Jurgen Appelo's Blog

A wicked problem is a complex issue that is difficult to define and has no clear solution. It often involves conflicting stakeholders, incomplete information, and interconnected challenges. Wicked problems emerge in systems loaded with uncertainty. We can begin taming wicked problems when we understand at least six kinds of uncertainty.

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When we think about systems, we tend to categorize them into a few system types, such as simple, complex, and complicated. But such a classification can often oversimplify reality. To better understand the complexity of wicked problems, we can evaluate them across six dimensions: Volatility, Intricacy, Modularity, Scalability, Ambiguity, and Reflexivity. Each aspect helps reveal how systems operate and interact, allowing for a more nuanced perspective than a reductionist approach of just a few system types.

Volatility: From Static to Chaotic

The first kind of uncertainty is Volatility, the degree to which a system’s behavior or outcomes fluctuate over time. Such behavior can range from static—predictable and stable—to chaotic—with highly unpredictable outcomes. The FIFA World Cup provides a good example. On one hand, the structure of the event is static: it takes place every four years, with fixed rules and teams, and we know the format will remain pretty much constant. However, the outcomes, such as which teams will win or advance in the rankings, can appear chaotic, driven by a combination of skill, luck, and unexpected events—similar to stock market dynamics. This shows that even within a seemingly ordered system, chaos can be observed depending on how we define the system.

In business, when systems are more volatile, we need to adopt flexible plans to adjust for the unpredictability of outcomes. Static systems, on the other hand, allow for traditional, linear planning. Understanding where a system falls on this volatility spectrum helps organizations choose the right strategies for managing this uncertainty.

Intricacy: From Simple to Complicated

The second kind of uncertainty in systems and wicked problems is their Intricacy or the complicatedness of their internal structure. Systems can be simple, with few components that interact in straightforward ways, or complicated, involving numerous interconnected elements. For instance, consider something as commonplace as a “smart” electric car key. From the driver’s perspective, the key may seem simple—just a few buttons and obvious icons. However, a mechanic or engineer would likely view it as a complicated system, given the intricate circuits and technology on the inside that make the car key do its job.

This relativity of the observer highlights that a system’s intricacy or complicatedness often depends on the observer’s focal point or level of expertise. For managers, recognizing when a system is simple or complicated informs the decision-making process: fixing simple problems requires little more than common sense while addressing complicated systems might require expert analysis​.

Modularity: From Tightly Integrated to Loosely Coupled

The third kind of uncertainty is Modularity: this refers to how easily a system’s components can be separated or interchanged without disrupting the whole. Some systems are tightly integrated, allowing for little or no reconfiguration, like a network of railroads. In contrast, air traffic systems are more loosely coupled, allowing for greater adaptability. Airlines can adjust schedules, routes, and capacities to match demand, while a railway timetable is more rigid and limited to a tightly integrated railroad pattern.

Increased modularity makes systems more adaptable but also introduces uncertainty as the number of potential configurations increases. For example, while railways themselves are tightly integrated, individual trains are nevertheless entirely modular—train cars can be easily reconfigured. On the other hand, airplanes are much less modular; you can’t just modify the parts of an aircraft the way you can rearrange train cars. The balance between integration and modularity in a system influences how it reacts to change and how resilient or flexible it can be in the face of disruption​.

Scalability: From Sublinear to Superlinear Growth

The fourth kind of uncertainty is Scalability: the ability of a system to grow or handle increased workloads without losing efficiency. Systems can scale in different ways—sublinear, linear, or superlinear. Sublinear scaling, where growth slows relative to input, is observed in large species or infrastructure like skyscrapers. Superlinear growth, on the other hand, occurs when systems, such as swarms, cities, or social networks, expand faster than the increase in their inputs.

