Outline
What is AI?
Introduction to AI Levels
Types of Artificial Intelligence
AI vs Machine Learning vs Deep Learning
Where is AI used in pharma?
AI use cases in life sciences & drug development
Why is AI booming now—especially in healthcare?
AI trends in 2025 and beyond
Slide1: Starting Slide!
Myth: “AI Can’t Be Trusted in Pharma” AI will replace scientists. AI is too complex for regulatory use. AI only works with massive datasets. AI is just a buzzword in pharma. AI is only useful in R&D.
Alternative Pointer: When we think of AI, the first thing that comes to our mind is Chatgpt but what if I told you
AI is NOT Just ChatGPT — It’s the Engine Behind a Global Shift
Slide 2: What is AI?
Title:
What Exactly is Artificial Intelligence?
Content:
Artificial Intelligence is the science of making machines think and learn like humans—so they can analyze, decide, and act without being explicitly told every step.
Think of it as teaching machines to "learn from data" the way we learn from experience.
Like how, Netflix recommends shows based on what you watch — that’s AI understanding your preferences.
Amazon and Flipkart suggests similar products based on what you browse.
Google/Apple Photos groups your photos by people.
Airlines/Uber/Hotels adjusts prices in real-time based on demand, location, and time.
Slide 3: Introduction to AI Levels
Title:
The 3 Levels of AI – Where Are We Today?
Content:
Specializes in performing one task very well. Cannot adapt beyond its programmed domain. ChatGPT answering questions Google Maps navigating traffic Facial recognition on smartphones This is the AI we use every day. General AI (AGI – Artificial General Intelligence) Mimics human-level understanding and reasoning across multiple domains. Can learn, adapt, and transfer knowledge like a human. Still theoretical – no AGI exists yet. Research labs are exploring it (e.g., OpenAI, DeepMind, Anthropic). Super AI (ASI – Artificial Superintelligence) Hypothetical AI that surpasses human intelligence in all aspects. Could outperform humans in science, creativity, social intelligence, and beyond. Raises ethical debates and long-term safety concerns.
Slide 4: Types of AI
Title:
Types of AI – From Rules to Reasoning
Content:
No memory or learning. Reacts to current input only. Example: IBM Deep Blue (chess computer), basic spam filters Learns from historical data to make better decisions. Example: Self-driving cars using past driving data, recommendation systems Theory of Mind (Emerging Research) AI that can understand emotions, intentions, and beliefs. Not yet achieved — key to social interactions and empathetic AI. Self-Aware AI (Hypothetical) AI with consciousness, self-awareness, and emotions. Still completely theoretical. Raises deep philosophical and ethical questions.
Slide 5: AI vs ML vs DL
Title:
AI vs ML vs DL – Let’s Clear the Confusion
What’s the Difference?:
AI – The big umbrella. Any system that can mimic human intelligence — reasoning, learning, problem-solving, or decision-making.
→ Example: A smart assistant like Siri answering your voice commands. Machine Learning (ML) – A subset of AI that enables systems to learn from data without being explicitly programmed.
→ Example: Netflix recommending shows based on your watch history. Deep Learning (DL) – A subset of ML that uses artificial neural networks (inspired by the human brain) to analyze very complex data.
→ Especially powerful in recognizing patterns in images, audio, and large datasets.
→ Example: Face unlock on your phone, or AI generating artwork from a photo.
Slide 6: Where is AI Used in Daily Life? [Number Crunch, PDF, Visualization]
Title:
What exactly you can do and what deep research they can do etc.
Slide 8: Why Is AI Booming Now?
Title:
Why the Sudden Surge in AI Adoption?
Content:
From smartphones, smartwatches, sensors, cameras, online behavior, and even your voice commands — the world is producing more data than ever before. Massive Compute Power Is Now Affordable Cloud platforms (like AWS, Azure, Google Cloud) and powerful GPUs/TPUs have made it possible to train complex AI models faster and cheaper. Business Pressure to Be Faster, Smarter, Leaner Companies are using AI to automate routine tasks, optimize operations, reduce costs, and make faster decisions. Digital Mindset After the Pandemic COVID-19 forced rapid digital transformation — from remote work to telehealth to online education. Regulatory Momentum & Trust Frameworks Governments and regulatory bodies (like the EU AI Act, FDA AI Framework, India’s Digital Health Mission) are beginning to embrace AI — with rules, not resistance.
It’s not that AI suddenly got smarter —
It’s that the world finally became ready for it.
Slide 9: AI Trends in 2025–2026
Title:
AI in Pharma – What’s Next?
Top Trends:
Multimodal AI
One model that understands text, images, video & voice — all at once. AI Agents
Task-driven, autonomous systems that can reason, plan, and act with minimal human input.
Think: a research assistant, project manager, or sales rep — powered by AI. Digital Twins
Virtual replicas of humans or systems for simulation and optimization. Foundation Models
Large-scale models adapted for domains like law, finance, and biology. Explainable AI (XAI)
Transparent AI that shows “why” it made a decision — critical for trust. AI-Augmented Workflows
Automated labs, predictive systems, and smart process copilots.
