Fundamentals of Agentic AI - Business Implications and Ethical Insights
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Understanding the Evolution of Agentic AI
What is agentic AI?
- Generative AI responds to prompts
- Agentic AI operates autonomously and makes decisions independently to achieve goals
How does agentic AI broadly function?
- Perception (sensor and data input)
- Decision-Making (analyze the input)
- Autonomous Action (act on predefined parameters to accomplish a task)
- Learning/Adaptation (from past experiences/actions)
Historical improvements and limitations
- Reinforcement Learning (receive feedback from actions)
- Convolutional Neural Networks (process grid-like data, similar to the brain)
- Superficial Processing (lack of awareness to contextual details)
Business Applications of Agentic AI
Impacts on processes
- Automate routine/repetitive tasks, e.g.
- purchasing: monitor inventory -> predict demand -> place orders
- customer service: answer FAQs, handle returns
- Integrate processes, e.g.
- coordinate schedules
- set up meetings
- assign tasks
Changing team dynamics
- For example, project managers will have to learn to assign tasks to AI and monitor their performance.
Varying implications across industries
- Healthcare: Analyze images, recommend treatments, monitor patient health
- Retail: Recommend deals, manage inventory, optimizing supply chain
- Finance: Detect fraud, manage risk, recommend investment strategies
Agentic AI Theoretical and Ethical Considerations
Realized versus expected applications of agentic AI
- Today:
- Energy: Adjust power flow, balance supply/demand
- Agriculture: Planting, watering, harvesting crops, applying fertilizer where needed, adjust to weather conditions
- Future:
- End-to-end development of complete software/apps, designs
- Analyze big data, predict trends, optimize outcomes
Ethical concerns of implementing AI into business practices
- Undue Semantics: When AI fails to understands the context of the processed data
- Blast Radius: Understanding the impact of an error in an AI system (think of healthcare or finance)
- Principle of Least Privilege
Planning for AI ethics
- Develop clear ethical guidelines
- Stresstest AI systems under various scenarios
- Who is responsible for the decisions made by the AI
- Who will monitor the results
- Ensure AI systems are transparent in their decision-making processes
- Ensure AI tools are aligned with societal norms
- Testing and monitoring
- Informed accountability: Both developers and users should be aware of how AI makes decisions, what could go wrong, and what to do if that happens
Future Outlook on Agentic AI
How agentic AI may evolve in the next ten years
- Managing entire projects in complex environments
- Enhanced human-AI collaboration (seen as coworkers instead of tools)
- Improved voice-to-text
- Specialized, domain-specific agents
Upcoming challenges with rapid innovation
- Keeping up with technological advancements
- Organisations must encourage continuous learning and agility
- Ensuring data privacy and security
- Protect against cyber attacks
- Workforce displacement
- Organisations should reskill/upskill employees
- May be more disruptive to our sense of self than actually causing unemployment
Strategic Planning for Agentic AI Implementation
Strategic planning for AI integration
- Assess business needs
- Where can agentic AI bring most value: operations, customer experience, innovation,…?
- Which repetitive tasks overwhelm us?
- Create roadmap
- Set short-term goals
- Start with small projects
- Training, workshops etc. for employeers
- Clear communication about how AI will benefit both employers and the organisation
Balancing innovation with practicality
- Set realistic goals
- Measurable objectives
- what does success look like
- what outcomes are we aiming for
- how will we measure progress
- Measurable objectives
- Start with small projects to be able to afford mistakes
- Start with a single department or project
- Encourage employees to explore new AI applications