How to Implement Decision Intelligence for Improved Decision-Making 

How to Implement Decision Intelligence for Improved Decision-Making 

By Marta Teneva

August 21, 2023

In an era saturated with data, making accurate, timely decisions is crucial for businesses seeking to thrive and stay competitive. But with the sheer volume and complexity of information at our fingertips, how do we transform this flood of data into meaningful, actionable insights? The answer is decision intelligence. 

Decision intelligence (DI) is a modern approach that employs artificial intelligence (AI) and machine learning (ML) to translate complex data into clear, concise insights, revolutionizing decision-making processes in various industries. But to truly understand decision intelligence and reap its full benefits, it’s essential to delve deeper than the surface. 

In this comprehensive guide, we will navigate the ins and outs of decision intelligence, exploring its myriad applications, potential benefits, and how to implement decision intelligence in your organization effectively. We’ll look at examples of decision intelligence in action across diverse industries and list the top decision intelligence platforms at the moment. What’s more, we’ll consider the challenges, limitations, and crucial considerations that come with introducing decision intelligence into your organization’s operations. 

Feel free to read the entire guide or use the clickable navigation below to jump to a section that piques your interest: 

What is Decision Intelligence?

Do You Need Decision Intelligence?

What Does Decision Intelligence Do for Your Organization?

What is the Best Strategy for Implementing Decision Intelligence?

Top Decision Intelligence Platforms

Examples of Decision Intelligence Across Industries

What are the Considerations, Limits and Challenges When Starting Decision Intelligence?

Decision Intelligence FAQs

B EYE’s Approach to Decision Intelligence

What’s Next for Decision Intelligence

What is Decision Intelligence?

Decision Intelligence (DI) is a practical, outcome-focused combination of disciplines and techniques enabling organizations to harness the power of AI and data for swift, precise, and uniform decision-making. It doesn’t just address various business needs and challenges. It deploys data collection and machine learning modeling for predicting reliable outcomes, thus facilitating optimal choices. 

Contrary to popular belief, decision intelligence doesn’t mean ousting humans from the decision-making loop. Instead, it’s all about equipping them with AI tools and a comprehensive, easily navigable view of their business data, so they can make the most effective decisions possible. 

What Is Decision Intelligence

Decision intelligence merges cutting-edge technologies like machine learning, optimization, analytics, and process automation. It bolsters business decisions through three distinct approaches: 

  • Decision support, where the AI provides analytical tools to aid human decision-making  
  • Decision augmentation, where the AI proposes decisions for a human to evaluate and approve 
  • Decision automation, where the process is entirely automated, with the machine making and executing decisions independently, albeit under human supervision.

Through the efficient processing and predictive capabilities of decision intelligence, companies can make more enlightened decisions across all echelons, gain a clearer understanding of their operations, and drive outcomes that change the game. 

Now that we’ve shed light on what decision intelligence is, let’s look at the numbers that prove its undeniable impact: 

  • Expectations suggest that the global decision intelligence market will balloon to a staggering $39.3 billion by 2031, with a compound annual growth rate (CAGR) of 15.2%.
  • Projections indicate that by 2024, a whopping 75% of enterprises will transition from testing to actualizing AI, igniting a fivefold surge in streaming data and analytics infrastructure, according to the data trends reported in Gartner’s Top 10 Trends in Data and Analytics for 2020
  • According to IDC, organizations that fail to achieve substantial advancements in decision intelligence risk being outpaced as others successfully apply decision intelligence across their entire operation and all business segments.

Do You Need Decision Intelligence? 

In the forthcoming Decision Intelligence Handbook, esteemed authorities L.Y. Pratt and N.E. Malcolm lay out a simple path to ascertain whether decision intelligence is the missing piece in your organizational puzzle. 

Do You Need Decision Intelligence in your company

Here are 5 Yes/No questions you need to answer to discover if decision intelligence is the right next step for your organization: 

  1. Does the idea of making your organization’s decisions more fact-driven resonate with you?  
  1. Have you been aware of the data and AI revolution changing the business landscape, and don’t want your enterprise to be left on the sidelines?  
  1. Are you seeking a way to effectively navigate assumptions, uncertainties, and risks for superior strategic decision-making?  
  1. Are you looking for a technological solution to respond when your organization’s KPIs flash warning signals effectively? 

