A tech giant's AI-powered content revolution: a blueprint for the future of enterprise content management
In the rapidly evolving landscape of artificial intelligence (AI), a leading global technology company is not just keeping pace—it's setting the tempo. This tech giant has developed a comprehensive, multi-faceted AI strategy that serves as an impressive blueprint for how enterprises can integrate AI responsibly across their operations. While their approach encompasses various aspects of AI implementation, it's their focus on leveraging AI for internal operations and fostering collaboration within an open AI ecosystem that's making waves in enterprise content management.
The company's strategy focuses on two key areas: first, leveraging AI to enhance internal operations and sharing these best practices with customers; and second, emphasizing collaboration with strategic partners within an open AI ecosystem. Nowhere is this more evident than in their approach to content creation and delivery. By applying AI throughout the content lifecycle and partnering with industry leaders, this company isn't just adapting to the future of content management—they're defining it.
Their AI-driven content strategy offers a glimpse into what may become the new gold standard for content operations in global enterprises. And driving this AI-driven content revolution is the manager of the technology group for the company's content engineering and translation team. We sat down with him after a recent innovation event to talk about how he and his team are using AI to solve real-world challenges in content creation and delivery.
The content colossus: A staggering scale
The manager supports a global workforce of about 300 content creators and translators, in addition to their translation service providers. His own team spans multiple continents, with members in the US, Ireland, Romania, and India.
The scale of their content and translation efforts is staggering:
The manager emphasizes the importance of this scale: "It's a sizable operation that needs to be reliable, consistent, and available 24/7, considering we support teams from China to the West Coast of the US."
This vast content ecosystem forms the foundation upon which the company is building its AI-driven content strategy, making their innovations not just impressive, but necessary for maintaining efficiency and quality at such a massive scale.
AI-driven content lifecycle: An innovative approach
The organization's journey into AI-driven content management is built on a solid foundation of industry-leading tools. As the manager points out, they have been using leading content management and translation management systems for several years. This long-standing relationship with their technology providers has provided them with a robust platform upon which to build their AI initiatives.
Like many forward-thinking companies, they recognize the potential of AI to revolutionize content management. However, they're not jumping on the AI bandwagon without careful consideration. Instead, they're taking a thoughtful, goal-oriented approach, asking key questions to guide their AI strategy:
Productivity: How can AI boost speed and operational efficiency? Can it automate time-consuming tasks, improve content accuracy and quality, and reduce time-to-market for both translations and original content?
Searchability: How can AI optimize content for search engines and incorporate more effective keywords, ensuring users can easily find the information they need?
Accessibility: With accessibility becoming increasingly crucial, how can AI ensure all content meets compliance standards, from alt text for images to proper table descriptions?
Customer experience: How can AI help target the right audience with the right content, provide insights into customer engagement, and ultimately deliver personalized content that meets customer needs precisely when they need it?These questions form the foundation of the company's AI strategy in content management. Their goal is not just to implement AI for its own sake, but to leverage it in ways that tangibly improve both the content creation process and the end-user experience.
With these objectives in mind, they have developed a comprehensive strategy they call "Content Lifecycle AI." This approach aims to address each of these areas, integrating AI throughout the content lifecycle to create a more efficient, effective, and user-centric content ecosystem.
Content Lifecycle AI: Strategy in action
Their Content Lifecycle AI strategy is transforming how they create, manage, and deliver content at every stage of the process. Here's a look at how they're bringing this vision to life:
Content creation: The company is leveraging AI to assist writers in generating new content, particularly for new product deliverables. The system can autogenerate SEO-related content, including short descriptions - a task that the manager identifies as "one of the most difficult and time-consuming activities for most individuals creating content." AI is also being used to autogenerate accessibility-related content, such as table descriptions and alternate text for images, ensuring compliance with accessibility standards without burdening writers with these technical details.
Content tagging: AI plays a crucial role in automatically tagging content with relevant taxonomical terms. This includes content type, task labels, product codes, and keywords. As the manager explains, "We have started at the publication level, and we have begun to auto-suggest and auto-tag content at the time of publication, with validation by the writers before it's published." This ensures consistency in content classification and lays the groundwork for more personalized content delivery.
