Chloe Maraina brings a sophisticated perspective to the intersection of big data and organizational intelligence, specializing in how large-scale data science can be harnessed to bridge the gap between information silos and user needs. In this conversation, we explore the radical shift from static, manually intensive knowledge management to a dynamic, AI-driven ecosystem. We delve into the challenges of maintaining thousands of articles across fragmented platforms like SharePoint and internal help desks, the specific logic used to transform messy support tickets into structured knowledge, and the substantial productivity gains achieved when a system moves from a reactive state to a proactive, “living” knowledge base. By examining the mechanics of AI enrichment and the “human-in-the-loop” philosophy, we gain insight into how modern enterprises can unlock the silent gold mines of their own data to empower employees and streamline global support operations.
Maintaining thousands of self-service and agent-facing articles every few months can become an overwhelming administrative burden for any organization. Could you describe the specific challenges your team faced with the legacy manual review process and the impact it had on the quality of support?
The weight of manual maintenance is something that truly grinds productivity to a halt, especially when you consider that a single five-member team was responsible for reviewing 1,900 self-service articles and 1,700 agent-facing articles every six months. This wasn’t just a side task; it was essentially their full-time job, and even then, they were barely keeping their heads above water because knowledge is spread across so many different SharePoint sites and repositories. When the content becomes stale or incomplete, the entire support experience breaks down immediately because search engines and AI agents begin serving up outdated or incorrect answers. It creates a palpable sense of frustration for employees who hit a dead end, forcing them to escalate issues to advanced support and creating a bottleneck that ripples through the whole company. This reactive cycle meant that some problems were only discovered after they had already caused significant friction, proving that a manual approach simply cannot scale in an environment where information evolves as fast as it does today.
You have described raw support data as a “silent gold mine” for the organization. What is the process for taking those messy, unrefined support tickets and transforming them into a structured pipeline that can actually improve your knowledge base?
The real magic happens when you treat every resolved support ticket as a vital signal rather than just a record to be archived. We start by ingesting massive volumes of raw incident data from our ticketing systems, which is often incredibly noisy and contains everything from abandoned issues to inconsistent writing styles and fragmented conversation notes. Our AI enrichment layer steps in to scrub this data, turning those disorganized details into structured fields that clearly define the reported problem versus the actual root cause and the specific remediation steps taken. We then use clustering to find patterns across these incidents, which allows us to identify recurring pain points without getting distracted by one-off scenarios that would otherwise clutter the knowledge base. It is a process of making the data “sing” by organizing the chaos into a coherent narrative that tells us exactly where our knowledge gaps are and where our existing documentation is failing to meet the needs of our users.
One of the most interesting aspects of this AI system is how it evaluates existing content. How does the system determine whether to create a new article or update an old one, and what specific thresholds do you use to ensure the information remains accurate?
The system employs a very specific ranking logic to determine the next steps for any given piece of information, ensuring we aren’t just creating content for the sake of it. When we compare identified patterns against our current knowledge, we look at the alignment percentage: if the resolution aligns at less than 40 percent, the system triggers the creation of an entirely new knowledge article. If the alignment sits between 40 and 80 percent, we recognize that the existing knowledge is fundamentally correct but likely missing the nuanced details required to solve the current iteration of the problem, so we flag it for an update. For anything above an 80 percent match, we determine that no update is needed, which prevents the redundant proliferation of articles that often plagues traditional systems. This structured approach allows us to proactively fill gaps before an employee ever reports a problem, turning a static database into a living, breathing asset that adapts in real-time.
While AI handles the heavy lifting of data processing, you emphasize the importance of having humans in the loop. How does the collaboration between the AI pipeline and subject-matter experts work to maintain the high standard of quality required for global support?
We firmly believe that while AI is incredible at processing scale, humans are essential for ensuring that the final output is truly useful and accurate for another human being. Once the AI has identified a gap and generated a structured draft, the system automatically notifies the appropriate subject-matter experts via email so they can validate the changes and add any necessary context. This collaborative loop means that our knowledge managers aren’t wasting their time searching through mountains of data or performing repetitive reviews; instead, they focus their energy on high-value quality control. Their feedback is then fed back into the system, which helps tune the AI model and improve its accuracy over time, creating a virtuous cycle of constant improvement. This balance frees our people from the drudgery of manual maintenance while ensuring that the “last mile” of knowledge delivery remains grounded in human expertise and practical experience.
Now that this platform is being utilized internally, what have been the most significant measurable impacts on organizational efficiency and the support experience for employees?
The impact has been nothing short of transformative, with our Global Help Desk projecting an annual savings of roughly 16,000 hours that were previously lost to manual content reviews. Beyond just saving time, we are seeing a 10 percent reduction in overall support tickets because employees are finally able to find the right answers the first time they look, which drastically reduces the need for escalations to advanced support. You can feel the shift in the organization as our knowledge base moves from being a static, reactive repository to an intelligent, proactive capability that unblocks people faster and boosts general productivity. This success has even caught the attention of other departments, such as HR, who are looking to leverage the platform to manage their own vast stores of information. By turning our knowledge into a living asset, we have moved away from struggling with maintenance and towards a model where we can apply what we know more effectively across the entire enterprise.
What is your forecast for the future of knowledge management as these AI-driven systems become more integrated into the standard corporate infrastructure?
In the coming years, I expect we will see the total disappearance of the “static” knowledge base as every piece of corporate information becomes part of a fluid, self-healing ecosystem. We are moving toward a world where knowledge management isn’t a separate department or a chore, but an invisible, intelligent layer that anticipates an employee’s needs before they even finish typing their query. As these systems move beyond IT and into HR, legal, and operations, the “silent gold mine” of internal data will become the primary driver of organizational agility, allowing companies to pivot instantly based on the real-time patterns found in their own internal communications. The ultimate goal is a frictionless environment where the distance between a question and a verified, accurate answer is essentially zero, powered by AI that learns at the speed of the business itself. Organizations that master this transition will find themselves far more resilient, as they will no longer be held back by the decay of their own internal expertise.
