Start Delivering Business Results with the Three AI Agents
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When it comes to the adoption of artificial intelligence (AI), we have reached a tipping point. Technologies that were once accessible to only a few are now broadly available. This has led to an explosion in AI investment. However, according to research firm McKinsey, for AI to make a sizable contribution to a company’s bottom line, they “must scale the technology across the organization, infusing it in core business processes” — and based on conversations with our customers, we couldn’t agree more.
While investments in pure data science continue to be essential for many, widespread adoption of AI increasingly involves a category of applications and services that we call AI agents. These are technologies that let customers apply the best of AI to common business challenges, with limited technical expertise required by employees, and include Google Cloud products like Document AI and Contact Center AI. Today, at Google Cloud Next ‘22, we’re announcing new features to our existing AI agents and a brand new one with Translation Hub.
“AI is becoming a key investment for many companies’ long term success. However, most companies are still in the experimental phases with AI and haven’t fully put the technology into production because of long deployment timelines, IT staffing needs, and more,” said Ritu Jyoti, group vice president, worldwide AI and automation research practice global AI research lead, at IDC. “Organizations need AI products that can be immediately applied to automate processes and solve business problems. Google Cloud is answering this problem by providing fully managed, scalable AI agents that can be deployed fast and deliver immediate results.”
Translation Hub: An enterprise-scale translation AI agent
At I/O this year, we announced the addition of 24 new languages to Google Translate to allow consumers in more locations, especially those whose languages aren’t represented in most technology, to help reduce communication barriers through the power of translation. Businesses strive for the same goals, but unfortunately it is often out of reach due to the high costs that come with scaling translation.
That’s why today, we are announcing Translation Hub, our AI agent that provides customers with self-service document translation. With 135 languages, Translation Hub can create impactful, inclusive, and cost-effective global communications in a few clicks.
With Translation Hub, now researchers are able to share their findings instantly across the world, goods and services providers can reach underserved markets, and public sector administrators can reach more members of their communities in a language they understand — all of which ultimately help make for a more connected, inclusive world.
Translation Hub brings together Google Cloud AI technology, like Neural Machine Translation and AutoML, to help make it easy to ingest and translate content from the most common enterprise document types, including Google Docs and Slides, PDFs, and Microsoft Word. It not only preserves layouts and formatting, but also provides granular management controls such as support for post-editing human-in-the-loop feedback and document review.
“In just three months of using Translation Hub and AutoML translation models, we saw our translated page count go up by 700% and translation cost reduced by 90%,” said Murali Nathan, digital innovation and employee experience lead, at materials science company Avery Dennison. “Beyond numbers, Google’s enterprise translation technology is driving a feeling of inclusion among our employees. Every Avery Dennison employee has access to on-demand, general purpose, and company-specific translations. English language fluency is no longer a barrier, and our employees are beginning to broadly express themselves right in their native language.”
Document AI: A document processing AI agent to automate workflows
Every organization needs to process documents, understand their content, and make them available to the appropriate people. Whether it’s during procurement cycles involving invoices and receipts, contract processes to close deals, or for general increases in efficiency, Document AI simplifies and automates various document processing. With two new features launching today, Document AI can allow employees to focus on higher impact tasks and better serve their own customers.
For example, payments provider Libeo used Document AI to uptrain an invoice parser with 1,600 documents and increase its testing accuracy from 75.6% to 83.9%. “Thanks to uptraining, the Document AI results now beat the results of a competitor and will help Libeo save ~20% on the overall cost for model training over the long run,” said Libeo chief technology officer, Pierre-Antoine Glandier.
Today, we’re announcing these new features to our existing Document AI agent:
- Document AI Workbench can remove the barriers around building custom document parsers, helping organizations extract fields of interest that are specific to their business needs. Relative to more traditional development approaches, it requires less training data and offers a simple interface for both labeling data and one-click model training.
- Document AI Warehouse can eliminate the challenges that many enterprises face when tagging and extracting data in documents by bringing Google’s Search technologies to Document AI. This feature can make it simpler and easier to search for and manage documents like workflow controls to accommodate invoice processing, contracts, approvals, and custom workflows.
