When it comes to development, the aim is to build software that acts as a rep’s aid and something that actually fits into the natural flow of their working day. To make that happen, the entire system’s architecture needs a fresh perspective. It is not a case of tweaking what is already there; it is about rethinking the whole thing from scratch. While doing that, there are a few core principles that should be in place at every stage.
Here are the five key layers needed to build generative AI in life sciences that truly scales:
Infrastructure
In today’s fast-moving tech world, infrastructure needs to be flexible enough to keep up. To make that happen, there are a few key ideas to stick to:
- Developers should be able to set up what they need quickly and easily, so different projects can run side by side without delays.
- When it comes to generative AI, it is smart to create services, tools and agents that work well across various systems instead of starting from scratch every time.
- Stick to one or two main platforms to stay efficient, but leave room to plug in a couple of extra tools for testing new ideas.
Data
Most organizations are comfortable handling structured data. But it is the unstructured, multimodal data that holds the next big opportunity for AI patient engagement. This is the kind of data that comes from all over the place: text, audio, video, images, sensors and more.
- One will need a solid system in place to manage and oversee all that mixed data. Think of it as the foundation that keeps everything organized, with good metadata and quality controls built in from the start.
- By creating integrated, reusable data products, one can make it easier for different apps and teams to tap into the information they need.
Applications
Business apps used to be built with just one job in mind. But times have changed. Now, they need to be flexible, solve problems on the fly and scale up as needed. To keep up, the focus is shifting to a few key areas:
- Reusable agents: Instead of starting from scratch for every task, businesses are building modular, decision-making agents that can work on their own or be combined with others.
- Context-aware design: The future lies in smart, adaptable agents that understand the situation they are in and respond based on what the user actually needs.
- Unified co-pilot experiences: Many businesses now rely on co-pilot. But having too many separate ones can get messy. The aim is to bring them together into a single, intelligent system that knows where to send each request.
Workflow integration
In the enterprise and SaaS world, it is still people who often end up stitching together disjointed systems and processes. While AI holds real promise to lighten that load, it will only work if it is introduced thoughtfully and with a genuine focus on people.
- Get systems AI-ready: Make sure the existing data, tools and services are in good enough shape to work alongside AI agents.
- Design with people in mind: Taking a design-thinking approach ensures AI tools slot into daily workflows in a way that makes sense for the people using them.
Operations
To stay ahead in keeping AI systems running smoothly, focus on these key areas:
- Design with scale in mind
- Strengthen operations at an enterprise level
- Be clear about costs throughout the life cycle
- Make the most of what vendors offer out of the box
These key areas make it easier for teams to stay focused while ensuring key principles are followed at every level.
