By Mahesh Maan
When we steer the conversation towards cloud technology
There are obviously important considerations, but they are the basics—well understood and widely adopted by businesses today.
Let’s take a look beyond the obvious and at the future, focusing now on the new synergies emerging between cloud technology, AI, and big data analytics.
In the past, various departments in a company passed data and decisions like a baton in a relay race—each waiting for the other to finish its part. With real-time cloud analytics and AI, the company now functions more like a single organism, seamlessly integrating feedback and implementing changes. For instance, when a customer complains about a delivery delay, the system could instantaneously analyze similar feedback, identify the logistical bottleneck, and prompt the supply chain team to take corrective action—all in the span of a few days.
But speed isn’t the only gain. Innovation, the lifeblood of any competitive business, gets a substantial boost. Take a pet food company. They use AI in the cloud to sift through public vet studies and find something missing: good food options for older dogs with arthritis. Armed with this insight, which their competitors haven’t even sniffed out, they quickly fill the gap with a new product line. Forward-thinking companies such as this will use real-time data not just to tweak existing products but to envision new ones.
Any innovative activity is a cycle of testing and refining prototypes. The faster you do these, the faster you can validate, refine, and bring your ideas to market
There is also ample opportunity to leverage these latest advances in cloud and AI technology for innovation in the company’s internal operations. It is now possible for language models within a company’s cloud to analyse internal communication to identify problems nobody would otherwise spot. A company might find, for example, that client dissatisfaction emails often follow internal discussions about resource allocation, pointing to a lack of staff as the root cause for the failed projects.
Now consider another everyday challenge within an organization – retrieving information. Employees often find themselves wading through an ocean of documents, emails, and databases to fish out a single piece of crucial information. It’s a drain on productivity. By integrating cloud and AI, you effectively create a smart, internal search system. Imagine a savvy librarian who knows exactly where that elusive report or project file resides in your cloud storage. You ask, and you receive—swiftly and precisely.
Emerging capabilities like ChatGPT can elevate this process further by understanding context and nuance in queries, ensuring an even more targeted search. Think of it as upgrading your smart librarian to a subject-matter expert who not only knows where things are but also understands what you’re truly looking for.
These capabilities are also making it possible for any teams to ‘talk with the data’ regardless of whether they have any data manipulation skills or not. Intelligent AI models residing in the cloud can refer to the company’s databases behind the scenes to answer a question asked by an employee during a natural conversation.
Automated customer interaction is another lucrative innovation that AI and cloud technology promise to all kinds of businesses. There are already cloud platforms today that businesses can use to assist their customers with minimal or no dedicated customer support staff. These customer chat bots can hold realistic conversations with the customers, can understand their issues, pull information about orders/policies and provide meaningful resolutions with no human interventions.
In conclusion, the new frontier in cloud technology isn’t just about doing things faster or cheaper. It’s about doing things so well that it wasn’t possible before and opening up avenues that couldn’t be imagined earlier. Companies eager to ride this wave of innovation will not only future-proof themselves but will set new standards for what business operations, customer engagement, and product refinement should look like.
The author is head, Data Science, GreyB
(With insights from Cointelegraph)