Many of us have now had some experience with Generative AI. We’ve played with ChatGPT, and perhaps looked at some alternatives like Claude, Gemini, CoPilot, Llama, etc. We have witnessed the transformative power of this new technology.
There are practical applications where Generative AI has shown great promise:
- Software Development: Developers utilizing AI coding assistants have noted substantial productivity gains.
- Professional Content Creation: Professionals across various fields are using AI tools to generate diverse content, including text, images, and videos.
- Education: Students leverage AI for assistance in essay writing and organizing their assignments and papers.
These examples all really on Large Language Models (LLMs) that are created using public data – e.g. the entire content of the internet.
Recently though we have seen use cases that go beyond these examples. In the case of AWS, at their Re:invent conference last November they introduced Amazon Q, an AI assistant deisgned to answer queries using private data. We can now enhance these LLMs with proprietary organizational data, paving the way for AI assistants that can address specific questions pertaining to an organisation’s internal data, such as:
- Sales figures for the last three months across different regions.
- Identification of employees skilled in LLMs.
- Status updates on overdue invoices from suppliers.
- Probation review schedules for the month.
- Onboarding tasks for new employees.
- Instructions for creating new AWS accounts for teams.
The list is endless. I believe we are seeing a glimpse of the future of work. Imagine being able to ask any question regarding our company and getting an instant answer? No more trolling through web pages and wiki pages and shared drives looking for information. AI Enterprise Search for Enterprise Knowledge is set to redefine workplace efficiency.
We are at the beginning of this journey and have just been given brand new tools to make Enterprise Search happen. All the cloud vendors are working on solutions to Enterprise Knowledge (e.g. Google’s Gemini and Microsoft CoPilot). Last week, Atlassian released their offering called Rovo. We will be looking at Amazon Q for Business, which also released it’s first generally available version last week, and documenting our progress towards efficient Enterprise Search.
What is Enterprise Knowledge?
Enterprise knowledge is all the data that your organisation owns and collects that relates to your business. Enterprise Knowledge includes all the data in your Sharepoint sites, your Confluence spaces and your shared drives. But it also includes things like sales data, service data and HR data from tools like SalesForce and ServiceNow. Imagine having all of this data at your fingertips, without needing to log in to various systems, switch tools, or navigate through extensive documentation, all while ensuring data security and access permissions.
The ability to search like this seems so obvious. In a few years time, seamless enterprise search will be so ubiquitous we will wonder how we survived “before”.
What’s Next?
Over the next few weeks, we will be documenting our journey to build AI Enterprise Search for Enterprise Knowledge. The upcoming part 2 will discuss how to start this journey. We will then go on to look at Data Management and Cost Management and how we can best make use of these new tools to improve workplace efficiency while keeping an eye on costs and carbon emissions. This journey promises not just to change the way we work but also to set new standards in how we manage and leverage data in the enterprise realm.
This is Part 1 of our AI for Enterprise Knowledge series. see best practices for creating Enterprise Knowledge AI assistant in Part 2, learn about the Importance of data management in Part 3, explore cost management in Part 4, and discover the Sales Knowledge use case in Part 5 and finish with AI solution comparison in Part 6.