Campaigntrack, Australia’s largest real estate marketing company, faced several challenges in its customer service department. The company initially relied heavily on individual agents for support, primarily via phone and email. However, as the company grew, this structure proved unsuitable due to the dependency on individual agents, leading to issues when they were unavailable. To address this, Campaigntrack tried creating agent groups to handle issues, but this led to difficulties in coordination, especially when the team was not in the same location, resulting in missed inquiries. Furthermore, there was a lack of transparency regarding the team's activities and workload balance. The company was also receiving a high volume of phone calls, approximately 8000, which needed to be reduced and distributed more evenly across other channels such as email and self-service.

Mindvalley, an ed-tech company, was facing a challenge in driving up lead acquisition for its masterclasses. The company was primarily dependent on swipe up stories and link in bio to drive people from awareness to the masterclasses. With over 1.3 million Instagram followers, the company was receiving a large number of direct messages about its masterclasses. The team was looking for a solution to respond to these messages more quickly than manually answering them. The goal was to use Instagram Automation not only to increase masterclass sign-ups but also to provide a more personalized experience for followers and drive more engagement.

Pricemoov, a yield management solution provider, faced a significant challenge in handling and cleaning data from old SI systems, Oracle, or MySql. The data was dirty and required a full-time developer to perform long ETL (extract-transform-load) steps in PHP for cleaning. Once cleaned, the datasets were painstakingly entered into a model, as they were custom-built pipelines. The replication and deployment process for the next customer was taking weeks. This slow and inefficient process was hindering Pricemoov's ability to provide optimal pricing suggestions and solutions to its customers in a timely manner.

Across Europe, billions of Euros are being invested in upgrading rail infrastructure with the aim of carrying more passengers, on more trains, more regularly, with on-time arrival, at a lower cost. Siemens, a leader in engineering solutions for the rail industry, is at the forefront of this transformation. The company uses data collected from over 300 sensors on each train, combined with historical data, to predict when components might fail. This IoT-driven approach, dubbed the 'Internet of Trains', ensures greater uptime for train operators, fewer delays for passengers, and more cost-effective maintenance. However, the challenge for Siemens lies not in the collection, but in the storage, management, and analytics of this vast variety of data. Furthermore, as an international company, Siemens must ensure that the data is stored according to local laws in the most cost-effective way possible.

Simplitium, a UK company delivering ModEx, a catastrophe risk modeling platform for the (re)insurance industry, faced a significant challenge. The industry had been relying on outdated technology for the better part of three decades, using resource-heavy, legacy in-house hosting systems. These systems were used to analyze risks and potential damage of events such as natural catastrophes, helping (re)insurers calculate the financial implications to ensure they have enough capital to pay the claims and remain solvent. However, as cloud and hosted environments developed significantly, these legacy systems became increasingly inefficient and costly. Simplitium needed a reliable colocation partner with a strong presence in the insurance sector to help grow its customer base. They required a solution that would offer clients faster roll outs, lower costs, and zero downtime.

Riyad Bank, one of the largest financial institutions in Saudi Arabia, was undergoing a company-wide digital transformation. The aim was to automate and energize processes, from customer experience to the recruitment process. This required not only better tech solutions but also attracting and hiring top tech professionals. The bank was facing the challenge of streamlining its recruitment process to attract top talent and stay ahead of the competition. The traditional recruitment process was time-consuming and less efficient, making it difficult to attract and hire the right candidates.

Specsavers, a global optician with a 51% market share, was facing a significant challenge in its growth strategy due to the scarcity of both new and experienced talent in the healthcare sector. The competition for healthcare talent was becoming increasingly difficult, posing a major risk to the company's expansion plans. Specsavers had an existing database of candidates but lacked an effective way to engage these candidates and identify those interested in applying for a role. The company needed a solution that could tap into this database and refresh it, a key strategy in recruiting top talent in a competitive job market.

Autofleet, a leading solution provider for fleet and mobility operators, was faced with the challenge of offering its customers a platform that was secure, reliable, and scalable. The company provides solutions for asset-heavy fleets and mobility operators to optimize their operations and launch new on-demand ride services. The comprehensive and modular nature of the company’s solutions meant that reliability, security, and performance were crucial to its operations. Failures and downtime could have a negative impact on their customers' core businesses. Furthermore, the company needed to be able to scale at will and with little notice. Whenever Autofleet signed a new client, the platform was immediately subjected to high volumes of extra traffic, which posed a significant challenge.

