AI Advisory for Pharmaceutical R&D

AI regulators can trust.Results leaders can measure.

SpordaTech is an independent advisory partner that takes AI from first idea to validated, inspection-ready production across Clinical Operations, Pharmacovigilance, Regulatory Affairs and Quality.

Book a 30-minute Discovery Call Download the brochure
IndependentVendor-neutral advice, driven by your business case
Regulator-readyGxP, GAMP 5 and Part 11 designed in, aligned to FDA, EMA and PMDA
Business-firstEvery engagement starts from a quantified business problem
Why now

AI is reshaping drug development,
and the rules are being written now.

Drug development is still slow, costly and uncertain. AI can change that, but only if it can be scaled, validated and defended in front of regulators.

$2.6B

Estimated cost to develop a new medicine that wins approval1

7.9%

Share of drug candidates entering Phase I that reach approval2

10

Joint FDA–EMA principles for good AI practice in drug development, published Jan 20263

The regulatory clock is running

Jan 2025

FDA draft guidance introduces a 7-step, risk-based credibility framework for AI used to support regulatory decisions4

Jan 2026

FDA and EMA publish 10 shared principles for good AI practice across the medicine lifecycle3

2026

FDA Operation TrialBlazer pilots an expedited IND pathway to speed up early-phase development5

Aug 2026

EU AI Act transparency obligations take effect6

Dec 2027

EU AI Act high-risk obligations (Annex III) apply, deferred by the 2026 Digital Omnibus6

Aug 2028

EU AI Act obligations for AI embedded in regulated products (Annex I) apply6

The gap
Pilots are easy. Validated, scaled, defensible AI is hard.

Most organizations have the ambition and plenty of vendors. What they often lack is a senior partner who speaks science, compliance and technology, and can turn AI ambition into a funded, inspection-ready result.

That is SpordaTech's role.

What we do

Seven capabilities.
One accountable partner.

For 20 years, we have worked inside Global Development, alongside Clinical Operations, Pharmacovigilance, Regulatory Affairs and Quality. We find the AI use cases that matter, build business cases that get funded, and deliver GxP-compliant solutions that speed up drug development.

01

Strategic Business Partnership

From ambiguity to a funded, executive-backed plan.

  • Pre-Concept DiscoveryWorkshops that define the real problem before any solution is proposed
  • Requirements SynthesisBRDs, FRDs, user stories and process maps
  • Business Case DevelopmentValue propositions and ROI models that secure funding
  • Executive PresentationsMaterials ready for steering committee decisions
  • Roadmap DevelopmentMulti-year roadmaps tied to scientific and operational goals
  • Stakeholder FacilitationClinical, technical, quality and vendor teams aligned across regions
02

AI Strategy & Implementation

From promising pilot to validated production.

  • AI Use Case IdentificationPrioritized AI/ML opportunities across ClinOps, Safety, Regulatory and Quality
  • Feasibility AssessmentsData readiness, technical feasibility and compliance impact
  • Proof-of-Concept DesignFocused PoCs built to show measurable value
  • AI Governance FrameworksModel credibility and lifecycle management under GxP
  • Vendor & Platform EvaluationAI vendors assessed on business and compliance criteria
  • From PoC to ProductionA clear path from successful pilot to scaled, validated rollout
Signature practice
03

Pharmacovigilance AI

Faster case intake and earlier signals, with compliance intact.

  • Case Intake AutomationNLP that extracts adverse events from emails, literature and reports
  • Signal Detection SupportAI-assisted pattern recognition and prioritization
  • Aggregate Report EfficiencyAutomated reconciliation, authoring support and version control
  • Safety Data IntegrationClinical and post-market safety data brought together
  • Predictive PharmacovigilanceEHR, wearable and real-world data for earlier signal detection
  • Compliance GuardrailsEvery solution meets GxP, E2B and global regulatory requirements
04

Global Regulatory Strategy

One governance model that works for FDA, EMA and PMDA.

