Early-phase clinical trials are among the most critical and complex stages in drug development. Recruiting suitable volunteers for these studies often presents significant operational challenges — from manual eligibility screening and time-consuming outreach to maintaining diversity and ensuring compliance. These hurdles can delay trial initiation and increase overall study costs.
As the life sciences industry continues its digital transformation, leading organizations are increasingly turning to AI-powered recruitment platforms to streamline and accelerate early-phase enrollment.
By integrating the Cloudbyz Patient Recruitment solution built on Salesforce Health Cloud with Salesforce Agentforce AI, organizations can create an intelligent recruitment ecosystem that connects data, processes, and people — enhancing speed, precision, and regulatory readiness.
Salesforce Agentforce introduces a new AI-native paradigm for the life sciences industry. By combining Data Cloud, Prompt Builder, and Agent Studio, Agentforce enables intelligent, context-aware agents that automate repetitive workflows, surface predictive insights, and engage with users in natural language.
When paired with the Cloudbyz Patient Recruitment platform, Agentforce AI delivers:
Automated screening and prequalification of volunteers
Personalized engagement and multilingual communication
Predictive forecasting of enrollment timelines
Diversity monitoring and compliance automation
Real-time, conversational insights for study teams and leadership
This synergy empowers life sciences organizations to modernize patient recruitment operations and accelerate time to study initiation.
The first stage of AI adoption focuses on automating manual tasks and improving team efficiency.
Using Einstein Copilot and Data Cloud, AI automatically evaluates volunteer profiles, EHR data, and eligibility criteria to produce a qualification score. This reduces manual pre-screening time and ensures that coordinators prioritize high-fit candidates.
Powered by Agent Studio and Prompt Builder, the chatbot interacts with potential volunteers to answer questions, guide them through screening steps, and capture pre-qualification data directly into Salesforce Health Cloud — improving engagement and responsiveness.
Coordinators can dictate notes or screening outcomes, and the AI transcribes them into structured Cloudbyz records. This voice-enabled functionality saves hours of data entry while maintaining accuracy and consistency.
Together, these quick wins build foundational momentum for broader AI-driven transformation.
Once operational data is centralized, AI can begin to predict outcomes and automate actions that were traditionally manual.
Leveraging Einstein Discovery and Data Cloud, this agent forecasts enrollment completion dates across sites. It analyzes trends, site capacity, and participant engagement to help teams proactively adjust recruitment strategies.
AI determines the next best action for each participant — sending reminders, scheduling follow-ups, or sharing study materials. This automation reduces drop-off rates and ensures consistent participant engagement throughout the study.
AI continuously tracks digital consent versions, expirations, and updates, sending automated alerts to maintain compliance readiness and audit preparedness.
These predictive use cases enable smarter decision-making and reduce operational inefficiencies.
At this stage, organizations can unlock the full potential of generative AI for insights, global reach, and executive intelligence.
Einstein GPT automatically translates recruitment materials, consent forms, and communication templates into multiple languages, supporting multilingual and cross-regional recruitment with speed and accuracy.
AI analyzes demographic patterns to ensure balanced representation and compliance with diversity goals. It recommends targeted outreach campaigns to underrepresented populations, helping studies achieve inclusivity.
Executives and study managers can query the system conversationally — for example, “Which sites are behind on enrollment?” or “What’s the predicted completion timeline?” — and receive AI-generated insights and summaries in real time.
These cognitive tools enable data-driven leadership and global scalability.
In the most advanced stage, AI agents evolve into self-learning and adaptive systems, improving continuously with new data and user feedback.
Learns from coordinator interactions and participant outcomes to refine recommendations, increasing recruitment performance over time.
Integrates Cloudbyz CTMS and CTFM financial data to predict cost per enrollment and optimize site-level spending, ensuring financial efficiency in recruitment operations.
Automatically compiles audit-ready documentation, compliance summaries, and study reports using natural language generation — reducing audit preparation time by as much as 80%.
This phase establishes an intelligent, self-optimizing ecosystem that drives operational excellence and compliance assurance.
With Cloudbyz and Salesforce Data Cloud integration, AI-driven dashboards offer unified, real-time visibility into key recruitment metrics:
Recruitment Efficiency: Enrollment velocity, screening throughput, and eligibility conversion rates
Diversity & Inclusion: Representation metrics and compliance tracking
Coordinator Productivity: Task completion, contact frequency, and engagement ratios
Regulatory Readiness: eConsent completion, audit trail coverage, and compliance alerts
Executive Insights: AI-generated forecasts, risk predictions, and cost optimization summaries
These dashboards enable life sciences leaders to visualize operational performance across studies and make decisions supported by real-time intelligence.
AI adoption in clinical trial recruitment marks a pivotal shift in how studies are planned and executed. By combining Cloudbyz’s unified patient recruitment solution with Salesforce Agentforce AI, organizations can automate manual workflows, improve patient diversity, ensure compliance, and deliver faster, more efficient recruitment outcomes.
As AI continues to evolve, intelligent agents will play an increasingly central role — collaborating with clinical teams, adapting to dynamic study conditions, and continuously learning to optimize every step of the recruitment journey.
The result is a smarter, faster, and more inclusive pathway to advancing clinical research — powered by AI, data, and human insight.