The pulp and paper market, valued at USD519 billion in 2019, is projected to reach USD680 billion by 2027. With this rapid growth, safety standards, particularly fire protection, have become a priority. Paper dust, a byproduct of the manufacturing process, is highly combustible, leading to the risk of dust explosions at manufacturing facilities. A paper pulp mill in eastern Canada recognized the need for enhanced fire protection at their facility. The existing system, a vertical turbine pump, was insufficient. The facility managers sought a more comprehensive solution that would meet the future demands of the mill and provide peace of mind.

The case study presents three different enterprises: a Top-5 Global Retail Bank, a UK Retailer, and a European Multiline Insurer, each facing unique challenges in enhancing their customer experience (CX). The bank was struggling with personalizing CX, requiring more granular detail in their data and analytics, and managing CX across all their digital channels. The UK Retailer was unable to maximize customer relationships due to a lack of insight into their customers' online activities. Their aggregated data was always 24 hours out of date, and they could only infer what customers wanted based on past behavior. The European Multiline Insurer was finding it difficult to capture insights from customers self-serving online. The limited data they had was typically 48 hours old, making it impossible to react to customers in the moment.

Exchange Agency, a digital media agency specializing in PPC advertising, faced a significant challenge in managing client meetings. As a digital agency, they often worked remotely with their clients, which necessitated taking notes during conversations. However, this process often slowed down their discussions, which was inconvenient for their busy clients. The impact of this problem was twofold. Firstly, important notes would sometimes be missed during a conversation, as the team would get caught up in formulating the perfect response to an unexpected question. Secondly, this issue often resulted in missing crucial feedback or questions during the meeting, which could potentially affect the quality of their campaigns and client satisfaction.

Titan Flow Control, an industrial pipeline equipment manufacturer, was facing a significant challenge in increasing its 'spec rate' - the frequency with which its products are specified in project plans by engineering or architecture firms. This specification is a prerequisite for sales in Titan's industry. Traditionally, companies relied on field sales teams making in-person calls to drive awareness and understanding of their product catalog. However, in the modern era, companies like Titan need to scale and accelerate this process. Furthermore, Titan's end customers typically require a significant level of customization of the firm’s core products, meaning customers need to be able to both understand the products and communicate their own customization requirements. Another challenge was the language barrier and lack of clarity in communication with suppliers, especially from China, due to reliance on text-based communication.

CTI Solutions Group was facing a significant challenge in managing their reporting solution for customers. The company's clients were increasingly involved in monitoring various aspects of their businesses, including wholesale inventory, sales by style, and SKUs. CTI Solutions offered analytics via Excel and other tools in both HTML and downloadable formats. However, the company was struggling to keep up with programming and maintenance, despite having a team of 12 local full-time programmers and another 30 offshore. Six of these programmers were dedicated to maintaining task reporting for 93 countries throughout the world. The company realized that they needed a more automated, more uniform solution that could present information in a variety of ways, both online and downloadable. If they didn't move to a more agile reporting and data handling system, they would not be able to satisfy their clients' growing needs for timely information or hire the talent they needed to grow.

The client, a global Univar solution company specializing in chemical manufacturing, was facing several challenges with their Azure Cloud Services and Azure Kubernetes Service (AKS). The issues included unavailability of application for 30 minutes during AKS update, AKS vulnerability, lack of support for on-premise systems, complex deployment in CPT and their PDMS application involving AI/ML, redundant storage accounts and application service plans, insufficient pods and inefficient pod deployments. The client also expressed a need for continuous support and monitoring of their AKS, and a desire to reduce their turnaround time by implementing Azure best practices.

Shopify operates two ecommerce stores: The Shopify Hardware Store and Shopify Supply. The Hardware Store sells hardware products for brands’ retail locations, including card readers, label printers, barcode scanners, sustainable packaging, and retail stands. However, Shopify noticed that merchants visiting the Hardware Store were having difficulty deciding what hardware they needed and which products they should bundle together. They identified three key challenges: the need for a virtual consultation to help merchants navigate which hardware is best for their store’s setup, the need to collect zero-party data to learn more about the merchants visiting the hardware store, and the need to collect merchant’s email addresses to notify them when a product they wanted was back in stock.

Premier Search Inc., a professional staffing company, was facing a significant challenge in sourcing fresh candidates for their clients. The company, led by President and Executive Recruiter Mike Albanese, specializes in hiring the best outside sales representatives. However, they were struggling with the limitations of LinkedIn Recruiter. Despite using various search options, they kept encountering the same pool of candidates, which was limiting their ability to provide a diverse range of potential hires for their clients. The repetitive results from LinkedIn's algorithms were causing frustration and hindering the company's ability to meet its clients' staffing needs effectively.