  • North America · FDAAI credibility assessments, context-of-use documentation, engagement strategy for AI-enabled submissions, single pivotal trial strategy
  • Europe · EMAEMA Reflection Paper on AI, EU AI Act readiness, GxP for AI/ML, launch sequencing under new EU pharma legislation
  • Asia-Pacific · PMDA & beyondEarly consultation, reliance pathways, lifecycle evaluation frameworks, multi-market submission sequencing
  • Cross-RegionalHarmonized governance frameworks that meet FDA, EMA and PMDA requirements at once
05

Digital Transformation & Data Strategy

The data foundation AI needs to succeed.

  • Transformation RoadmapsMulti-year R&D technology strategy with prioritized investment
  • Data HarmonizationFragmented sources turned into clean, AI-ready datasets
  • Data Lakes & PipelinesReal-time architectures for clinical, safety and regulatory data
  • Data GovernanceStewardship, quality controls and compliance guardrails
  • Process AutomationAutomation for clinical ops, safety reporting and submissions
  • IoT & Wearable DataPatient device data in compliant, real-time analytics
06

GxP Compliance & Validation

Innovation that stands up to inspection.

  • GAMP 5 ValidationRisk-based validation planning and execution
  • 21 CFR Part 11Compliant electronic records and signatures
  • Data Integrity (ALCOA+)Assessment, remediation and ongoing governance
  • Audit ReadinessInspection preparation and audit trail design
  • CAPA ManagementCorrective and preventive action for quality events
  • AI ComplianceModel credibility and regulatory defensibility
07

RFI/RFP Management & Vendor Selection

The right vendor, chosen on evidence rather than demos.

  • RFI/RFP DevelopmentStructured to your business and compliance needs
  • Weighted ScorecardsCriteria based on business value, not vendor marketing
  • Vendor EvaluationDemos, workshops and PoCs with the shortlisted vendors
  • Decision-Ready BriefingsClear recommendations for leadership
  • Contract Negotiation SupportSLAs, RFQs and onboarding with procurement and legal
  • Consultant OrchestrationExternal consultants kept aligned to your objectives
Case studies

Real AI. Real results.
Details kept confidential.

Selected work led by our team inside top-5 pharmaceutical companies, global pharma and biotech. We cover the full path, from building the data foundation to governed AI in production. Client names and specific details are anonymized to respect confidentiality. The challenges, approaches and outcomes are real.

01

AI in action

Governed AI applied to pharmacovigilance, regulatory operations and enterprise R&D.

Case 01Global Pharma
Pharmacovigilance · Global Patient Safety

AI-powered signal detection in pharmacovigilance

40%faster signal detection in the PoC, with no loss in sensitivity

Challenge. Adverse event volume from trials and post-market sources was overwhelming the safety team. Manual ICSR review was slow and inconsistent, and signal detection relied on periodic reviews held weeks or months after the data arrived. The team also needed to know how to adopt AI under GxP without adding risk.

AI angleAI applied directly to patient safety, with explainability, human oversight and regulatory defensibility built in.

Read the full case

Our approach

  • Ran workshops with Safety Scientists, PV Operations and Quality, and used a value-versus-feasibility matrix to prioritize AI-assisted signal detection, with AI case triage as a secondary use case
  • Assessed data readiness across the safety database, literature and real-world data, and built a data remediation plan into the PoC
  • Defined requirements: NLP for unstructured sources, ML for anomaly detection, integration with the safety database, human-in-the-loop validation, explainability and a full audit trail
  • Set up AI governance with QA: a model credibility framework, GAMP 5 risk-based validation, human review before any escalation, lifecycle monitoring and 21 CFR Part 11 audit trails
  • Led the vendor RFI and ran the PoC against clear success criteria: sensitivity, specificity, time savings and user acceptance

