Mastering digital decision making


Written by Professor Alan Brown, Professor in Digital Economy, Exeter Business School

In recent years, I’ve engaged with many organizations in both the public and private sectors, exploring their unique digital transformation journeys. While their circumstances and concerns vary significantly, a common question unites them: Why can’t we go faster?

The main stumbling block they highlight is often the difficulty they face to accelerate decision-making processes, enabling teams to swiftly navigate uncertain terrain. They want to try out new approaches, explore different ways of working, and engage in more experimentation to learn quickly. Yet, they too often fail at the first hurdle. How can organizations master digital decision making?

In the changing world of digital transformation, speeding up decision making in times of uncertainty is critical. Unfortunately, organizations find themselves paralyzed by the myriad of choices and alternatives they face. For managers and leaders at all levels, navigating through this complexity requires a strategic approach that encourages a different way of thinking about change. In my experience, this can be supported by adopting three important techniques: An agile decision making process; a focus on data-driven automation; and a structured approach to defining risk.


The OODA loop: A blueprint for agility

There are many ways to define the process of agile decision making. However, I’ve found that a great place to start is with the OODA loop, a concept pioneered by military strategist and retired United States Air Force Colonel John Boyd. OODA, an acronym for “Observation, Orientation, Decision, and Action”, is an important concept with a long history. It provides a dynamic framework for decision-making and problem-solving in complex, rapidly changing situations.

Let’s break down each stage of the OODA loop:

  1. Observation: The initial step involves gathering information about the environment, competitors, market trends, and other relevant factors. It entails collecting data, assessing the current situation, and recognizing patterns and changes.
  2. Orientation: This stage involves analyzing gathered information to develop a comprehensive mental model of the situation. Considerations include organizational strengths and weaknesses, as well as those of competitors and partners. Orientation is influenced by many factors, including previous knowledge, experiences, cultural background, and adaptability to new information.
  3. Decision: With a clear understanding of the situation, decision-makers can make informed decisions based on their view of the current context. These decisions consider objectives and potential courses of action, emphasizing both speed and impact.
  4. Action: Following the decision making phase, actions are implemented. This stage involves executing the chosen course of action and adapting as needed based on feedback and results.

While simple in concept, this approach provides a clear focus to drive decisions forward. Crucially, the OODA loop is not a linear process; it is iterative and continuous. Constantly cycling through the stages allows for adaptation to new information and changing circumstances. The OODA loop’s key advantage lies in its emphasis on agility, speed, and the ability to respond effectively to evolving situations, providing individuals and organizations with a competitive edge.

While its origins are in military contexts, where agile decision making is literally the difference between life and death, it now has application across a wide set of fields, including:

While the OODA loop is a widely recognized concept, its application may vary based on specific needs and contexts. The adaptability of the OODA loop is evident in its diverse applications, showcasing its relevance where rapid decision making and adaptability are essential.


A New paradigm for AI-driven decision making

But, is the OODA loop still relevant in today’s digital world? In the rapidly advancing landscape of AI and data-driven autonomous systems, people such as Steve Blank believe that a new decision making paradigm is emerging: the “Sense-Predict-Agree-Act” (SPAA) loop. This concept represents a series of steps that autonomous systems or AI-driven entities follow to make decisions and take actions in real-time driven by data from a variety of sources:

  1. Sense: In the initial step, the system gathers data and information from sensors or the environment. These sensors may include cameras, microphones, temperature sensors, or any other data sources providing information about the system’s surroundings.
  2. Predict: After data collection, the system uses algorithms and models to process information, making predictions about future events. This step involves analyzing data to anticipate potential outcomes or events.
  3. Agree: In this stage, the system compares predictions with pre-defined objectives or goals, evaluating alignment with desired results and identifying any deviations or anomalies. Much of this is automated, but human input may be essential in some scenarios.
  4. Act: If predictions and objectives align, the system proceeds to take action based on its predictions. This action may involve making decisions, adjusting parameters, or executing specific tasks.

The SPAA loop finds application in the development of autonomous vehicles, robotics, and other AI-driven systems, where real-time decision-making and adaptability are imperative. By continuously cycling through the SPAA loop, these systems can sense their environment, predict future events, and take appropriate actions to achieve their goals.

Of course, the impact of automation in decision making in AI scenarios raises any concerns. The extent to which humans are removed from the decision making loop is widely debated. There are many situations where such decision making can have important implications in aspects such denying access to public services, making life-changing decisions in healthcare, education, and transportation, and much more. Understanding the implications of such decisions to manage risk is critical.


Understanding blast radius in decision-making

To understand risk in decision making, one approach is to consider the impact of key decisions on those affected, and how negative effects can be limited. Often used in data security, an approach that has broader use in digital decision making is sometimes referred to as “managing the blast radius”. In the realm of decision-making, particularly in security, risk management, and technology, the term “blast radius” plays a crucial role. Coined in the military, it describes the potential impact or scope of damage resulting from a specific event, such as a security breach or system failure.

Considering the blast radius is essential for assessing the potential consequences of a decision or action. It involves understanding not only the immediate impact but also the broader and often cascading effects on interconnected systems, processes, and stakeholders. When making decisions regarding many types of digital technology delivery, it can be useful to ask openly about the extent of the blast radius should things go wrong and how those impacts can be managed. This can help you to quickly rule out some alternatives, while helping to put in place risk mitigation plans for others.

For instance, in cybersecurity, decision-makers evaluating new software deployments or changes in system configurations must consider the potential blast radius. Understanding the extent of potential damage, including compromised data and system downtime, allows for informed choices and the implementation of mitigating measures. Such thinking can be effective in much wider digital decision making scenarios, including upgrades to existing systems, changes to ways of working, and the introduction of new system deployments.


Between a rock and a hard place

These 3 areas are important for digital decision making. In navigating the complex landscape of digital decision-making, they lead to three key recommendations for managers and leaders:

Embrace iterative decision-making

Cultivate a culture of continuous learning and adaptation. Encourage teams to iterate through the decision-making process using an OODA approach, learning from each cycle and adapting strategies accordingly. In the digital era, where change is constant, the ability to iterate rapidly is a strategic advantage.

Invest in agility

Stay on top of emerging technologies and adapt decision making to their capabilities. Foster a culture that embraces technological agility, enabling the organization to integrate innovative solutions promptly. Leverage approaches such as SPAA that enhance the decision making process to take advantage of AI and data-driven autonomous systems, ensuring the organization remains at the forefront of responsible ways to apply digital advances.

Manage risks to limit negative effects

In an era of increasing digitization, understand the potential negative effects of digital decision making. Regularly assess the blast radius of potential digital decisions and implement proactive strategies to safeguard critical assets. A resilient digital decision making strategy is fundamental to sustaining trust and protecting the organization from potential disruptions.


The digital decision making mandate

Mastering digital decision-making requires a strategic blend of proven methodologies like the OODA loop, innovative concepts like the SPAA loop, and a vigilant approach to keep ahead of risk considerations such as defining the blast radius. As leaders, navigating the digital landscape becomes not just a necessity but a strategic advantage. By adopting these 3 techniques, you can more effectively embrace the dynamism of the digital era, empower your teams with agile decision-making frameworks, and steer your organization towards sustained success in the ever-evolving digital landscape.

Originally posted here

Read More Transformation

Comments are closed.