikaCr