Clinical development is drowning in complexity: protocol designs are more intricate, data modalities are exploding, and sites are stretched thin. The winners are not simply “AI adopters,” but organizations that operationalize AI inside day-to-day workflows, with trust, governance, and measurable outcomes.
Salesforce Agentforce provides an enterprise-grade AI runtime—identity, security, policy, and orchestration—while Cloudbyz eClinical brings domain-specific data models and processes (CTMS, EDC, eTMF, RTSM, eCOA/ePRO, Safety). Together they convert AI from a pilot into a productivity platform: faster builds, cleaner data, fewer manual steps, and proactive risk management across studies.
Data sprawl → decision friction. eSource, wearables, labs, imaging, PROs, and safety data are distributed across tools and teams. AI condenses signals, flags risk earlier, and routes work to the right role at the right time.
Regulatory pressure + cost inflation. GCP E6(R3) expectations for risk-based quality management, transparency, and traceability fit AI-assisted oversight and explainable audit trails.
Talent constraints. Study teams can’t simply “hire their way out.” Automating the 30–50% of repetitive tasks is now a competitiveness issue, not a luxury.
Agentforce equips clinical organizations with:
Trust layer: enterprise identity, permissioning, data residency, audit logs, and policy controls to keep AI usage compliant.
Orchestration layer: tool-use, function calling, and multi-step plans so agents can follow SOPs (e.g., SDV workflows, deviation handling).
Model abstraction: switch/ensemble best-fit models while retaining consistent prompts, guardrails, and monitoring.
Observability: telemetry on prompts, outcomes, drift, and exception handling to satisfy validation and inspection readiness.
Cloudbyz unifies CTMS, EDC, eTMF, RTSM, eCOA/ePRO, and Safety on Salesforce, giving AI agents structured, governed objects (studies, sites, subjects, visits, CRFs, TMF artifacts, shipments, cases). This alignment ensures:
Process-level unification: one backbone for startup, conduct, monitoring, data management, safety, and closeout.
Metadata richness: AI doesn’t guess context; it reads it—protocol constraints, visit schedules, training status, and country-specific rules.
Closed-loop execution: AI not only “advises” but also acts—creating tasks, updating records, routing documents, triggering signals—subject to your permissions and change-control.
AI Shortcuts (Assistive UX)
Inline copilots embedded in Cloudbyz pages and records to draft, summarize, classify, and guide.
Examples:
Protocol & plan drafting: first-pass authoring for monitoring plans, country appendices, and site Feasibility questionnaires.
Narrative & memo generation: safety case narratives, deviation rationales, meeting minutes.
Smart search across eTMF/EDC: “Show all consent form versions impacted by the new amendment in Germany.”
AI Automation (Workflow robotics)
Background automations that observe events and execute deterministic steps before humans need to intervene.
Examples:
Risk-based monitoring prep: ingest KRIs/KPIs from EDC, lab, and CTMS; pre-populate a monitoring visit agenda and SDV focus list.
TMF housekeeping: auto-classify documents to TMF Reference Model, detect gaps, request remediation from owners.
Enrollment velocity tuning: detect screen-fail patterns, recommend inclusion/exclusion clarifications and site enablement actions.
AI Agents (Policy-aware digital teammates)
Goal-seeking agents operating under SOPs with escalation rules—auditable, reproducible, and measurable.
Examples:
Site-enablement agent: orchestrates green-light by checking essential docs, training, budget, and IRB/EC statuses, then issues activation tasks.
CRA companion: compiles pre-visit packets, drafts follow-up letters, reconciles action items, and tracks close-out readiness.
Data-review agent: runs edit checks, proposes query bundles, prioritizes by subject risk, and schedules reconciliation sprints.
PV triage agent: normalizes ICSR intake, deduplicates, codes with MedDRA, drafts case narratives, and routes to safety physicians.
Feasibility intelligence from historical performance + KOL networks; draft CTAs and budget benchmarks; accelerate essential document collection with auto-classification and reminders.
Outcome: weeks shaved from site activation; fewer back-and-forth loops.
Risk-based monitoring signals synthesized from EDC, deviations, and site comms; SDV focus lists and visit letter drafts; real-time enrollment and protocol-deviation heatmaps.
Outcome: 20–40% fewer non-value-add site visits; earlier risk detection.
AI-augmented edit checks, query clustering, reconciliation with external labs/ECG/imaging; statistical anomaly and pattern detection.
Outcome: 30–50% faster data review cycles; cleaner locks with fewer last-minute scrambles.
Automated classification to TMF taxonomy, version tracking, and completeness scoring; change-impact analysis across docs after amendments.
Outcome: audit-ready TMF with continuous health scoring.
Enrollment forecasting for IP demand; shipment and expiry optimization; temperature excursion triage with suggested dispositions.
Outcome: minimized stockouts and waste; better budget control.
Conversational visit prep and adherence nudges; multilingual assistance; on-device anomaly checks to reduce bad data.
Outcome: higher compliance and richer RWD with less site burden.
Intake normalization, deduplication, and coding; narrative drafts; signal detection across ICSRs, literature, and social mentions (as permitted).
Outcome: faster case throughput and earlier signal surfacing.
Data fabric: Cloudbyz as the governed record system; connectors to lab/imaging/EDC sources; CDISC/FHIR mappings where applicable.
Guardrails: role-based access control, prompt & tool-use policies, PHI/PII handling, data minimization, and audit logs.
Validation approach: GxP CSV aligned to GAMP 5—intended use definition for each Shortcut/Automation/Agent; test evidence; change control with continuous monitoring.
Explainability: agent decisions captured with inputs/outputs, confidence, and human-in-the-loop checkpoints.
Time: study start-up cycle time, days from LPLV to DBL, query turnaround, narrative lead time.
Quality: protocol deviation rate, TMF completeness, reconciliation defects, case processing errors.
Cost & capacity: CRA hours per site, DM hours per subject, IP waste, rework rate.
Experience: site NPS, patient adherence, team satisfaction.
Weeks 0–2 — Prioritize & baseline. Pick 3 use cases with measurable pain (e.g., TMF classification, CRA visit letters, DM query bundling). Establish KPIs and risk controls.
Weeks 3–6 — Configure & validate. Deploy Shortcuts first; wire Automations; pilot one Agent with clear SOPs, RBAC, and escalation.
Weeks 7–10 — Scale across roles. Expand to adjacent teams; enable observability dashboards; refine prompts/tools from telemetry.
Weeks 11–13 — Institutionalize. Add to training and SOPs, finalize validation package, and publish the value report to sponsors/CROs.
Multimodal review: agents that read tables, documents, and images (e.g., central reads) to provide a single risk story per subject.
Federated learning: privacy-preserving benchmarking for enrollment forecasts and site performance.
Synthetic test data: safer validation and SOP testing without exposure of real PHI/PII.
If you already run on Salesforce, you’re halfway there. Agentforce supplies the guardrails and orchestration; Cloudbyz eClinical delivers the clinical context and execution fabric. Start with three practical use cases, validate quickly, and scale with confidence. The path to faster, safer, and more affordable trials isn’t “doing AI”—it’s operationalizing AI inside the workflows your teams use every day.