Agentic AI: A Short Tutorial for Asset and Wealth Managers
Agentic AI is rapidly emerging as a transformative technology with the potential to revolutionize various industries1. This tutorial provides a comprehensive overview of agentic AI, covering its definition, applications, benefits, risks, and ethical considerations.
What is Agentic AI?
AI chatbots use generative AI to provide responses based on a single interaction. A person makes a query and the chatbot uses natural language processing to reply. The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries. Agentic AI systems ingest vast amounts of data from multiple sources to independently analyze challenges, develop strategies and execute tasks like creating territory plans, content for campaigns as well as campaigns themselves, creating lists of advisors, and other tedious and time-consuming tasks.
Agentic AI refers to a class of artificial intelligence (AI) systems designed to operate autonomously and achieve goals with minimal human intervention2. These AI agents can perceive their environment, reason about optimal actions, and execute those actions to fulfill specific goals3. It's important to distinguish between Agentic AI as an overarching concept and AI agents as the individual components within this system that perform specific tasks. By leveraging large language models, massive training datasets, scalable computing power, and connectivity, agentic AI can set its own goals, plan optimized workflows, make nuanced decisions, and adapt to changing circumstances.
Key characteristics of agentic AI include:
● Goal Orientation: AI agents are designed to achieve specific goals, and they can plan and execute actions to reach those goals.
● Autonomy: AI agents can operate independently, making decisions and taking actions without continuous human oversight.
● Adaptability: AI agents can learn from their experiences and adjust to new data and changing situations.
● Reasoning: AI agents can analyze information, evaluate options, and make informed decisions.
Applications in Sales, Marketing, and Management
Agentic AI has the potential to transform sales, marketing, and management functions across various industries. Here are some examples of how businesses can leverage agentic AI:
● Personalized Customer Interactions: Analyze customer data to provide personalized product recommendations and support, improving customer satisfaction and driving sales.
● Lead Generation and Qualification: Identify and qualify potential leads, saving sales teams time and resources.
● Sales Automation: Automate repetitive sales tasks, such as scheduling meetings, sending follow-up emails, and updating CRM systems.
● Personalized Marketing Campaigns: Analyze customer data to create targeted marketing campaigns, improving engagement and conversion rates.
● Content Creation and Optimization: Generate marketing content, such as social media posts, blog articles, and email newsletters, and optimize it for different channels and audiences.
● Market Research and Analysis: Analyze market trends and competitor activities to provide valuable insights for marketing strategies.
● Performance Monitoring and Analysis: Track key performance indicators (KPIs) and provide real-time insights into sales and marketing performance.
● Predictive Analytics: Forecast future sales and marketing trends, helping managers make informed decisions about resource allocation and strategy.
The applications of agentic AI are vast, ranging from personalized customer interactions to market research and analysis. This range of use cases directly translates into significant benefits for businesses.
Revenue Generation: Agentic AI enhances customer experiences through personalized interactions and continuous support, leading to increased customer satisfaction and potentially higher sales. Its data analysis capabilities provide insights that can inform product development, marketing strategies, and sales targeting, driving revenue growth.
Cost Management: Automating tasks through agentic AI reduces operational costs by minimizing the need for human intervention in routine processes. For example, agentic AI can address discrepancies in invoice processing without human involvement, eliminating manual effort and reducing errors, thus saving costs associated with corrections and delays.
Efficiency Gains: Agentic AI significantly improves efficiency and productivity. By automating tasks, it frees up human personnel to focus on higher-value, strategic work. LLMs within agentic AI enhance natural language understanding and support multi-step reasoning, enabling more complex automation and further boosting efficiency. The increased adaptability and workflow optimization facilitated by agentic AI also contribute to greater operational efficiency. Finally, agentic AI increases scalability by managing large task volumes, handling increasing workloads without a proportional increase in human resources.
Where Does Agentic AI Go From Here?
Agentic AI is a rapidly evolving field, with ongoing research and development focused on improving its capabilities and addressing its limitations1. Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. Several areas hold significant potential for future innovation:
● Specialized AI Agents: We are moving from single, powerful AI models to networks of specialized AI agents . This shift mirrors the evolution of human organizations, where specialized roles and expertise lead to greater efficiency and effectiveness. Expect to see the development of AI agents tailored to specific industries (healthcare, legal, manufacturing) and business functions (marketing, sales, customer service).
● AI Agent Marketplaces: Similar to app stores for mobile devices, we can anticipate the emergence of marketplaces where organizations can discover, purchase, and deploy specialized AI agents . This will democratize access to agentic AI and accelerate its adoption across various sectors.
● Self-Organizing AI Networks: Future AI systems will be able to autonomously form temporary coalitions of agents to solve complex problems. This collaborative approach will enable AI agents to leverage their individual strengths and expertise to tackle challenges more effectively.
● Enhanced Human-AI Collaboration: As AI agents become more sophisticated, they will be able to collaborate more effectively with humans. This could involve AI agents taking on routine tasks, freeing up humans for more creative and strategic work, or AI agents providing expert advice and support to human decision-makers.
This is a powerful technology with the potential to transform various industries. By understanding its capabilities, benefits, risks, and ethical considerations, businesses can leverage agentic AI to improve efficiency, productivity, and customer experiences. As research and development continue, we can expect to see even more innovative applications of agentic AI in the years to come.
Looking ahead, the future of agentic AI holds both exciting possibilities and significant challenges. Emerging trends suggest that AI agents will become increasingly integrated into our daily lives, automating tasks, assisting with decision-making, and even interacting with us in more human-like ways.