Content review: The tech giant is developing a learning model based on comments from reviewers to autosuggest content during creation. This AI-driven approach aims to leverage past feedback to improve future content drafts, reducing errors and inconsistencies. The system also verifies that the content complies with the company's standards and libraries, automating a process that was previously manual.
Content publishing: Their system automatically delivers data to relevant delivery channels, learning from customer searches and personas to personalize delivered content. This approach aligns with the company's goal of syndication to Large Language Models (LLMs), allowing for more dynamic and responsive content delivery across various platforms.
Content analytics: The company is looking into using AI to analyze customer content usage, providing insights into content performance. This data helps identify what content to develop, when to deliver it, and how to achieve the best results. As the manager emphasizes, this feedback loop is crucial for "enabling us to focus on creating the right type of content that's most engaging and most used, and stop developing content that is of little use."Vision for AI-powered localization
On the translation side of the house, the company is about to embark on an ambitious project with their technology partner, migrating to a new Translation Management System. This move aims to fully integrate AI into their translation workflows, with the promise of reducing effort and costs while increasing translation volume and improving the experience for global customers.
Envisioned AI-enabled translation workflow:
How the company's future AI-driven translation process will work:
Source content creation/generation: The process will begin with content created by various teams across the company. An automated system will identify which content needs translation and into which languages.
Validated content reuse: The system will evaluate new content against the translation memory, leveraging existing translations at zero cost. This step aims to maximize efficiency by reusing previously translated content wherever possible.
Adaptive neural machine translation (nMT): For content not found in the translation memory, they plan to employ adaptive nMT enhanced with memory matching and Machine Translation Quality Estimation (MTQE). This AI-driven approach is expected to significantly reduce the workload for human translators and post-editors.
AI target evaluation: An automated Language Quality Assurance (LQA) process will evaluate the machine-translated content. This step will determine whether the translation meets the company's quality standards without needing human intervention.Human validation: Only when the AI detects inadequate MT quality will the workflow involve human translators. This "single human step" is planned to be performed within their translation management system by professional linguists, ensuring high-quality output while minimizing manual effort.
Publishing: Once approved, whether by AI evaluation or human validation, the content will be published across the company's various platforms.The manager emphasizes that this streamlined, AI-enabled workflow has the potential to revolutionize their translation process: "It will enable us to radically simplify our workflows. If we have set a standard of one quality level, then we don't need to differentiate via workflow."
This approach is expected not only to make the process more efficient for the company's internal teams but also to open up new possibilities for content localization that may have been previously unfeasible due to time or cost constraints.
Precision amplified; complexity simplified
Their commitment to AI-driven content management is already yielding remarkable results. Currently, 80% of all translation work and 80% of all publishing work is automated, significantly streamlining their processes. The company has achieved a 50% topic reuse rate, maximizing the efficiency of their content creation efforts. Perhaps most impressively, 90% of all content is syndicated through automation, ensuring rapid and consistent distribution across various platforms. These figures underscore the transformative impact of their AI initiatives, demonstrating substantial improvements in efficiency, consistency, and scalability in their content operations.
This innovative use of AI in content management isn't just an operational upgrade—it's a reimagining of what's possible in enterprise content strategy. As AI continues to evolve, this approach provides a roadmap for enterprises aiming to stay ahead in the content game. Those who follow this global technology company’s lead in embracing AI across the content lifecycle may find themselves not just keeping pace with the future of content management, but actively shaping it.
The company's AI journey in content management is still evolving. As the manager puts it, "There are lots of areas where we can leverage AI, which will keep us on our toes for some time." While they've made significant strides, the team knows this is just the beginning. They're continually refining their approach, learning from each implementation, and pushing the boundaries of what's possible. It's a complex challenge, but one that promises to revolutionize how they create and deliver content to their customers. And in today's fast-paced tech world, that could make all the difference.
Fraser Doig
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