Is your supply chain delivering what your customers want, when they want it, while costing your organization as little as possible to accomplish that goal? If your answer isn’t a resounding yes, then you are probably missing opportunities to improve your supply chain and deliver significant value to your organization. The on-demand economy is all […]
Taking a computer-aided engineering (CAE) approach to business transformation can help businesses manage program risks, maximize value, decrease costs and accelerate innovation.
To thrive in world that is constantly morphing due to the acceleration of innovation and the velocity of disruption, we must move the AI cursor to generate a wider domain of intelligence.
The pursuit of AI assumes that human intelligence is worth replicating and will create a benefit for end users. But do we really want to replicate the flaws of human intelligence like prejudices, greed, and procrastination? Or the shortcomings of our processing capabilities? Merely replicating human intelligence using known patterns and outcomes might unburden us from menial tasks, but it won’t solve the most pressing problems of the future.
Generative intelligence pairs human perception and decision making capabilities of artificial intelligence (AI) with the scientific disciplines of dynamic complexity and perturbation theory, supported by causal deconstruction, to create a systemic and iterative collection of rational and unbiased knowledge that exceeds human intelligence.
The systemic enterprise is an integrated “systems-based” operations management approach that uses mathematical emulation, generative intelligence and automation to model, analyze, and measure operational risk so that system owners can optimally manage the health of business processes and underlying information technology.
