🚨 The Silent Revolution in Clinical Trials: How TCS Is Using AI to Catch Problems Before They Exist

• TCS unveils major AI-driven upgrades to its clinical trial oversight platform.

• New modules detect risk far earlier than traditional systems.

• The Connected Clinical Trials (CCT) platform enhances patient experience and adherence.

• Aligned with global guidelines and already deployed across 1,300+ studies.

• A sign that AI-driven clinical oversight is fast becoming the industry’s new operating system.

• Who:

Tata Consultancy Services (TCS), global biotech & pharma sponsors, researchers, and patients participating in clinical trials.

• What:

Launch of an AI-enhanced Risk-Based Quality Management (RBQM) platform + the Connected Clinical Trials (CCT) SaaS suite.

• When:

Latest updates released as the life sciences industry enters a new era of decentralised and adaptive trials.

• Where:

Deployed across 32,000 clinical trial sites globally, headquartered operations in India and used by major pharma companies like Janssen.

• Why:

Trials are becoming more complex, regulations stricter, and patient engagement more difficult—AI offers earlier insights, fewer errors, and dynamic oversight.

If you’ve ever sat through a traditional clinical monitoring cycle, you know the drill: data checks, audits, late discoveries, and the occasional frantic “why didn’t we catch this sooner?” moment 😅.

But modern clinical trials are sprawling, hyper-complex ecosystems. We’re talking global patient pools, decentralised designs, adaptive protocols, and regulatory landmines.

Manual oversight simply can’t keep up.

AI can.

TCS’s upgraded ADD RBQM platform introduces four AI-infused modules that work like a digital command centre for clinical quality.

✨ The Four New AI Modules

• 🔮 Risk Assessment: Predict issues before they escalate.

• 📉 Quality Tolerance Limits: Automatically enforce guardrails around trial performance.

• 📊 Trial Analytics: Transform raw data into strategic insight.

• 🧬 Subject-Level Monitoring: Catch anomalies patient by patient—in real time.

Collectively, these tools give teams the superpower of early detection, something nearly impossible with traditional systems.

The life sciences landscape is in flux:

• Decentralised trials are rising.

• Regulatory expectations are tightening.

• Patient engagement is harder and more fragmented.

• Data volumes are exploding.

AI adoption in India’s healthcare GCCs, for instance, jumped from 65% in 2019 to 86% in 2024 🤯—a clear sign that AI is now baseline infrastructure.

TCS’s Global Head of ADD, Rachna Malik, summed it up perfectly:

Translation?

The future requires intelligence, not just information.

Numbers don’t lie:

• 1,300+ studies

• 32,000 sites

• Global deployment

• Aligned with ICH E6(R2) and ready for E6(R3)

This isn’t a pilot.

It’s a paradigm shift already in motion.

The ADD platform focuses on oversight.

CCT focuses on people.

It turns the traditionally confusing trial journey into a guided, tech-enabled experience that patients can easily follow.

📦 The Four CCT Modules

• CCT Tracking:

  Prevents medication mix-ups using scanner-based verification.

• CCT Communication:

  Sends personalised reminders, instructions, videos, and adverse-event capture prompts 📱.

• CCT Label:

  Generates multilingual electronic labels—clear, accessible, patient-friendly.

• CCT Adherence:

  Uses smart medication packaging to confirm dosage in real time.

  (Goodbye guesswork 👋)

Janssen partnered with TCS to refine the CCT platform as part of their Smart Trial and Engagement ecosystem.

CCT was then tested in TCS’s user experience labs with global patient advisory boards and the CISCRP—ensuring the platform reflects real human needs, not just digital ambition.

A clinical research coordinator put it simply:

That’s what transformation looks like: accessible, reliable, human-centred.

TCS Life Sciences isn’t just building tools.

It’s reshaping how biotech organizations:

• Accelerate drug discovery

• Maintain trial integrity

• Improve patient engagement

• Scale digital maturity

• Ensure compliance

• And ultimately deliver therapies faster

AI is no longer an add-on.

It’s becoming the backbone.

AI won’t replace clinical researchers.

But researchers who use AI will absolutely outperform those who don’t.

Whether you’re a founder, clinical lead, investor, or technologist, this shift is directly relevant to your future strategy.

🔥 Your Actionable Playbook

• Adopt AI early in trial oversight to reduce risk and accelerate timelines.

• Evaluate RBQM maturity within your organization—most teams underutilize its potential.

• Explore patient engagement tech like CCT to improve adherence and retention.

• Align your systems with ICH E6(R3) now—avoid rushed compliance later.

• Integrate real-time analytics dashboards to empower proactive decision-making.

• Run pilot projects using AI-driven monitoring to benchmark ROI.

• Upskill your teams in AI literacy for trials—make intelligence part of workflow.

• Audit your trial data ecosystems to ensure interoperability, a key strength of TCS platforms.

• Leverage user-experience testing with real patients early in your design cycle.

• Choose platforms that scale globally, not tools that break under regulatory complexity.

At Think Ahead With AI (TAWAI), we believe the future belongs to those who can blend human leadership with intelligent technologies. The world is shifting faster than any previous generation has experienced, and navigating that shift requires more than technical skill—it demands vision, adaptability, and a mindset built for exponential change.

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• Build solutions that balance innovation with responsibility

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1. Medidata AI (Dassault Systèmes)

An industry-leading platform that uses AI to enhance patient recruitment, detect site-level risks, and optimise trial design.

Use cases:

• Predicting enrollment challenges

• Automating anomaly detection

• Benchmarking protocol performance across historic data

2. Saama AI Clinical Insights Platform

A cognitive intelligence engine used widely by pharma companies to speed up data cleaning, monitoring, and regulatory reporting.

Use cases:

• Automated medical coding

• Faster database locks

• Real-time statistical anomaly detection

3. IQVIA Orchestrated Clinical Trials (OCT) with AI

IQVIA integrates AI-driven analytics to streamline trial planning and operational execution.

Use cases:

• Site selection optimization

• Risk-based monitoring insights

• Predictive performance dashboards

4. Deep 6 AI

A specialised AI tool that uses natural language processing to match patients to trials within minutes instead of months.

Use cases:

• Precision patient recruitment

• Accelerated feasibility assessments

• Real-time EMR-driven cohort generation

5. Clario AI-Powered eCOA & Trial Endpoints

Clario uses AI to analyse patient-reported outcomes, imaging, and physiological signals to improve endpoint accuracy.

Use cases:

• AI scoring for neurological, cardiovascular, and respiratory endpoints

• Adaptive trial monitoring

• Adverse event detection via wearables

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