Avanza, a leading business process outsourcing (BPO) provider in Spain, faced several challenges in its operations. The company, which offers a wide range of services from retail operations to human resources to call center support, had to maintain efficiency while dealing with the cyclical nature of the BPO sector and tight margins. With over 7,000 employees spread across six countries, Avanza struggled with collaboration and communication, largely due to its outdated email platform that led to siloed working and the burden of maintaining its own servers. Furthermore, Avanza wanted to incorporate machine learning and artificial intelligence into its business solutions, but its existing IT infrastructure was not equipped to handle these advanced technologies. The company was at a crossroads in 2016, deciding whether to continue with its current platform or explore new options.

Balgrist Campus, an internationally renowned research institute for musculoskeletal issues, was facing challenges in expanding its research on lower-back pain. The Integrative Spinal Research (ISR) group at the campus was gathering data from patients across a broad range of symptom durations, using an on-premises cloud setup linked to a network of supercomputers to process and analyze the information. However, this setup had limitations. The growth of the ISR research team and the success of their projects presented problems, including the need for more workstations and increased computing power. The expense of purchasing additional infrastructure and the desire for a more cost-effective solution led the team to seek alternatives.

Banco BV, one of Brazil's largest private banks, was seeking to drive technological innovation more effectively. A significant part of this journey was migrating its infrastructure to Google Cloud from another cloud provider for more flexibility and improved asset allocation. The bank's long-term goals with Google Cloud included using data intelligence to transform customer experience, increasing the potential of open platforms, creating new digital products, and refining the company's data science models. However, the bank faced the challenge of building a team to implement a FinOps culture, which involves adopting financial management best practices for its technology infrastructure. This was crucial as Banco BV was increasing its investment in digital and strategic evolution and needed enhanced cost visibility to create cost optimization and reallocation processes.

Band Aid 30, a global philanthropic initiative aimed at raising funds for Ebola relief, was faced with the challenge of organizing a live performance featuring dozens of top music artists. The project was to be executed in less than 14 days, with a small team of two web developers, Mukesh Randev and Jonathan Horne from Adtrak, a UK-based media agency. The task was to deploy a website that could handle heavy traffic from millions of followers of the participating artists. The website was to be hosted on Google Cloud Platform, a platform that was relatively new to the developers. The challenge was to ensure the website remained online and could handle the anticipated heavy traffic, while also being cost-effective.

Banlinea, a Colombian fintech company, was in search of robust technologies to leverage data for its digital transformation and online sale of financial products. The company aimed to change people's lives by helping them make better financial decisions. To achieve this, it needed to understand users' behaviors using digital tools. However, it was critical for Banlinea to have a robust infrastructure that provided the necessary security and certifications to assure its customers. The company also needed the ability to scale its products globally and required new data analysis tools to detect patterns from people's behaviors to develop products that meet their financial needs. Furthermore, Banlinea needed a technology that was not limited to a single type or brand, allowing for versatility, capacity, and scalability regardless of the increasing number of simultaneous customers. Business challenges included quick responsiveness in the development of its products, timely delivery of information, and a high level of security.

BasisAI, a company that helps enterprises accelerate AI adoption, faced several challenges in its mission to deploy responsible AI applications. The company needed to ensure that the AI systems it helped develop were free of biases, which could potentially lead to loss of consumer trust if certain groups of customers were favored over others due to AI system biases. The process of taking AI from code to production required a tight collaboration between data scientists and DevOps within an organization, which could be complex and time-consuming. Additionally, managing the infrastructure for machine learning operations (MLOps) was a significant burden, particularly in terms of resource allocation and dealing with traffic spikes. BasisAI also needed to ensure robust monitoring of AI models to prevent downtime and manage cloud consumption costs. Finally, ensuring data privacy and security was crucial, especially for customers in regulated industries.

BE International, a direct marketing company, faced significant challenges with its e-commerce application, BE4U. The application was initially built on a monolithic architecture, which began to show its limitations as the company's membership base and transaction volume increased. Three major events - flash sales, new product launches, and month-end transaction processing - would cause the application to shut down, leading to a chaotic situation where invoices had to be manually generated. This not only jeopardized the company's image and sales revenue but also led to numerous complaints from members. The company needed a solution that could handle high traffic without compromising performance, allow the in-house team to focus on development rather than infrastructure issues, and improve overall member and customer experiences.

