Improving future decision making

The aim of collaborative AI is to collate all relevant data about the decisions being made that impact the organisation and use it, in combination with human inputs, to improve future decision making and generate the desired outcome, every time. This is the next phase of a fully digital environment.


From the beginning we work in partnership with our clients. We have expert facilitators who take you through the process in a focused collaboration space. We bring our interaction and experience designers, together with our data scientists and engineers to tease out the decision-making process and start to collaborate around the decision(s) that need to be made.


We identify the key drivers that are currently used to arrive at decisions. In addition to the available data, we also capture the alternatives and outliers that are either too far out from the norm or have no way of determining if the drivers are either available or possible. No decision driver is left out of the capture process.


Following on from capture, we use the drivers to model and simulate outcomes that can be learned from the heuristics and the data. We use both simulation techniques and machine learning techniques to tease out which decision-making aspects can be automated, from those which are reliant on humans. This is a crucial stage which helps us understand the balance needed in future automation interaction design.


We take the results of simulation and machine or deep learning and we start to collaborate around model explainability. This helps inform the team where decision fidelity is high or low and subsequently, those interactions and experiences that need to be designed and should be progressed into AI product development.


Decide provides a point of reflection to understand if we are really making the right decisions: do the decisions that are being made challenge or confirm what a good decision is. Does the digital twin reflect reality, or is it way off?


This is where we now filter down the candidate decision making solutions to get an understanding of which ones will amplify business decision making. How the resulting models, if implemented will drive considerable business value, both in terms of decision accuracy and decision speed.


This builds the interaction model, the digital twin, that enables the team to understand how the humans and the AI will interact around a decision-making flow. The resulting interaction design will be carried forward into the product development lifecycle.

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