"...My hope is that we move from fixing problems in buildings after they happen to a more proactive approach. That’s been the goal of the Healthy Buildings program. Before COVID, healthy buildings was a nascent field. Now, it has exploded and everyone is rightly talking about indoor environments. I feel a deep responsibility to help shape what comes next, now that everyone is paying attention. The healthy buildings movement will be a failure if it’s confined to those with resources. This must be a movement that serves all."

"By 2024, the market is expected to pass $500 billion, growing about 18% a year until then....[yet] while 59% of organizations have a strategy for AI, many of the promised benefits remain elusive."

Tellius announced an $8 million Series A funding round led by Sands Capital Ventures with participation from Grotech Ventures.

"While 70% of business leaders say they understand how AI will change how their organization generates business value, only 10% report significant financial benefits in 2020.

So while 59% of organizations have a strategy for AI, many of the promised benefits remain elusive."

"Building decision intelligence applications and delivering prescriptive analytics wherever and whenever needed is challenging in the best of times. Establishing a future-proof platform with analytic and AI powered insights, state-of-the-art optimization, and decision rules solutions can help businesses transform into customer-centric, automated and intelligent enterprises."

If annual averages were the only information we considered, 79 AD in Pompeii was a slightly warmer year than usual—hardly a situation worthy of serious concern. As you can see with this example, averages, when assessing outlier risks, often make very bad metrics.

Orphaned Analytics are one-off Machine Learning (ML) models written to address a specific business or operational problem, but never engineered for sharing, re-use and continuous-learning and adapting.

Many of us were made aware of an alleged near-miss between two satellites in low earth orbit, one operated by SpaceX and the other by OneWeb....we seek to understand, use and manage information in such a way that leads to desired outcomes. This ability is known as Decision Intelligence, and it’s the reason for developing situational or domain awareness."

Social media bombards us with big lies. The juicier and click-baitier, the better, and the happier are advertisers. Unfortunately, disinformation is often plausible as well as attractive. By promising to right an imaginary or misplaced wrong it can tempt us to engage in bullying, persecution, and—in the extreme—bloodshed and even murder. The good news is that there’s a pattern to big lies, and so a pattern as well to beating them.

In artificial intelligence, machine learning, decision intelligence, statistics, and science, we use the word “decision” to mean a lot of things. Let’s tease out some distinctions:

"...quality AI information is clouded by the AI apocalypse narrative. If you google the field, you’ll be challenged to separate medical imaging wheat from AGI chaff. (Don’t tell anyone, friends, there’s no magic here, it’s just math.) AI alone is no more likely to take over the world than is your calculator. Well, unless it’s used as a deniability smokescreen: “It’s not my fault the killer robot smashed your house, it was the AI that did it”.

Are you thinking: “I’ve heard that AI can provide some value to my organization?” but wondering where to start?

Our mission is to improve lives by empowering students with essential skills and dispositions for making better decisions

"Lack of causal understanding makes it very hard to make predictions and deal with novel situations. This is why you see self-driving cars make weird and dangerous mistakes even after having trained for millions of miles.
...
Generalizing well outside the i.i.d. setting requires learning not mere statistical associations between variables, but an underlying causal model,” the AI researchers write.

Even though the concept of decision intelligence is still relatively new on local shores, the potential to disrupt the AI market is significant. Companies must be cognisant of this framework and gain an understanding of how to leverage it for optimal data analysis.

To be able to spot business sense in data and find a true answer to the initially asked questions at the required time and adapted to the audience, result visualization is crucial. Humans only have a limited capacity to process information. This is why insight-driven organization’s intelligent tools that serve people by producing target-group specific dashboards ease the decision-making process. In the event of missing or simply not identifiable capabilities in your workforce, the most efficient solution is to get a professional data analyst aboard. In order to create C-level insights and derive first implications a special skill mix between science and art is required. 

Create a forum for rapid debate to take place. Be clear that everyone has a voice but not a vote.
When following this approach, it is possible to involve a large number of stakeholders and experts without sacrificing speed. Especially when things are unfamiliar and the decisions you are considering are bold, you need many points of view to make sure the decision makers aren’t missing something.

It is expected that many organizations will have analysts practicing decision intelligence including decision modeling in the future.

How can we leverage our understanding of human physiology, neuroscience, and technology to guide and improve the decision making process for organizations and teams?

The industrialization of AI platforms enables the reusability, scalability and safety of AI, which accelerates its adoption and growth. This industrialization aims at getting new adopters of AI on par with early adopters. 

According to a recent Gartner survey, the C-suite is steering AI projects, with nearly 30% of projects directed by CEOs. Having the C-suite in the driver’s seat accelerates AI adoption and investment in AI solutions.

For example, decision intelligence indicates that companies want to use AI to make better decisions faster, such as selecting best treatment options for patients or accelerating discovery and prevention of anomalies and vulnerabilities.

In my situation, I believe that DI could have assisted me with the best action to take to achieve my ultimate goal, which is to drive engineering pride across SAP. …

While we may be facing the biggest threat of our lifetime, the good news is that Decision Intelligence (DI) can leverage AI as well as epidemiological, social, and other knowledge sources to help. Including beautiful visualizations, DI can be used by policymakers, media, business leaders and individuals, to make and communicate the impact of their decisions in a complex world.