November 5, 2023 by Mark Seall

Solving real problems with organizational intelligence 

Making sure that AI is not just a solution searching for a problem

Our goal is to build AI solutions that solve real problems and which contribute to our vision for “building organizational intelligence”.  Staying true to this idea when selecting new features to develop means that InferenceCloud can really deliver the fundamental promise of AI. 

We spend as much time as possible understanding customer issues so that we can apply this intelligence where it has the most immediate benefit. Looking at data and talking with customers is critical to this process, particularly since AI represents a new frontier in delivering business benefits. 

Example – The Blank Canvass Problem

InferenceCloud is designed based on a human in the loop concept. AI does the work, the human does the core thinking and is an essential part of the process at every step. However, this often means asking questions which are simple on the surface but require a lot of very deep thought in places where we also want InferenceCloud to be very fast and efficient to use. 

For example, we ask users to define communications objectives so that we can create strategies and content that are specifically targeted. This sounds like a simple request, but objectives can also be full of nuance and complexity. At the same time, we have a lot of data that we can use to help define objectives, such as the topics selected, audience profiles, and other objectives for communications projects across your organization where similar problems may have been solved already.

We are currently developing and testing a feature called Smart Suggest that takes a wide range of signals from the use of InferenceCloud across your organization to help suggest contexts, topics, audience, objectives and even which research documents to upload. Using organizational intelligence we can help predict and shape the right answers. This way, everybody, including the system continually optimizes together. 

Initial tests are already showing just how much AI can help us uncover useful and valuable data in organizations – if an organization knows what an organization knows – and are providing that surprise factor “it knew that..?”.

Customers play an important role during the development phase also, acting as a sounding board for our ideas and more often than not suggesting useful improvements or other directions we hadn’t considered. This is also the most fun and rewarding part of the process. 

Over the next weeks we will test, finalize, harden and ship the Smart Suggest feature before coming back to the fundamental question once more: what’s the next feature we can develop that will solve a real fundamental problem and help us continue to deliver organizational intelligence?