In organizational terms, deciding whether to scale up (centralizing growth into a larger single entity) or scale out (expanding into multiple smaller entities) can significantly impact efficiency and adaptability. For example, a company that grows into a massive organization may face bureaucratic inefficiencies (sublinear growth), while one that spreads into smaller, autonomous units might enjoy more flexibility and faster innovation (superlinear growth). Understanding how a system scales helps leaders decide the best path for sustainable growth​.

Ambiguity: From Lucid to Fuzzy

The fifth kind of uncertainty is Ambiguity. Ambiguity in systems comes from unclear boundaries, relationships, or outcomes. In some systems, boundaries and roles are clearly defined, leading to predictability—what we might call lucid systems. Boundaries can be much more blurred in other systems, such as human communities or organizations. For example, defining who belongs to the LGBTQ+ community can be tricky, as the lines between who is “in” and who is “out” are often subjective and contested. Whether someone is an enemy or an ally can be a matter of intensive debate.

In organizational contexts, ambiguity often arises around team dynamics. Team members might disagree on what constitutes “the team,” leading to different interpretations of roles and responsibilities. When systems are ambiguous, management must focus on probing and experimenting to gain clearer insights rather than relying on straightforward decision-making​.

Reflexivity: From Isolated to Immersed

The sixth kind of uncertainty is Reflexivity. Reflexivity refers to how much a system’s behavior is influenced by the behavior of its observers and participants. In isolated systems, external perceptions and interactions have little impact. For example, a dog on a leash may behave in a relatively predictable way, regardless of how its human caretakers talk about the dog. On the other hand, human systems are deeply reflexive. A group of children labeled as “troublemakers” might begin to behave in exactly that way, fulfilling the expectations placed on them. This feedback loop between perception and behavior creates increased complexity in human systems.

A system’s reflexivity complicates efforts to observe or manage it objectively. For example, employees’ awareness of how they are being evaluated can influence their behavior, creating a cycle that might reinforce or challenge the initial assumptions. Recognizing the reflexive nature of a system helps managers understand the limitations of traditional observation and control methods​ .

Taming Wicked Problems

We now have six dimensions or qualifications of systems: Volatility, Intricacy, Modularity, Scalability, Ambiguity, and Reflexivity. Are these the only ones? Certainly not. Some readers of this article will be able to produce a few more. But this is a workable set. The benefit of this model, and why I prefer it over more reductionist, simplistic ones, is that it allows us to assess and discuss a much wider variety of systems. Recognizing only four or five system types doesn’t do justice to the incredible variety of systems in the universe. With the model described here, we get a hundred or more patterns of wicked problems and systemic behaviors, and each could be worth discussing.

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In understanding these six dimensions—volatility, intricacy, modularity, scalability, ambiguity, and reflexivity—we can better navigate the complexity of our systems. No single model can fully capture the range of behaviors systems exhibit—and the Wicked Framework is still a simplification of reality—but evaluating systems along these six axes allows for more tailored and adaptive strategies. Instead of applying a one-size-fits-all solution, this framework might help us recognize the unique characteristics of the systems we manage and choose appropriate methods to handle at least six different kinds of uncertainty.

Authors: Nicolas Pascaud & Javier Camarasa Garcia

Introduction: A Legacy of Innovation

Founded in 2002, BeTomorrow has grown into a leading tech and digital innovation agency, housing over 90 experts across disciplines such as strategy, IT development, data, design, and marketing. Their primary focus is creating custom digital solutions tailored to the unique needs of various industries, including finance, health, and luxury. Since he became CEO of BeTomorrow, and now also as the President, Alexandre Ribeiro has emphasized the importance of human experience, which has been the cornerstone of the company’s growth and success.

The Evolution of BeTomorrow

Over the past two decades, BeTomorrow has established itself as a dynamic and forward-thinking organization, constantly evolving to meet the demands of the digital landscape. Their approach to innovation has been more and more people-centric, fostering an environment that values open communication, autonomy, and collaboration. However, as the company expanded, it began to face several challenges that necessitated a re-evaluation of its management practices.

