P2P

74 P E E R T O P E E R : I L T A ' S Q U A R T E R L Y M A G A Z I N E | W I N T E R 2 0 2 3 The technology architecture behind this dictates that the more that can be inferred from what we know about content, the better we can identify content relevant to the user's prompt. The reason is that the user's prompt is broken down into different "sub prompts", some of which can simply be thought of sub-searches that are happening behind the scenes. This is why the challenges with AI are very similar to those of enterprise search. But arguably, when – and if – you consider using Generative AI to draft an overview of relevant specialisms for a proposal, article or summary, for instance, the challenges and consequences go deeper. To illustrate this, consider the following prompt (ignoring any system prompt): "For a tender response to Bank of Laska, provide an overview of our relevant specialisms, each with a header and a 100-word description, based on other tender responses to the same or similar clients." An experienced human would obviously conduct a few searches and put the answer together with a high degree of confidence. But what will the Gen AI do? First, we must assume it can access "your data" across your platforms. Second, it must be able to infer similar clients from the content or metadata. Third, it will have to search for the most relevant documents from which it will extract content. And so on. Just by looking at these three steps, we can quickly identify areas to focus on when getting ready for AI. 1. Consolidate content in a modern content management system, such as Microsoft 365, or connect and streamline content indexing from multiple systems (iManage, Netdocs, etc). 2. Enrich your content with metadata, making it more identifiable and permitting the relationships to be more confidently inferred. 3. Remove redundant and outdated content and *at minimum* adopt proper file-level version control rather than named versions. This solves a big part of the headache, but interestingly, we are still left with some more esoteric challenges. When looking at the content, faced with 100 examples of previous tender responses, which ones come first? (Don't say all of them, as this has massive cost implications.) This moves us to the next level of content preparation, which might include: 1. Identifying the "right" types and rules for content that is to be made available for Gen AI. 2. Checking for accuracy, completeness, consistency, and reliability. 3. Cleaning and preparing content repositories, accordingly, including checking permissions. Considering that most organizations will hold hundreds of gigabytes of data per employee, it is not surprising that most CIOs, CISOs and CDOs will look at these challenges as a very big mountain to climb. Possibly too big a mountain. The alternative is to focus on making the higher graded content, the gold standard content, available to AI. The organizations with the most foresight and ambition will implement a modern knowledge platform to better create, maintain and manage (including disposing of) this higher graded content effectively and dynamically. Your high-priority use cases will be those that determine if *all content* should be in scope or if you can prepare and make smaller sets of content available for specific use cases and tasks. We believe the latter will be the popular choice for most, and indeed, in Atlas, we developed an innovative concept to support this. Called "knowledge collections," this allows organizations to efficiently manage how their content is made available for specific tasks and use cases. How an Intelligent Knowledge Platform Can Accelerate the Journey To make AI sing to your tune, a lot of groundwork is needed. A Knowledge Platform with capabilities to prepare for, utilize, or deliver on use cases supported by Generative AI can significantly accelerate the journey. Such a platform, also known as an Intelligent Knowledge Platform, should be seen as an orchestration tool for leveraging AI capabilities while delivering on hygiene requirements and advanced features for driving knowledge-centric productivity, collaboration, and communication. ILTA F E A T U R E S

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