With nearly 2,300 stores, The Home Depot is the world’s largest home-improvement chain — a brand that professional contractors and DIYers alike have come to depend on. The home improvement industry continues to experience unprecedented demand and dramatic increases in online ordering accompanied by expanding consumer expectations for things like curbside pickup and same day delivery. The Home Depot’s decision to migrate to cloud-based infrastructure, including the migration of the company’s SAP applications on Google Cloud which began in 2017, has set it up for success in an increasingly digital world, and helped the company adapt to changing market conditions quickly. 

Interconnected retail at scale

Building on a strong customer-first philosophy, The Home Depot aims to create what it calls interconnected retail—allowing customers to shop however, whenever, and wherever they want. “So many companies are focused on omni-channel retail,” explains Sam Moses, Vice President of Corporate Systems. “At The Home Depot, we wanted to take it to the next level. Interconnected retail puts the customer at the center of everything and enables them to shop in store, online, or both. Customers can begin a transaction online and continue in-store, or vice-versa.”

To support this strategy, the company’s SAP environment needed to be more agile. Running  everything on-premises, from central finance to POS systems, meant that The Home Depot’s IT teams experienced redundancy and repetitive, manual processes. Their data warehouse needed an upgrade to process and analyze growing and increasingly diverse data sets. The Home Depot chose to migrate its SAP environment to Google Cloud to support both the velocity and scale needed for the business as well as critical analytics capabilities needed for its bold digital initiatives. “We chose Google Cloud to support our SAP implementation. Our decision had a lot to do with the relationship between Google Cloud and SAP and also for the applications and services that are offered by Google Cloud, like BigQuery, which are helping to enable data and analytics within our organization,” Moses explains.

After migrating its SAP applications—including S/4HANA, its customer activity repository (CAR), general ledger, e-commerce system, enterprise data warehouse and more to Google Cloud, the company now has the speed, scale and flexibility to tackle enormous spikes in the business, all while staying fully available for their customers. Additionally, The Home Depot was able to transform its financial systems and make them more agile to deliver critical information across multiple business functions in real time.

Maximizing data insights to support customer experiences

By migrating to Google Cloud, The Home Depot is leveraging Google Cloud analytics to build   the industry’s most efficient supply chain including more robust demand forecasting, supplier lead times, estimated delivery times and more, all while maintaining better security than before. “We experienced unprecedented change in our customers’ behavior and their buying patterns, which puts a lot of pressure on our supply chain,” explains Moses. “So having the ability to leverage data and analytics gives us insights to know exactly what it is that our customers need.” 

The company’s analysts now use BigQuery ML for machine learning directly against the company’s BigQuery data and use AutoML to determine the best model for predictions. The Home Depot’s engineers have also adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time—capabilities that were not as seamless in the on-premises environment.

With hundreds of projects on Google Cloud, The Home Depot’s cloud journey is well on track, but the company is always looking to the future. “As our customers’ needs have continued to evolve, and as technology has continued to evolve, our relationship with Google will continue to advance — to be able to innovate together, to be able to find new solutions together, to better serve our customers.”

Learn more about how The Home Depot is renovating its retail operation with SAP on Google Cloud.

Alphabet CEO Sundar Pichai has compared the potential impact of artificial intelligence (AI) to the impact of electricity—so it may be no surprise that at Google Cloud, we expect to see increased AI and machine learning (ML) momentum across the spectrum of users and use cases.

Some of the momentum is more foundational, such as the hundreds of academic citations that Google AI researchers earn each year, or products like Google Cloud Vertex AI accelerating ML development and experimentation by 5x, with 80% fewer lines of code required. Some are more concrete, like mortgage servicer Mr. Cooper using Google Cloud Document AI to process documents 75% faster with 40% cost savings; Ford leveraging Google Cloud AI services for predictive maintenance and other manufacturing modernizations; and customers across a wide range of industries deploying ML platforms atop Google Cloud.

