To remain competitive and thrive, organizations must differentiate their brand through outstanding customer service experiences. As customer expectations and business needs continuously shift, enterprises need the agility to rapidly create, maintain, and optimize those experiences with the latest technologies, including sophisticated AI, without relying on external vendors. What enterprises need are options that keep them in control--a choice of no-code, low-code, and pro-code AI development tools that offer the freedom to quickly build the conversational AI applications they need. After the recent launch of the Microsoft Digital Contact Center Platform, organizations now have those options. Some organizations want the flexibility and agility to build their own conversational solution in-house on an end-to-end software-as-a-service (SaaS) solution that is quick to deploy, monitor and tune; one that can easily be self-managed by their subject experts.

One of the bigger announcements at this year's Microsoft Inspire event was the launch announcement of its Digital Contact Center Platform. Intended to be a cross-channel contact platform, it brings together a mix of existing applications and services as well as the recently acquired Nuance voice recognition tools. Microsoft has had many of the features needed to build a contact center in its portfolio for some time, with Dynamics 365 offering a fully featured CRM platform, Power Platform providing no-code and low-code workflow tools, and Teams providing a multi-channel desktop with voice, text and video all built in to one application for internal and external communications. It's a timely response to competitors like Twilio and Salesforce who have deep experience in features essential for contact centers, but who lack the breadth of Microsoft's product portfolio. Putting all these tools together and adding features from Azure's Cognitive Services suite of pre-built machine learning models makes a lot of sense. Users expect companies to meet them where they are, not chase down phone trees to sit on hold for hours, and if contact can be automated where possible while still maintaining a natural flow, organizations can use smart tooling to triage and manage calls, only handing over to humans where necessary.

Arabot, an Amman-based'Bot as a Service' startup has raised $1 million in seed funding led Saudi's Riyad Taqnia Fund (RTF) with participation from existing investors, RTF announced in a statement to MENAbytes. Founded in 2016 by Abdallah Faza and Kais Hassan in 2016, Arabot allows businesses to integrate and use its intelligent Arabic bot (that also speaks English) through their website, mobile app, or contact center platform to engage with customers. Arabot according to its website is built upon Arabic NLP engine which can understand and analyze Arabic content and conversation in different Arabic dialects. "We are building a conversation/dialogue management as a hybrid rule-based system fused with Deep Machine Learning to reach needed levels of Natural Language Understanding (NLU)," notes Arabot's website. The service that could be used by businesses mainly for customer service and promotion can be integrated in their website, mobile app, contact center platform, or messaging platform.

Blue Jeans Network Inc. and Voicera have partnered to offer an in-meeting transcription service within the Blue Jeans platform. The transcription technology uses Voicera's artificial intelligence assistant, Eva. Blue Jeans, a cloud-based video conferencing provider based in Mountain View, Calif., initially announced support for Eva in November for features such as note taking and identifying key moments in the meeting. The partnership has expanded Eva's capabilities to include full-meeting recordings with a searchable transcript. The transcription technology integration with the Eva assistant is available to all Blue Jeans users.

Due to Apple's success with a closed-platform solution, many software companies have opted to forgo an open-platform to provide a more consistent user experience, and of course, for the benefit of increased profits. But, when it comes to chatbots, a closed-platform completely defeats the purpose of a chatbot solution. Closed platforms are walled gardens that isolate the chatbot from a world of possibility. A chatbot on a closed platform can never become the ultimate solution because it can't be customized to address the specific problems a company faces. For example, integrating with internally developed software would likely be a lengthy process and at the discretion of the software developer. Any changes or added functionality to the platform have to be reviewed, approved, and implemented by the vendor.