Outcome

  • Successful PoC: 40% faster signal detection with no loss in sensitivity
  • High confidence among safety scientists, driven by explainable flags
  • A GxP-compliant AI governance framework for future PV AI
  • A recommended path to production with a phased rollout
  • Model documentation and governance aligned to FDA and EMA expectations
PV AIModel credibilityGxP AI governanceVendor PoC
Case 02Mid-Size Biotech
Regulatory Affairs

AI-assisted regulatory submission assembly

30%projected reduction in submission assembly time

Challenge. Manual assembly of Clinical, Nonclinical, Manufacturing and Labeling documents took up more than half of every submission cycle. Health Authority Questions needed fast precedent research, and a small team was burning out under the risk of errors.

AI angleGenerative and NLP-based AI for regulatory operations, with humans approving every output.

Read the full case

Our approach

  • Mapped the submission workflow from document gathering through eCTD formatting, cross-referencing, QC and publishing, and quantified manual work at over 50% of cycle time
  • Identified two AI opportunities: NLP-driven document assembly, and ML precedent search to speed up HAQ responses
  • Defined requirements: integration with the document management and RIM systems, an audit trail for every AI action, human approval of all AI-generated content, and GxP, Part 11 and ALCOA+ compliance
  • Led the vendor RFI on integration, explainability, compliance support and vendor stability, and built the ROI case
  • Set up AI governance and an adoption plan that positions AI as augmentation rather than replacement

Outcome

  • A recommended AI solution with a phased implementation roadmap
  • Projected 30% reduction in submission assembly time
  • More consistent and faster HAQ responses
  • A governance framework for future regulatory AI
Regulatory AINLP · MLROI modelingChange management
Case 03Top-5 Global Pharma
R&D Technology Strategy · AI at Scale

Enterprise AI orchestration for R&D

6functions united under one enterprise AI governance council

Challenge. AI pilots across Clinical, Safety and Regulatory were built in silos, with no shared infrastructure and inconsistent governance. Some stalled over compliance concerns, leadership saw little ROI, and the organization was stuck in pilot purgatory.

AI angleMoving from scattered pilots to governed, enterprise-scale AI, including a path to agentic AI.

Read the full case

Our approach

  • Inventoried every R&D AI/ML initiative by status, value, barriers, dependencies and overlaps
  • Defined the target architecture: a shared data foundation, common AI governance, a central model registry, an integration layer and an orchestration engine
  • Prioritized a roadmap of quick wins (0–6 months), foundations (6–18 months) and transformational agentic AI (18–36 months)
  • Set up an AI governance council with ClinOps, Patient Safety, Regulatory, Quality, IT and Data Science to set standards, approve use cases and monitor performance
  • Built the enterprise business case on cost avoidance, speed to value, compliance confidence and scalability, and secured sponsorship and funding

Outcome

  • Enterprise AI orchestration strategy approved and funded
  • A clear governance model and prioritization framework
  • A roadmap for scaling AI across R&D
  • A foundation for agentic AI and advanced analytics
  • A clear path from idea to production
Enterprise AIAI orchestrationGovernance councilAgentic AI roadmap
02

Building the foundation for AI

Every successful AI program starts with data, process and strategy. These projects built that foundation.

Case 04Top-5 Global Pharma
Clinical Development · AI-Ready Data

IoT wearable data platform and AI proofs of concept

60%less time spent pre-processing data by clinicians

Challenge. Hundreds of trial sites sent wearable data in inconsistent formats. Clinicians spent up to 70% of their time reconciling it, there was no real-time visibility, audit gaps put FDA submissions at risk, and no data foundation existed for AI.

AI angleAI starts with data. We built the foundation and then delivered AI proofs of concept on top of it.

