As AI adoption accelerates, cloud platforms have become the backbone for building, training, and deploying intelligent systems.
This session takes a deep dive into the AI toolkits offered by leading cloud providers, exploring the full stack—from compute and data infrastructure to pre-built models and automation tools.
Whether you’re just starting out or scaling enterprise AI, discover how to leverage the right cloud-native solutions to drive innovation and efficiency without any additional infrastructure build cost.
Agenda:
1. Who Offers What – A Service-by-Service Breakdown
2. Why it matters – Evaluating Services from Aws, Azure & GCP
3. Business Cases – Simple to Integrate into existing systems and use for High value deliveries
4. Conclusion – Beyond the Hype: Selecting AI That Delivers Measurable ROI
# 4. Conclusion
## Factors in choosing the right AI:
* Ease of integration with existing systems
* Scalability to accommodate future growth
* Vendor Support & reliability
* Alignment with Strategic Business Objectives
## AI Success Metrics & ROI
Business Objective Alignment
Such as:
Ensure AI supports strategic goals (e.g., efficiency, revenue growth, customer satisfaction).
Key Performance Indicators (KPIs)
Such as:
Define measurable outcomes such as cost savings, productivity improvement, and user engagement.
Pre-Implementation Baseline
Such as:
Establish baseline metrics (e.g., processing time, costs) to compare post-implementation performance.
Quantitative ROI Metrics
Such as:
Cost Reduction: Savings from automation.
Revenue Growth: Impact of AI-driven initiatives on sales.
Time Savings: Reduced time spent on manual tasks.
Qualitative ROI Metrics
Such as:
User Adoption: Engagement and satisfaction with AI tools.
Employee Efficiency: Time saved for strategic tasks.
Time to Value (TTV)
Such as:
Measure how quickly AI generates measurable benefits after implementation.
Long-Term Business Impact
Assess competitive advantage, innovation, and growth driven by AI.