The Quest for Organizational Clarity

In early 2023, BeTomorrow embarked on a journey to explore alternative management ways. The organization recognized that while its open and autonomous culture had fueled its success, it also led to certain inefficiencies. Key challenges include the need for clearer organizational structure, rapid scaling, and the management of information that was often concentrated in the hands of a few individuals.

To address these issues, BeTomorrow began researching various management approaches, including Sociocracy and Holacracy. In doing so, Betomorrow discovered the unFIX Model and it resonated most with the company’s needs, offering a flexible and human-centered approach to organizational design.

Experimenting with the unFIX Model: A Transformational Journey

Using the unFIX Model at BeTomorrow marked a significant shift in how the company approached organizational challenges. This model, which BeTomorrow affectionately refers to as the “unFIX Burger,” provided a framework for visualizing and clarifying the organization’s structure.

Clarification and Visualization

One of the primary benefits of the unFIX Model has been its ability to bring clarity to BeTomorrow’s organizational setup. The process of aligning everyone with the unFIX vocabulary and understanding happened organically, largely driven by groups of volunteers who were passionate both about contributing to the company's future, and the model's potential to drive change. These volunteers gathered in various settings to train, discuss, and apply the unFIX Model within the context of BeTomorrow.

Figure 1: unFIX Ignition Workshop at BeTomorrow

Through these efforts, the company has achieved greater transparency in its operations, enabling all Base members to better understand how the organization is currently working and how it might evolve in the future. The unFIX Model has also enhanced BeTomorrow’s ability to place the human experience at the center of everything it does, both internally and in its work with clients.

Defining Roles with Precision

Internally, BeTomorrow had a clear understanding of roles, but communicating these roles to clients was often challenging. Clients sometimes found it difficult to determine whom to approach within the teams, whether it be the Product Owner, Scrum Master, or other crew members. To address this, BeTomorrow is in the process of using the Role Attributes pattern from the unFIX Model, which clearly delineates the responsibilities of each team member.

The company is applying the Role Attributes pattern within the Value Stream Crews, combining them with the Delegation Levels pattern, and found substantial value in the process. This method not only clarified internal roles but also improved the external communication of these roles to clients, leading to a more efficient and transparent collaboration process. Encouraged by the success, BeTomorrow is now exploring how to extend these patterns to other crews and forums across the organization to ensure consistency and base-wide adoption.

Figure 2: BeTomorrow v.2.0

Managing Tensions

In any organization, tensions and conflicts are inevitable, particularly when different perspectives collide. BeTomorrow is no exception. The unFIX Model provides a structured approach to addressing these challenges, fostering open discussions and enabling the organization to align on potential solutions.

By asking the right questions and facilitating conversations around these tensions, BeTomorrow is working on finding better ways of working both within teams and across the organization. They see it as a way to become even better in sustaining and improving both internal harmony and the company’s overall effectiveness and efficiency.

Enhancing Coordination Amid Rapid Growth

BeTomorrow has experienced significant growth in recent years, with a 37% increase in size over the past two years alone, according to LinkedIn Insights. This rapid expansion has made it increasingly difficult to maintain consistent communication across the organization. More people means more opportunities, but it also introduces complexity in terms of coordination and information sharing.

To address these challenges, BeTomorrow turned to unFIX Initiatives, a tool designed to improve alignment across the company. By focusing on aligning everyone around the right priorities, rather than simply increasing the workload, BeTomorrow expects to continue nurturing, or even, increasing, its agility and focus, even as it scales.

The Road Ahead: Embracing Continuous Feedback

BeTomorrow’s journey with the unFIX Model is ongoing, with the company continually refining its approach based on feedback and evolving needs. One of the key goals moving forward is to further integrate the unFIX patterns into the company’s DNA, enabling BeTomorrow’s consultants and coaches to support clients more effectively.