Read the full case

Our approach

  • Led discovery with Clinical Operations, Data Management, Biostatistics, Quality and site partners
  • Defined requirements for raw near-real-time data, a sponsor-controlled GxP platform and a self-improving harmonization model
  • Championed a cloud data lake with ML-assisted data mapping, site APIs, role-based access and a raw data zone for AI/ML
  • Embedded GAMP 5 and Part 11 from day one, and secured executive funding
  • After launch, championed two AI/ML PoCs on the new foundation: predictive enrollment optimization and AI-assisted clinical data review

Outcome

  • 60% reduction in clinician data pre-processing time
  • An AI-ready data foundation: raw data lake, harmonized data and real-time pipelines
  • Two AI/ML proofs of concept delivered
  • A GxP-compliant, audit-ready platform
ClinOpsAI-ready dataML data mappingGAMP 5 · Part 11
Case 05Global Pharma
R&D Data Strategy · AI Foundation

Enterprise data harmonization for R&D

40%fewer redundant data pipelines in phase one

Challenge. Many groups sourced the same core data. Teams worked from different versions of the truth, duplicate pipelines drained budget, audit traceability was close to impossible, and the fragmented data ruled out AI.

AI angleHarmonized data is a prerequisite for AI. Without it, AI projects fail.

Read the full case

Our approach

  • Mapped the data flows across Clinical Development, Biostatistics, Patient Safety and IT, and found more than 15 redundant channels
  • Defined a single authoritative source using a publish-and-subscribe architecture
  • Built the ROI case on lower maintenance cost, faster analysis, lower regulatory risk and AI readiness
  • Planned phased adoption with each business unit

Outcome

  • Fully funded, multi-year data program
  • 40% reduction in redundant data pipelines in phase one
  • Clean, harmonized, governed data that is ready for AI
  • A roadmap to rationalize the entire data portfolio
Data strategyAI foundationChange management
Case 06Global Pharma
Global Patient Safety · Pharmacovigilance

Safety aggregate report automation

~40 hrsof manual work per report identified and targeted

Challenge. PBRERs and DSURs were authored in Word, with version control over email and sign-off by up to 15 reviewers. Deadlines slipped, Quality raised repeated audit findings, and there was no foundation for AI-assisted safety work.

AI angleYou can't apply AI to signal detection when safety data and processes are chaotic. This project built that foundation.

Read the full case

Our approach

  • Mapped the end-to-end process and quantified about 40 wasted hours per report
  • Defined requirements for one platform linking safety data, authoring and publishing, with structured workflows, audit history and AI readiness
  • Led the RFI on safety data integration, authoring, publishing, GxP, E2B, Part 11, and API and data access for future ML
  • Built the business case: fewer audit findings, about 30% shorter report cycle time, and experts freed up for signal detection

Outcome

  • Funding secured for phased implementation
  • A prioritized roadmap to a controlled workflow
  • A data and process foundation for AI-assisted signal detection
PharmacovigilanceRFI leadershipGxP · E2B · Part 11
Case 07Mid-Size Biotech
Global Regulatory Affairs · Regulatory Intelligence

Global regulatory submission sequencing

4regulatory regions sequenced into one prioritized roadmap

Challenge. A global launch across FDA, EMA, PMDA and APAC, with limited resources, inconsistent requirements, no reliance strategy, and an overwhelming amount of manual regulatory intelligence work.

AI angleRecommended AI-powered regulatory intelligence to monitor changing requirements and assess their impact.

Read the full case

Our approach

  • Analyzed requirements, timelines and reliance pathways across FDA, EMA, PMDA and selected APAC markets
  • Built a market scoring model on commercial value, complexity, reliance availability and resources
  • Created a phased roadmap that uses the FDA approval as a reliance reference and aligns with expedited programs
  • Recommended an AI-powered regulatory intelligence approach: NLP monitoring of agency updates, ML impact assessment and automated alerts

Outcome

  • A clear, prioritized submission roadmap
  • Reliance pathways identified to reduce duplicate effort
  • Faster time to market in priority regions
  • An AI regulatory intelligence approach recommended for future use
Regulatory strategyReliance pathwaysAI reg-intel design
Case 08Global Pharma
R&D Technology Strategy · AI-First Transformation

AI-first digital transformation roadmap for R&D

36-moAI-first roadmap approved by executives

Challenge. Legacy technical debt, a fragmented landscape and competing priorities across Clinical, Safety, Regulatory and Quality, with ad hoc, siloed AI and no enterprise strategy.

