Most business transactions begin, involve, or end with a document. But working with documents can be tricky, as leaders across industries seeking digital transformation can attest to.

These enterprises face similar challenges as they seek to extract information from documents. The process can be costly, time consuming, and prone to errors with manual data entry.

Learn how to use machine learning to organize, process, and extract data within documents. Also, learn about some examples of how various customers have found success using Google Cloud Document AI.

In this video Sudheera Vanguri, Product Manager, Google Cloud AI, highlights new Document AI capabilities. She walks you through of the building blocks of Document AI and demonstrates the new UI. She also highlights specialized Document AI models pre-trained for invoice and healthcare document processing as well as shows customer examples and live demos.

How about selfie analysis of users to recommend them the right lipstick color?

That’s just one of the many ideas folks at Purplle.com came up with to improve the buying experience of Indian consumers.

And without the power of Google Cloud, it would probably have remained just that…an idea.

But today, thanks to Google Cloud, “Nothing seems impossible,” says Suyash Katyayani, CTO, Purplle.

Purplle.com is an online e-commerce company in India and one of the pioneers in creating a digitally-native beauty brands in India.

“The beauty industry is so data intensive that we needed to have a strong data strategy and we were looking out for solutions which would enable us to have a strong data pipeline and a strong data warehousing solution,” says Katyayani.

That’s when it turned to Google Cloud.

Additionally, Purplle.com, says Katyayani, does not have to worry about at what scale the company operates at because they have access to state-of-the-art infrastructure from Google Cloud available to them so that their developers can run experiments.

“The biggest plus point for us has been the agility that Google Cloud has added,” says Katyayani.

AI can help organizations improve the decision-making process across most business functions. However, building an effective AI capability encompasses more than just creating a technology platform.

To do this effectively requires alignment to business objectives, strong executive sponsorship, and collaboration between skilled employees and strategic partners.

Additionally, you need your initiatives to be powered by secure data management and cloud-native services to scale and automate ML workloads, and ensure all of this is underpinned by responsible AI principles.

Successfully adopting AI in your business is determined by your practices in these areas. Learn more about the AI journey and how you can gain value every step of the way.

For many enterprises, the wish to tap into the power of AI is negated only by a lack of know-how: How AI works, what sort and how much data they will need to gather and structure correctly, and how to use the platforms that make AI adoption easier.

Google understands that. Which is why the AI solutions they have built “are basically plug and play, ready to go with our partners, so that you (businesses) can have immediate business value for your specific use case, and for your specific workflow without a deep investment of any kind into machine learning,” says Levent Besik, Group Product Manager, Google Cloud.

Among the more interesting use cases that’s seeing adoption is contact center AI.

“So one thing that we see quite often, especially from our B2C customers, is that they often have this growing pain in their call centers. They face a trade-off between operational efficiency and great customer service. With the advances in language and conversational AI, that doesn’t have to be the case anymore,” says Besik.

That’s exactly the problem in front of Akash Parmar, Enterprise Architect,. “One of the big challenge we had was: how do we effectively manage 14 million calls, which come into our contact center, stores and head office? In the past, these calls were managed by completely different platforms with their own IVRs, with their own routing and reporting solutions. It was expensive, plus the experience across them was very inconsistent.”

Digging deeper, Parmar and team figured that the challenge was in the fact that an IVR couldn’t really capture the hundreds of reasons customers call.

To get around the problem Parmar and team decided to let customer tell them why they were calling. They then used AI to decipher what the customer was saying, extract the customer’s intent from that, and then help them directly, or re-route them to the best department.

Overall, it proved to be a big success, says Parmer. Although there were funny moments.

“In our early days, we were getting some transcriptions from the Speech API, which were not what we expected. It was not word by word and we had to kind of train the model to make sense of it. For example, we started to get calls about Brad Pitt. We were like, we’ve really won the Oscar here. Brad Pitt is calling us. It was not actually call for Brad Pitt. These were calls about bra fit, which it’s a big business for M&S,” remembers Parmer.

Find out how three companies, including Marks and Spencer, are using AI today.