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Generative AI Falling Short? Unleash the Potential of Decision Intelligence

 

In the realm of business, there’s a mystical force known as Decision Intelligence, a concept so powerful that it’s reshaping the way organizations make crucial choices. Imagine a world where decisions aren’t just based on mountains of data but are instead guided by a harmonious blend of traditional wisdom and cutting-edge innovation.

Gartner, the sage of technology, describes Decision Intelligence as a practical field that brings together a diverse array of decision-making techniques. It’s about more than just crunching numbers; it’s about designing, modeling, aligning, executing, monitoring, and refining decision models and processes.

For years, the business world has been dazzled by the allure of data-driven decision-making. Companies invested heavily in big data solutions and adorned their offices with flashy dashboards, hoping to unlock the secrets of success. But as time passed, they realized that simply drowning in data wasn’t enough.

The problem? Current business intelligence solutions, even those powered by artificial intelligence, often fall short when it comes to making sense of the data. Sure, they can deliver flashy reports and dashboards, but they struggle to provide the context, timeliness, and completeness needed for effective decision-making in today’s fast-paced digital economy.

Enter Decision Intelligence. Instead of fixating on the data itself, Decision Intelligence focuses on the decisions that need to be made. It’s about asking the right questions and identifying the necessary data to support those decisions. With Decision Intelligence, organizations can formulate, engineer, and coordinate decisions in a systematic manner, enhancing decision consistency, velocity, governance, control, and quality.

But implementing Decision Intelligence isn’t without its challenges. Organizations often struggle to scale Decision Intelligence due to the complexity of their decision processes, workflows, and a lack of confidence in automation. To succeed, they must establish governance, testing, and controls to ensure transparency and accountability in decision-making processes.

Despite these challenges, the benefits of Decision Intelligence are clear. By striking a balance between speed and control, Decision Intelligence can expedite decision velocity and enhance decision quality. It standardizes decision-making processes, preserves institutional knowledge, and reduces variability and inconsistency.

As organizations explore the world of Decision Intelligence, several trends are emerging. Explainable and auditable AI is becoming increasingly important, particularly as AI/ML is integrated into decision-making processes. Human involvement remains crucial in many decision flows, and the adoption of low-code and no-code interfaces is on the rise.

In this ever-evolving landscape, a new class of users is emerging decision engineers or decision scientists. These individuals work closely with business users to model workflows and decision processes effectively, ensuring that Decision Intelligence continues to thrive and evolve in the years to come.

These decision scientists move beyond mere machine learning, they now engage in a process we at Zinia call ‘Reciprocal Learning’ – an exciting shift towards collaboration with machines.

While the decision scientists may still delve into the mathematical intricacies of algorithms, the focus now lies on a more holistic approach. They navigate the realm of decision intelligence, crafting AI test cases with precision to ensure conclusive delivery of business value. This involves targeting key performance indicators (KPIs) that align with organizational goals and strategically balancing optimality with risk in model outcomes.

In this new landscape, the decision scientists grapple with questions of machine-led recommendations: How much autonomy should machines have in decision-making? How do they design and implement tests to validate these recommendations in operational settings? And crucially, how do they iteratively refine these processes to strike the perfect balance between machine insights and human expertise?

These decision scientists must ensure stakeholders have confidence in AI outcomes by providing transparent explanations of each step in the decision-making process.

In essence, the decision intelligence journey is no longer solitary but collaborative, where learning from machines is as essential as teaching them. It’s a dynamic dance between data and human ingenuity, driving innovation and unlocking new realms of possibility in the ever-evolving world of AI.

It’s no wonder that Gartner estimated that by the end of 2023, over 33% of large organizations have analysts practicing decision intelligence, including decision modeling. However, this is just the beginning. According to a McKinsey study, Decision Intelligence is projected to generate 63% of all business value from AI by 2030, surpassing other forms of AI such as Generative AI!

Zinia has been at the forefront of the Decision Intelligence revolution, with our multi-award-winning, cloud-based, no-code AI platform which has been built as an Enterprise grade platform underpinned by the following design principles:

  • Simplicity to Use: building AI models with minimal prior knowledge and possibly scarce data, through a hands-off no code approach
  • Explainability and Transparency: extracting clear information from data, provide simple and see-through predictions and explainable decisions
  • Assurance and Trust: attaching guarantees to predictions/decisions, namely simple certificates on their quality
  • No Biases: spotting biases in data and take fair model-based decisions across the board
  • Adaptivity: adapting to customers’ changing needs, to market’s variability across time, to scarce and outlier data


Author: A. Mishra CEO Zinia.ai
Date: 24/04/2024

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