If you’ve nodded along to any of these points, then decision intelligence may indeed be your next strategic move. 

At this point, you’re probably pondering over the question that everyone is asking: do companies need analytics, or is decision intelligence the way forward? To give you a proper answer, let’s see what decision intelligence can do for your company

What Does Decision Intelligence Do for Your Organization? 

Decision intelligence isn’t merely an ephemeral trend; it’s a meticulously structured discipline that stakeholders need to embrace to reshape their organizations on a systemic level. Building upon over a century of management innovation, decision Intelligence centers around pinpointing data that allows decision-makers to draw a clear line between actions and results. It’s especially useful for non-technical personnel, such as CEOs and VPs, who need actionable insights distilled from their organization’s data. 

If you’ve been wondering how to utilize your vast organizational data to meet your objectives, or if you’re finding it challenging to make heads or tails of the flood of AI- and math-derived insights, patterns, trends, graphs, and charts, then decision intelligence might just be your beacon in the fog. 

Decision intelligence primarily bridges three significant divides that exist between data and decision-making within organizations:

1. It fosters a mutual understanding between analysts and non-technical decision-makers regarding data utilization, goals, actions, and performance metrics. 

2. It enables the efficient communication of a decision’s context from the non-technical decision-maker to the analyst, allowing them to deliver data that directly links actions to outcomes. 

3. It clarifies the relationship between your aspired outcomes, the actions you can execute, and the impact of your external environment. 

In a compelling video, Gareth Herschel, Research Vice President at Gartner, showcases how decision intelligence can amplify business outcomes:

What is the Best Strategy for Implementing Decision Intelligence? 

Navigating the world of decision intelligence might seem complex, but with the right strategy, it can be a game-changer for any organization. To help you understand how to implement decision intelligence effectively, we delve into two robust models, each offering unique perspectives on successful implementation.  

An effective strategy for implementing decision intelligence can be found in the Gartner Decision Intelligence (GDI) Model. This model acknowledges the nonlinear character of decisions and centers the desired outcome within the model. It’s crucial to remember that decision intelligence isn’t just about technology; it also involves understanding the decision-making process, including how decisions are tracked and enhanced and how relevant knowledge, such as data literacy initiatives, is imparted.

An alternative strategic approach to implementing decision intelligence in your business is the Nine Decision Intelligence Process Model proposed by L.Y. Pratt and N.E. Malcolm. This comprehensive model includes the following:

1. Decision Requirements
• Formulating the decision objective statement

• Framing the decision

2. Decision Modeling
• Designing the decision

• Investigating decision assets

3. Decision Reasoning
• Simulating the decision

• Assessing the decision

4. Decision Action
• Monitoring the decision

5. Decision Review
• Retaining decision artifacts

• Conducting a decision retrospective

For an in-depth exploration on how to implement decision intelligence in your organization, you might consider getting a sneak peek at their book available on O’Reilly’s website.

Top Decision Intelligence Platforms

In the vast landscape of decision intelligence, several platforms have established their mark as reliable tools for driving smarter, data-led choices. These platforms, armed with sophisticated technology, are transforming the way businesses make decisions. Here’s an insider’s look at some of the best decision intelligence platforms that are steering the future:

Top Decision Intelligence Platforms

Data Innovation AI

Picture this: a platform that uses the horsepower of AI to turn heaps of raw data into gold nuggets of actionable insights. That’s Data Innovation AI for you. It offers custom-fit solutions, so whether you’re in forecasting, anomaly detection, or optimization, you’re covered. Essentially, it’s like having a smart assistant to help you make decisions faster and smarter.


Imagine a strategic planning tool that’s like a Swiss Army knife for decision-makers in the complex modern business landscape. C-Plan.IT is exactly that. With its ability to incorporate data from multiple sources and serve up strategic insights like a seasoned sommelier, it’s your go-to platform for scenario planning and forecasting.


Ever wished you could have a platform that makes real-time decisions, almost as if it has a brain of its own? Meet Aera. It’s a Cognitive Operating System that not only learns from your data but can also make decisions and recommendations. Ideal for businesses that want to leave mundane operational decisions to an AI while they focus on the big picture.


Here’s the deal with Peak: it’s your AI-powered wingman in the world of decision intelligence. Its strength lies in its unified approach, bringing together data from various sources to generate insights that matter. Designed to assist organizations in making great decisions, all day, every day – kind of like having your cake and eating it too.