Heetch, a French mobility company, was struggling with the management of large quantities of data gathered from drivers, passengers, and global operations. As the company grew, the costs of their data warehouse were spiraling out of control and performance was suffering due to the increasing volume of data. They needed a solution that would allow anyone in the organization to work with large amounts of data while also ensuring optimized resource allocation. The challenge was to find a way to leverage big data with good performance and at reasonable costs, which required serious computational power, optimized resource consumption, and isolated environments for development and production. Managing all these aspects was becoming increasingly complex for the organization.

Freo, a neo-bank for millennial Indians, was grappling with a significant challenge common to many Fintech brands - customer drop-offs during the customer journey. The complex set of steps that customers had to complete, such as KYC registrations, made customer retention a major issue. The company was struggling to maintain engagement and keep customers on board throughout the entire process. This was a significant problem as it was affecting their conversion rates and overall customer satisfaction.

ClearGate, a company that provides merchant services and a payment gateway, was facing challenges with the limitations of third-party software. The company was in need of a new engagement platform that could offer superior customer service, seamless integration, and efficient execution. The existing software was not able to meet the dynamic needs of ClearGate, which was affecting their ability to manage administrative tasks and workflow effectively. The company was looking for a solution that could help them overcome these limitations and improve their overall operational efficiency.

Confiar, a Colombian Financial Cooperative, was facing a significant challenge in safeguarding its clients' sensitive data against fast-moving threats like ransomware. Despite having traditional security tools such as firewalls, anti-virus, and email protection, the Cooperative needed a solution that could understand user behavior and protect against novel and sophisticated threats that could evade these traditional solutions. The Cooperative was also aware of the increasing prevalence of social engineering, hidden malware, and unauthorized data transfers, and recognized the need to incorporate AI into its security stack to protect its users and data in this new era of dynamic cyber threats. During the initial trial of Darktrace's Self-Learning AI, several vulnerabilities across employee devices were identified that the organization wasn't previously aware of.

GeBBS Healthcare Solutions, a global provider of healthcare revenue cycle management (RCM) and health information management (HIM) solutions, was faced with the challenge of securing their office and ensuring compliance with HIPAA standards. The company handles a significant amount of private healthcare data, making it crucial to have a robust access control solution. With offices located internationally, GeBBS had to manage the constant inflow and outflow of employees and visitors at their Culver City headquarters. The company sought a solution that would not only provide stringent security but also offer the flexibility to grant temporary access to visitors and keep a record of all individuals entering the office.

SKIDATA, a tech-forward vehicle access company with multiple offices across the US, was struggling with its existing on-premise security systems. These systems were not keeping pace with the technology required to operate an efficient company. Each office required its own servers and management, leading to a complex and inefficient system. Employees needed different credentials for each location, adding to the complexity and inefficiency. Furthermore, with the growing trend of contactless technology in their industry, SKIDATA was in need of a system that could support contactless functionality.

C & L Aqua Professionals, Inc., a wholesale chemical distributor and manufacturer, was facing operational inefficiencies due to the use of non-industry specific software, Peachtree Accounting, for 17 years. The software forced the company to re-key information and duplicate data into other software packages, leading to redundancy and inefficiency. The company was in dire need of a software solution that could eliminate these redundancies and streamline their business processes. The challenge was to find a software vendor that could offer a complete solution tailored to their industry-specific needs.

PING, a leading golf equipment manufacturer, faced a significant challenge in managing its supply chain due to the high level of customization it offers. The company custom-fits each club to the owner's body type, swing style, physical strength, and personal preference, resulting in over 7 million possible specification combinations for its irons alone. This complexity, coupled with the need to pre-build finished goods based on projected orders, created a profound manufacturing and supply chain planning challenge. Additionally, PING had to accommodate changing player preferences and trends in playing styles. The company's existing planning process, which relied heavily on Excel, was unable to handle the volume of SKU configurations or exploit the value of real-time sell-through information from retailers.

PandoLogic, an AI-based programmatic job advertising platform, was facing challenges in operationalizing machine learning (ML) on premises due to lack of DevOps or infrastructure. Despite having a lean team of data scientists, their productivity was constantly interrupted with DevOps tasks and infrastructure challenges. They lacked the resources and infrastructure to operationalize their impressive models to achieve real business results. They were limited to on-premise deployment which caused technical challenges and DevOps overhead. They required dynamic Spark Clusters to handle terabytes of data, which wasted weeks of set up time, and caused major overhead costs to maintain. The PandoLogic team wanted a way to train and deploy on premise and in multiple clouds, without being locked into a single cloud. They needed an easy way to leverage open source tools and the compute resources they already had.

ailslab, a small bioinformatics research group, is dedicated to building machine learning models to predict cardiovascular disease development using clinical, imaging, and genetics data. The research process is intense, requiring a custom infrastructure to extract features from various data types, including Electronic Health Records (EHR), time-to-event data, images, structured data, and ECG. The goal is to create precise machine learning models for risk stratification for primary cardiovascular prevention. However, as the team grew, collaboration became more challenging, and new problems began to emerge. These included issues with data privacy, workflow standardization, feature and model selection, experiment management, and information logging.