Contact Center AI: A contact center AI agent to improve customer experiences
Scaling call center support can be expensive and difficult, especially when implementing AI technologies to support representatives. Contact Center AI is an AI agent for virtually all contact center needs, from intelligently routing customers, to facilitating handoffs between virtual and human customer support representatives, to analyzing call center transcripts for trends and much more.
Just days ago, we announced that Contact Center AI Platform is now generally available to provide additional deployment choice and flexibility. With this addition to Contact Center AI, we are furthering our commitment to providing an AI agent that can assist organizations to quickly scale their contact centers to improve customer experiences and create value via data-driven decisions.
Dean Kontul, division chief information officer at KeyBank, had this to say about powering their contact center with Contact Center AI from Google Cloud: “With Google Cloud and Contact Center AI, we will quickly move our contact center to the Cloud, supporting both our customers and agents with industry-leading customer experience innovations, all while streamlining operations through more efficient customer care operations.”
Start delivering business results with AI agents, today!
If you’re ready to get started with Translation Hub, this Next ‘22 session has the details, including a deeper dive into Avery Dennison’s use of the AI agent.
To learn more about our Document AI announcements, check out our session with Commerzbank, “Improve document efficiency with AI,” as well as “Transform digital experiences with Google AI powered search and recommendations.”
And, to explore Contact Center AI Platform, watch “Delight customers in every interaction with Contact Center AI,” featuring more insight into KeyBank’s use case.
Data-first Digitization Helps Leverage the Cloud for Your Mainframe Assets
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For many enterprises, the venerable mainframe is home to decades’ worth of data about the company’s customers, processes and operations. And it goes without saying that the business would like access to that mainframe data — to report on it, to analyze it with big data analysis tools, or to use it as the basis of new machine learning and artificial intelligence initiatives.
At Google Cloud, we are eager to work with organizations to help them transform their mainframe assets for the cloud era. Of course, we can help them modernize their mainframe applications by migrating them to the cloud. At the same time, working with partners and customers, we’ve developed another, more lightweight approach that can help them start to leverage the cloud for their mainframe assets much more quickly than performing a full-fledged migration. We call this approach data-first digitization.
In this rapidly evolving digital ecosystem, it’s imperative to understand the difference between ‘modernization’ and ‘digitization.’ With modernization you start with the current state and look forward, and rely on mainframe application migration approaches such as rehosting (emulation), refactoring (automated code transformation), reengineering — or simply replacing a custom application with a commercial package. With digitization, you start with the future state that you want to achieve, and work back to what is required to get there.
This data-first digitization approach includes a mainframe data-first integration framework comprising in-house and partner products and tools to migrate heterogeneous data sources from the mainframe to Google Cloud Storage. Once mainframe data has been copied to Cloud Storage, it can then be integrated and leveraged by Google Cloud tools such as BigQuery, AI and machine learning prodcuts and Smart and Stream analytics platforms. The integration framework covers both bulk batch data transfers and real-time data replication (change data capture).
Data-first digitization is based on the tenet that ‘applications are transient, data is permanent.’ By bringing data first to Google Cloud instead of traditional ways of modernizing applications (for example, with Gartner’s 7 options to Modernize), this allows organizations to leapfrog to new business models, use cases and innovative ways to serve end customers. For example:
- Making decisions with smart and stream analytics platforms and AI/ML engines. These tools need data to make decisions. Google is a pioneer in extracting information and value from the raw structured and unstructured data, and this approach opens up mainframe data for use by BigQuery and AI/ML models.
- Building new reporting applications. With access to mainframe data, you can use Google cloud products like Looker and Appsheet to build net-new reporting applications, expediting the process of retiring mainframe reporting applications, and accelerating your overall transformation.
In our experience, taking a data-first digitization approach to your mainframe offers a number of benefits:
- Faster time-to-business: Because data-first modernization is built on existing products, the implementation cycle is much shorter.
- Less capital investment: You spend your time integrating products, not developing applications.
- Minimized risk: Data-first integrates with existing, proven and reliable Google Cloud products.