Recommendations for the Future

As BeTomorrow continues to evolve, several key recommendations can help the company maximize the benefits of the unFIX Model:

1. Measures and Metrics Patterns: Experimenting with these patterns can help establish a baseline of the company’s current business indicators. This will allow BeTomorrow to evaluate the impact of each unFIX experiment and make data-driven decisions. 

2. Strategic Dimension Patterns: These patterns can be leveraged to align the entire organization around a unified strategic direction. By using these exercises, BeTomorrow can empower its leadership to share information more effectively and ensure that everyone becomes a strategist in the organization and has all the necessary information to make decisions faster than ever before.

3. Innovation Vortex Pattern: Given BeTomorrow’s focus on innovation, and exploring the Innovation Vortex pattern could help the company refine its processes and become even more effective in driving innovation for its clients.

Conclusion: A Partnership Made in Heaven

This case study is a testament to the collaborative spirit of BeTomorrow and its commitment to continuous improvement and innovation. This case study highlights the ongoing partnership and commitment to the unFIX Model. 

As BeTomorrow continues its journey, the lessons learned and the recommendations provided will serve as a roadmap for navigating the challenges and opportunities that lie ahead. With the unFIX Model as a guiding model, BeTomorrow is well-positioned to continue its legacy of innovation, delivering exceptional value to its clients and creating a positive impact on the digital landscape.

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This article has been co-created by BeTomorrow and tandi , an independent consultancy firm. The main collaborators from BeTomorrow are Nicolas Pascaud , Doriane Ribeaut , Farouk Choulak , Thibault Baleinier , and Anais Parenteau . The main collaborator from tandi is Javier Camarasa Garcia .

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Author: Jurgen Appelo

Anyone who has delved into innovative product development is familiar with the five-step Design Thinking process by IDEO and Lean Startup’s Build-Measure-Learn loop. However, the model that aligns most closely with the scientific method could be the Innovation Vortex.

The scientific method, a cornerstone of scientific discovery for centuries, follows a systematic approach to inquiry. Its formalization, credited to philosophers like Francis Bacon and René Descartes and later refined by Karl Popper, emphasizes observation, hypothesis formation, experimentation, and analysis of empirical evidence. This systematic process has driven the remarkable advancements in science and technology that we enjoy today.

In product innovation, the rigor of the scientific method has been championed by figures like Steve Blank, creator of the Customer Development Model, and Eric Ries, author of Lean Startup. They and others have effectively brought scientific rigor to product innovation, guiding development from initial concept to market-ready product through continuous iterations of experiments.

The Innovation Vortex is a merger of both Design Thinking and Lean Startup, bringing together the insights from both product innovation models.

Note: Newer iterations of IDEO’s process show six steps instead of five . They’ve recently added Frame a Question as an extra step at the beginning which, we are pleased to say, was already the first step of the Innovation Vortex for several years.

Now, let’s be honest here. Building innovative products is not the same thing as developing scientific theories. However, the parallels are obvious, as you can see in the following comparison:

Specialization (Frame in the Innovation Vortex): While not typically shown in visuals of the scientific method, academic specialization is crucial. Scientists specialize in preferred research areas, much like organizations focus on particular markets, framing the problem they aim to solve. However, unlike most scientists, organizations frequently shift focus areas, hence the inclusion of this stage in the Innovation Vortex.

Observation (Discover in the Innovation Vortex): This stage involves identifying a market problem or need, akin to scientists observing phenomena for further understanding. In business, this translates to observing gaps in current solutions or emerging trends, paralleling scientific researchers observing knowledge gaps they want to explore.

Question (Define in the Innovation Vortex): Scientists formulate specific questions about their observations, defining their investigative focus. Similarly, in product development, observations are synthesized into questions we can explore through experimentation.

Research: Although not explicitly mentioned in the Innovation Vortex, research is vital to the scientific method. Reviewing existing studies and current knowledge informs both scientific and product innovation, ensuring a solid foundation for new hypotheses or product ideas.

