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Data & AI
(Business
Perspective)
Agenda
- AI 101
- GenAI (Generative AI) hype
- AI business use cases
- The bigger picture
- Pursuing a career in data & AI?
- Mistakes to avoid!
GenAI (Generative AI) hype
• Big folks competition
• AI unicorns
Simple Tasks
Expressive and coherent
text generation
Few shot learning
Extraordinary interactivity
Problem solving through
human instruction
Web search
• Computational
resources
• More data
• Mature system
architectures
GenAI Hype?
Business Cases: Marketing Mix Modeling
- Predictive analysis for business KPIs
- Marketing contribution
- Baseline estimation
- ROIs
- Budget optimization
Data & AI business use cases trends
Medical imaging
analysis and drug
discovery: using
Computer Vision in X-
rays, MRI scans, and CT
scans & simulating and
predicting the interactions
between molecules and
biological targets
Fraud detection and Financial
documents generation: crime
prevention using anomaly
detection technique & loan
agreements tailored to individual
borrowers
Sustainability and smart
manufacturing: optimizing
resource usage for production
and distribution & reducing
food waste
Network optimization
and speech synthesis:
using RL for identifying the
optimal network setup &
interactive voice response
(IVR) systems
Dynamic pricing and customer
service automation: using AI for
estimating market dynamics and
pricing strategies & efficient chatbots
Business cycle
Understand:
• Business priorities, use cases and
KPIs, customer needs and journey and
teams specific pain points
• Processes, systems and data maturity
level
• Internal resources
• Competition
Execute:
• Data infrastructure & Governance
• AI capabilities
• Business & tech alignment
(explainable AI)
Iterate:
• Data quality monitoring
• Processes optimization and automation
• Feedback loop dependent on business
KPIs and goals
The bigger picture
• Data stack modernization and cloud adoption
• Decentralization for
development and maintenance
and cross-functional teams with
representatives from difference
business units & talent
development
• Data governance framework that
includes data quality standards,
metadata management and data
ownership, security and privacy
• Sustainability constraints in terms of carbon
intelligence and material traceability
• Centralized data architecture in terms of
storage, ETL processes, data integration layer,
single source of truth for key data elements etc
• Data compliance; GDPR, sensitive data,
consent management, data retention and
deletion, etc
• Optimizing data processing
and analytics processes and
developing dashboards and
visualization tools for
leadership
• Using AI for sales forecasting, supply chain
management and manufacturing optimization,
customer insights
Pursuing a career in data & AI?
Software
Engineer
Data Engineer
Data Analyst
Data Scientist/
Machine Learning
Engineer
MLOps
Engineer
Not a tech person?
Mistakes to avoid (discussion)
Let’s connect!
https://www.linkedin.com/in/mustafaawny/

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Potential of AI (Generative AI) in Business: Learnings and Insights

  • 2. Agenda - AI 101 - GenAI (Generative AI) hype - AI business use cases - The bigger picture - Pursuing a career in data & AI? - Mistakes to avoid!
  • 3.
  • 4. GenAI (Generative AI) hype • Big folks competition • AI unicorns Simple Tasks Expressive and coherent text generation Few shot learning Extraordinary interactivity Problem solving through human instruction Web search • Computational resources • More data • Mature system architectures GenAI Hype?
  • 5. Business Cases: Marketing Mix Modeling - Predictive analysis for business KPIs - Marketing contribution - Baseline estimation - ROIs - Budget optimization
  • 6. Data & AI business use cases trends Medical imaging analysis and drug discovery: using Computer Vision in X- rays, MRI scans, and CT scans & simulating and predicting the interactions between molecules and biological targets Fraud detection and Financial documents generation: crime prevention using anomaly detection technique & loan agreements tailored to individual borrowers Sustainability and smart manufacturing: optimizing resource usage for production and distribution & reducing food waste Network optimization and speech synthesis: using RL for identifying the optimal network setup & interactive voice response (IVR) systems Dynamic pricing and customer service automation: using AI for estimating market dynamics and pricing strategies & efficient chatbots
  • 7. Business cycle Understand: • Business priorities, use cases and KPIs, customer needs and journey and teams specific pain points • Processes, systems and data maturity level • Internal resources • Competition Execute: • Data infrastructure & Governance • AI capabilities • Business & tech alignment (explainable AI) Iterate: • Data quality monitoring • Processes optimization and automation • Feedback loop dependent on business KPIs and goals
  • 8. The bigger picture • Data stack modernization and cloud adoption • Decentralization for development and maintenance and cross-functional teams with representatives from difference business units & talent development • Data governance framework that includes data quality standards, metadata management and data ownership, security and privacy • Sustainability constraints in terms of carbon intelligence and material traceability • Centralized data architecture in terms of storage, ETL processes, data integration layer, single source of truth for key data elements etc • Data compliance; GDPR, sensitive data, consent management, data retention and deletion, etc • Optimizing data processing and analytics processes and developing dashboards and visualization tools for leadership • Using AI for sales forecasting, supply chain management and manufacturing optimization, customer insights
  • 9. Pursuing a career in data & AI? Software Engineer Data Engineer Data Analyst Data Scientist/ Machine Learning Engineer MLOps Engineer
  • 10. Not a tech person?
  • 11. Mistakes to avoid (discussion)