- Faster overall mainframe transformation: When you shift your modernization center of gravity from the application to the data, you look at mainframe applications from a business perspective instead of just “keeping the lights on.” As a result, only the most business-critical applications are modernized and many support applications can be decommissioned, accelerating your transformation journey.
Taking a data-first approach to digitization is still relatively new, but we’re heartened by customers’ early successes. Watch this space for additional insights, reference architectures and technical white papers around data-first. And if you think this approach may be right for you, reach out to mainframe@google.com.
Learn more:
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Google Cloud’s ML-based Image Classification App: A Key to Global Wildlife Conservation
Wildlife provides critical benefits to support nature and people. Unfortunately, wildlife is slowly but surely disappearing from our planet and we lack reliable and up-to-date information to understand and prevent this loss. By harnessing the power of technology and science, we can unite millions of photos from [motion sensored cameras] around the world and reveal how wildlife is faring, in near real-time…and make better decisions
wildlifeinsights.org/about
Held Back by Database Scalability, This Financial Services Company Switches to Google Cloud and Cloud Spanner
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Azimut Group operates an international network of companies handling investment and asset management, mutual funds, hedge funds, and insurance. Founded in Milan, Italy in 1988, Azimut Group today has branches in fifteen countries, including Brazil, China, and the USA.
“We have subsidiaries and manage funds all over the world,” explains Simone Bertolotti, IT Manager at Azimut Holding S.p.a. “That means that any technology that we put in place has to cover needs from many different countries.”
“When complicated analysis has to be executed, we have to increase our table space in a couple of minutes so that the AI can drill down into the data and deliver the information we need.”
—Simone Bertolotti, IT Manager, Azimut Holding S.p.a.
Azimut manages its funds with investment advisors who use information sourced from Bloomberg, Reuters and others. “They use a huge amount of data,” says Simone. “They work with spreadsheets, algorithms, formulae and they analyse data in minutes.” In finance, every second is crucial, which is why Azimut decided to develop a risk management dashboard that can process information even more quickly, then distribute it worldwide.
“When an advisor manages data, that data is used to make immediate decisions on funds, capital movements or whether to sell stock,” says Simone. “They have to be ready to make recommendations for any amount of data that comes to them. For our dashboard, that means that when additional information arrives or complicated analysis has to be executed, we have to increase our table space in a couple of minutes so that the AI can drill down into the data and deliver the information we need.”
Generating insights at speed
Investors and investment managers make decisions based on the most accurate, up-to-date information possible. For Azimut Group, information sourced through financial data vendors such as Bloomberg and Reuters provided only part of the data that the group required.
“We looked to collect information from a range of different providers,” explains Simone, “then analyse it to develop a predictive algorithm that could work faster than an advisor stationed at the terminal. We set ourselves the challenge to try to manipulate that data to add new insights into our matrix, so that every one of our branches across the world can see risk information about the funds in real-time.”
“We compared Google Cloud Platform’s performance with our previous cloud provider, and saw huge benefits of switching to Google. For me, the key performance issue is scaling. With Google Cloud Platform I know that I can increase and decrease my infrastructure quickly, when I need it.
—Simone Bertolotti, IT Manager, Azimut Holding S.p.a.
The first cloud provider Azimut used to build its system struggled to scale quickly to meet different kinds of data challenges. “If we wanted to add more cores, that was fine,” says Simone. “But the previous cloud provider made it complicated to raise the amount of space in a database infrastructure and scale up to demand. Scaling up for more in-depth analysis would take a day, and our need was immediate.”
That’s why Azimut switched one year ago to Google Cloud Platform to run the 150 VMs on its risk analysis platform. “We compared Google Cloud Platform’s performance with our previous cloud provider, and saw huge benefits of switching to Google. For me, the key performance issue is scaling,” says Simone. “With Google Cloud Platform I know that I can increase and decrease my infrastructure quickly, when I need it. Instead of waiting a day to scale up infrastructure, we can request and add space to our database in a couple of minutes.”
