Let your users ask “What’s my next step?” – a very useful AI addition to your apps

One example of how #AI can make it easier for your staff, customers or suppliers to interact with your software tools is to add a combined”Next Step / Tell me what you want to do” facility.

This uses natural language processing (NLP) combined with knowledge of who the user is (and what their role is, e.g. whether they are a member of staff, a customer, or a supplier, or a user with admin rights for example) and the context (which page or part of the app they are on, and what data they have stored in the system), to add two powerful new ways for the user to interact (with minimal training) with the app:

What’s my next step?

On any page, simply clicking the Go button asks the system “What’s my next step?”.  The system then look intelligently at the user’s identity, role, data and location within the app and makes one or more suggestions as to what the user could usefully do next to make the most of the app.

Here are a couple of examples, taken from InQA’s WebPocketMoney application (referred to in this previous post).

Some reasons why your company/organisation should start using AI now!

AI built in to the heart of user interfaces

Within a few short years, some companies and organisations will have adopted Artificial Intelligence (AI) in at least one part of their work: interfacing with their customers.  (I’m using customers in the widest sense of the word: it could be students in education, or patients in healthcare for example).

Imagine the following:

  • Instead of having to log in to a website or an application, the application simply recognises the user’s face or voice
  • Instead of having to click on a menu to navigate the app, the user can just talk to it, either by speaking or using a chatbot type interface.
  • Instead of calling customer service (and being told “you are currently number two in a queue” or “Our business hours are 0900 to 1700 Monday to Friday, please call back during those times” ), they can get an immediate response (24 hours a day, 365 days a year) from a chatbot.

If customers have a choice between interacting with one organisation in that way, or another in the more traditional way, I think they will vote with their feet.

It’s a straightforward matter of economics

Why adding chatbots makes financial sense for your organisation

Adding a chatbot to your organisation’s website can provide a more interactive experience for your users while at the same time reducing demands on your staff’s time. Chatbots can help to:

  • free your team to deal with more complex enquiries or tasks
  • speed up employee training by providing a very accessible and intuitive source for staff to obtain information internally
  • automate complex workflows (such as providing quotes or booking services)
  • provide availability 24/7, 365 days a year
  • provide an alternative user interface for your apps than the traditional point and click menu/button system

Microsoft Teams definitely DOES allow Guest users, and this is fantastic!

Further to my previous post about this, we have managed to get this working successfully now with a variety of guest users (with email addresses which are outlook.com, or associated with Azure Active Directory or Azure ADB2C accounts).

Why is this so useful? Because it means that in order to collaborate with users outside your organisation (including being able to share files, hold online conversations, video chats, do online voting within your team), all you need is one of the following Office 365 subscriptions (see this Microsoft link)

Guest access is included with all Office 365 Business Premium, Office 365 Enterprise, and Office 365 Education subscriptions. No additional Office 365 license is necessary. Guest access is a tenant-level setting in Microsoft Teams and is turned off by default.

This should not only be much cheaper than alternative collaboration software (e.g. box.com) but also allows your staff and guest users to use tools that they will increasingly become familiar with (Office 365).

 

 

 

 

Why do data scientists use R and Python, as opposed to other languages like C#?

As a “proper” programmer, used to programming in heavy duty, compiled languages like C# (and before that C++ and C), my reaction on discovering during my Data Science journey that R and Python are heavily used by data scientists was: why??

Why would anyone use an interpreted language, which is therefore bound to be slower, and why would anyone go to the trouble of using yet another language when there are perfectly good compiled languages around like C#, F# and VB.net?

The answer seems to be partly that R and Python are free (open source), and also because R and Python have excellent visualisation tools, which the other languages currently lack.