Organizations around the world today are coping with a difficult decision environment. From competitive threats and demands for innovations to global uncertainty in markets and post-pandemic workforce disparities, there are numerous challenges driving organizations to take new and agile approaches to teamwork and decision making. However, traditional methods of strategic planning and decision making are insufficient for the scope and speed required in today’s competitive and rapidly changing landscape.

So how can organizations handle the modern pressures of decision making and not just survive, but thrive?

The answer? Artificial intelligence (AI). In just the next few years, the global AI market is predicted to snowball, reaching a $190.61 billion market value in 2025, which is no doubt fueled by the machine learning application industry receiving $37 billion of funding in the U.S. just two years ago. 

Some organizations are still hesitant to invest in AI, which makes sense since a large proportion of AI is focused on replacing people by automating routine tasks. However, this sentiment is expected to quickly change, especially since some AI such as CrowdSmart focuses instead on helping people getting better by their jobs by blending human and artificial intelligence. According to Forbes, 43% of enterprises believe that their AI and Machine Learning (ML) initiatives matter “more than we thought,” with one in four saying AI and ML should have been their top priority sooner. Furthermore, 83% of enterprises have increased their budgets for AI and machine learning year-over-year from 2019 to 2020. As more and more businesses begin to unlock the benefits of AI, it’s crucial not to get left behind. That’s why we’re sharing exactly what you need to know about powering your decisions with AI so you can improve your decision process, reduce risks, and transform organizational learning.

The Modern Pressures of Decision Making

Decisions these days are more complex and time-sensitive than ever. We’ve all experienced the tricky nature of collaborative decisions, and most of us have also probably experienced the lost opportunities and consequences of poorly made decisions. Before we dive into how AI can improve this error-prone aspect of business, it’s helpful to understand and identify the top five modern decision process challenges:

1. Domain knowledge extends across an organization’s ecosystem, and more people than ever consider themselves stakeholders in the outcome of a decision
2. Accuracy depends on understanding risks and mitigating bias
3. Speed is essential and mistakes are often very costly
4. Transparency and buy-in are essential for long-term success
5. Decision-making as an organization should improve over time

Each of these issues contributes to billions of dollars in lost economic value in organizations like yours. Although many companies have pre-defined decision rubrics and heuristics for collaborative decisions, they lack a technology platform to manage these modern demands on the decision process. Moreover, organizations are hesitant to invest in technology and implement AI for a variety of reasons, the top three being staff skills (56%), the fear of the unknown (42%), and finding a starting point (26%).

These are all valid concerns, especially since not all AI is created equal. We all experience the limitations of current AI models in the form of failure of speech recognition systems, failure of navigation systems, and more. Today’s systems do not have an understanding of their context of best performance or know when to switch to a different model or approach to solve a problem. Simply put, there is too much reliance on data-only approaches. 

So where does that leave business leaders who are hoping to use AI to drive better decisions?

The New Wave of AI

Many scientists believe that the next breakthrough in AI will come from the human brain itself, with researchers at institutions such as MIT, Stanford, and UC Berkeley looking to instrument and automate how humans use knowledge to solve problems and make predictions. This new wave combines AI models with collective intelligence, enabling the aggregate predictions of a diverse group, and providing the capability to make smarter decisions than any person, group, or computer could do alone. Human-empowered AI tackles the top modern decision process challenges mentioned above by:

1. Building trust through explanation and integration with human cognitive processes
2. Leveraging collective human skills of imagination and judgement
3. Providing a framework for contextual adaptation

For investors who struggle to stay clear of the biases and blind spots as they invest millions of dollars in new ventures, human-empowered AI brings a much higher degree of accuracy in predicting which companies are most likely to produce a profitable return on investment. Better yet, the technology supports continuous improvement so their investment team will become smarter and even more accurate at predicting success as they work together with the AI system over time. 

The CrowdSmart Solution

Unlike many forms of AI that are trained using historical data, CrowdSmart learns from human interactions and converts opinions into computational models that generate new data. 

Battle-tested, CrowdSmart was originally created as a technology platform for an AI-driven venture fund. A team of 2,5000 investment experts used CrowdSmart technology to collectively create 80% accuracy in predicting follow-on funding momentum for a cohort of 150 companies.

With each use of CrowdSmart in your organization, the accuracy of your key decisions and your institutional knowledge grows in depth and intelligence over time, creating an atmosphere of constant improvement and better returns.

Ready to level up? Let’s schedule a demo today and see how we can help your organization make better decisions by combining the best of human and artificial intelligence.