The infrastructure of Azimut’s solution handles around 800TB of data per month, and Google’s global network of servers and high-speed connections ensure that it gets to where it’s most needed by the most direct route. Impressed by the speed, security and availability of Google Cloud Platform, Azimut has moved its intranet on to Google Cloud Platform, too, eliminating the need for staff to login with VPNs.
“Instead of waiting a day to scale up infrastructure, we can request and add space to our database in a couple of minutes.”
—Simone Bertolotti, IT Manager, Azimut Holding S.p.a.
Driving ahead with Noovle
For Azimut, migrating the risk management dashboard is the latest of many Google product collaborations with cloud consultancy Noovle. “Everything started five years ago,” says Simone, “when Noovle assisted us in migrating to Gmail from our on-premise email solution. From G Suite to Google Cloud Platform, we’ve had a great relationship. Noovle provides consultancy services, support for mobility, and external advisors who work on our premises, such as when they trained us how to broadcast our meetings on Google Hangouts. As an independent company, we know we can trust them for transparent advice. All they care about is the best way to get a job done and to help us reach our goals.”
New app, new customers
In a business case comparison, Google Cloud Platform cost Azimut 35% less to run than the previous cloud provider. Now the group is building a major new mobile application on Google App Engine to be released in 2018.
“The new mobile application will allow customers to trade directly, without human advisors, by proposing different investment solutions depending on targets the customers set,” says Simone. “So if a customer aims to make money with investments, they enter their relevant personal information and we carry out the necessary regulatory checks and suggest what they could buy. The entire project will be based on Google Cloud Platform, so customers can control their investments through the app while we manage the fund, using Google Cloud Spanner on the backend.”
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How Apna is using data and AI to drive the gig economy in India
When it comes to the adoption of artificial intelligence (AI), we have reached a tipping point. Technologies that were once accessible to only a few are now broadly available. This has led to an explosion in AI investment. However, according to research firm McKinsey, for AI to make a sizable contribution to a company’s bottom line, they “must scale the technology across the organization, infusing it in core business processes” — and based on conversations with our customers, we couldn’t agree more.
While investments in pure data science continue to be essential for many, widespread adoption of AI increasingly involves a category of applications and services that we call AI agents. These are technologies that let customers apply the best of AI to common business challenges, with limited technical expertise required by employees, and include Google Cloud products like Document AI and Contact Center AI. Today, at Google Cloud Next ‘22, we’re announcing new features to our existing AI agents and a brand new one with Translation Hub.
“AI is becoming a key investment for many companies’ long term success. However, most companies are still in the experimental phases with AI and haven’t fully put the technology into production because of long deployment timelines, IT staffing needs, and more,” said Ritu Jyoti, group vice president, worldwide AI and automation research practice global AI research lead, at IDC. “Organizations need AI products that can be immediately applied to automate processes and solve business problems. Google Cloud is answering this problem by providing fully managed, scalable AI agents that can be deployed fast and deliver immediate results.”
Translation Hub: An enterprise-scale translation AI agent
At I/O this year, we announced the addition of 24 new languages to Google Translate to allow consumers in more locations, especially those whose languages aren’t represented in most technology, to help reduce communication barriers through the power of translation. Businesses strive for the same goals, but unfortunately it is often out of reach due to the high costs that come with scaling translation.
That’s why today, we are announcing Translation Hub, our AI agent that provides customers with self-service document translation. With 135 languages, Translation Hub can create impactful, inclusive, and cost-effective global communications in a few clicks.
With Translation Hub, now researchers are able to share their findings instantly across the world, goods and services providers can reach underserved markets, and public sector administrators can reach more members of their communities in a language they understand — all of which ultimately help make for a more connected, inclusive world.
Translation Hub brings together Google Cloud AI technology, like Neural Machine Translation and AutoML, to help make it easy to ingest and translate content from the most common enterprise document types, including Google Docs and Slides, PDFs, and Microsoft Word. It not only preserves layouts and formatting, but also provides granular management controls such as support for post-editing human-in-the-loop feedback and document review.
