Artificial Intelligence (AI) has driven digital transformation across various industries, accelerating innovation and improving efficiency. Among the many types of AI, Generative AI and Agentic AI have been in the frontline of transforming operations. Understanding the distinctions and synergies of these AI types can help organisations make informed decisions about leveraging them effectively.
What are the differences between Generative AI and Agentic AI?
Generative AI and Agentic AI each offer distinct capabilities that serve different purposes. Generative AI refers to AI systems that create new content, whether text, images, music, and more, based on the data they have been trained on. These models learn patterns and structures from existing data and use that knowledge to generate new, original content. Well-known examples of generative AI include Microsoft CoPilot or GPT-4.
On the other hand, Agentic AI refers to AI-powered agents that perform tasks autonomously on behalf of users or processes. These agents are designed to understand the nature of tasks and act accordingly, providing support across various organisational roles, teams, and functions. Unlike generative AI, agentic AI focuses on automating complex workflows and decision-making processes.
Generative AI
- Content generation: Automating the creation of marketing materials such as social media content or articles.
- Design and art: Generating unique designs or visual content for branding and marketing.
- Entertainment: Developing scripts, stories, and dialogues.
- Personalised recommendations: Generating personalised product recommendations based on user preferences and interactions.
Agentic AI
- Organisation process automation: Streamlining administrative tasks such as data entry, invoicing, and scheduling.
- Customer support: Automating responses to customer inquiries, handling common issues, and providing real-time assistance.
- Supply chain management: Optimising inventory management, logistics, and demand forecasting.
- Financial services: Automating fraud detection, risk assessment, and transaction processing.
Combining Generative AI's creativity with Agentic AI's efficiency
While Generative AI and Agentic AI serve different primary functions, they can intersect and complement each other in driving even greater value for organisations:
Enhanced customer experiences
Combining generative AI’s content creation capabilities with agentic AI’s task automation can create highly personalised and efficient customer interactions. For example, generative AI can craft tailored responses or marketing materials, while agentic AI automates the delivery and timing of these communications based on customer interactions and preferences.
Data-driven decision making
Agentic AI can automate data collection and analysis processes, providing valuable insights that generative AI can use to create more relevant and targeted content. This synergy enhances the overall impact and effectiveness of AI-driven initiatives.
Streamlined content management
Generative AI can produce a wide range of content, from articles to marketing copy, while agentic AI can manage the distribution, scheduling, and optimisation of this content across various channels, ensuring it reaches the right audience at the right time.
But where do you start?
Now that you have a better understanding of Generative AI and Agentic AI, it’s time to explore how to integrate these technologies into your organisation.
Assess your organisation’s needs and goals
Have you thought about what your organisation could achieve with AI? Whether it’s boosting employee productivity, improving products, or reducing operating costs, AI can offer significant benefits. In fact, the Digital Landscape Study 2024 by Ecosystm found that these are the top three drivers for AI implementation among Australian organisations. Identifying where AI can add the most value to your organisation is crucial for guiding your AI strategy effectively.
Evaluate data readiness
AI technology heavily relies on data. High-quality proprietary data enhances AI’s ability to deliver valuable insights and efficiencies. According to McKinsey, organisations with strong data practices are 2.6 times more likely to report successful AI implementations.
Start small, scale strategically
Implementing AI on a limited scale allows you to measure its impact and make necessary adjustments before scaling up. For example, according to the same Ecosystm study, organisations have successfully started deploying AI primarily for data analytics, IT documentation, and IT and customer support. This targeted approach helps in understanding the potential value and fine-tuning AI applications before broader implementation.
Generative AI and Agentic AI each offer unique capabilities that can drive significant value for organisations. By understanding their key differences, and use cases, you can make informed decisions about which type of AI to leverage for your specific needs. Whether you’re looking to create innovative content or streamline operations, both generative and agentic AI have the potential to transform your organisation and drive success.
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