With people expecting to access products and services through easy, always-on experiences delivered across channels, transforming approaches to digital services is a must for every business. As a result, business leaders have to strive to provide employees with these same frictionless experiences. 

Sopra Steria is a European leader in consulting, digital services and software development, with 46,000 employees in 25 countries that generated revenue of €4.3 billion in 2020. It provides end-to-end solutions that help customers drive their digital transformation to obtain tangible and sustainable benefits, by combining in-depth knowledge of a wide range of business sectors and innovative technologies.

To accomplish its goals, Sopra Steria planned to use conversational artificial intelligence as part of their strategy and began working closely with Google Cloud, Cisco, and Activeo. It believed that, by building out more advanced Virtual Agents for its customers to use to serve them, it could usher in a new era of frictionless experiences in the End-User support services.

“To address customer demands for office and business applications support services, we looked to integrate a new generation of virtual voice assistant into our platform,” says Xavier Leroux, CTO End User Services at Sopra Steria. “We did this using Google’s proven AI and integrated it with our Cisco Contact Center by Activeo.”

As many companies have learned, the need to provide customers with advanced experiences is directly attached to the services offered to their employees. By providing more contextual information and other forms of digital support to staff, those same team members will be better positioned to serve customers.

Solving a complex problem with ease

Sopra Steria sought to solve several challenges for its customers, enabling them to:

• Provide all their employees with seamless, quick access to IT and business support,
• Offer 24/7 phone support capabilities for employees,
• Make services easily deployable, flexible, agile, and available in multiple languages,
• Reduce costs associated with delivering better support services.

Sopra Steria chose Google Cloud Contact Center AI (CCAI), built by Google Cloud and Cisco, as the best option to achieve its vision. Google Cloud CCAI is currently used in some of the world’s largest call centers, building on Google’s expertise in natural language processing.

Sopra Steria implemented CCAI as its new Virtual Assistant on phone channels managed by a Cisco telephony solution, and used Activeo for its integration and implementation support.

The new Virtual Assistant allows Sopra Steria clients to qualify employee requests and direct them to the right agent based on Natural Language Understanding while offering self-service options for basic requests such as password resets. These Virtual Assistants also fully automate complex incident tickets creation in synchronization within the IT service management system.

Google Cloud CCAI covers three main use cases:

1. Autonomously handles full-length conversations using natural language,
2. Augments operator support through contextual assistance through a desktop application based on live conversation analysis,
3. Semantic analysis of all audio and text conversations processed by customer service through Google Cloud machine learning.

Cisco provides native integration of CCAI within its Cisco Contact Center solution as a part of its global partnership with Google Cloud.

“The work we have done with Google Cloud has allowed Sopra Steria to create innovative and personalized conversational services to enhance both employee satisfaction and operator productivity without sacrificing security,” says Christian Laloy, EMEA Contact Center Sales Specialist at Cisco.

Unlocking new, more powerful contact center experiences

Since standing up the new CCAI solution, Sopra Steria has been able to enhance its service catalog through Virtual Assistants. The offering has generated immense interest among existing and new Sopra Steria clients because it can simultaneously reduce average waiting and handling and resolution times while dramatically decreasing service costs.

“The Virtual Assistant serves both the user and operator alike,” says Xavier Leroux. “The user gets a conversational experience like no other and a seamless journey to resolve their requests. With Google Cloud, Cisco and Activeo, we have increased operator efficiency, so they can focus more on adding value to every business interaction.”

SAP Build Process Automation is designed to optimize business processes and boost efficiency. The platform helps both business users and developers alike digitize core workflows and incorporate artificial intelligence (AI) into time consuming and error-prone manual tasks.

All digital paths can benefit from automation. The pandemic, supply chain shortages, and other disruptive events have upped the pressure on businesses and their workers to perform in more efficient and flexible ways.

Google Cloud and SAP have responded by providing an integrated toolbox that can fundamentally change the way businesses operate — all while creating value for their customers.

With a focus on taking process automation to an even more advanced level and removing inefficiencies from workflows, SAP has introduced integrations with Google Cloud Document AI, and Google Workspace for SAP Build Process Automation customers. This integration can reduce or eliminate many repetitive and error-prone tasks, so that companies can help save money, operate more productively, and scale more easily.

AI unleashes innovation

Continuous advances in digital systems and advanced technology introduce new opportunities to rethink workflows. SAP recognizes the role that AI-powered automation can play in transforming workflows, and that the benefits of doing so extend beyond basic time savings and cost cutting. By plugging Google Cloud Document AI into the application, SAP Build Process Automation’s low-code, no-code platform enables SAP to help its customers in lines of business and IT integrate multiple applications while democratizing access to governed machine learning technology.

Machine learning and process automation can drive efficiency with SAP S/4HANA

Customers using SAP S/4HANA can build workflow improvements into all major core processes, such as order entry, invoice creation, asset posting, and many others, helping them to be faster and more efficient in the process. Google Cloud Document AI extracts key elements — including addresses, article numbers, price, quantity, and more — within emails, PDFs, handwritten notes, and other formats.

Integrated automation can drive results for invoice and purchase order processing

An example of the improvements these integrations have made to SAP Build Process Automation is the use of AI, productivity tools, and automation for processing purchase orders. In the past, workers had to manually select relevant orders in their Gmail accounts and extract key data from large PDF files, including the order date, supplier details, article numbers, quantity, unit price, and the total amount. Then, workers would have to enter all of the individual line items one by one into the SAP S/4HANA system.