Hypothesis (Ideate in the Innovation Vortex): Scientists propose explanations or predictions based on observations. In product development, once we identify a customer need, we can develop a hypothesis to address it. This hypothesis serves as a testable solution, outlining expected outcomes.

Experiment (Build in the Innovation Vortex): Scientists design experiments to test hypotheses, carefully controlling variables. Product innovation involves developing prototypes and conducting tests, iterating based on feedback and performance data.

Analysis (Test in the Innovation Vortex): Analyzing experimental results determines the validity of the hypothesis. In product development, we assess metrics such as user satisfaction and functionality. Supported hypotheses move the product closer to launch, while unsupported ones require hypothesis adjustments and further testing.

Conclusion (Learn in the Innovation Vortex): Conclusions drawn from data inform the next steps. Successful hypotheses might lead to scientific publications or market introduction of new products. Flawed hypotheses prompt additional observations and experiments, restarting the cycle.

Note: Some visuals of the scientific model show (scientific) Publication as a separate ninth step. This is similar to the final step in IDEO’s current six-step model , where they call it Share the Story . We chose not to include Publication/Storytelling as an extra step in the Innovation Vortex because the other steps of the process can (and should) be done iteratively. This is not the case with Publication/Storytelling, which usually happens only once when the iterative cycles are completed and the end result is worth sharing. Including it as part of the visual would be confusing and could make people misinterpret the model as a sequential process that happens only once.

When the scientific method is applied to product innovation, this structured approach mitigates the risk of failed product launches and enhances the chance of market success. The Innovation Vortex effectively encapsulates the scientific process, making it an innovation model that looks remarkably similar to pictures that visualize the scientific model.

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We’ve uncovered fifteen AI use case patterns (there are probably more) and given each one a name. This is the second of the fifteen.

Creator of immersive, interactive, and multisensory experiences.

AIs can enable experiences that go far beyond what is possible with other technologies.

The Experience Generator is the AI pattern for crafting rich, multisensory, and interactive experiences. It combines elements of virtual and augmented reality with advanced conversational interfaces to create engaging environments for entertainment, education, training, and more. By leveraging AI's capacity for real-time content generation and adaptation, machines can produce highly responsive experiences that blur the lines between the digital and physical worlds.

More examples:

A major museum might employ the Experience Generator pattern to create a time-traveling historical tour. Visitors wear AR glasses that overlay historically accurate, AI-generated scenes and characters onto the physical exhibits, with the AI adapting the narrative based on each visitor's interests, age, and engagement level.

A global corporation could utilize the Experience Generator for advanced employee training. It creates a VR environment simulating complex, high-stakes business scenarios and the AI dynamically adjusts their difficulty and introduces unexpected challenges based on the trainee's performance.

A mental health startup might leverage the Experience Generator to develop an innovative therapy platform. The AI creates personalized, immersive environments that help patients confront and overcome phobias or anxiety triggers. It adapts the intensity of the simulations in real-time based on biometric feedback and verbal responses.

The Experience Generator takes the concepts from several other AI use case patterns and turns up the dial with the immersion of users in AI-generated digital environments.

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We’ve uncovered fifteen AI use case patterns (there are probably more) and given each one a name. This is the fourteenth of the fifteen.

Customer service system automating interactions and resolving issues.

Anyone who's ever had to talk with a help desk agent knows there is a world to win in the area of customer service and support.

The Support Automaton is the AI pattern that can revolutionize customer service by automating interactions and streamlining issue resolution processes. Provided the AIs are sophisticated enough, they can enhance both customer experience and operational efficiency by providing instant, accurate responses to inquiries and guiding users through troubleshooting steps.

Capable of handling a wide range of customer interactions, from simple FAQ responses to complex problem-solving scenarios, the Support Automaton learns and improves from each interaction. By analyzing customer feedback and interaction data, this pattern would also provide valuable insights for continuous service improvement and product development.