AI angleAI embedded in the transformation strategy from the start, not added later.

Read the full case

Our approach

  • Assessed the current state across all Global Development domains
  • Defined a target state with AI and automation as foundational capabilities
  • Prioritized the gaps by value, feasibility and dependencies
  • Built an AI-first roadmap: AI PoC quick wins (0–6 months), data, governance and cloud foundations (6–18 months), and agentic AI and autonomous workflows (18–36 months)
  • Positioned AI as the accelerator for R&D productivity and secured executive buy-in

Outcome

  • An approved multi-year R&D technology roadmap
  • Clear prioritization and sequencing
  • Executive alignment on investment
  • An AI-first foundation for transformation
Digital strategyAI-first roadmapExecutive alignment
Our method

Chaos to Actionable
From problem to funded roadmap.

Good technology decisions start with the business problem, not a vendor pitch or a demo. Our framework moves every engagement from uncertainty to a decision in four disciplined phases. Each phase ends with a concrete deliverable.

1

Discovery & Problem Framing

We resist the urge to jump to a solution. We map the current state, quantify the pain and state the problem in your terms.

  • Stakeholder interviews and workshops
  • Current-state process mapping
  • Pain point quantification
  • Root cause analysis
DeliverableClear problem statement and baseline metrics
2

Structuring & Requirements

We define the future state, success metrics and constraints, turning broad goals into measurable success criteria.

  • Future-state visioning
  • Requirements: BRD, FRD, user stories
  • Success metric definition
  • Compliance, budget and timeline constraints
DeliverablePrioritized requirements and success criteria
3

Options & Feasibility

We weigh solution options against your business criteria and set vendor hype aside.

  • Market and vendor scanning
  • RFI/RFP management
  • Weighted scorecard evaluation
  • Feasibility assessment
  • Business case development
DeliverableSide-by-side comparison and a recommendation
4

Roadmap & Business Case

A decision-ready package: the recommended solution, a phased roadmap, a business case with ROI and clear next steps.

  • Implementation roadmap
  • Phased delivery plan
  • Business case and ROI model
  • Executive presentation
  • Governance and change management
DeliverableFunded roadmap with clear next steps
Clarity. Confidence. Action.

With the business leading the way at every step.

Topics we advise on
AI in pharmacovigilance & predictive safetyModel credibility & regulatory defensibilityAI orchestration across clinical, safety & regulatoryGxP-compliant AI governance & validationAgentic AI for discovery & developmentReal-world data & digital biomarkersSingle pivotal trial strategyEU pharma legislation & launch sequencingAPAC reliance & expedited pathwaysScaling AI from PoC to production
Trust by design

Trust is engineered,
not claimed.

Every AI solution we shape is built to answer an inspector's questions. Our approach follows the FDA's risk-based credibility framework and the joint FDA–EMA principles for good AI practice.

Defined context of use

Every model has a documented question, scope and decision it supports.

Risk-based credibility

The level of validation matches model influence and decision consequence, following GAMP 5.

Data integrity by design

ALCOA+ principles and 21 CFR Part 11 controls are built in from the first requirement.

Human oversight

Clear roles for human review where decisions affect patients or submissions.

Traceability & audit readiness

Every step from data to decision can be traced and shown to inspectors.

Lifecycle monitoring

Performance and drift monitoring under change control once the model is live.