“In just three months of using Translation Hub and AutoML translation models, we saw our translated page count go up by 700% and translation cost reduced by 90%,” said Murali Nathan, digital innovation and employee experience lead, at materials science company Avery Dennison. “Beyond numbers, Google’s enterprise translation technology is driving a feeling of inclusion among our employees. Every Avery Dennison employee has access to on-demand, general purpose, and company-specific translations. English language fluency is no longer a barrier, and our employees are beginning to broadly express themselves right in their native language.”
Document AI: A document processing AI agent to automate workflows
Every organization needs to process documents, understand their content, and make them available to the appropriate people. Whether it’s during procurement cycles involving invoices and receipts, contract processes to close deals, or for general increases in efficiency, Document AI simplifies and automates various document processing. With two new features launching today, Document AI can allow employees to focus on higher impact tasks and better serve their own customers.
For example, payments provider Libeo used Document AI to uptrain an invoice parser with 1,600 documents and increase its testing accuracy from 75.6% to 83.9%. “Thanks to uptraining, the Document AI results now beat the results of a competitor and will help Libeo save ~20% on the overall cost for model training over the long run,” said Libeo chief technology officer, Pierre-Antoine Glandier.
Today, we’re announcing these new features to our existing Document AI agent:
- Document AI Workbench can remove the barriers around building custom document parsers, helping organizations extract fields of interest that are specific to their business needs. Relative to more traditional development approaches, it requires less training data and offers a simple interface for both labeling data and one-click model training.
- Document AI Warehouse can eliminate the challenges that many enterprises face when tagging and extracting data in documents by bringing Google’s Search technologies to Document AI. This feature can make it simpler and easier to search for and manage documents like workflow controls to accommodate invoice processing, contracts, approvals, and custom workflows.
Contact Center AI: A contact center AI agent to improve customer experiences
Scaling call center support can be expensive and difficult, especially when implementing AI technologies to support representatives. Contact Center AI is an AI agent for virtually all contact center needs, from intelligently routing customers, to facilitating handoffs between virtual and human customer support representatives, to analyzing call center transcripts for trends and much more.
Just days ago, we announced that Contact Center AI Platform is now generally available to provide additional deployment choice and flexibility. With this addition to Contact Center AI, we are furthering our commitment to providing an AI agent that can assist organizations to quickly scale their contact centers to improve customer experiences and create value via data-driven decisions.
Dean Kontul, division chief information officer at KeyBank, had this to say about powering their contact center with Contact Center AI from Google Cloud: “With Google Cloud and Contact Center AI, we will quickly move our contact center to the Cloud, supporting both our customers and agents with industry-leading customer experience innovations, all while streamlining operations through more efficient customer care operations.”
Start delivering business results with AI agents, today!
If you’re ready to get started with Translation Hub, this Next ‘22 session has the details, including a deeper dive into Avery Dennison’s use of the AI agent.
To learn more about our Document AI announcements, check out our session with Commerzbank, “Improve document efficiency with AI,” as well as “Transform digital experiences with Google AI powered search and recommendations.”
And, to explore Contact Center AI Platform, watch “Delight customers in every interaction with Contact Center AI,” featuring more insight into KeyBank’s use case.
Built on Google Cloud, Enexor’s Bio-CHP Unit Powers 100 Homes with Renewable Energy!
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Editor’s note: Earth Day reminds us that we all can contribute to creating a cleaner, healthier, and more sustainable future. Google Cloud is excited to celebrate innovative startup companies developing new technology and driving sustainable change. On this year’s Earth Day we’re highlighting Enexor BioEnergy and their initiative to produce clean, sustainable energy from plastic and agro-waste with the help of Google Cloud solutions.
Picture a world without electricity where billions of people burn dangerous fuels and trash from landfills to cook meals and heat their homes. Children constantly cough, choking on toxic smoke that lingers in houses and sickens entire families. Because there are no pumps or purification plants, contaminated water is hauled in buckets from dirty rivers and polluted wells. Without power or lights, hospitals and emergency medical clinics can only provide basic services during the day.
Although it is 2022, this harsh life is a reality for billions of people living in developing countries around the world.
Producing clean energy from plastics and organic-waste
This Earth Day, let’s imagine what life would be like in these countries if people had access to safe and inexpensive bioenergy. In this world, indoor air is fresher. Clean water runs from faucets, while homes, businesses, and schools have electricity. Doctors and nurses have the power to treat patients 24 hours a day and save lives with advanced medical equipment. Local economies boom and entrepreneurs thrive.
