It’s simply amazing to witness how some of our systems integrators employ Google Cloud solutions to drive innovation in ways we at Google may never have considered—especially in healthcare.  According to analyst firm MarketsandMarkets, the market for the Cloud in healthcare is projected to grow 43% between 2020 and 2025 to nearly $65 billion, fueled by the need for better technology infrastructures and faster digital transformation.  

Healthcare and life sciences communities are looking to Google Cloud and its Service engagement model partners to improve collaboration and activate the power of medical data.  These transformations deliver robust data analytics and bring a much deeper perspective into health epidemics like COVID-19 to help save lives.

In the healthcare and life sciences industry, our Google Cloud partners and customers provide constant energy and inspiration–and are the magic in some key healthcare innovations globally.  

Let me show you how they are solving real-world business challenges.

Improving collaboration in Healthcare

Cloudbakers and Comanche County Memorial Hospital transitioned 2,000 employees to Google Workspace to improve collaboration, reduce costs, and increase security. By implementing a system that requires less maintenance while enabling mobile access to data and true collaboration, Comanche County Memorial Hospital saves $175,000 annually on licenses and helps medical professionals spend more time with patients.

“We chose Google Workspace because it cost a quarter of what we were paying previously, offers the kind of modern features that healthcare facilities need, and gave us data security and peace of mind.” —James Wellman, CIO, Comanche County Memorial Hospital

Accessing previously locked down medical data

Google Cloud and Quantiphi supported advances in cloud-based machine learning services to reduce infrastructure costs, unlock new paths of treatment, and dramatically reduce the amount of time it takes to evaluate scanned imagery following a stroke.  John Hopkins University BIOS Division has been working on medical imaging to accelerate insights from scans on approximately 500 patients from 2,500 hours to 90 minutes, and lead to more accurate decision-making for brain injury patients that will ultimately improve medical outcomes.  

“We’ve aligned closely with the goal of showing that a cloud-based, AI-driven approach is robust and that it can be performed while protecting personal PHI. In terms of costs, the cloud has definitely reduced some of the traditional financial demands of our research.” —Daniel F. Hanley, Jr., M.D., Director, Johns Hopkins University BIOS Division

Improving data access with the flexibility of Google Cloud

With the help of MediaAgility, TRIARQ migrated to Google Cloud to modernize their platform, build new applications, and expand their global footprint.  With BigQuery, TRIARQ can now consolidate all transactional data, past and present, into a single location to report on specific insights and predict future data from e-prescriptions to complex revenue-cycle data and value-base analysis.

“The future of this industry will need to be a global one, and we can no longer be stuck in legacy systems if we want to survive. Having a team that is passionate about supporting us in this journey is definitely a plus point.”—Yaw Kwakye, Co-founder and Chief Architect, TRIARQ

Increasing visibility to COVID-19 outbreaks for actionable insights

As part of its response to the COVID-19 pandemic, HCA Healthcare chose to work with Google Cloud and SADA to create a national portal that increases visibility into outbreaks in 3,100 counties across the country in just 8 weeks.  Now, the portal generates 30,000 new analytical views each day that can help inform private and public sector decision making for reopenings, closures, hot spots, and many other population health management activities. 

“This project required deep knowledge of AI, and consumer-facing platforms, as well as healthcare. Google brought together the ideal combination of product, people, and partners. Google Cloud’s healthcare-specific products along with SADA’s expertise in the healthcare IT space made this partnership the perfect choice to move quickly and intelligently.”—Dr. Edmund Jackson, Chief Data Officer, HCA Healthcare

We’re committed to building the technology, resources, and services through Partner Advantage to help our partners address this opportunity.  Looking for a solution focused partner in your region who has achieved Expertise and/or Specialization in your industry?  Search our Global Partner Directory.  Not yet a Google Cloud partner? Visit Partner Advantage and learn how to become one today!

As we think about economic recovery from COVID-19—both inside Google and outside through working with Google Cloud customers—we’ve made many important observations. Among them is the recognition that the ways software developers and IT practitioners work together will shift in the post COVID-19 world. Our economic recovery today will look different than past recoveries, and on a fundamental level, the way we innovate will be different than it’s ever been before.

Right now, we’re entering a new phase of cloud computing, where businesses have shifted from making tactical infrastructure decisions, to making larger IT decisions with an eye towards enabling transformation throughout the company. Data, and what we can do with that data, is key to this transformation. And how companies put data in the hands of every employee to help catalyze transformation and solve the most important and impactful opportunities in their industries is at the core.  

A recent Google-commissioned study by IDG highlighted the role of data analytics and intelligent solutions when it comes to helping businesses separate from their competition. The survey of 2,000 IT leaders across the globe reinforced the notion that the ability to derive insights from data will go a long way towards determining which companies win in this new era.