Tellius is the friend you’ve always wanted in the vast world of data. It combines machine learning with intelligent automation and, best of all, you can ask it questions just like you’d ask a friend – in natural language. It’s like having a data wizard in your pocket, making data-driven decisions more accessible and more human.


Paretos is like the Sherlock Holmes of decision-making platforms. It marries data analytics with visual interfaces, helping you not just see your data but understand it. It’s adaptable to different business landscapes, so whether you’re navigating the bustling streets of retail or the high-stakes world of finance, Paretos has your back.

Examples of Decision Intelligence Across Industries 

Leveraging decision intelligence equates to consistently excellent commercial decision-making. No longer the exclusive domain of data science teams at tech behemoths, decision intelligence democratizes the transformational capabilities of AI and machine learning, making them accessible to all. 

Several industries have tapped into the potential of decision intelligence, transforming their operations – ranging from life sciences and pharmaceuticals to medical devices, supply chain management, retail, and manufacturing.

Decision Intelligence in Life Sciences

Take Genentech, a biotechnology frontrunner, which integrates decision intelligence into their R&D endeavors. They exploit advanced analytics, AI, and machine learning to scrutinize biological data, earmark potential drug targets, and streamline drug discovery processes, culminating in efficient and informed decision-making. 

Decision Intelligence Tools In Lifesciences

Biogen, focusing on neurology and immunology, and Moderna, leading the charge in mRNA-based therapeutics, both incorporate decision intelligence to maximize their R&D, enhancing patient stratification and accelerating development timelines, respectively. 

Decision Intelligence in Pharmaceuticals

Look at Pfizer and Johnson & Johnson, two of the world’s largest pharmaceutical companies, both of which have adopted decision intelligence in their R&D and operational areas. Novartis also taps into the power of decision intelligence, leveraging diverse data sources to aid in drug development and personalized medicine. 

Decision Intelligence in Medical Devices

Consider Medtronic and Philips Healthcare, both of which harness decision intelligence to optimize product performance and personalize healthcare delivery. Similarly, Siemens Healthineers and Boston Scientific utilize decision intelligence for precise diagnosis, personalized treatment planning, and informed treatment decisions. 

Decision Intelligence in Supply Chain Management

Amazon, DHL, and UPS – they all utilize decision intelligence in their supply chain operations, using advanced analytics and AI to optimize logistics, manage inventory, and streamline processes. 

Decision Intelligence in Retail

Retail giants such as Walmart, Target, and Alibaba heavily rely on decision intelligence, optimizing inventory management, personalizing shopping experiences, and driving sales through data-driven decisions. 

Decision Intelligence in Manufacturing

General Electric (GE), Siemens, and Toyota – all giants in the manufacturing arena – utilize decision intelligence to enhance productivity, optimize resource allocation, and improve product quality. 

Each of these industry leaders demonstrates the power and potential of decision intelligence, proving it to be a game-changer across diverse sectors

What are the Considerations, Limits, and Challenges When Starting Decision Intelligence? 

Understanding what decision intelligence is and knowing how to implement it are just the first steps in a much larger process. Real progress requires addressing complex challenges, acknowledging inherent limitations, and meticulously considering a wide array of factors. While the advantages of decision intelligence are undeniable, like all new journeys, embarking on a decision intelligence initiative comes with its share of considerations, limitations, and potential stumbling blocks. 

Firstly, the consideration of data quality is paramount. Decision intelligence is only as effective as the data it has to work with. If your data is incomplete, outdated, or inconsistent, the insights generated may be misleading, and the decisions based thereupon flawed. Consequently, investing in reliable data collection, management, and cleaning mechanisms is a crucial starting point. 

Decision Intelligence Challenges for companies

Secondly, embracing decision intelligence involves grappling with technology limitations. While AI and machine learning are powerful tools, they are not infallible and can generate inaccurate predictions if not properly managed. Moreover, different decision intelligence platforms come with their own sets of capabilities and limitations. Careful evaluation is essential to select the one that aligns best with your specific business needs. 

Thirdly, the challenge of change management is substantial. As with any digital transformation, shifting towards a more data-driven, AI-enhanced decision-making process involves significant cultural and operational changes. These might include training employees on new tools, fostering a culture that values data-driven decision-making, and adapting processes to include these new decision-support mechanisms. 