More examples:

A global telecommunications company implements the Support Automaton pattern to handle customer inquiries across multiple channels (chat, voice, and email), with the AI successfully resolving most customer issues without human intervention.

An e-commerce giant utilizes the Support Automaton to create a personalized assistant. The AI analyzes customer purchase history, browsing behavior, and current inquiries to provide tailored assistance.

A software company employs the pattern to create an intelligent troubleshooting system for their complex enterprise software. The AI guides users through step-by-step problem-solving processes, adapting its approach based on user responses and system diagnostics.

Similar to the Personal Weaver pattern, the Support Automaton improves the user experience. However, the focus is on support and assistance rather than personalization based on preferences.

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We’ve uncovered fifteen AI use case patterns (there are probably more) and given each one a name. This is the thirteenth of the fifteen.

Solution finder for complex problems across various domains.

How about we use AI to solve our problems for us? The machines aren't smarter than us but they are certainly faster.

The Decision Optimizer is how we call the AI pattern for identifying optimal solutions, configurations, and strategies when dealing with complex systems and problems. Leveraging advanced algorithms and data analysis, AIs can streamline operations, enhance resource allocation, and support strategic decision-making processes. They can consider numerous variables and constraints to arrive at the most efficient and effective outcomes.

By enabling organizations to make better data-informed decisions, the Decision Optimizer improves operational efficiency and could provide a competitive edge.

More examples:

A global shipping company could employ the Decision Optimizer pattern to dynamically adjust its entire fleet's routes and cargo assignments in real-time, considering factors such as weather patterns, port congestion, fuel prices, and delivery urgency.

A major healthcare network might utilize the pattern to optimize staff scheduling across multiple hospitals, taking into account factors like patient influx predictions, staff specializations, fatigue management, and emergency preparedness.

A renewable energy company could use the Decision Optimizer to manage a complex grid of solar, wind, and hydroelectric power sources, balancing energy production, storage, and distribution in response to real-time demand fluctuations and weather forecasts.

Summarizing, the Decision Optimizer can significantly shorten the time to solve some of the most complex optimization problems.

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We’ve uncovered fifteen AI use case patterns (there are probably more) and given each one a name. This is the twelfth of the fifteen.

Forecasting system for trend prediction and scenario simulation.

Wouldn't it be nice to be able to look into the future? A well-trained AI can do that for you.

The Predictive Oracle is the AI pattern that leverages advanced modeling and simulation techniques to forecast trends, predict outcomes, and test hypotheses across various domains. By employing complex algorithms and analyzing vast datasets, it can create detailed simulations of potential scenarios, offering invaluable insights for strategic planning and risk assessment. For example:

“I’m uploading the productivity data and remaining backlog of our team. Please give me your forecast for when you think we’ll be finished, in multiple scenarios with percentages.”

The Predictive Oracle pattern empowers organizations and individuals to make more informed choices by providing a glimpse into possible futures and preparing for a range of potential outcomes.

More examples:

A global investment bank utilizes the Predictive Oracle to simulate thousands of economic scenarios, incorporating variables such as geopolitical events, natural disasters, and technological breakthroughs.

A multinational agricultural corporation employs the pattern to create detailed climate simulations for the next 50 years. These simulations predict crop yields, water availability, and pest patterns across different regions.

A metropolitan government could use the Predictive Oracle to model the impact of various urban development plans on traffic flow, air quality, and social equity over the next few decades.

Provided you have the necessary data sets and modeling system to work with, the Predictive Oracle can help you prepare for the future.

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We’ve uncovered fifteen AI use case patterns (there are probably more) and given each one a name. This is the eleventh of the fifteen.

Synthetic data generator for model training and testing.

If AIs are good at showing fake people, it means they're excellent at generating fake data.

The Data Synthesizer is the AI pattern that specializes in creating synthetic data for various applications. By generating artificial datasets that augment existing information, we can enhance machine learning models, and create realistic simulations. This is about more than just generating deep fake photos and videos.

