Free AI Strategy Consultation for your Pharma Company
Explore how AI and intelligent agents can boost your sales, supply chain, and operations in a no-cost, AI Impact assesment
What You’ll Get – Free AI Impact Assessment
🔍 Personalized AI Roadmap
We identify 2–3 high-impact opportunities for AI — from predictive supply planning to tender automation.💰 ROI & Feasibility Insights
See what’s possible — and how quickly it can pay off.🤝 Pharma-Specific Expertise
Speak 1:1 with our pharma AI strategists. No pressure. No jargon. Just real insight.
💬 “In one session, we helped a pharma client cut tender response time by 80% — imagine what we could find for you.”
RGS - Pharma Sales Use Cases
Tender Win-Rate Optimization
Use Case: AI systems analyze historical tender outcomes, competitor bids, and scoring criteria to pinpoint what factors lead to wins. By learning from past tenders, the AI can guide proposal strategy for new bids (e.g. suggesting optimal pricing ranges, flagging likely high-impact requirements).
Impact: Sales teams can focus on the most promising tenders and tailor their proposals to score higher. For instance, if AI analysis shows that including certain real-world evidence documents tends to win hospital tenders, the team can prioritize those.
This data-driven approach can significantly improve win rates.
Example: A pharma supplier saw a 15% increase in tender win rate after the AI identified patterns (such as specific phrasing or faster response times) that influence evaluator decisions. This directly translates to more contracts won with the same resources.
Lead Generation & Prioritization
Use Case: AI combs through vast data (e.g. prescribing data, formulary listings, clinical trial pipelines, news) to identify high-potential leads for drugs or products. Instead of generic prospect lists, the AI finds healthcare organizations or physicians likely to have interest based on data signals. It can also qualify leads by predicting which are more likely to convert (using factors like past engagement or demographic fit).
Impact: Pharma sales reps (or KAMs) get a prioritized list of leads, saving time on research and enabling more targeted outreach. This increases efficiency and ensures no promising opportunity is overlooked.
Example: An AI model might flag that Hospital X has an expiring contract for a therapy area where your product fits, suggesting a lead. Or it might analyze that hospital procurement groups have recently awarded multiple tenders in a related therapeutic area — signaling an upcoming procurement opportunity for your product.
Omnichannel Pharma Marketing
Use Case: Pharma is increasingly adopting omnichannel marketing (combining rep visits, emails, webinars, etc.). AI can analyze how healthcare professionals engage with various content and then recommend the next best action – e.g., after a doctor attends a webinar, the AI might suggest the rep send a follow-up with a specific study reprint that matches interests.
Impact: This maximizes the impact of each interaction by personalizing outreach. For sales teams working in tandem with marketing, it ensures the customer gets relevant touches, which can shorten the sales cycle or improve uptake.
Example: For a client we implemented AI in their CRM systems to boost engagement – which increased sales by a notable margin. RGS can incorporate such a use case especially if pitching to commercial excellence teams.
RGS - Pharma Operations Use Cases
Document Automation and Knowledge Management
Use Case: pharma companies manage a massive volume of repetitive documentation — from bioequivalence studies, SOPs, and stability protocols to change control justifications and PSURs.
Our AI agent works right out of the box — simply upload your Word, PDF, or Excel files in your protected environment. It instantly analyzes, summarizes, and generates content using your past submissions and templates. No integration, no IT setup — just value.
Impact: A regulatory team uploads their last 3 dossiers and asks: “Draft a new Module 2.5 summary for our upcoming clarithromycin filing.”
Within seconds, the AI returns a draft based on previous dossiers, updated data, and reference documents — ready to review before submission
You can also ask:
“Find the justification we used for impurity X in our 2023 submission.”
“Pre-fill this PSUR template based on last year’s version.”
Real-World Data Analysis for Medical Affairs
Use Case: Medical Affairs teams often monitor real-world data (publications, patient registries, social media, conference abstracts) to find insights on how products perform or where gaps are. AI can continuously scan these unstructured data sources to alert the company of new safety signals, off-label usage patterns, or emerging competitor evidence.
Impact: The company stays ahead in understanding the landscape and can respond proactively (e.g., update a safety section in a brochure, or start planning a new study to address a gap).
Example: An AI agent might alert that several doctors at a recent conference reported using the company’s drug in a new patient population – something that marketing can then investigate as a potential new indication or ensure communications are proper. This shows that AI can also assist in keeping the company’s knowledge up-to-date, which is valuable for strategy and compliance.
AI-Powered Supplier Segmentation
Use Case: Generic manufacturers rely on complex global supply chains — but assessing and segmenting suppliers by risk and criticality is often manual, inconsistent, and slow. AI eliminates that burden by transforming disconnected data (Excel sheets, audit reports, delivery logs) into a clear supplier risk profile — no integrations required.
Impact: Instead of spending hours updating segmentation models manually or aligning across departments, teams receive a real-time, AI-generated supplier risk map. This enables faster decisions on stockpiling, requalification, audits, and tender prioritization
Example: A generic manufacturer uses AI to continuously monitor supplier data — when a sole-source API supplier showed rising deviations and late deliveries, the system flagged it in real time. The team adjusted tender plans and launched requalification before it became a supply issue.