Data analytics and intelligence were prioritized during COVID-19

The results of the IDG study show a separation amongst those organizations that embrace the capabilities of today’s data analytics and AI/ML tools and those that do not. When COVID-19 hit, many organizations cancelled IT initiatives, with 55% of respondents delaying or cancelling at least one technology project. However, 32% of respondents accelerated or introduced initiatives around building out or improving the use of data analytics and intelligence. IT leaders realize how critical data is to their future success, even when resources are scarce.

Digital-focused companies are faster to embrace advanced intelligence tools

Furthermore, enthusiasm for big data analytics, AI, and ML technologies is highest among companies who are further along in their digital transformation journeys. Fifty-four percent of companies who identify as “Fully digitally transformed” or “Digital native” are using or considering using these tools, vs. the global average of 37%. And, these same organizations are embracing the promise of AI more than their peers. Forty-eight percent felt that “Embedded AI across our full stack of cloud solutions will be critical” vs 39% of digital conservatives. These companies realize these digital tools enable them to be more resilient, agile, and prepared for whatever the future brings.

Companies are turning to cloud to maximize insights from data

As companies tap into the promise of data analytics and AI/ML, they are turning to cloud for help. When considering which cloud providers to work with, 78% of respondents said big data analysis is a “must have” or a “major consideration,” which placed this capability at the top of the list of consideration factors. This is not surprising, as cloud solutions address the most common pain points and barriers to innovation. Three of the respondents’ top four areas impeding innovation are addressed by cloud: Insufficient IT & developer skill sets (1st), security risks and concerns (2nd), and legacy systems and technologies (4th). Plus, cloud makes it easier to quickly launch a project, scale up or scale down, and pay for only what you use.

COVID-19 changed the very nature of business, and of IT. It forced IT leaders to decide where to put their scarce resources and big data analytics and AI/ML were, understandably, at the top of the list. To learn more about the findings, download the IDG report “No turning back: How the pandemic reshaped digital business agendas.”

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.

Advances in artificial intelligence (AI) and machine learning (ML) have led to increased adoption in the financial services sector. A prominent use for this technology is to assist in key compliance and risk functions, including the detection of fraud, money laundering, and other financial crimes and illicit finance, as well as trade manipulation — collectively referred to as “Risk AI/ML.” As the use of these models grows, so do questions about managing risks associated with the models. 

In particular, regulators, financial institutions, and technology service providers have been looking into whether existing Model Risk Management (MRM) guidance — which has traditionally been the regulatory regime applicable to managing model risk in the financial services industry — continues to be relevant for AI/ML models. And, if so, how should the guidance be interpreted and applied to this new technology?

As the financial sector increasingly adopts artificial intelligence and machine learning techniques, it is critical for regulators, financial companies and technology providers to work together to assure that there are clear rules of the road,” says Jo Ann Barefoot, AIR CEO and co-founder. “Updated guidelines on the responsible use of these models can help prevent novel technologies from causing harm, and can also open up better ways to combat risk in areas like money laundering, illicit finance, and fraud.

Our new white paper, written in partnership with the Alliance for Innovative Regulation (AIR), seeks to address that question, with the aim of fostering thought and dialogue among agencies, the financial services industry, risk model vendors, and entities interested in the performance, outputs, and compliance of models used to identify, mitigate, and combat risks in financial services. This white paper does not address issues that may arise with other applications of AI/ML in the financial services industry, such as consumer credit underwriting or models using generative AI or Large Language Models, which are better addressed iteratively. 

The paper argues that MRM guidance, given its broad, principles-based approach, continues to provide an appropriate framework for assessing financial institutions’ management of model risk, even for Risk AI/ML models. Working within an existing framework takes advantage of the knowledge and operational capabilities of institutions that already understand this framework, instead of having to create an entirely new approach, which generally takes longer to implement and make effective. Nonetheless, the paper recognizes that AI/ML models have unique traits and characteristics compared to conventional models, including their potential dynamism and pattern recognition capabilities. These distinctions must be in focus when considering how MRM guidance should be applied to Risk AI/ML models. 

Taking into account those unique aspects of AI/ML models, the paper offers specific observations and recommendations regarding the application of MRM guidance to Risk AI/ML models, including:

• Risk assessment: In assessing risk, it is important to recognize that AI/ML models are not inherently more risky than conventional models. A risk-tiering assessment must consider the targeted business application or process for which a model is used, as well as the model’s complexity and materiality. To assist in these assessments, regulators could clarify that the use of AI/ML alone does not place a model into a high-risk tier and publish further guidance to help set expectations regarding the materiality/risk ratings of AI/ML models as applied to common use cases.
• Safety and soundness: Due to the dynamic nature of Risk AI/ML models, reliance on extensive and ongoing testing focused on outcomes throughout the development and implementation stages of such models should be primary in satisfying regulatory expectations of soundness. To that end, the development of technical metrics and related testing benchmarks should be encouraged. Model “explainability,” while useful for purposes of understanding the specific outputs of AI/ML models, may be less effective or insufficient for establishing whether the model as a whole is sound and fit for purpose.
• Model documentation: The touchstone for the sufficiency of documentation should be what is needed for the bank to use and validate the model, and understand its design, theory, and logic. Disclosure of proprietary details, such as model code, is unnecessary and unhelpful in verifying the sufficiency of a model and would deter model builders from sharing best-in-class technology with financial institutions.
• Industry standards and best practices: Regulators should support the development of global standards and their use across the financial services and regulatory landscape by explicitly recognizing such standards as presumptive evidence of compliance with the MRM guidance and sound AI/ML risk mitigation practices. In addition, regulators should foster industry collaboration and training based on such standards.

Governance controls: Regulators should use guidance to advance the use of governance controls, including incremental rollouts and circuit breakers, as essential tools in mitigating risks associated with Risk AI/ML models.

In an era where AI technology has the potential to revolutionize financial services, we acknowledge the foresight of our regulators in setting a solid foundation and blueprint for navigating the labyrinth of potential risks through the MRM guidance,” says Philip Moyer, Global VP, AI and Business Solutions at Google Cloud. “We believe there is room for greater coherence and precision, enhanced risk-mitigation approaches, and refined best practices surrounding AI and ML risk models. Whether it’s in capacity building or information sharing, our call to action is for greater collaboration between regulators and financial institutions. We’re confident that our collective efforts today will help shape a more robust and resilient future for financial services.

We invite a discussion of additional considerations, including the importance of examiner and industry training and collaboration, as well as openness by regulators to continue to refine the MRM guidance as AI/ML technologies develop and standards emerge. 

Implementing our recommendations would advance several goals. It would help regulators, financial institutions, and technology providers work together to better serve their shared purpose of protecting the safety and soundness of the financial system. At the same time, implementing the recommendations and continuing work in this space would promote the adoption of cutting-edge technologies in the industry, including those that combat such scourges as money laundering, illicit finance, and fraud.

You can read the full white paper here.

Last week we announced the private preview of Duet AI for Google Cloud, an always-on AI collaborator that uses generative AI to provide help to developers and cloud users. This article gives you a detailed look at Duet AI for developers, showing how Duet AI can help provide developers with real-time code suggestions, chat assistance, and enterprise-focused customization. You can sign-up here to join our waitlist for Google Cloud’s AI trusted tester program.  

We believe that addressing these use case​​s with large language models (LLMs) will usher in a major leap in productivity in enterprise development. Duet AI uses Codey, a family of code models built on PaLM 2. 

Developers continuously seek ways to improve their productivity and, over the past few decades, these efforts have resulted in massive productivity leaps due to technological changes. From advanced debuggers and online developer communities, to modern IDEs/notebooks and cloud computing, each advance brought massive changes in productivity. Despite these improvements, developers still face numerous challenges, some of which are unique to cloud development:

• Disruptive context switching and friction when integrating a new tool or service
• Excessive time spent on repetitive tasks
• Time required to understand a new code base or project
• Large cognitive workload when working on large code bases or complex APIs

Duet AI for developers focuses on challenges and tasks across the development lifecycle:

Code/Boilerplate Generation — Developers can describe the tasks they have in mind as a comment or function name, such as creating a Cloud Pub/Sub topic. Duet AI will generate a reference implementation that can be reviewed and modified, so developers don’t need to spend time reading through multiple documentation pages.

Inline Code Completion — To reduce the time spent on repetitive tasks and minimize the cognitive workload of tasks such as writing repetitive code or retrieving variable names, Duet AI provides intelligent, context-aware code completion, helping reduce the time spent on coding and enhancing the quality of the written code.

Enterprise Customization — Organizations frequently have massive code bases and specific recommended frameworks and best practices, which generic code assistance solutions may not be best positioned to support. With Vertex AI, developers will be able to tune and customize the underlying models and connect them to the Duet AI experience, allowing for assistance optimized to the needs of the organization.

Code Explanation — Developers spend significant time and effort reading and understanding code written by their peers or external contributors. To help assist in this process, Duet AI for code assistance provides an “Explain this code” option available whenever a developer selects their code, allowing them to more quickly understand, map, and navigate unfamiliar code bases.

Code security guardrails — Code generated by Duet AI can also be scanned for vulnerable dependencies via Source Protect, helping surface known public vulnerabilities impacting code, along with suggested fixes when available, bringing additional security.

By harnessing the power of AI-driven developer assistance such as the one provided by Duet AI for developers, businesses can unlock unprecedented levels of productivity and efficiency in software development, paving the way for a new era of innovation and growth.

These early features of Duet AI for Google Cloud will be available for limited users and we will be expanding access very soon. Sign up here to join Google Cloud’s AI Trusted Tester Program.