1. MLOps: Industrialized AI
(Scaling model development and operations with a dose of engineering and operational discipline)
This concept, known as model drift, is one of the leading reasons that models miss performance targets. For example, COVID-19 disrupted many supply chains because demand planning models weren’t updated frequently enough to account for the quickly emerging “new normal” as the pandemic began. As discussed in the chapter Supply unchained, many businesses had either too much or too little supply, in large part because
Their demand planning models were operating on data and assumptions that became outdated nearly overnight. MLS helps organizations monitor model performance and manage model drift’s predictive inaccuracies by helping standardize processes for maintaining alignment of AI models with evolving business and customer data. Human ML experts can monitor production models, observe how they change and behave as they scale, and decide when they need to be retrained or replaced. As a result of this planning and monitoring, model drift is diminished, and development and deployment become more flexible and responsive.
2. Machine data revolution: Feeding the machine
(Disrupting the data management value chain for the ML age)
With machine learning (ML) poised to
augment and, in some cases, replace
human decision-making, chief
data officers, data scientists, and CIOs recognize that traditional ways of organizing data for human consumption will not suffice in the coming age of artificial intelligence (AI)– based decision-making. This leaves a growing number of future-focused companies with only one path forward: For their ML strategies to succeed, they will need to data management value chain from end to end.
In the next 18 to 24 months, we expect to see companies begin addressing this challenge by reengineering the way they capture, store, and process data. As part of this effort, they will deploy an array of tools and approaches, including advanced data capture and structuring capabilities, analytics to identify connections among random data, and next-
generation cloud-based data stores to support complex modeling.
3. Zero trust: Never trust, always verify
(Security in the age of the porous perimeter)
The anticipated growth of smart devices, 5G, edge computing, and artificial intelligence promises to create even more data, connected nodes, and expanded attack surfaces.
By removing the assumption of trust from the security architecture and authenticating every action, user, and device, zero trust helps enable a more robust and resilient security posture. The organizational benefits are complemented by a considerable end-user perk: seamless access to the tools and data needed to work efficiently.
4. Rebooting the digital workplace
(Data can drive new ways of working remotely and in the office)
If it’s true that you can’t manage what you don’t measure, then the digital workplace is eminently manageable. While the office may be undergoing a pandemic-driven
existential crisis, it’s not down for the count. How do we reconcile these competing needs? The office may not be dead, but it’s unlikely to return in its previous incarnation. Employers could find that creating exciting environments that employees are eager to experience is the best way to entice them back to the office. Perhaps, as architecture and design.
As the office evolves to become both a collaboration hub for project teams and a creative center for client meetings, While the office may be undergoing a pandemic-driven existential crisis, it’s not down for the count. Firm Gensler predicts that the post–COVID-19 workplace will shift from where people work to where teams meet, socialize, and connect. The smart money is on flexible and configurable work environments, technology-driven workplace services, and new ways of managing fewer people and less space without sacrificing collaboration and innovation. (The everyday commute, on the other hand, appears to be on life support.) However, just as e-commerce changed the way consumers and retailers think about brick-and-mortar storefronts, so is the forced mass adoption of remote work
changing the way employers and employees think about the physical workplace.
5. Supply unchained
(Transforming a traditional cost center into a value driver)
The idea of transforming the supply chain from a cost center to a value driver is not new. Over the past two decades, leading companies have fine-tuned strategies for optimizing incentives and disincentives for online purchasing and delivery timing strategies that manufacturing, retail, and other sectors may find helpful as they transform their supply chains. (See Lessons from the front lines “Pactiv Evergreen gets proactive with factory asset intelligence,” page 55).
Online retailers were among those pioneering the art of using predictive models to optimize the location and volume of inventory, procurement, and replenishment. Using customer data, they developed highly detailed customer cost-to-serve profiles used to segment customers into groups based on location, preferences, and service expectations. These retailers found that in some cases, customers will pay premium prices for premium delivery services, while more price-sensitive customers will accept
longer delivery time frames.
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