Use Cases Solved by Activepieces (Automation-Focused)
(No AI model required — purely workflow and task automation)
1. R&D / Drug Discovery
New Research Digest Distribution
→ Fetch new PubMed alerts → Email to R&D leads daily. Form-Driven Idea Collection System
→ Scientists submit molecule ideas via form → auto-logged into database + notified. 2. Clinical Trials
Site Report Collector
→ Automate collection of trial site updates via forms → aggregate into Google Sheets or Slack. Trial Deadline Reminder Bot
→ Set timelines for protocol tasks → trigger reminders to stakeholders via WhatsApp/Email. 3. Regulatory & QA
SOP Update Alerts
→ Monitor folders for file changes → auto-notify compliance team with changelog link. Audit Calendar Workflow
→ Create scheduled reminders + auto-generate task lists before audits. 4. Manufacturing
Deviation Reporting Workflow
→ QC fills form → entry logged → notification to QA + PDF auto-generated. Inventory Low-Level Alerts
→ Monitor stock in Google Sheets → auto-send alert to procurement when thresholds are hit. 5. Sales & Commercial
HCP Meeting Follow-Up Trigger
→ Sales rep submits meeting form → auto-email follow-up is triggered to internal team. Event Registration + Reminder Flow
→ Doctors register → calendar invites, reminders, and post-event thank-yous are fully automated. 6. Internal
Monthly Departmental Summary Submission Flow
→ Teams submit summaries via form → centralized + shared via Slack/Drive. Birthday/Work Anniversary Bots
→ Auto-fetch dates from HR sheet → send wishes to employees/team WhatsApp groups.
Use Cases
Meeting Summary Delivery: Upload meeting transcript → GPT summarizes → Activepieces sends to relevant team leads VIA SLACK, ETC. Input Excel → Clean & format → Push to Google Sheets dashboard for leadership. Check Tally for unpaid invoices past 30/60 days → Send auto-reminders to regional sales heads. Competitor News Alert
→ Use RSS/Google Alerts for "Sun Pharma US", "Lupin + FDA", etc. → auto-summarized via GPT → delivered weekly. US Doctor Review Monitoring (via Web Scraper)
→ Track reviews for competitor products on platforms like Drugs.com or WebMD → summarize sentiment trends. Weekly sales performance report: Pull sales data from CRM or sheets → GPT returns bullet pointers by region/product/person → send via email or preferred communication channel Invoice Auto Reminder: Detect Overdue Invoices [Tally/Excel] → Send Email Reminders Payment run reminder: Alert accounts team on upcoming scheduled payments Employee Onboarding Bot: New Hire form triggers account creation, sends SOPs & orientation docs [Pucho Studio + Google Drive + Slack] Daily Business Snapshot: Pull data from sales, collections, support, HR → email summary AI Q&A Dashboard: Ask internal data questions like: “Sales by product category in Q2?”
USE CASES PERPLEXITY
Product Launch Tracking
→ Ask: “Which Indian pharma companies launched generics in the US in 2024?” FDA Approval Monitoring
→ Get summaries of recent FDA drug approvals in H&H’s focus areas (e.g., antibiotics, vitamins). Pricing Benchmarking
→ Compare: “What are the retail prices of common Vitamin D3 supplements across US brands?” Market Entry Strategy Reports
→ Ask: “What regulatory hurdles do Indian pharma exporters face when entering the US market?” Doctor/Prescriber Trends
→ Summarize: “Top prescribing patterns for antihistamines in the US market (2023)” Competitor Differentiators
→ Example: “How is Cipla positioning its respiratory portfolio in the US?” M&A and Partnership Tracking
→ Example: “Recent acquisitions by Dr. Reddy’s or Zydus in the US between 2022–2024”
OUTLINE
Slide 1: Opening Hook – Breaking the AI Myth
Title:
“AI is NOT Just ChatGPT – It’s the Engine Behind a Global Shift”
Core Myths to Debunk (visually):
AI is too complex for real businesses AI only works with big data Alternate Opener:
“What if your daily reports, research, and replies could be done in minutes — not hours?”
Slide 2: What is AI? (Explained Simply)
Title:
“Understanding AI Like a Human Learns”
AI = systems that analyze, learn, predict, and automate without needing to be told exactly what to do
Like Netflix learning your taste Like Google Photos grouping people’s faces Like Uber changing prices based on demand Like Gmail suggesting your next sentence Slide 3: The 3 Levels of AI
Title:
“Understanding AI: How Smart Are These Systems, Really?”
1. Basic AI – The Doers
These AIs follow clear instructions. They can only do one thing at a time — like sorting emails or recommending songs. This is where most AI today exists.
2. Smart AI – The Learners
These AIs can learn from experience. They improve over time based on data — like ChatGPT or Siri. They can write, talk, create — but they don’t “understand” like humans. ChatGPT answering questions Canva AI creating designs Voice assistants like Alexa This is what we call Generative AI — it creates content, but it's still task-specific.
3. Super AI – The Thinkers (Still Future)
This AI would think, reason, and act like a human — or better. It could solve problems, learn anything, and adapt like we do. But it doesn’t exist yet — it’s still a research goal. Slide 4: AI vs ML vs DL
Title:
“Let’s Clear the Confusion”
AI: The big umbrella (reasoning, automation, decision-making) ML: AI that improves with data DL: Neural networks learning from huge, complex data Example Flow:
You watch a show → Netflix (ML) recommends similar Your camera detects faces → Deep Learning Siri responds to voice → AI + ML + DL combo 