In addition, ethical and privacy considerations need to be accounted for. The use of AI and data analytics often raises concerns about data privacy and bias in decision-making. Thus, your organization must establish robust policies around data usage and algorithm transparency. 

Lastly, the hype surrounding decision intelligence might lead to unrealistic expectations. Although decision intelligence can undoubtedly improve decision-making, it is not a magic bullet that will solve all business problems. Like any framework, its effectiveness depends largely on how it is used and in what context. Setting realistic goals and expectations is therefore a crucial step in a successful decision intelligence initiative. 

So, while embarking on a decision intelligence journey can deliver significant rewards, navigating these considerations, understanding the inherent limitations, and preparing for potential challenges will be instrumental in ensuring a smooth and successful transition.

Decision Intelligence FAQs 

  • u003cstrongu003eWhat are the benefits of decision intelligence?u003c/strongu003e

    Decision intelligence (DI) harnesses the power of data, AI, and machine learning to optimize decision-making across an organization. It provides a structured approach to transforming raw data into actionable insights, helping businesses make faster, more accurate decisions. Decision intelligence can increase efficiency, improve predictability, and yield more robust strategic planning. Ultimately, it leads to better operational outcomes and a competitive edge in today’s data-driven business landscape.

  • u003cstrongu003eHow to get started with Decision Intelligence in your organization?u003c/strongu003e

    Initiating a decision intelligence program starts with a clear definition of your business goals and an understanding of the key decisions that impact these goals. Next, you’ll need to gather, clean, and structure your data for analysis. Then, consider partnering with a trusted decision intelligence provider or investing in a decision intelligence platform suitable for your organization’s needs. It’s essential to train your team on using these tools effectively, foster a data-driven culture, and ensure proper governance mechanisms are in place.

  • u003cstrongu003eWhat are the best strategies for implementing Decision Intelligence?u003c/strongu003e 

    Successful decision intelligence implementation rests on a few strategies: clearly defining business objectives; identifying critical decisions impacting these objectives; evaluating the quality, relevance, and completeness of your data; choosing the right decision intelligence platform; and most importantly, fostering a data-centric culture. Additionally, maintaining transparency in your decision-making process, encouraging continuous learning, and regularly reviewing and refining your decision intelligence strategies ensure your Ddcision intelligence initiatives remain effective and relevant.

  • u003cstrongu003eWhat are the features of Decision Intelligence?u003c/strongu003e

    Key features of decision intelligence include data aggregation and cleaning, predictive analytics, machine learning algorithms, AI-enhanced decision support, and decision automation capabilities. Additionally, decision intelligence platforms often provide visualization tools for better understanding of data patterns and trends. At the heart of decision intelligence is its ability to bridge the gap between raw data and actionable business decisions, thus enabling a more effective decision-making process.

  • u003cstrongu003eWhat is an example of Decision Intelligence?u003c/strongu003e

    Consider an eCommerce company using decision intelligence to enhance its supply chain efficiency. By harnessing decision intelligence, the company can analyze vast datasets to forecast product demand accurately, optimize inventory management, and improve logistics planning. With machine learning algorithms, it can identify trends and patterns that help predict future demand spikes, avoid stockouts, and reduce storage costs. Consequently, the company can make informed, data-driven decisions that enhance operational efficiency and customer satisfaction.

B EYE’s Approach to Decision Intelligence 

At B EYE, we holistically assess your data analytics strategy to align with your unique needs. Our tailored solutions are geared to address these needs, helping businesses overcome data complications and inefficiencies. With a strong focus on some of the key features of decision intelligence — data analytics, planning, and automation — we champion continuous and sustainable growth while refining your decision-making process with cutting-edge technology. 

Explore Our Services 

What’s Next For Decision Intelligence 

Looking ahead, decision intelligence is set to take an even more prominent role in how we make business decisions. Leveraging AI and data analytics, decision intelligence helps convert complex data into understandable, actionable insights, a process that will become increasingly essential in our data-rich world. 

We at B EYE, along with other forward-thinking companies, stand ready to drive this transformation, helping businesses tackle data issues while fostering continuous, sustainable growth. As more and more industries recognize the power of decision intelligence, it’s clear that this field is not just a passing trend, but a new standard in decision-making. 

With the shift from testing AI to fully integrating it into operational processes, the demand for decision intelligence is expected to rise significantly. In 2023, we can say that the future.