Beryl, a leading micromobility company in the UK, was faced with the challenge of ensuring its bikes, e-bikes, e-cargo bikes, and e-scooters were available at the right places and times to meet customer demand. The company aimed to encourage people to switch to sustainable transport to reduce traffic congestion, carbon emissions, and improve mental and physical health. However, to achieve this, Beryl needed a reliable, glitch-free customer-facing app and fully functioning vehicles. The company also needed to manage and analyze the vast amount of data generated by docking stations, vehicles, users, logistics teams, and maintenance crews to continuously improve its service.

BigChange, a company launched in 2013, has been instrumental in monitoring and managing over eight million jobs and tracking driving miles equivalent to over 100,000 trips to the moon. However, they identified a significant challenge in the industry. Many traditional companies requiring fleet and workforce management were wasting time, money, and fuel managing their mobile workforces due to reliance on outdated technology, manual reporting, or paper-based systems. BigChange aimed to build a system that would eliminate many of these antiquated processes, thereby improving efficiency and productivity.

Birlasoft, a global enterprise IT solutions leader based in India, was faced with the challenge of migrating its own SAP systems from on-premise to the new cloud-based RISE platform. This was a necessary step for Birlasoft to gain firsthand knowledge of the solutions it offers and provide best-in-class RISE transformation to its clients. The migration required selecting a public cloud hosting infrastructure that offered unlimited scale and agility to fully utilize the power of RISE. Birlasoft considered various providers before deciding on SAP on Google Cloud, a custom solution of Google Cloud to build, deploy, and manage SAP systems. The decision was influenced by Google Cloud's world-class compute capabilities, secure infrastructure, and intelligent cloud tools for machine learning and artificial intelligence transformation.

BK Medical, a manufacturer of ultrasound devices with operations in Denmark and the United States, faced a significant challenge when it decided to separate from its parent company. The company had been running SAP applications in-house with on-premises servers. The divestiture necessitated the separation of its data and systems, which presented an ideal opportunity to transition to cloud-based IT. However, the company needed to maintain its own data center while minimizing capital expenditure and reducing its on-premises footprint. The challenge was magnified due to the complexities of manufacturing and distributing medical devices globally, which included compliance, data and process management challenges, distribution, resource planning, and customer experience complexities.

Aerospike, an open-source, flash-optimized, in-memory NoSQL database, was looking to push the boundaries of Google's speed on Google Compute Engine. The challenge was to meet high throughput, consistently low latency, and real-time processing, which are characteristic of future cloud applications. The team at Aerospike was inspired by Ivan Santa Maria Filho, Performance Engineering Lead at Google, who demonstrated 1 Million Writes Per Second with Cassandra on Google Compute Engine. The goal was to benchmark Aerospike's product performance on Google Compute Engine and see if it could scale with consistently low latency, require smaller clusters, and be simpler to operate.

Almundo, a leading travel technology company and omnichannel agency, was facing a challenge in transforming its ecosystem into a more collaborative model. With operations expanding throughout Latin America, the company had over 800 professionals and more than 330 travel experts working in various areas of the business. The goal was to create a more coordinated work environment that would have a positive impact on productivity. However, the company was relying on legacy tools that were not conducive to collaborative work, distributed access, and productivity of work teams. The challenge was to find a solution that would meet these specific requirements and help the company maintain its leadership position in Latin America.

ARIGATOBANK Inc., a Tokyo-based financial services business, operates a platform called kifutown that connects donors with those in need of financial aid. The platform has grown significantly, handling over 2,000 donation projects as of March 2022. However, the company faced challenges in managing sudden traffic surges, particularly due to the influence of high-profile shareholder Yusaku Maezawa. A single social media post from Maezawa could lead to a significant increase in traffic, posing a challenge to the platform's infrastructure. The company needed a solution that could handle these traffic surges without impacting availability and latency. Additionally, ARIGATOBANK wanted to maintain development speeds and quality while undertaking progressive development, which required clear delineation of responsibilities and efficient operation of the application server.

Asahi Group, a leading food and beverage company, faced significant challenges in its global operations due to its legacy on-premises infrastructure. The company's global units, often born of acquisitions, needed to collaborate seamlessly and in real time. However, the existing infrastructure was causing bottlenecks even in routine tasks such as onboarding new employees, as data needed to be funneled through a centralized hub server. The company's ambitious multi-year cloud modernization program aimed to overhaul its legacy infrastructure to a modernized cloud architecture by 2027. However, the sprawling global business could not afford to skip a beat even in the midst of this transformation. The challenge was to ensure seamless data sharing and cooperation across platforms, offering transformative agility, scaling power, and robust security.

