May

Microsoft AI products

                                                 

Microsoft product strategy has always been and still remains that of ‘zero alternative’. Their ultimate policy is for their customers to have no choice but to embrace only Microsoft products. Consequently they created and are offering products and solutions in (almost) every segment of IT enterprise and consumer market, including, but certainly not limited to, their own data base, their own cloud services, operating system, office tools, programming language, and many more.

Not only do Microsoft offer wide variety of products, they tie them up together in a unified ecosystem that makes it easy for components to connect and interact. At the same time, this ecosystem is hostile to non-Microsoft products.

Microsoft strategy for the burgeoning, fast growing AI segment is similar:

Create products to address all parts of the AI market, add them to the ecosystem to ensure easy compatibility from within and difficulty of use from outside.

Currently the products on offer are:

- Microsoft AI engine, called LUIS. It is supposed to compete with other major industrial AI systems such as IBM Watson, and has similar training methodology. It offers webhook interfacing via endpoints.  

- Microsoft chatbot building platform, called, surprisingly, Microsoft Bot Platform. It addresses the popular demand for easy chatbot design and provides seamless connectivity with main user interfaces, such as web interface, SMS, mobile, and messaging platforms.

- In addition Microsoft offers their own messaging platform in Skype.

The main advantage of  using Microsoft AI products is the built-in connectivity with user interfaces.

The main disadvantage is in their ‘zero alternative’ policy - once you’ve chosen a Microsoft product you are likely will be forced to choose only Microsoft products for the duration of your project.

 

Interested in reading more? Check out our other blogs:

AI unmasked: What is conversational AI



Conversational AI is the use of speech-based assistants (such as chatbots, voicebots) in order to create an easier and more user-friendly experience for the customers.

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Artificial Intelligence Chat Is Evolving Faster Than IVR

                                                         

Although it doesn’t feel like all that long ago, way back in the 90s one of the most important factors to a call center’s success was the ability to route a customer to the right support agent with the IVR (Interactive Voice Response). Countless hours were spent identifying the most efficient call routing patterns and expert agent capabilities to ensure that your request reached the right person quickly. This technology is still widely used today and there are still teams in the largest companies programming IVR systems to accomplish pretty much the same goal.

As the standard for customer support evolved there have been many attempts to improve the function and the customer experience associated with IVRs to reduce hold times and provide more relevant support faster. Even today some companies will use their IVR system as a way to keep a customer on hold, rather than provide a solution, when agents are inundated with calls.

For those of us who’ve worked in the voice industry for some time, we’ve seen first-hand the attempts to accomplish a customer’s need before reaching an agent. First there was expert agent routing that delivered your call to the agent most qualified to help you. Then came advances in voice recognition, which today has evolved to be a very effective tool to increase containment rates and deflect calls from reaching a live agent. My two favorite examples of the power of voice recognition are Cox Communications and Capital One, two examples of great voice recognition and routing.

Our memory, however, is short. It wasn’t so long ago that we were all pulling our hair out punching digits into the phone or constantly repeating “agent”, “Agent”, “AGENT”, AGENT!!!!!”.

Whether it was a limit of computational power or the sheer cost of developing and implementing advanced call center technology, it took decades for phone systems to be able to front end the customer support process as efficiently as they do today. Thankfully we all survived to see it without boiling over from the hypertension usually associated with calling with a customer service department.

Bad customer experience is definitely not the case with Chat Artificial Intelligence (Chat AI). While we seem to hear about the shortcomings of Chat AI like the disconnected conversations and the robotic like responses, these experiences are usually the product of Chatbots with limited AI functionality or early stage deployments. The increases in both computational power and the massive advancements in machine learning are driving excellent customer experiences that improve over time.

When was the last time you heard of technology actually performing better, on its own, without a ton of additional development work or continuous updates? Well, that’s the case with Artificial Intelligence. Like a person, the more experience it has interacting with customers and information, the better it performs with little need to be manually improved or fine-tuned.

Today, AI Chat can be used to answer a large majority of customer requests and because Artificial Intelligence learns as it is used, customers prefer to interact through AI chat to avoid all of the frustrations commonly associated with calling a contact center agent. 

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