Newer doesn’t always mean better.
Whether it’s a parent convincing a child that they don’t need another new toy because they hardly play with the 30 they already have, or the owner of a thrift store explaining that the cracked vase and dogeared paperback book they’re trying to sell you have ‘character’ –everyone has heard some variation of the phrase throughout their lives.
And that’s because there’s wisdom in it.
While the latest shiny new gadget, fashion must-have, or top-of-the-range car certainly draws the eye, they are more often than not unnecessary and unneeded.
So, where does agentic AI fit into all this?
We aren’t suggesting that the tech taking the customer service and experience space by storm isn’t impressive, far from it; it’s more the case that it might not be the right fit for every organization.
Unfortunately, figuring out whether or not agentic AI is the best option for your contact center or customer service department can be difficult.
According to Sabio’s Chief Innovation Officer, Stuart Dorman, this is partly due to the fact that many tech vendors are pushing agentic AI as a one-size-fits-all solution.
This can make it difficult to cut through the hype, particularly for companies that are less experienced and knowledgeable about the differences between agentic AI and other AI offerings.
For Dorman, at its core, agentic AI requires three things: a conversational interface, reasoning capability, and the autonomy to act on a customer’s behalf.
He said: “Some businesses aren’t quite ready for that full stack – but that doesn’t mean they can’t start somewhere.”
The Building Blocks to AI Success
Like many tech implementations, the extent to which an organization can reap the rewards is often determined by its pre-existing foundations.
In Dorman’s experience, to fully take advantage of agentic AI, businesses must have the following:
- Strong data infrastructure
- Accessible systems via APIs
- A joined-up view of the customer journey
“If a company doesn’t have those foundational elements – say their data is siloed or inaccessible – that doesn't mean they can't use AI, it just means the scope will be limited,” he explained.
Indeed, Dorman is particularly keen to emphasize the importance of data, which he describes as the “fuel for AI” and can be broken down into four areas: internal data, customer data, task completion, and consumer sentiment.
Before a company seriously considers implementing agentic AI solutions, it needs to ensure that its internal data and knowledge are accurate and up-to-date. Otherwise, the AI could produce incorrect information.
“That’s the simplest way it can go wrong,” Dorman explained.
From a customer data perspective, it is crucial that the systems used by organizations to store the data, such as CRMs and ERPs, are accessible.
In addition, a company’s systems must be designed to allow AI to take action and access the necessary information to complete tasks.