By transmuting raw data into valuable synthetic information, we can address data scarcity, privacy concerns, and the need for diverse, balanced datasets. The Data Synthesizer plays a crucial role in computer vision, natural language processing, and financial modeling. It enables researchers and developers to overcome the limitations of real-world data collection.

More examples:

A cybersecurity firm uses the Data Synthesizer pattern to generate millions of synthetic network traffic patterns, including rare attack scenarios, to train and test advanced intrusion detection systems.

A healthcare startup employs the pattern to create a diverse set of synthetic medical images, complete with rare pathologies, to train a diagnostic AI system for early cancer detection, overcoming the scarcity of real patient data and ethical concerns surrounding data privacy.

An autonomous vehicle company may utilize the Data Synthesizer to generate complex virtual driving scenarios, including edge cases and hazardous conditions rarely encountered in real-world testing.

The Data Synthesizer pattern shows that fake data is not necessarily a bad thing. Using it can be crucial when real data is too hard to come by.

We’ve uncovered fifteen AI use case patterns (there are probably more) and given each one a name. This is the tenth of the fifteen.

Information synthesizer, transforming diverse data into actionable insights.

When you’re looking for actionable insights drawn from a vast trove of data, you would be smart to consider consulting an AI. Like, “Hey, ChatGPT. Here are the results from our latest customer survey. Which conclusions can I draw from this?”

The Knowledge Gatherer is our name for the AI pattern that deals with collecting, analyzing, and synthesizing vast amounts of information from diverse sources. AIs can distill complicated knowledge into actionable insights, supporting knowledge work and enhancing decision-making processes. For example:

“From these five hundred job candidates, pick the twenty we should invite for an interview, and give us your motivation behind your suggestions.”

By processing and interpreting large volumes of data, AIs can uncover hidden patterns, trends, and correlations, presenting them in a comprehensible and applicable format.

More examples:

A pharmaceutical company uses the Knowledge Gatherer when analyzing millions of scientific papers, clinical trial results, and patient data, uncovering unexpected correlations between genetic markers and drug efficacy.

A global investment firm employs the Knowledge Gatherer to continuously analyze financial reports, news articles, social media sentiment, and economic indicators across multiple markets, to provide real-time investment recommendations.

A government think tank could utilize this pattern to synthesize climate data, economic reports, and policy documents to generate comprehensive policy recommendations.

In short, the Knowledge Gatherer is your friend when it comes to turning data into decisions.

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We’ve uncovered fifteen AI use case patterns (there are probably more) and given each one a name. This is the ninth of the fifteen.

Personalization engine tailoring content and experiences for individual users.

Sometimes, as a user, you want an experience that perfectly matches your preferences. Like, “I only want to see sci-fi movies, not fantasy, and please skip anything that is scary.”

The Personal Weaver is the name we give to the AI pattern that crafts personalized content, recommendations, and experiences for individual users at scale. By analyzing user data, preferences, and behavior patterns, this AI creates a unique and engaging journey for each person across various platforms and services.

This pattern is applied in diverse fields such as e-commerce, entertainment, education, health, and finance. By leveraging machine learning algorithms and data analytics, the Personal Weaver continuously refines its understanding of each user, ensuring that the personalized content and recommendations evolve alongside the user's changing interests and needs.

More examples:

Streaming platforms employ the Personal Weaver pattern to create dynamic, personalized homepages for each user, featuring a mix of content recommendations based on viewing history, time of day, and current trends.

A major online retailer could integrate the Personal Weaver into its shopping experience, offering personalized product recommendations, personalized deals, and tailored search results based on each customer's browsing history, purchase patterns, and demographic information.

Fitness apps utilize this pattern to create adaptive workout plans for users. They adjust workout difficulty, duration, and type based on real-time performance data, personal goals, and physiological metrics from wearable devices.

Summarizing, the Personal Weaver pattern is fully dedicated to modifying experiences so that they feel entirely personal.

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