Top 50 use-cases of AI for the commercial teams in the MedTech and the Pharma Industry

AI utilisation is growing in the MedTech and Pharmaceutical sectors as companies aim to boost their operating margins amidst economic challenges. The future victors will use AI to enhance performance and pinpoint trends and signals that will mould upcoming markets. Plus, numerous pilot projects are underway across the industry. Over the past three years, we’ve held over a thousand discussions and carried out multiple implementations / projects of our AI platform in tendering, pricing, market access, and sales.

Below are the top 50 use-cases (alphabetically organised):

1. Automated Reporting: Generate sales reports automatically.
2. Bid Success Rate Prediction: Estimate the likelihood of winning a tender.
3. Competitor Analysis: Monitor and analyse competitor strategies.
4. Cost-Effectiveness Analysis: Evaluate the cost-effectiveness of products.
5. Demand Forecasting: Predict demand based on tender data.
6. Discount Analysis: Assess the impact of discounts on profitability.
7. Document Automation: Auto-generate tender documents.
8. E-commerce Integration: AI-driven online sales platforms and ERP data harmonisation.
9. Historical Data Analysis: Analyse past tender data for patterns.
10. Historical Pricing Trends: Analyse past pricing data for insights.
11. Inventory Management: Predict inventory needs and shortages.
12. Lead Scoring: Prioritise leads based on data-driven insights.
13. Licensing Pricing Analysis: Set prices for licensing deals.
14. Margin Analysis: Analyse profit margins on tender bids.
15. Market Entry Strategy: Analyse the best routes for entering new markets.
16. Market Growth Predictions: Predict potential market growth rates.
17. Market Segmentation: Segment markets for targeted approaches.
18. Market Share Analysis: Based on won and lost tenders.
19. Market Trend Analysis: Predict and adapt to emerging market trends.
20. Multichannel Marketing: Coordinate campaigns across channels.
21. New Product Pricing Analysis: Determine prices for new product launches.
22. Order Management: Automate processing of customer orders.
23. Patient Access Analysis: Understand patient accessibility to products.
24. Policy Impact Analysis: Predict impacts of health policies.
25. Population Health Analysis: Understand health trends in populations.
26. Post-Market Surveillance: Monitor products post-launch.
27. Predictive Analytics for Pricing: Predict future pricing outcomes.
28. Price Anomaly Detection: Detect and correct pricing errors.
29. Price Optimisation Algorithms: Develop algorithms for automated pricing decisions.
30. Price Segmentation: Set different prices for different customer segments.
31. Pricing A/B Testing: Test different prices to determine the best strategy.
32. Pricing Contract Analysis: Analyse and optimise pricing contracts.
33. Pricing Simulation: Simulate pricing scenarios to predict outcomes.
34. Pricing Strategy Optimisation: Determine optimal bid pricing.
35. Product Recommendations: For sales reps during client meetings.
36. Quote Management: Automated quote generation.
37. Real-time Analytics: Provide real-time sales data.
38. Real-time Tender Data Analysis: Real-time insights during bidding.
39. Rebate Analysis: Evaluate the impact of rebates on overall pricing strategy.
40. Sales Playbooks: AI-generated sales strategies.
41. Supply Chain Impact on Pricing: Analyse how supply chain factors affect pricing.
42. Tender Alert Systems: Notify teams of relevant tenders.
43. Tender Data Cleansing and CRM Management: Cleanse and refine tender data.
44. Tender Data Visualisation: Visual tools for tender data.
45. Tender Financial Modelling: Model financial aspects of tenders.
46. Tender Market Analysis: Analyse specific tender markets.
47. Tender Outcome Predictions: Predict the outcome of tender submissions.
48. Tender Prediction: Predict upcoming tender opportunities.
49. Tender Reporting: Automated generation of tender reports.
50. Value-based Pricing: Price products based on perceived value.