Gemini Spark vs. Cortana (discontinued)

It's easy for anyone who is expecting some simple AI problems like fetching the keywords, understanding the intent, language translation, etc. to be solved from an existing database and all they need is to connect to their APIs via a subscription model. But for complex use cases, there is still room for improvement like customization of underlying AI models for a specific use case like identifying some unique identifiers with respect to industry.

Incentivized

• Being hosted in Azure solves a massive hosting problem
• The language understanding system has the ability to revolutionize many vertical markets
• Integrating with Cortana Analytics was really simple due to easy to understand documentation

Incentivized

• More partnerships with colleges and schools to increase the workforce with technical knowledge (increase local workforce)
• Have more online training and documentation in other languages
• Have affordable prices for students

Incentivized

IBM Watson Assistant has been early into this market and has improved a lot over time compared to Azure AI Cortana. More documentation related to the services. But Ease of integration Azure AI ranks over IBM Watson Assistant. And again in terms of services offered under the ecosystem, Azure AI precedes IBM Watson Assitant.

Incentivized

• Difficult to ascertain the ROI as we are a software house who have developed a module in our application using Cortana. However for companies that use our software I would say the use of sentiment analysis in our application could free up at least 1 full time resource to be used elsewhere in their organisation.

Incentivized