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| CEO Guide to Gen AI use cases 2024 | |
| Codegen IDE | |
| Codegen Tools | |
AlphaCodium-ai-generate-activity-test-2024-gpt4 | |
Key Concepts
ceo-guide-to-genai-use-cases-2024.pdf. link
ceo-guide-to-genai-use-cases-2024.pdf. file
Introduction Leadership can’t be automated 3
Section 1 AI-enabled people 7 Chapter 1 Talent and skills 9 Chapter 2 Customer service 19 Chapter 3 Customer and employee experience 29
Section 2 AI-powered data and technology 39 Chapter 4 Platforms, data, and governance 41 Chapter 5 Open innovation and ecosystems 51 Chapter 6 Application modernization 61 Chapter 7 Responsible AI and ethics 71 Chapter 8 Tech spend 81
Section 3 AI-fueled operations 91 Chapter 9 Supply chain 93 Chapter 10 Marketing 103 Chapter 11 Cybersecurity 113 Chapter 12 Sustainability 123
Conclusion Lay the groundwork for greatness 133
IBM > Generative AI won’t replace people, but people who use generative AI will replace people who don’t
IBM assumes people centric vs value centric process models
Genai is only type of use case that AI and ML apply to
see AI use cases for: optimization, prediction, pattern recognition, anomaly detection, responsible behavior ( digital twins ), content generation for audiences, codegen, testgen, datagen, trustgen, decisions, governance
IBM > With generative AI, organizations can get the best of both worlds—automation + humanity.
Combining technologies to meet use cases: AI, DLT, Web3, Digital Twins, Quantum, Chaos, Automation, SXM ( Smart X Management = Services, Trust, Ledger, Data, Quality, Governance, Analytics, Choices, Predictions ),
WHO is your customer now? Purchasing Mgr or Solution Designer? Consumer or PCA ( Personal Consumer Assistant )
IBM focuses on customer service to people now w GenAI - part of today's needs
What does the customer want? Difference between Expectations, Value and Consequences Challenge
How do different customer groups, use cases and scenarios learn?
IBM > Assumption > Value increases when technology meets design. Value explodes when generative AI meets experience.
Reality > Value increases when it's realized ( FACTUR3DT.io ) by setting, meeting value criteria and metrics for a group and use case scenario
What is the road to success? VCRS > Value, Costs, Risks, Success Keys
IBM > success comes from reducing frictions for consumers, workers
IBM > Assumption > ethical journeys that build customer confidence.
Reality > different groups have different definitions of ethics
IBM > Generative AI is disrupting the disruptors—and platform-based businesses have the edge < Agree on Time to Value for Platforms
Reality > ML models and platforms perfrom well when the data is automatically grounded, tested AND verifiable > SLT and Data Governance are Critical
Challenge: How to Mature Value Delivery capability for a community and set of use cases effectively?
IBM > key > Ecosystem partnerships, where solution and service providers combine their skills and capabilities to deliver strategic outcomes,
Reality > VCE ( Value Chain Economy ) succeeds with open engineering where all stakeholders benefit (. Crew sport is the model )
Reality > successful VCE driven by open community strategy with tactical adjustments > align your resources to the community VCE model
IBM > model > With generative AI, technology drives innovation—and the business propels the technology.
Reality > Continuous business innovation for VCE use cases drives all value creation, innovation
Reality > key > how can we future proof VCE for easy, continuous improvement?
IBM > key > build foundational models that will give you such a network effect advantage
Reality > key > design VCE for network effect growing the community for faster success
IBM > assumption > Human values are at the heart of responsible AI.
Reality > key > design VCE find acceptable value goals for the community: understand expectations > id consequences > agree on value set
IBM > spend smart on AI
Reality > engage business stakeholders up front for Opportunity Assessments with ISR > use VCRS to measure value, costs, risks
AI Operations
IBM > Supply chain automation just got an upgrade <. Reality - Agreed but the other SXMs are key to VCE value
Reality > the tools are different but the VCE model and operations metrics are not < KSG network effect 15 years before Amazon < demand forecasting trends
IBM > AI can improve marketing personalization, responsiveness and automation < Agreed
IBM > Generative AI amplifies risk— and resilience for security
Reality > Any Trust domain has risks. AI can be both an offensive and defensive weapon to eliminate threats but needs verifiable data always - https://trust.mit.edu/
IBM > Generative AI can help scale sustainability— ushering in a new era of responsible growth
Reality > need better transparency and governance effectively on the values, policies driving Gen AI solutions in sustainability as well as the processes and outcomes
IBM > SIMPLE THINKING EXAMPLE > What has got to happen over the next 30 years is all of the primary gas and petroleum has got to be removed from [the] mix. At the same time, you’ve got to massively ramp up electricity production. Right now, some of the big bottlenecks are areas where AI can help.
Reality > SMART THINKING EXAMPLE > prioritize both solutions and threats based on their value, impacts short-term and long-term > simple "religious beliefs on the environment are dangerous"
Potential Value Opportunities
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ai-apps-use-cases-msft-azure-ebook-2024.pdf. link
AI apps employ machine learning to continually learn and adapt, using advanced models powered by cloud computing to optimize their results over time. The insights they provide are much more informative and actionable than their non-AI counterparts.
Compare Traditional to AI Apps
| Traditional Apps | Intelligent Apps | Outcomes | |
| Learning and automation | Depends on the code written by the programmer to perform a specific task | Programmed to learn to perform the task by using data, algorithms, computation, and method | Intelligent AI apps can adapt to changing situations and user preferences, while traditional apps are limited by predefined rules and logic |
| Responsiveness | Can only respond to user inputs or requests | Can anticipate user needs and offer suggestions or solutions | Intelligent AI apps are proactive, making them more personalized and engaging than reactive traditional apps |
| Data Capabilities | Designed only to handle certain types of data or inputs | Designed to handle various types of data or inputs and even generate new data or output | AI apps are flexible and creative, allowing users to engage beyond traditional app limitations in ways they didn’t expect |
| Implementation | Typically built on a monolithic architecture and deployed on-premises | Built on the cloud using a microservices architecture | AI apps have enhanced scalability that lets them handle unlimited traffic and data |
Consulting Use Case
To maximize the collective knowledge of its consultants, Arthur D. Little created an internal solution that draws on text analytics and other AI enrichment capabilities in Azure AI services to improve indexing and deliver consolidated data insights. Using this solution, consultants have access to summaries of documents with the abstractive summarization feature in Azure AI Language. Unlike extractive summarization—which only extracts sentences with relevant information—abstractive summarization generates concise and coherent summaries, saving the consultants from scanning long documents for information.
1. Enhanced summarization capabilities speed up consultant workflows
2. Improved security and confidentiality
3. Rapid innovation for products and services
Synthesized Voice for Customer Service Use Case
TIM pioneers synthesized voice service to increase customer satisfaction
Azure AI Services
Azure provides a wide range of tools and services that support AI development:
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