Key Takeaways
- AI investment is rising fast, but many projects struggle to show real business results beyond simple chatbots.
- Most current AI can talk, but can’t easily use or act on a company’s internal data.
- Agentic AI is presented as the next step, designed to connect with business systems, use data securely, and automate tasks.
- This shifts AI from just answering questions to becoming a tool that actively helps operations and saves costs.
- A major hurdle is “data readiness” – accessing and using scattered, messy company data.
- Agentic AI aims to overcome this by integrating directly with workflows and using data as it is.
- Challenges like security concerns and resistance to change can slow down adoption.
- Starting with small, focused agentic AI projects is suggested for better results.
Companies are pouring money into artificial intelligence, but often find it difficult to see a real return on that investment. Many AI tools end up stuck in trial phases, unable to make a significant impact.
A common issue is that many AI solutions are essentially sophisticated chatbots. They can chat, but they can’t easily access or act upon the vast amounts of data stored within a company’s systems.
Agentic AI is being discussed as the next evolution. Unlike chatbots that just respond, agentic AI aims to securely connect with a business’s data, understand it using large language models, and then take action, according to an article from Forbes.
This capability turns AI from a passive assistant into an active part of the workforce. Instead of just answering questions about support tickets, for example, agentic AI could potentially identify an issue, understand the request, fulfill it, and close the ticket automatically.
The goal is to integrate AI directly into business operations, automating workflows and freeing up employees for more complex tasks. This helps companies find real value and cost savings from their AI efforts.
However, one of the biggest roadblocks isn’t the AI technology itself, but getting the data ready. Businesses have huge amounts of data, often scattered across different departments, systems, and formats, including on-premise servers and various cloud platforms.
Agentic AI attempts to tackle this by working directly within existing workflows. It’s designed to pull in different types of data in real-time while adhering to the company’s security rules.
Some businesses are already exploring agentic AI. In sales, it might automate CRM updates. Financial institutions could use it for automating risk checks. Executives might benefit from real-time dashboards powered by AI insights.
Despite the potential, adopting agentic AI faces hurdles. Security and data privacy are major concerns, especially in regulated industries. Ensuring AI agents follow access rules and compliance standards is crucial.
Resistance to change within organizations also plays a role. Some leaders are hesitant due to uncertainty or worries about disruption. However, implementing agentic AI doesn’t necessarily require a complete overhaul; companies can start small.
Experts suggest testing agentic AI in specific, high-impact areas first. By proving its value on a smaller scale, businesses can then expand its use more broadly and strategically.
The thinking is that companies embracing agentic AI now, even incrementally, could gain a significant competitive edge as the technology matures and reshapes industries.
