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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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- Use GenAI tools to improve the overall skill set of the workforce.
- Position GenAI as a force multiplier in solving both new and perennial problems.
- Meet unconventional threats by creating new roles to mitigate risk.
Savvy executive leaders must broaden the horizons of IT professionals and business teams alike. They will stress the need to experiment with GenAI to learn its possibilities. They will embrace the risks of using GenAI so they can reap its rewards
Genai is opportunity and risk
GenAI breaks that mold. The popularity of ChatGPT has spurred many to action well past technology innovation. The existence of large language models (LLMs) covers a broad range of creative capabilities that keep building more excitement. But opposite that excitement is healthy skepticism and concerns about risk. GenAI can produce hallucinations or create suboptimal responses, it is little understood, and it creates both legal and ethical dilemmas.
Predictions
Smart Objects add AI Agent capability
Economics of AI Agents - tokenomics - Lisa Tan
Expect tokenising AI agents will only accelerate. I believe AI-agent tokens are part of an infrastructure mechanism set up, and its token economics model and structure isunlikeany other consumer token design. Let’s understand more about the AI token-ecosystem structure and in the following weeks, we will dive into case studies of how AI agents and tokens work.
1. AI Token-Ecosystem Mapping
What is the protocol or system’s purpose?
AI agents are autonomous digital entities designed to execute tasks, make decisions, and generate economic value. They redefine workflows by taking on roles traditionally reserved for humans or software tools, often improving efficiency and scalability.
Who are the participants, and what roles do they play?
- AI Agents: Perform tasks autonomously (e.g., customer service, workflow management).
- Users: Consumers or businesses that deploy AI agents for tasks like financial analysis, content creation, or logistical coordination.
- Developers: Create and train AI agents with domain-specific expertise, essentially programming them with high-value “skills”.
- Blockchain Infrastructure: Provides trustless, decentralized systems for payments and interactions.
2. Value Creation
How do AI agents create value?
AI agents are not just tools; they are economic actors. As in the past posts, I talked about how AI agents redefine what “work” is. As of today, they are mainly focused on the cost of work. That is a value in itself. But moving forward, it will revolutionise value creation beyond our imagination.
- Efficiency Gains: Automate repetitive and complex workflows (e.g., supply chain optimisation, customer support).
- Innovation: Generate solutions in research, design, and strategy beyond human capabilities (e.g., personalised marketing strategies).
- Personalisation: Tailor experiences to individual user preferences in areas like healthcare or education. (e.g. a personal health doctor-bot that tracks your activity and food intake)
- Revenue Generation: Operate as creators in the digital economy (e.g., influencers, ad negotiators).
3. Incentives and Mechanisms
If you are thinking about incentives in the traditional consumer sense, think again. AI Agents are machines, and the way to think about economy design in an agent-based system is similar to that of tokens in a layer 1 protocol. Focusing on economic efficiency and encouraging, to some degree, economic integrity of data and economic security of network.
What mechanisms encourage participation?
- Token Rewards: Users and agents are incentivised through token systems, allowing them to earn rewards for completing tasks. These tokens can be used to access premium services, pay for additional resources, or trade in secondary markets.
- Staking and Governance: Token holders can stake their tokens to vote on agent behaviour (e.g., prioritising sustainability or innovation in workflows).
- AI Agent Cooperation: Agents interact, competing or collaborating for optimised outcomes (e.g., AI influencer agents negotiating ad deals with corporate AI marketing agents).
4. Transactions and Tokenomics
What is the medium of exchange?
Currently, AI agents rely on crypto tokens as a medium for payment and resource access. Unlike traditional software that requires human approval for transactions, AI agents transact autonomously through crypto wallets. There are also cases where revenue is used to engage in buyback and burn mechanisms to reduce total circulating supply, hence increasing the value of these tokens.
How do transactions occur?
- Agent-to-Agent Payments: Agents trade resources or services autonomously, paying each other using tokens. For instance, one AI agent might purchase processing power or access data from another agent.
- Tokenisation of Services: Each service an AI agent provides can be tokenised, creating a seamless economy for decentralised services.
5. Governance
Who makes decisions in the system?
- Decentralised Autonomous Organizations (DAOs): Govern the behaviour of AI agents and the broader ecosystem. Stakeholders vote on major upgrades or changes.
- Embedded Logic in Agents: Agents are pre-programmed with decision-making protocols that align with user or system objectives.
6. Revenue Models
How do AI agents make money?
- Task-Based Revenue: Charging for services (e.g., content creation, customer support).
- Profit from Optimisation: AI agents save money by optimising resource use (e.g., energy or logistics).
- Ad Revenue: Acting as influencers, agents generate income from ad placements.
- Data Monetisation: Selling insights from anonymised datasets.
- Subscription Models: Offering premium services like advanced analytics or priority task handling.
7. Sustainability
How is economic equilibrium maintained?
- Bonding Curves: Ensure stability by dynamically adjusting token supply and demand.
- Reinvestment of Earnings: Tokens earned by agents are reinvested into acquiring more resources or improving functionality.
- Feedback Loops: AI agents continually learn from their environments, improving their ability to create value.
Potential Challenges
Candidate Solutions
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