Today, through SAP’s integrated automation platform, customers can automatically extract and organize data by Google Workspace (Google Sheets, Google Drive, and Gmail) and Document AI. This works by extracting data contained in Gmail attachments, downloading it into Google Drive and extracting fields using Document AI’s pretrained models. The tool then enters the consolidated order information from Google Sheets into SAP S/4HANA. For example, the Canton of Zurich in Switzerland experienced a significant reduction of workload to process compensation forms once the organization implemented this automation. Furthermore, implementing the automation can avoid audit and compliance issues, and improve data quality.

The screenshot below shows how a workflow operates within the SAP Build Process Automation software.

Sales, procurement, finance, and other functions are also able to handle more strategic work that delivers greater value to customers with these SAP and Google Cloud integrations. They’ve also boosted both security and regulatory compliance for customers, including those in the financial services space.

For example, the Google Cloud Document AI technology can process thousands of supply chain invoices, validates and systematically approves them. And, over time, the machine learning and AI components improve the analysis process and ensure that data processed with SAP Build Process Automation is adhering to a company’s best practices and essential regulatory requirements.

SAP and Google Cloud put automation to work

Achieving the most accurate, efficient business processes is possible with an automation framework that embeds collaboration and productivity applications while giving lines of business and IT users access to machine learning technology. SAP Build Process Automation combined with Google Cloud Document AI, and Google Workspace has proven to be a catalyst in driving innovation and business transformation for customers across multiple industries, leading to an average of 22% to 30% faster time to market, and significant financial gains, including up to 20% accounts payable savings potential. An example of these improvements includes those experienced by TasNetworks, which had a 25% reduction in back-office processing efforts after implementing these technologies.

To learn more about how Google Cloud and SAP are building solutions for accelerating business value, visit cloud.google.com/solutions/sap. You can find more information about SAP Build Process Automation at sap.com/build-automation.

To start your transformation journey today, choose the SAP Business Technology Platform region that’s best for you. We’re also happy to announce that Google Cloud offers the first and only option to run BTP in the cloud in India — learn more here: Google Cloud’s newest SAP Business Technology Platform Region.

Vertex AI launched with the premise “one AI platform, every ML tool you need.” Let’s talk about how Vertex AI streamlines modeling universally for a broad range of use cases. 

The overall purpose of Vertex AI is to simplify modeling so that enterprises can fast track their innovation, accelerate time to market, and ultimately increase return on ML investments. Vertex AI facilitates this in several ways. Features like Vertex AI Workbench, for example, speed up training and deployment of models by five times compared to traditional notebooks. Vertex AI Workbench’s native integration with BigQuery and Spark means that users without data science expertise can more easily perform machine learning work. Tools integrated into the unified Vertex AI platform, such as state of the art pre-trained APIs and AutoML, make it easier for data scientists to build models in less time. And for modeling work that lends itself best to custom modeling, Vertex AI’s custom model tooling supports advanced ML coding, with nearly 80% fewer lines of code required (compared to competitive platforms) to train a model with custom libraries. Vertex AI delivers all this while maintaining a strong focus on Explainable AI. 

Yet organizations with the largest investments in AI and machine learning, with teams of ML experts, require extremely advanced toolsets to deliver on their most complex problems. Simplified ML modeling isn’t relegated to simple use cases only.

Let’s look at Vertex AI Neural Architecture Search (NAS), for instance. 

Vertex AI NAS enables ML experts at the highest level to perform their most complex tasks with higher accuracy, lower latency, and low power requirements. Vertex AI NAS originates from the deep experience Alphabet has with building advanced AI at scale. In 2017, the Google Brain team recognized we need a better way to scale AI modeling, so they developed Neural Architecture Search technology to create an AI that generates other neural networks, trained to optimize their performance in a specific task the user provides. To the astonishment of many in the field, these AI-optimized models were able to beat a number of state of the art benchmarks, such as ImageNet and SOTA mobilenets, setting a new standard for many of the applications we see in use today, including many Google-internal products. Google Cloud saw the potential of such a technology and shipped in less than a year a productized version of the technique (under the brand AutoML). Vertex AI NAS is the newest and most powerful version of this idea, using the most sophisticated innovation that has emerged since the initial research.

Customer organizations are already implementing Vertex AI NAS for their most advanced workloads. Autonomous vehicle company Nuro is using Vertex AI NAS, and Jack Guo, Head of Autonomy Platform at the company, states, “Nuro’s perception team has accelerated their AI model development with Vertex AI NAS. Vertex AI NAS have enabled us to innovate AI models to achieve good accuracy and optimize memory and latency for the target hardware. Overall, this has increased our team’s productivity for developing and deploying perception AI models.” 

And our partner ecosystem is growing for Vertex AI NAS. Google Cloud and Qualcomm Technologies have collaborated to bring Vertex AI NAS to the Qualcomm Technologies Neural Processing SDK, optimized for Snapdragon 8. This will bring AI to different device types and use cases, such as those involving IoT, mixed reality, automobiles, and mobile.

Google Cloud’s commitments to making machine learning more accessible and useful for data users, from the novice to the expert, and to increasing the efficacy of machine learning for enterprises are at the core of everything we do. With the suite of unified machine learning tools within Vertex AI, organizations can take advantage of every ML tool they need on one AI platform. 

Ready to start ML modeling with Vertex AI? Start building for free. Want to know how Vertex AI Platform can help your enterprise increase return on ML investments? Contact us.