Key Points

1. AI fits many use cases in software today
2. code assist
3. testing
4. requirements automation
5. RPA
6. deployments

References

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

AlphaCodium-ai-generate-activity-test-2024-gpt4

There's a new open-source, state-of-the-art code generation tool. It's a new approach that improves the performance of Large Language Models generating code.



The paper's authors call the process "AlphaCodium" and tested it on the CodeContests dataset, which contains around 10,000 competitive programming problems.



The results put AlphaCodium as the best approach to generate code we've seen. It beats DeepMind's AlphaCode and their new AlphaCode2 without needing to fine-tune a model!



I'm linking to the paper, the GitHub repository, and a blog post below, but let me give you a 10-second summary of how the process works:



Instead of using a single prompt to solve problems, AlphaCodium relies on an iterative process that repeatedly runs and fixes the generated code using the testing data.



1. The first step is to have the model reason about the problem. They describe it using bullet points and focus on the goal, inputs, outputs, rules, constraints, and any other relevant details.



2. Then, they make the model reason about the public tests and come up with an explanation of why the input leads to that particular output.



3. The model generates two to three potential solutions in text and ranks them in terms of correctness, simplicity, and robustness.



4. Then, it generates more diverse tests for the problem, covering cases not part of the original public tests.



5. Iteratively, pick a solution, generate the code, and run it on a few test cases. If the tests fail, improve the code and repeat the process until the code passes every test.



There's a lot more information in the paper and the blog post. Here are the links:



• Paper: https://lnkd.in/g9zkc_AK

• Blog: https://lnkd.in/g_wx88xj

• Code: https://lnkd.in/gJAxtgzn

https://github.com/Codium-ai/AlphaCodium



I attached an image comparing AlphaCodium with direct prompting using different models.

We tested AlphaCodium on a challenging code generation dataset called CodeContests, which includes competitive programming problems from platforms such as Codeforces. The proposed flow consistently and significantly improves results. On the validation set, for example, GPT-4 accuracy (pass@5) increased from 19% with a single well-designed direct prompt to 44% with the AlphaCodium flow.

Many of the principles and best practices we acquired in this work, we believe, are broadly applicable to general code generation tasks.

Azure AI Use Cases

ai-apps-use-cases-msft-azure-ebook-2024.pdf.    link

Consulting Use Case

Synthesized Voice for Customer Service Use Case

Azure AI Services

Azure provides a wide range of tools and services that support AI development:

• Azure OpenAI
  Service Azure OpenAI Service provides access to powerful language models from OpenAI, such as GPT-4, GPT-3.5 Turbo, Codex, DALL-E, and Whisper, that perform tasks such as content generation, summarization, semantic search, and natural language to code translation. Enterprises use this service to improve digital customer experience by adding chatbot/generative AI capabilities to customer-facing solutions with Azure AI services and Azure OpenAI.
  
  
• Azure AI Search
  Azure AI Search lets enterprises build rich search experiences over their private and heterogeneous data sources in web, mobile, and enterprise applications. Azure AI Search utilizes advanced deep-learning models to provide contextual and relevant results. It also supports features such as semantic search, knowledge mining, summary results, faceting, suggestions, synonyms, geo-search, and more.
  
  
• Azure AI services
  Azure AI services is a suite of out-of-the-box and customizable AI tools, APIs, and models that help modernize business processes faster. Azure AI services include services for vision, speech, language, decision, metrics advisor, immersive reader, and more. Enterprises use these services to build intelligent applications that automate document processing, improve customer service, understand the root cause of anomalies, and extract insights from content.
  
  
• Azure Kubernetes Service
  Azure Kubernetes Service simplifies deploying managed Kubernetes clusters in Azure by offloading the operational overhead to Azure. Kubernetes is a popular open-source platform for orchestrating containers that run applications. Enterprises use AKS to run their containerized applications at scale with high availability and performance.
  
  
• Azure Cosmos DB
  Azure Cosmos DB is a globally distributed, multi-model database service that offers single-digit millisecond response times, automatic and instant scalability, and guaranteed speed at any scale. Azure Cosmos DB supports multiple data models including document, key-value, graph, and column-family data. It also supports multiple APIs, such as native NoSQL, MongoDB API, PostgreSQL API, Apache Cassandra API, and more. Enterprises use Azure Cosmos DB to store and query their data in the most suitable model and API for their application needs

Potential Challenges

Candidate Solutions

Step-by-step guide for Example

sample code block

Recommended Next Steps

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