This is the sustainable future Enexor BioEnergy and its partners are building—one village at a time. With the Enexor Bio-CHP™ system, we produce clean and sustainable energy from discarded plastics, organic, and biomass-waste such as rice and corn husks, seaweed, coconuts, and other biomatter while offsetting significant Carbon emissions. The Bio-CHP can be commissioned and easily installed in just a single day, with multiple systems installed side-by-side when more power is needed. By being offered under its novel Energy-as-a-Service business model, Enexor ensures that the Bio-CHP can generate immediate environmental positive impact and customer adoption.
Each Bio-CHP unit generates enough energy to power over 100 homes—and provides much-needed renewable electricity and thermal power for schools, businesses, manufacturing facilities, water pumps, Wi-Fi systems, and telecommunications towers. The Bio-CHP creates clean energy by safely oxidizing plastics and biomatter in a secure container using a high-temperature system to power a micro-turbine. This produces bioenergy, significantly reducing harmful greenhouse gas emissions.
By solving a community’s waste issues while providing more affordable renewable energy, the Bio-CHP also creates new economic opportunities and improved outcomes where it is installed. This includes using its inexpensive power to expand local services and offerings, and empowering local community clean up efforts by incentivizing waste collection via blockchain-enabled digital currencies and services such as PayGo. Additionally, the Bio-CHP is an ideal solution for businesses who desire to maximize their own sustainability efforts, gain greater energy resiliency, and save money on their current energy and waste costs.
Engaging in Google for Startups Accelerator: Climate Change
When developing the Bio-CHP, we realized we needed a reliable and experienced partner to help us incorporate the best in class machine learning and artificial intelligence into our technology. That’s why we joined the Google for Startups Accelerator and participated in the Google for Startups Accelerator: Climate Change.
The accelerator introduced us to other companies working to create a sustainable future and opened new opportunities for collaboration and partnerships. The accelerator also continues to give our small team access to the best of Google Cloud’s people, programs, and solutions. We especially want to highlight Google Cloud’s dedicated startup experts and the incredible technical support they provide, as well as the Google Cloud research credits we used to explore new solutions.
As an example, Enexor is developing a predictive maintenance tool that automatically adjusts Bio-CHP operations to match fuel composition. Another tool in development proactively identifies and flags potential Bio-CHP system and component failures—before they occur.
Google Cloud’s dedicated startup experts work closely with Enexor to develop predictive models for these tools and Enexor also used the Google Cloud research credits that were provided to build these advanced models on TensorFlow, Vertex AI, Cloud CDN and AutoML. Since each system is remotely monitored from Enexor’s global headquarters in Tennessee, predictive maintenance tools also increase the safety, reliability, and efficiency of Bio-CHP in off-grid locations.
Generating 13 billion kWh of clean power by 2040
In the future, Enexor plans to explore additional Google Cloud solutions such as BigQuery to crunch larger datasets, Google Data Studio to build dashboards, and perhaps even Google Cloud Tensor Processing Units (TPUs) to more efficiently process machine learning (ML) workloads.
These solutions will help Enexor to achieve three primary goals by 2040: to generate 13 billion kWh of clean power, reduce 120 million tons of CO₂, and provide one million people with access to clean and sustainable energy. With each Bio-CHP system, Enexor annually reduces up to 2,000 metric tons of CO₂ equivalent emissions by decreasing methane emissions released from landfills, offsetting fossil fuel-based power generation with carbon credits and minimizing waste disposal transportation emissions.
Enexor is now preparing to deploy the Bio-CHP in Accra, Ghana where it will convert organic and plastic manufacturing waste into sustainable energy. Enexor is also looking forward to working with the local Accra community and NGOs to collect discarded plastics and organic waste diverting it from ending in the rivers and oceans and instead using them as renewable fuel sources. We can’t wait to see what we accomplish next as we celebrate Earth Day 2022 and think about the sustainable future Enexor is helping to empower.
If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
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