Show related SlideShares at end. WordPress Shortcode. Published in: Technology. Full Name Comment goes here. Are you sure you want to Yes No. No Downloads. Views Total views. Actions Shares. Embeds 0 No embeds. No notes for slide. Decision Intelligence: a new discipline emerges 1.
How much should we invest in clean, energy-efficient manufacturing equipment, given that we have to pay a carbon tax? Traditional View What will be the outcome? What decisions can we make?
Decision intelligence is a new academic discipline concerned with all aspects of selecting between options. It brings together the best of applied data science. Decision intelligence is an engineering discipline that augments data science with theory from social science, decision theory, and managerial science.
Decision Intelligence view What data, analytics, reports, human expertise, and other assets are relevant? What outcomes do we need or want to reach? What decisions will get us there? Source: SAP Leon Source: Raytheon Source: The Millenium Project All rights reserved.
You just clipped your first slide! If Mr. Algorithm is going to drive most enterprise decisions of tomorrow, we need to create some checks and balances to ensure that it does not go awry.
Real-Time Insights Real-time monitoring, real-time sales and marketing data dashboards, and real-time alerts help clients stay one step ahead. Successfully reported this slideshow. Looking for a reliable IT solutions provider? Oracle Analytics Cloud Analytics permeates every aspect of our lives. The second step is to define how you would make the decision if you had access to any information you wanted. Solutions Business Intelligence.
It is more critical today than ever before that the algorithmic suite developed by enterprises has a strong grounding in ethics and can handle situations appropriately for which explicit training may not have been provided. Ushering into an AI-centric era of decision-making will require organizational transformation from business, cultural and technical standpoints. Instrumenting AI in the enterprise requires a combination of data scientists and computer scientists. As AI matures in the enterprise, the users, use cases and data will increase exponentially.
To deliver impactful AI applications, scale and extensibility is critically important. This is where having an engineering mindset comes in. Imbibing an engineering mindset will help standardize the use of these applications while ensuring that they are scalable and extensible.
The other critical aspect to a culture where AI can thrive is creating an environment supporting continuous unlearning and relearning. AI can succeed if the people developing and operating it are rewarded for continuous experimentation and exploration. And just like AI, people should be encouraged to incorporate feedback loops and learn continuously. As technology matures it's important that the existing workforce keeps up. For one, it's critical that the knowledge of algorithm theory, applied math alongside training on AI library and developer tools, is imparted into the workforce — and is continuously updated to reflect new breakthroughs in this space.
Finally, given the nature of AI applications, it's critical that they are consumed voraciously. User input very often activates the learning cycles of artificial intelligence applications. To ensure high usage of these applications, it's very important that we put the user at the center while designing these applications.
This is where the application of behavioral sciences and human-centered design will deliver impact. As we augment decision-making with algorithmic, AI-centered systems and platforms — the big expectation is that they will bring untold efficiencies in terms of cost, alongside improvement in the speed and quality with which decisions get made.
It's time to reimagine and deliver on enterprise decision-making that is increasingly shaped through artificial intelligence. These aspects — how the AI is progressing and how to exploit its potential are of paramount importance to keep in mind for an AI transformation. Share to facebook Share to twitter Share to linkedin.