Making Scientific Workflows Smarter: Using AI to Accelerate Search and Discovery

Why Smarter Scientific Workflows Are Key to Moving Faster

Life sciences organizations are racing to discover faster, commercialize smarter, and collaborate more efficiently. But many are still slowed by the basics: searching for prior work, waiting on internal experts, or manually recreating what already exists.

Smarter workflows are essential for accelerating search and discovery. When information is fragmented across formats, teams lose time and risk duplication. Strategic AI adoption makes it easier to locate knowledge, reuse valuable content, and keep research moving.

That’s why 93% of healthcare and life sciences companies plan to increase their AI investments in 2025 (Vultr, 2024). AI in life science has become a strategic priority—not for automation alone, but for improving how research teams work and collaborate in practice.

AI for Drug Discovery: Removing Barriers to Scientific Progress

Scientific teams need more than digitized systems. They need AI-powered tools that help them:

• Use natural language prompts to search across protocols, experimental data, and regulatory responses—even when those sources aren’t formally harmonized
• Map and align content from disconnected formats and systems, speeding up collaboration and version control
• Translate key documentation to support coordination across global research teams

These are not future goals. They’re essential capabilities for organizations that want to reduce delays, protect institutional knowledge, and move discoveries forward with confidence.

The real blocker isn’t lack of expertise. It’s the inaccessibility of that expertise. Valuable information is often scattered across notebooks, hard drives, email threads, or siloed portals—impossible to trace, tough to replicate, and vulnerable to loss. Even with cloud adoption, many organizations still lack a unified, searchable environment for their most important research knowledge.

This is what slows down discovery. And this is where smarter scientific workflows, supported by AI for scientific discovery, make a measurable difference.

When Data Is Disconnected, Discovery Slows Down

Breakthroughs start with great ideas—but they only move forward when knowledge is accessible and connected. In many life sciences organizations, scientific workflows are fragmented across spreadsheets, emails, cloud drives, and legacy systems that don’t communicate.

Even when data exists, teams often struggle to locate, validate, or reuse it. The result: duplicated work, delayed timelines, and stalled collaboration.

This is not just a data management problem. It’s a structural challenge that affects how fast teams can work, how consistently they can apply prior learnings, and how reliably they can scale research.

AI for scientific discovery creates real value here. It reduces redundancy, improves access, and accelerates the path from data to insight—so teams spend less time searching and more time discovering.

Lay the Groundwork: Strategy That Connects the Dots

Most life sciences teams already know their data is fragmented. What they often lack is visibility into where fragmentation causes the greatest slowdowns or risks. Bottlenecks, redundancies, and gaps are rarely obvious until they start compounding.

An AI Readiness Assessment helps surface these friction points—whether it’s a missing integration, unclear ownership, or knowledge stuck in someone’s head. By mapping how data, systems, and teams interact, organizations gain clarity on where AI can create real value.

This phase connects more than systems. It aligns priorities across scientific, regulatory, and operational teams—so decisions about AI are tied to business goals and research realities.

Strategy becomes a bridge from vision to execution. Not a static report, but a practical foundation for smarter, more coordinated action.

Turn Expertise into Actionable Knowledge

Scientific expertise is one of an organization’s most valuable assets. But when that knowledge is undocumented or hard to access, teams lose time retracing steps, waiting on internal experts, or recreating what already exists.

These gaps cause avoidable slowdowns in research replication, regulatory preparation, onboarding, and even routine tasks.

AI-powered deliverables help solve this by structuring expert knowledge into accessible, searchable resources that support decision-making across functions.

Examples include:

• Internal playbooks that clarify key workflows
• Smart protocols for consistent research execution
• Role-specific user guides for tools and systems
• Decision frameworks to support compliance and commercialization

This isn’t about adding more documentation. It’s about making the right knowledge available—when and where it’s needed—so teams can work faster, train more effectively, and move discovery forward.

Build the Foundation: Infrastructure That Works Across Systems

Even the best AI tools can’t deliver results if they can’t reach the right data—or operate in isolation from the systems researchers use daily.

In many life sciences organizations, data is spread across legacy platforms, cloud repositories, and specialized tools that weren’t designed to work together. These silos make it hard to operationalize AI at scale.

Custom AI infrastructure creates secure, scalable environments that support long-term growth and interoperability.

This includes:

• Data mapping across systems and formats
• Governance controls to protect compliance and data integrity
• Technical integration for both structured and unstructured data

These steps make it possible to support real-time querying, automated reporting, and collaborative research spaces—so AI for scientific discovery functions consistently across teams and tools.

Infrastructure is more than a technical layer. It’s the backbone of smarter, more dependable workflows.

Bring Information to the Surface: Smarter Search with AI Agents

Even with strong documentation and infrastructure, scientists and operations teams often spend too much time searching for what already exists—previous study results, protocols, regulatory templates, or key decisions.

When information is scattered across systems, valuable time is lost.

AI agents make internal searches faster and more intuitive. Researchers can use natural language prompts to locate validated, role-specific information—without needing technical expertise.

This accelerates decision-making across research, regulatory, and commercial workflows.

AI Scouts take this further. They continuously retrieve, validate, and update key information across systems, reducing the need for manual upkeep and ensuring critical data stays current.

What was once scattered or outdated becomes reliable and actionable—when and where teams need it most.

Support the People Behind the Process

Technology alone won’t improve workflows. Adoption depends on enablement.

Researchers, analysts, and operational leaders need different types of support—from technical onboarding to strategic context.

AI Enablement and Upskilling services provide:

• Executive workshops to align leadership around strategy and priorities
• Sandbox environments for safe testing and experimentation
• Role-based learning paths tailored to research, regulatory, or commercial roles

This isn’t generic training. It’s a scalable, role-specific approach that builds confidence and supports long-term success.

Well-supported people unlock the full potential of smarter workflows.

A Unified Approach to Smarter Scientific Workflows

Smarter workflows don’t come from a single tool. They come from a coordinated, business-aligned framework that addresses the complexity of life sciences environments.

Viable Synergy helps life sciences organizations bring together five essential elements:

• Strategy to uncover fragmentation and align priorities
• AI-powered deliverables that make knowledge usable
• Custom infrastructure for secure, scalable integration
• AI agents and scouts to surface validated insights in real time
• Tailored enablement to support lasting adoption

This unified approach accelerates search and discovery, reduces duplication, and gives scientists and leaders the clarity to focus on what matters most.

Let’s Build an AI Roadmap for Smarter Discovery

For life sciences leaders, the path to faster, more connected discovery starts by identifying where friction still exists—and what’s possible when AI in life science is applied with purpose.

Viable Synergy serves as a strategic enabler for leaders who see both the opportunities and the risks ahead. Our real-world experience across scientific, regulatory, and commercial domains helps organizations move forward with clarity, precision, and measurable results.

Let’s build a roadmap together, tailored to your team, your priorities, and the breakthroughs you’re working toward.