A2P Messaging & AI

A2P SMS is plain and simple, yet most widely used due to it simplicity. Many a times it is chosen as a compliance guideline or as a most cost effective and efficient medium. The very important feature of  A2P SMS is - it is neutral and run under GSMA / ITU guidelines. 

With RCS and OTTs influx A2P SMS feels heat as those platforms are more sophisticated and there is AI (Artificial Intelligence) integration enabled.

For A2P SMS - AI can be added at the platform level (at the operator end) to avoid spam, virus and unnecessary interconnect charges. It can also prevent the use of grey routes abusing P2P channels.

Use of AI for SMS at the user level can be done using some primary application like 2FA, some apps to identify the spam / frauds, data collection on Virtual Long Numbers or Short Codes etc. There could be real time interaction and post message analysis on the collected data.

As mobile messaging becomes highly prevalent Brands wants to analyse the data collected during conversation using A2P SMS (and other sort of messaging) and make it interactive using AI. Brands want their customers to remain in constant touch with conversational messaging. 

Around 2016 Chatbot came into the existence, though the technology was very old but integration with OTTs, RCS and A2P SMS helped Brands to make the conversation interactive. Chatbots have been successful till some extent.  The need of more human like interaction led to further progress of Chatbots.

Around 2019 NLP (Natural Language Processing) was introduced for Contextual understanding and action. NLP was only  possible with AI. NLP was very good till some level and further the success was depended on the cultural Affinity, language recognition and demographics of the user.

Between 2020-2021 new ways made Conversational AI or Applied AI possible with the use of multiple technologies behind automated messaging and speech-enabled applications and offered human-like interactions between Apps and users by reading Text, Identifying the language, analyzing the context and responding with the right answer (or options to choose from).

Today we see Google’s RCS, WhatsApp Business Messaging, Telegram and other services have already made Conversational AI possible. Many Software companies are also developing their plugins as Add-ons to OTT with extended features and analytics using AI.

This is how now AI is used in Conversation and analytics helping Brands to minimize support & marketing team and adding ease to the end clients. ( everyone knows how tedious and boring to hold a customer care phone line versus using a chat bot with bank ) !!

We strongly believe that the easiest (and probably most cost effective) way for Brands is to integrate AI with NLP at the A2P Platform level to analyse their B2C users’ data and make it more interactive by integration with other technologies. ( we are heading to CPaaS now ).

AS per PRN News Wire : The global conversational AI market size was valued at $5.78 billion in 2020 and is projected to reach $32.62 billion by 2030. The forecasted compound annual growth rate (CAGR) is 20.0% from 2021 to 2030. This data helps us to understand the usability and acceptance of AI with messaging in the coming years.  

Though, with the PRN News wire data, everyone would feel like to build AI solution and get a pie out of that. Yes that should be the way. But it requires lot of efforts to train it, and apply it to the everyday conversations happening on the messaging apps and make consumers to learn how to use it effectively.

And the effect of wrong communication is known to everyone 😊 !!!

Brands and Developers should always consider major issues faced with AI integration ….

- Security

The biggest issue is about security of the device when you are integrating with various platforms. There are very high chances of spoofing of data at any level. For example, any financial data analysis might jeopardize the security of the user. 

- Multichannel Integration

For the ease of use SMS / Messaging has to be integrated with Voice, Email and other complex OTT channels , Payments, AI etc which is not a difficult task, but when users want them to work in synchronicity, it becomes challenge and bringing the analysis on a single dashboard is most difficult.

- Interactive Platform

AI Platform or solution must overcome few interactive issues to become successful and user-friendly..

• Time : in Messaging 10 ms response is also late. Need lightning fast response from AI
• Dialect : Language and Dialect keeps changing
• Use of Short forms and Slang : Another major challenges

- Content Based Rules

Depending on the content setting up the rule (again that is highly dependent on  business process or culture)

- Privacy

There should be enough arrangement not to track user and the AI data has to be kept safe as per GDPR laws

- Scalability

             Another major point to be considered for the higher number of interaction

- Dashboard

             This has to be most intuitive and user-friendly aspect for better user engagement

Let’s consolidate the efforts as a team and shape the development Messaging !