Global regulatory fluency

FDA

North America

  • AI credibility assessments and context-of-use documentation
  • Engagement strategy for AI-enabled submissions
  • Single pivotal trial and evidence narrative
  • FDA PreCheck and Operation TrialBlazer readiness
EMA

Europe

  • EMA Reflection Paper on AI across the product lifecycle
  • EU AI Act obligations for life sciences
  • GxP compliance for AI/ML systems
  • Launch sequencing under new EU pharma legislation
PMDA+

Asia-Pacific

  • Early consultation for AI-enabled products
  • Reliance pathway strategy across APAC
  • Lifecycle evaluation and traceability documentation
  • Multi-market submission sequencing
GLOBAL

Cross-Regional

  • Harmonized FDA, EMA and PMDA governance
  • Global AI policy monitoring and impact assessment
  • Regulatory intelligence on emerging AI guidance
Frameworks we work to
GxP (GCP · GLP · GMP)GAMP 521 CFR Part 11ALCOA+HIPAAICH E2BEU AI ActInspection Readiness
Why SpordaTech

Senior expertise. Independent advice.
Accountable for the outcome.

CriteriaSpordaTechLarge consultanciesTechnology vendors
Senior practitioner leads every engagementAlwaysVaries by teamSales-led
Vendor-neutral recommendationsBy designUsuallyOwn platform first
Deep ClinOps, PV, Regulatory & Quality expertiseTwo decadesSpecialist teamsProduct-specific
Business case before technologyAlwaysOftenRarely
GxP, GAMP 5 and Part 11 from day oneBuilt inOften a separate workstreamVaries
Right-sized for lean teamsFractional to fullRarelyLicense-driven
Ways to engage
Fixed scope

Discovery Assessment

Define the problem, assess feasibility and recommend next steps.

You receiveProblem statement, feasibility view and a recommendation
Best forOrganizations exploring AI or new technology
Fractional or interim

Embedded BSP/BRM

A senior Business Solution Partner working inside your team.

You receiveStrategic partnership without adding permanent headcount
Best forLean teams that need strategic support
Defined scope & timeline

Project-Based

Specific deliverables with a defined scope and timeline.

You receiveA business case, RFI/RFP or roadmap, delivered
Best forInitiatives with clear objectives
Ongoing

Advisory Retainer

Ongoing strategic advisory and support.

You receiveA trusted advisor and regulatory intelligence on call
Best forOrganizations that want an ongoing partnership
About SpordaTech

Insiders by experience.
Advisors by choice.

SpordaTech's experience was built inside Global Development at top-5 pharmaceutical companies, leading biotechs and healthcare technology firms. We have worked as Business Solution Partners and Business Relationship Managers alongside Clinical Operations, Pharmacovigilance, Regulatory Affairs and Quality. We know how decisions really get made, funded and inspected, because we have been part of that process.

How we work
  • Senior-led. You work directly with an experienced practitioner. Your engagement is never handed off to a junior team.
  • Independent. No platform to sell, so recommendations follow your business case.
  • Confidential. Your data, plans and pipeline stay protected, with NDAs as standard.
Industries we serve
  • Pharmaceutical R&D
  • Biotechnology
  • CROs
  • Healthcare Technology
  • Medical Devices
  • Life Sciences
20 yrs

Hands-on pharma R&D technology experience

Top-5

Pharma companies, leading biotechs and health tech firms

4

Functions: ClinOps, Pharmacovigilance, Regulatory, Quality

3

Regulatory regions: FDA, EMA and PMDA

Discretion is part of the service. Pharma work is confidential, so we don't publish client names or engagement details. Detailed credentials and relevant experience are shared one-to-one during your Discovery Call.

Let's talk

Bring us your hardest
AI question.

Exploring AI, choosing a new system, planning a digital transformation or working through global regulatory complexity? Start with a conversation.

Book a 30-minute Discovery Call contact@spordatech.ai

What to expect on the call

  • 30 minutes, with no obligation
  • We listen to your challenges and goals
  • You leave with at least one idea you can act on
  • If we're a good fit, we propose next steps