Together, these proof points reflect our belief that AI is for everyone, and that it should be easy to harness in workflows of all kinds and for people of all levels of technical expertise. We see our customers’ accomplishments as validation of this philosophy and a sign that we are taking away the right things from our conversations with business leaders. Likewise, we see validation in recognition from analysts, which recently includes Google being named a Leader by

Gartner® in the 2022 Magic Quadrant™ for Cloud AI Developer Services report

Forrester in the Forrester Wave™: AI Infrastructure, Q4 2021 report, the Forrester Wave™: Document-Oriented Text Analytics Platforms, Q2 2022 report, and The Forrester Wave™: People-Oriented Text Analytics Platforms, Q2 2022 report

In June, we talked about four pillars that guide our approach to creating products for MLOps and to accelerate development of ML models and their deployment into product. In this article, we’ll look more broadly at our AI and ML philosophy, and what it means to create “AI for everyone.”

AI should be for everyone

One of the pillars we discussed in June was “meeting users where they are,” and this idea extends far beyond products for data scientists. Technical expertise should not be a barrier to implementing AI—otherwise, use cases where AI can help will languish without modernization, and enterprises without well-developed AI practices will risk falling behind their competitors.

To this end, we focus on creating AI and ML services for all kinds of users, e.g.:

• DocumentAI, Contact Center AI, and other solutions that inject AI and ML into business workflows without imposing heavy technical requirements or retraining on users;
• Pre-trained APIs, ranging from Speech to Fleet Optimization, that let developers leverage pre-trained ML models and free them from having to develop core AI technologies from scratch;
• BigQuery ML to unite data analysis tasks with ML;
• AutoML for abstracted and low-code ML production without requiring ML expertise;
• Vertex AI to speed up ML experimentation and deployment, with every tool you need to build deploy and the lifecycle of ML projects
• AI Infrastructure options for training deep learning and machine learning models cost effectively. Including Deep Learning VMs optimized for data science and machine learning tasks and AI accelerators for every use case, from low-cost inference to high-performance training.

It’s important to provide not only leading tools for advanced AI practitioners, but also leading AI services for users of all kinds. Some of this involves abstracting or automating parts of the ML workflow to meet the needs of the job and technical aptitude of the user. Some of it involves integrating our AI and ML services with our broader range of enterprise products, whether that means smarter language models invisibly integrated into Google Docs or BigQuery making ML easily accessible to data analysts. Regardless of any particular angle, AI is turning into a multi-faceted, pervasive technology for businesses and users the world over, so we feel technology providers should reflect this by building platforms that help users harness the power of AI by meeting them wherever they are.

How we’re powering the next generation of AI

Creating products that help bring AI to everyone requires large research investments, including in areas where the path to productization may not be clear for years. We feel a foundation in research combines with our focus on business needs and users to inform sustainable AI products that are in keeping with our AI principles and encourages responsible use of AI.

Many of our recent updates to our AI and ML platforms began as Google research projects. Just consider how DeepMind’s breakthrough AlphaFold project has led to the ability to run protein prediction models in Vertex AI. Or how research into neural networks helped create Vertex AI NAS, which lets data science teams train models more accurately with lower latency and power requirements.

Research is crucial, but also only one way of validating an AI strategy. Products have to speak for themselves when they reach customers, and customers need to see their feedback reflected as products are iterated and updated. This reinforces the importance of seeing customer adoption and success across a range of industries, use cases, and user types. In this regard, we feel very fortunate to work with so many great customers, and very proud of the work we help them accomplish.

I’ve already mentioned Ford and Mr. Cooper, but those are just a small sampling. For example, Vodafone Commercial’s “AI Booster” platform uses the latest Google technology to enable cutting-edge AI use cases such as optimizing customer experiences, customer loyalty, and product recommendations. Our conversational AI technologies are used by companies ranging from Embodied, whose Moxie robot helps children overcome developmental challenges, to HubSpot connecting meeting notes to CRM data. Across our products and across industries around the world, customer stories grow by the day.

