In our previous articles, we explored how Generative AI can save you time by handling creative and communication tasks. Now, let's talk about the next step in this journey: Agentic AI.
Think of Generative AI as a brilliant writer or a creative artist. Agentic AI, on the other hand, is like a smart, highly organised personal assistant. Instead of just creating content, it's an AI system that can perform a series of tasks all on its own, learning and adapting as it goes. For instance, it could automatically follow up on emails, update a spreadsheet, and send a notification to your team, all as part of one workflow.
By delegating these tasks to Agentic AI, your team can focus on building relationships, solving complex problems, and providing that invaluable human touch where it matters most.
The Foundational Challenge: Data Quality and Consolidation
While the hype around Agentic AI is growing, the reality is that its effectiveness hinges on two critical factors: the quality of your data and its organisation.
I like to think about this like a hoarder's house. Of course, you might find a lot of things somewhere in the house, but first, it's hard to find anything, and second, what you do find probably won't be in good shape. Data and information are the same. We need to organise, clean, and store them in a systematic way to effectively leverage both Generative AI and Agentic AI.
Why Data Quality and Consolidation are Foundational
AI agents, by their nature, are "doers" that need to retrieve and act on information to complete a task. If the data they access is inconsistent, incomplete, or scattered across multiple, disconnected systems, the agent will fail.
- Garbage In, Garbage Out: An AI agent is only as good as the data it works with. Poor quality data (inaccurate, duplicated, or missing information) leads to poor decisions and unreliable outcomes, undermining the very purpose of automation.
- System Integration: Agents often need to pull information from various sources (CRM, spreadsheets, databases, etc.). If these systems are not consolidated or lack clean APIs, the agent cannot access the data it needs to function, making implementation a complex and costly project.
Your First Step: Getting Your Data House in Order
My recommendation is to start looking at all the data and information you have in your company and begin the process of "cleaning it" and storing it in one place as much as you can. The sooner you do this, the faster and easier you can get on the Agentic AI bandwagon.
This is not just about adopting a new tool; it's about building a solid foundation that allows these powerful systems to work for you, not against you.
Tip: If you want to know more about Agentic AI, ask AI!
I'd love to know: where do you currently store your business information and data? E.g., Excel, Databases, CRM?