--- title: Fundamentals of Agentic AI - Business Implications and Ethical Insights kind: note description: Link 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 E.g. Tesla… words: 530 readingMinutes: 2 created: '2024-12-26T00:00:00.000Z' updated: '2024-12-26T18:41:21+01:00' website: https://www.linkedin.com/learning/fundamentals-of-agentic-ai-business-implications-and-ethical-insights --- ## Link ## 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 - E.g. Tesla "self-driving"; Siri scheduling meetings ### 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 - Start with small projects to be able to afford mistakes - Start with a single department or project - Encourage employees to explore new AI applications ### Next steps in planning for agentic AI