We also see validation in our partner network. As we noted in the pillars discussed in June, partners like Nvidia help us to ensure customers have freedom of choice when building their AI stacks, and partners like Neo4j help our customers to expand our services into areas like graph structures. Partners support our mission to bring AI to everyone, helping more customers use our services for new and expanded use cases.

Accelerating the momentum

Overall, to create products that reflect AI’s potential and likely future ubiquity, we have to take all of the preceding factors, from research to customer and analyst conversations to working with partners, and turn them into products and product updates. We’ve been very active over the last year, from the launch of Call Center AI Platform in March, to the new Speech model we released in May, to a range of announcements at the Google Cloud Applied ML Summit in June. We have much more planned in coming months, and we’re excited to work with customers not just to maintain the pace of AI momentum, but to accelerate it. To learn more about Google Cloud’s AI and ML services, visit this link or browse recent AI and ML articles on the Google Cloud Blog.

GARTNER and MAGIC QUADRANT are registered trademarks and service marks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Google Workspace recently commissioned Economist Impact to complete a global survey (October 2021)* on the state of hybrid work, including its challenges and opportunities. We already knew that the pandemic had fundamentally changed the world of work, but the survey emphasizes the scale, reach, and longevity of those changes.. 

Over 75% of respondents believe that hybrid/flexible work will be a standard practice within their organizations in the coming three years. Given that 70% of respondents said they never worked remotely before the pandemic, it’s clear that hybrid has become the dominant model for work and that it’s here to stay. 

But it’s also clear, as we’ll see below, that hybrid has some serious gaps that need to be addressed if it’s going to be sustainable and successful in the long term.

Defining hybrid work

As part of our work on the hybrid work survey and a broader project with The Economist Group, we interviewed a group of experts across research, consulting, business, and the advocacy world to arrive at a definition of hybrid work that captures all ways work is changing.

As Brian Kropp, one of the interviewees and vice president at Gartner, puts it: “Hybrid work is not just about different locations, but also different timings and different schedules.”

Harriet Molyneaux, managing director at HSM Advisory, a future-of-work research and advisory group, adds to this notion by describing the time-location work spectrum: “At one end of the spectrum is everyone in the office, nine till five, so both restricted time and location. And then at the other end of the spectrum is anywhere around the world at any time. So no restricted time or location. A hybrid is anything that sits in the middle of that.”

I agree with Brian and Harriet’s views. Flexibility in both location and hours is a core part of our working definition of hybrid work: a spectrum of flexible work arrangements in which an employee’s work location and/or hours are not strictly standardized.

Given this definition and framework, how is hybrid work faring and what lies ahead? 

Individual wellbeing is coming at the cost of organizational connection

Early on in the pandemic, productivity remained steady or even increased for many organizations. But it came at a cost, with levels of burnout spiking as employees juggled caregiving, homeschooling, and other demands in their personal lives with work responsibilities.

Based on the survey data, wellbeing has made an upward shift, no doubt aided by things like students returning to schools in many regions and fewer demands being made of working parents. The majority of respondents said that hybrid work, based on their own experiences, can have a positive impact on the physical, mental, financial, and social wellbeing of employees.

But it appears that individual wellbeing is coming at the cost of organizational connection. The majority of respondents said they feel disconnected from their organization and co-workers (57%), that limited networking opportunities negatively impact career growth (62%), and that limited social interactions with co-workers has had a negative impact on their mental health (54%).

To be sustainable, hybrid work models must address this sense of disconnection in real and tangible ways. And it won’t happen just by increasing the number of virtual meetings. Although 72% of people say that virtual meetings improve inclusion and participation, 68% also say there are too many virtual meetings to begin with. We need new ways for people to connect spontaneously.

Hybrid workers are often using tools built for a bygone desktop era

When asked about the most important conditions needed to achieve the long-term success of hybrid work models, the number one choice globally was “new technologies that allow for time and location flexibility.” 

Naturally, it was gratifying to see this, since Google Workspace is built on a cloud-based platform that empowers collaboration from anywhere, on any device, but the survey answer also highlights how much organizations have had to scramble to meet the demands of the pandemic. Some had to take legacy, office-centric systems and make them work for a distributed workforce overnight, often leveraging less-than-ideal technologies like VPNs that introduce digital friction.

