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AI in life sciences offers exciting potential for the prevention, diagnosis, treatment, and perhaps even cure for disease. The layperson will benefit from AI in the form of wearables and increased control over their personalized health. Custom, targeted treatment plans are easier to draw up with AI, as no patient data falls through the cracks. AI is also the future of discovery regarding the intricacies of the human body and the diseases it’s prone to. The ability for AI to process vast amounts of data faster than a team of data scientists and doctors means that human talent can be funneled into the invention of new pharmaceuticals and therapies at a lower cost and shorter time-to-market than ever before.
Key Themes
Evolution from Early RDA to Full-Scale Attended Automation
Learning from Attended Automation Scalability Fails
Selecting the Correct Attended Automation Solution for Your Enterprise
Easing Your Attended Automation Deployment Through Managing Culture
AGENDA
30 JUNE
11:00am ET: The Continued Importance & Evolution of Change Management: A Fireside Chat
Anil Bhavnani, Director BPO, Pfizer
- Reviewing tried and true principles
- Realizing real-time lessons learned (during Covid-19)
- Utilizing cognitive tools on your intelligent automation journey
- Remaining focused on today while conceiving of tomorrow
12:00pm ET: Activating purpose-led technology
Bobby Abraham, Global VP of Finance Transformation, AstraZeneca
- Data is the only way to progression free survival
- Improve faster early stage innovation
- Informing late stage production
- Putting biopharmacologists and data scientists coming together
- Ultimately saving lives
1:00pm ET: Augmenting Your AI Data
- Divining your most valuable data based on enterprise and customer needs
- Applying cognitive tools to optimize and augment your most valuable data
- Increasing accuracy as your cognitive automation tools continually learn from your data
- Realizing both large scale and personalized accuracy for your enterprise, workforce and customer
2:00pm ET: Overcoming Challenges to Harness Predictive Analytics for your Enterprise
- Realizing the volume of required data is vast
- Understanding that accessing the true source of data is difficult
- Grappling with pattern recognition which is not easy at the start
- Uncovering the difficulty of system integration
- Truly examining the budget and talent needed for the task at hand
3:00pm ET: AI & Compliance
- Beginning with applications that identify, parse, and suggest changes in the face of consistent internal and external regulatory compliance changes
- Deliberately rolling out automation to ensure that regulatory compliance is never a question
- Working with with regulators and external governing bodies to ensure your automation can work at scale
- Consistently reviewing automation regulatory compliance
1 JULY
11:00am ET: AI & Fraud Detection
- Ensuring your enterprise is nearly free of fraud through the application of AI
- Vastly reducing false flags through geo-location, transaction history, etc. to stamp out damage to the bank/client relationship
- Continually up-skilling and re-skilling your AI fraud detection tools
12pm ET: Combatting Scaled Automation Risks
- Examining the ideal structure your risk organization
- Certifying that your AI/IA has technical governance at scale
- Collaborating with regulators and external governing bodies to ensure your automation can work at scale
- Demonstrate your technical governance ensure that your automation won’t fail at scale
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