SLAS to Develop 2026 Lab Automation Guidelines: Bridging the Skills Gap with AI and Robotics
SLAS secured a $199,884 grant from the Alfred P. Sloan Foundation to build the first open, comprehensive educational guidelines for laboratory automation. The multi-year effort, "Standards for Automated Science Education," will define tiered competencies that map directly to real job requirements across pharma, biotech, and academic research.
As Kennedy McDaniel Bae, PhD, who leads the project, put it: "The automation infrastructure investments have been massive, but training infrastructure hasn't kept pace. We're building that missing piece-the first comprehensive framework that bridges traditional education with the convergence of robotics, AI, and machine learning."
Vicki Loise, CMP, CAE, CEO of SLAS, emphasized a shared standards approach to strengthen the workforce. Padraic Foley of the Acceleration Consortium added that a workforce skilled in integrating robotics and data is essential to realize autonomous science.
What's Being Built
- Workforce needs assessment: Clear signal on the skills labs actually hire for, from instrument control to data engineering and MLOps.
- Tiered competency standards: Role-aligned levels (entry, practitioner, architect/lead) that guide both hiring and upskilling.
- Open teaching resources: Implementation-ready modules released under Creative Commons and hosted by SLAS.
- Integration playbooks: Documented models for successful curriculum adoption across institutions and industry.
The project is led by Kennedy McDaniel Bae, PhD, in collaboration with Joshua D. Kangas, PhD of Carnegie Mellon University and a diverse drafting committee. Guidelines are expected to begin rolling out publicly in late 2026 and will be freely available.
Why This Matters for IT and Development Teams
Labs have poured budget into robots, liquid handlers, and analytics. The bottleneck is software and systems talent that can connect instruments, data, and models into reliable, audited workflows.
These guidelines aim to align education with how real lab automation gets built and maintained. For engineering leaders, this sets a shared language for roles, roadmaps, and hiring signals.
Core Skill Areas You Can Expect to See
- Instrument control and standards: APIs, drivers, and protocols (e.g., SiLA 2, OPC UA), plus vendor SDKs.
- Protocol development: Scripted methods (Python-first approaches, Autoprotocol-style schemas), versioning, and review workflows.
- Scheduling and orchestration: Job runners, queueing, error handling, event-driven designs, and simulation before execution.
- Data engineering and provenance: ELN/LIMS integration, FAIR data practices, audit trails, metadata standards, and schema evolution.
- MLOps for experimental loops: Active learning, model lifecycle, dataset hygiene, and repeatability across instruments and sites.
- Robotics integration: Coordinating cobots and lab hardware, safety interlocks, and path planning via ROS or vendor frameworks.
- DevOps and validation: Containerization, CI/CD for methods, digital twins, test harnesses, and change control for regulated environments.
- Security and compliance: Identity, least privilege, network segmentation, electronic signatures, and data retention.
- Human-in-the-loop UX: Clear operator interfaces, exception handling, and run monitoring.
How to Prepare Now
- Map roles to tiers: Define expectations for automation engineer, data engineer, and MLOps roles. Cut overlap and skill mismatches.
- Standardize interfaces: Pick APIs and data contracts early. Reduce one-off integrations and driver drift.
- Put CI on your methods: Treat protocols like code. Add unit tests, simulation checks, and preflight validations.
- Tighten data contracts: Lock down metadata, sample IDs, and lineage. Make audit logs first-class.
- Pilot reskilling: Pair senior devs with lab staff for protocol scripting and incident response rotations.
- Document everything: Runbooks, escalation paths, versioned SOPs. Make it dull and reliable.
- Plan for interoperability: Expect multi-vendor stacks. Budget for adapters and schema bridges.
Timeline and Access
Resources will be hosted by SLAS and released under a Creative Commons license, with initial public availability expected in late 2026. Track progress and future releases here:
Keep Learning
If you're building your team's learning plan before the guidelines arrive, start with curated tracks that cover automation, MLOps, and data engineering fundamentals:
Your membership also unlocks:
