AI/BI/CI/DI: Decision intelligence (DI) solves the world's most complex problems by connecting actions to outcomes. It connects collaborating human decision makers to knowledge and also to technologies like machine learning, AI, deep learning, visual decision modeling, complex systems modeling, big data, predictive analytics, UX design, statistical analysis, business intelligence, business process management, causal reasoning, evidence-based analysis, and more. See https://en.wikipedia.org/wiki/Decision_Intelligence. For an overview, see the webinar at http://youtu.be/XRTJt3bVCaE, http://www.lorienpratt.com, and the Decision Intelligence group: http://www.linkedin.com/groups?gid=205078. Also, my company offers DI and machine learning consulting services. See http://bit.ly/1X8O2zF to learn more. Don't miss the latest DI news! Sign up at subscribe.decisionintelligencenews.com.
"Though it’s the stock response, 'insights' is precisely the wrong way to think about how an advance in AI will create value. In fact, we feel that 'insights' often is code for 'We don’t know what to do with our AI’s predictions.'
A much better answer would be to describe the decisions that the predictions will improve, because AI has value only if it leads to better decision-making."
Lorien Pratt's insight:
In examining how AI predictions affect decisions, this article goes beyond the need to focus on decisions vs. "insights". Because insights don't get you all the way to understanding the impact of your actions on outcomes.
"Decision intelligence (DI) is at the Innovation Trigger of the Hype Cycle. DI is a practical discipline used to improve decision making by explicitly understanding and engineering how decisions are made, and how outcomes are evaluated, managed and improved via feedback....
There is a need to transparently represent how decisions are being made.
From a pure business perspective, it makes sense to curtail unstructured ad-hoc decisions that are siloed and disjointed, and properly harmonize collective decision outcomes across an entire organization. Software tools are now emerging that will enable organizations to practically implement DI projects and strategies."
Lorien Pratt's insight:
Gartner now has DI on three hype cycles: AI, Data and Analytics Programs and Practices, and now Emerging Technologies in Finance. It is also significant that in adding DI to the Finance hype cycle, Gartner points out the importance DI in enabling cross-functional decision making.
"As the demand for robots increases in various industries, the need for collaborative robots (cobots) is also seemingly rising — basic robots might not be able to effectively complete various tasks for companies that demand collaborative elements without complicated programming to do so.
AvaWatz Inc. is a decision intelligence tech firm that supplies for the growing demand for cobots...
Powerful machine-learning methods combine the capabilities of individual mobile robots into a unified system for collaborative detection, decisioning and action. ARYA is scalable to support a range of indoor and outdoor scenarios.
Cobots can be used in a variety of fields to perform tasks that are too difficult, dangerous or tedious for humans alone. Cobot teams are adaptable and might help solve challenging problems for public- and private-sector customers in industries such as the military, aviation, search and rescue, construction, infrastructure and transportation."
Lorien Pratt's insight:
Hybrid--machine plus human--systems are the future of AI, a path from 70% solutions to commercially viable technology. DI provides an integration technology enabling AI orchestration and human-in-the-loop decision making. Cobots with DI as described in this article bring this collaborative decision making to robotics.
"Usually data is about the past. So how can it help us to make decisions about the future? Our main question: "If I make this decision today, what will happen?". Learn the answer here."
Lorien Pratt's insight:
Simulation to get data from the future is once again a hot topic.
"Read enough analyst reports, and listen to enough conference speakers, and one can be forgiven for assuming that all important decisions these days should be data-driven. However, business leaders and professionals should be selective as to which decisions should be tied to data analytics, and which should remain based on human experience.
…To help make decisions about making decisions, Gartner recently published an informative ebook, The Future of Decisions, that outlines the process of dissecting and reengineering decisions to both separate human input from machine involvement, as well as enable the two to work in unison."
Lorien Pratt's insight:
It’s exciting to see thought leaders like Forbes and Gartner emphasizing what we’ve known, which is that data is a critical element to decision making but it can’t replace the importance of humans in the loop.
"Government decisions tend to have complex, interconnected effects, all of which have impacts on government outcomes, often outside the purview of any given agency. Improving both trust and customer experience requires decisions to be made accurately in context. This requires the decision process to be clearly understood and be about delivering outcomes, not just adherence to process.…
Gartner estimates that by 2023, more than 33% of large organizations will have analysts practicing decision intelligence, and by 2024, 60% of government AI and data analytics investments will directly impact real-time operational decisions and outcomes."
Lorien Pratt's insight:
Government decisions impact each of us every day. It’s heartening to note that Gartner sees DI as critical to better government decision-making.
"Decision intelligence is a trending field that contains a range of decision-making methods to design, model, align, execute, and track decision models and processes. The implementation offers a structure for organizational decision-making and processes with the integration of machine learning algorithms."
Lorien Pratt's insight:
This article touches on a few key aspects of DI--aligning your decision team, integrating AI with human decision-making, and generating "data from the future" to lower decision risk.
"Decision intelligence (DI) is a new field that uses technology to support, augment and automate business decisions. DI was popularized in Lorien Pratt’s 2019 book, Link, and identified by Gartner as one of the most impactful technology trends for 2022...
Decision intelligence is a new lever for business success in our modern times, and the paths to implement it are clear. Has your company already started its journey toward decision intelligence?"
Lorien Pratt's insight:
Forbes has covered DI for a number of years; here's the latest. With companies like Gartner and Google recognizing how DI is an essential link in the chain from data and AI, to actions, to measurable outcomes, DI is at a unique point in the history of an emerging field, it's “crossing the chasm” from early adopters into widespread adoption.
"...the brain spontaneously breaks down decisions among multiple options to a series of two-choice decisions until only one option -- the one ultimately selected -- remains. This was found to result in animals exhibiting a series of abrupt changes in direction, each associated with the exclusion of one of the remaining options....
The team used immersive virtual reality to test their theoretical predictions in flying, walking, and swimming animals -- the fruit fly, desert locust, and zebrafish, respectively.... All species were found to exhibit exactly the same bifurcations as had been predicted....
'It is often thought that animals first decide where to go and then they move to the target,' says Sridhar. 'But our findings show that the interplay between movement and the changing neural representation of options considerably impacts how decisions are made. What is so exciting about these findings is that this response yields extremely effective decision-making in complex and diverse ecological contexts.'"
Lorien Pratt's insight:
Although not yet tested on humans, these findings suggest how little we can actually hold in our heads during decision making. The solution is to use at least simple tools - like diagrams of decisions - when faced with complex decisions.
"In this article, we’ll examine the phenomenon of bounded awareness—when cognitive blinders prevent a person from seeing, seeking, using, or sharing highly relevant, easily accessible, and readily perceivable information during the decision-making process. 'The information that life serves is not necessarily the information that one would order from the menu,' notes Dan Gilbert of Harvard University’s psychology department, 'but like polite dinner guests and other victims of circumstance, people generally seem to accept what is offered rather than banging their flatware and demanding carrots.'...
It’s important to note that bounded awareness differs from information overload, or having to make decisions with too much information and too little time. Even when spared a deluge of information and given sufficient time to make decisions, most individuals still fail to bring the right information into their conscious awareness at the right time."
Lorien Pratt's insight:
This article provides an in-depth exploration of bounded awareness and its detrimental effects on decision making. It provides a number of techniques to break out of the limitations of bounded awareness thinking, including “unpacking” a situation, sharing unique information, broadening focus, and asking "Why Not?" DI provides a methodology and framework for employing these and other techniques to remove blinders from decision makers.
"In March 2020, the NHS needed to make life-or-death decisions on the back of understanding whether curves were trending up or down. For the first time ever, they connected data flows from across the entire system.
That is a potentially overwhelming amount of data – but, crucially, they weren’t paralysed by this sudden torrent... Why was that?
Well, working alongside the NHS, we applied approaches from a nascent field called decision intelligence. We used this data to build the understanding the NHS needed to make confident decisions about which hospital wards to open or close, and which resources to send where.
Rather than just showing data on current levels of patient demand, we could also show demand for tomorrow or next month, the reasons why demand was changing, how that affected resources such as beds or ventilators, and what actions they could take.
Rather than making decisions on data alone, the NHS made them on an understanding of how and why events were unfolding. The collective results have been widely credited with saving thousands of lives."
Lorien Pratt's insight:
As Marc Warner points out, DI lets us create models, develop understanding about the behavior of these models, and use them to create the "data from the future" we need to make better decisions.
"Beyond decision making, the Decision Intelligence Platforms allow to work in what-if mode. The idea is to change the parameters generating the scenarios, to change the set of constraints to be respected as well as the objectives to be optimized.
By controlling this scenario generation at a very high level, it becomes possible to analyze potential futures, and to draw the most profitable one for a company."
“Effective decision making is more important than ever. 65% of executives have to make more complex decisions today than they did two years ago, according to a Gartner study, and 50% of them experience greater pressure to justify the decisions they make.…
To put some context, the term ‘decision intelligence’ was popularized in Lorien Pratt’s 2019 book, Link: how Decision Intelligence Connects Data, Actions, and Outcomes for a Better World...
According to McKinsey’s findings, 72% of executives say that bad decisions are as frequent as good ones, and the average S&P 500 company wastes an average of $250 million a year due to ineffective decision making.”
Lorien Pratt's insight:
This article focuses on DI and decision-making as part of a set of business processes that include not only being clear about the outcomes you want but also being clear about how you retrospectively examine your decisions.
This excerpt was translated using Bing translate. To translate this article into English, either select “Translate” when you open it in a browser or highlight the text, and select “Translate selection into English”.
“Recent developments in AI are about lowering the cost of prediction. Better predictions matter when you make decisions in the face of uncertainty, as every business does, constantly. But how do you think through what it would take to incorporate a prediction machine into your decision-making process? In teaching this subject to MBA graduates at the University of Toronto’s Rotman School of Management, the authors have introduced a simple decision-making tool: the AI Canvas. Each space on the canvas contains one of the requirements for machine-assisted decision making, beginning with a prediction. To explain how the AI Canvas works, the authors use an example crafted during one of their AI strategy workshops: home security.”
Lorien Pratt's insight:
This HBR article by Ajay Agrawal, Joshua Gans, and Avi Goldfarb focuses on bringing AI into human-in-the-loop decision making. It acknowledges the need for actions, outcomes, and human judgment. It provides an example using a tool, the AI Canvas, to analyze where actions, outcomes, AI, human judgment, and other factors fit into a decision process.
There are a couple of enhancements that it appears this approach is missing: 1) A causal diagramming tool like a CDD that helps to map the path from actions to outcomes 2) A connection to systems thinking / systems modeling which forms the basis for understanding of causal mechanisms, especially nonlinear effects
"The Latest Released Decision Intelligence market study has evaluated the future growth potential of Decision Intelligence market and provides information and useful stats on market structure and size... The study includes market share analysis and profiles of players such as IBM (United States), Aeye (United States), Busigence Technologies (United States), Aera Technolog (United States), Peak.in (United Kingdom), Tellius, Inc. (United States), PYRAMID ANALYTICS (Netherlands), Pegasystems Inc. (United States), Quantellia LLC. (United States) and Diwo (United States).
Decision intelligence is a practical domain that encompasses a broad range of decision-making techniques, bringing together multiple traditional and advanced disciplines to design, model, align, execute, monitor, and tune decision models and processes... It is outcome-oriented and must meet commercial objectives... It enables data to be collected and machine learning models to be used to predict accurate outcomes for optimal commercial decision-making."
Lorien Pratt's insight:
Quantellia is pleased to be recognized as a DI "major giant".
Ardee's article presents one of several DI market reports that have appeared recently spotlighting the developing DI market.
"The unpredictability of the outcomes in today’s decision models often arises from the inability to capture the uncertainty factors linked to these models’ 'behavior' in a business context. By introducing machine learning algorithms to decision-making processes, a new field called 'decision intelligence' is emerging to create strong decision models in a wide range of processes."
Lorien Pratt's insight:
It’s gratifying to see DI continue to receive coverage on a global scale. This article from AI Multiple is a great introduction to decision intelligence, and highlights vendors like Quantellia who provide DI solutions.
"The bottom line of our study is that diversity matters: When team members have more unique information, they make more intelligent decisions. But diversity alone is not enough. A bad information-sharing strategy in the team can leave the important information unsaid. The most efficient and scalable approach for information-intensive decisions is to leverage the wisdom of disagreement. It is by challenging the assumptions and opinions of our team members that we produce collective intelligence, a scarce but sorely needed quality for today’s teams."
Lorien Pratt's insight:
The decision intelligence methodology recommends employing diverse teams to make better decisions. DI also teaches that the way the team approaches the decision, that is, how they assemble and use the information that supports the decision, is important. Dr. Kudesia’s study provides peer-reviewed evidence supporting these decision intelligence practices.
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In examining how AI predictions affect decisions, this article goes beyond the need to focus on decisions vs. "insights". Because insights don't get you all the way to understanding the impact of your actions on outcomes.