AI lab informatics company Sapio Sciences has announced a new partnership with management consulting and technology firm ZS.
The pair will offer consulting and implementation services to support Sapio Platform deployments across biopharma R&D and chemistry, manufacturing and controls (CMC) laboratories.
ZA will also join Sapio's broader partner ecosystem, which includes companies such as AWS, NVIDIA and Elsevier.
The pair said that the partnership will combine technology and consulting to help biopharma organisations digitalise their laboratories. It will provide access to the Sapio Platform, along with support for systems integration, change management and validation to ensure successful deployment.
The new collaboration will aim to enable AI-ready, traceable lab data and governed workflows with clear human oversight — which the pair says is essential for organisations in highly regulated R&D and CMC environments.
Research from the Pistoia Alliance shows that shifting priorities and workload (49%) are the main challenges for change in R&D labs, while 34% say a lack of skilled individuals is a barrier to AI adoption.
"Biopharma organisations are investing heavily in digital laboratories, particularly across development and CMC environments where data integrity and operational coordination are critical," said Mike Hampton, Chief Commercial Officer, Sapio Sciences.
Partnering with ZS gives our customers access to deep life sciences consulting and systems integration expertise and through ZS’s global network and helps us reach more organisations undertaking that journey.
Sapio Platform customers will be able to access ZS support across the full deployment life cycle, from solution architecture and system integration through change management and adoption, while maintaining scientific context, data integrity and regulatory compliance.
"The need for results in R&D is extreme: speed, lower costs and higher success rates," added Rohan Fernando, Managing Principal, Life Sciences R&D and Medical, ZS.
ELN and LIMS systems are critical as modern science depends on AI-ready, traceable lab data to enable more effective collaboration and orchestration.