The Customer, a pioneer in Autonomous Systems, was faced with the challenge of migrating its computer vision cloud platform to the Amazon cloud within a four-month timeframe. The migration was necessary to enable the platform to perform highly scalable, real-time weapon detection to identify firearms and suspects in high-security environments. The goal was to provide security and safety to essential businesses, communities, and schools through real-time human behavior recognition and weapon detection technologies, enabled by AI & Machine Learning. The Customer was also looking to protect communities by bringing AI-driven visual imaging and human behavior recognition technology to every school, public building, and business across the country. They wanted to develop a weapon detection solution that they could integrate with their apps in the AWS cloud, to be able to deter, detect, and defend against shooters quickly and efficiently.

The advent of education technology has revolutionized classrooms worldwide, providing a plethora of tools to enhance learning. However, this surge in educational tools also presents numerous challenges, such as identifying the most effective learning devices and equipping them with reliable management and monitoring tools. Blocksi, a company launched in 2011, initially provided internet filtering services through a Chrome-native app. The company soon realized that a significant number of families and schools were downloading its app, prompting it to tailor its tools for the education sector. The challenge was to expand its reach in education and power its business while ensuring the safe and compliant use of devices in schools.

ETH Zurich's Biomedical Informatics (BMI) Group is working on creating the world's largest-ever DNA search index by processing 4 petabytes of sequencing data. The goal is to make the world's genetic code more accessible for medical and scientific research. However, the team faced significant challenges in terms of data accessibility and processing. Despite having access to a vast amount of information in the National Center for Biotechnology Information (NCBI) repository, existing methods did not allow for the most effective use of these datasets. The team's ambitions were curtailed by their other major obstacle: efficient accessibility. Before the switch to Google Cloud, the BMI Group had to limit its operations to smaller sequencing datasets of several terabytes in size, just to keep download and processing times manageable.

bofrost*, a European market leader in direct distribution of frozen food, was looking to evolve its business model to keep up with the growing global market for frozen foods, projected to reach $282.50 billion by 2023. The company's IT infrastructure relied heavily on legacy systems, with two on-premises data centers in Germany and Italy. Information about products was provided to customers via a twice-yearly print catalog, and salespeople used a basic device to access details about orders to be delivered. However, they couldn't access multichannel order histories and other additional information. The company wanted to transform its IT infrastructure to enable new ways of meeting its customers' needs, including a new point of sale (POS) platform to streamline delivery schedules and provide personalized suggestions to facilitate upselling opportunities.

Botmaker, an AI platform that creates and administers voice and text enabled bots, aimed to provide exceptional customer service across various channels. The challenge was to automate conversations between brands and people using AI, requiring an infrastructure capable of processing millions of messages, understanding them, and providing accurate responses in real time, 24/7/365. This was crucial to maintain the brand's goodwill with its customers, making data processing speed and scalability a significant technical challenge. Additionally, Botmaker had to meet strict safety requirements imposed by various customers, including large banks and international insurance companies.

Digital marketing firms are often faced with the challenge of managing new leads efficiently. The traditional method involves manually checking Facebook Lead Ads every hour or two, or exporting a list of new leads at the end of each day for follow-up. This process is not only monotonous but also time-consuming, leading to delays between a lead entering their information and the firm's response. The firms also have to monitor mentions of their brand on social media and across the internet, which is a full-time job in itself. Keeping an eye on competitors adds to the workload, making it almost impossible to track new mentions without missing some. The firms needed a solution that would automate their lead workflow, eliminating the need for manual work and ensuring quick response to new leads.

Lenovo, a multinational technology company, was facing a challenge in balancing supply and demand for its products among Brazilian retailers. The company aimed to predict the sell-out volume, the number of units of a product that retailers sell to customers, but was constrained by resources. The team had started developing R code to predict sell-out volume, with a goal to have it updated weekly for their top ten retail customers. However, with only 2 people writing 1,500 lines of R code for one customer each week, reaching their target of predictions for ten customers each week was impossible. The team needed to either invest in more data scientists or find a tool that could automate all the modeling and forecasting steps.