As a result, top technology concerns of respondents include:

• unreliable internet access (if only all our WiFi connections were hybrid-ready!)
• reliance on slow or outdated tools
• accessing and maintaining files in multiple places
• relying on too many applications in order to get work done

Hybrid work, it seems to me, doesn’t need more applications and collaboration surfaces. It needs deeper, more meaningful connections in the tools and surfaces we already have. 

As organizations assess their tech stacks, they should consider whether they can shape the behaviors they want from hybrid employees (e.g., real-time collaboration, ease of information sharing) with the tools they have in place. Or are the limits of the technology determining hybrid employee behavior?

The management and culture gap

In the same way that tools have often been built for a shared physical workspace, organizational culture seems to lag behind the hybrid moment. The majority of respondents said that a lack of face-to-face supervision creates a sense of distrust among managers and employees, and that they feel stressed by increased monitoring associated with flexible work. And more than 62% said that limited networking opportunities with senior employees and co-workers has a negative impact on career growth.

Strikingly, more than 70% of respondents indicated that the culture of trust between managers and employees needed improvement. Training and management best practices (60% of people want more of both) might help fill some of the gaps, but a manager can’t build trust with their hybrid teams by going through a training program. 

The role of manager must itself evolve to meet the demands of a hybrid model. How can we empower managers to be the bridge between the “office of one” and the “office of many”? And as I discussed previously on Forbes, driving towards impact rather than output is a more sustainable metric for team engagement and productivity. It also dispenses with monitoring as a core part of a manager’s role, freeing them up to be a coach-and-connector.

How Google Workspace can help bridge the hybrid work gaps

No one has all the answers for how to make hybrid work successful, and I suspect we will see iterations of the model into 2022 and beyond as organizations—including Google—experiment with the right mix of location, culture, processes, and tools. 

On the technology front, Google Workspace is uniquely positioned to bridge many of the emerging hybrid work gaps. Over the last year, we’ve delivered a set of innovations that are designed to deepen collaboration experiences while strengthening social connections within and across teams. 

For example, we launched smart canvas to bring new collaboration capabilities to the places where people are already working together, helping to keep them connected, rather than having them switch tabs or open new apps. And we delivered Spaces as a central place for collaboration, where teams can share ideas, work on documents together, and manage tasks from a single place. Because all their work is preserved for future reference, team members can easily jump in and contribute at a time that works best for them, seeing a full history of the conversations, context, and content along the way. Spaces helps people maintain individual wellbeing while preserving the health of a group project.

In Google Meet, we’ve introduced real-time captions in multiple languages (in preview now), the ability for people to customize their video tiles, including being able to turn off their own to help with meeting fatigue, and we’ve implemented automatic light adjustments and noise cancellation. These changes help ensure that everyone can be seen and heard.

Meanwhile, features like hand-raise, Q&A, and polls have helped make meetings more inclusive and companion mode (in preview) can bring these to life in hybrid settings, to help ensure that there’s one cohesive conversation between the people in the office and their colleagues working somewhere else. We view companion mode as a bridge between the office and “somewhere else.”

On the personal wellbeing front, we launched Focus Time, which lets people block out their calendars for uninterrupted focus work, and Time Insights in Google Calendar, where employees can look at how they’re spending their time and adjust as needed.

As hybrid work continues to evolve, Google Workspace is committed to supporting wellbeing and seamless collaboration with tools that can remove digital friction and help people maintain connections—to each other and the organizations they work for.

^{*Survey details: The survey, completed in October 2021, polled a total of 1,244 employees and managers in four regions (North America, Europe, APAC, and Latin America), from more than 15 industries, in every age group, and from both small and large organizations. The focus was on knowledge workers, though it’s important to note that some of those people have been working on the frontlines; 20% of respondents indicated they haven’t worked remotely at all during the pandemic. This includes people in hospitality, retail, transportation and logistics, and healthcare.}