WIP Research: Syntelic vs. Syncretic Ontology Approaches for Strategy & Innovation
Hi, Shep here. I’m exploring some new & novel research in the space of ontology-first management frameworks and organizational strategies, and I thought I’d share some thought-provoking WIP material with you.
I currently have a white paper on the drafting table, tentatively titled “Harnessing Strategic Tensions: A Framework for Syntelic and Syncretic Adaptive Ontologies in Innovation and Decision-Making”.
The focus is to outline how different ontological approaches can align with specific business processes, and outline how AI’s role as a cognitive load manager unlocks new, more complex approaches to leveraging ontology for business advantage.
To that end, here’s the scaffolding of my knowledge structure (an “ontology card”, as I call it) on the topic of Syntelic and Syncretic Ontologies and their application in strategy & innovation disciplines.
Both of these are new ways to think of ontologies in a business sense, so you’ll forgive my somewhat exhaustive anchoring of the material here. In the absence of a thorough literature review, I’m leaning heavily on good scaffolding of the concepts to ground the work in a digestible context.
I’ll likely leave this post up even once the white paper is live as it’s a good example of the human-AI collaboration and problem space explosion that is made possible through the cognitive load management assistance of an LLM.
I’d say “enjoy!” but this piece is a bit heady for a casual read. The junction of Ontology & LLMs is a point of fascination for me lately, and I think it’s a place where transcendent value can be created both academically and economically.
Instead, I’ll say: “Thanks for reading, reach out on LinkedIn if you want to chat more about this.” :)
And please note, I’m collaborating with AI models to develop the scaffolding and body of this material, but the thesis, intent, and concepts are my own.
If unfamiliar with ontologies, this is going to read like Greek to you. And even if you are familiar with ontologies… it may still read like Greek to you! Hah! If you’re new to the space, get your bearings by first checking out Ontologies 101 and then my paper on executable ontologies second. That should at least give you the underpinnings you need to grasp where this white paper is eventually headed.
1. Core Concepts
Syntelic Ontology
Definition: A syntelic ontology refers to a system that aligns and synthesizes diverse strategies into a unified, goal-oriented framework. This is an adaptive ontology designed to bring different strategic elements into coherence when the organization requires clarity and efficiency in its direction. The term "syntelic" is derived from Greek roots syn- (together) and telos (goal), emphasizing that the purpose is to converge diverse inputs toward a shared objective.
Objective: To streamline organizational perspectives toward a singular, cohesive goal, ensuring strategic clarity and operational coherence. The syntelic ontology adapts by integrating changing inputs into a harmonized strategy as conditions evolve.
Context: In regulated or stable environments, syntelic ontologies allow organizations to dynamically adapt by absorbing new inputs while maintaining a clear goal.
Related Concepts:
Exploitation: Focusing on refining and maximizing the use of existing resources.
Convergence: The process of aligning diverse strategies or viewpoints toward a singular, shared goal.
Example: A global pharmaceutical company synthesizes regulatory, marketing, and production strategies into a single cohesive framework to align its global expansion plan, enabling it to adapt to different regional regulations while maintaining a unified strategic vision.
Syncretic Ontology
Definition: A syncretic ontology maintains strategic contradictions and conflicting viewpoints, allowing for the coexistence of parallel strategies that can drive innovation. This is also an adaptive ontology, enabling organizations to preserve tensions and allow for dynamic shifts as the market or operational environment changes.
Objective: To stimulate creative tension and support parallel exploration of multiple strategies without prematurely forcing convergence. This framework adapts to organizational and environmental shifts by leveraging contradictions as drivers for innovation.
Context: Syncretic ontologies are ideal in volatile, fast-evolving environments where innovation is critical, and multiple strategies must be explored in parallel before making long-term decisions.
Related Concepts:
Exploration: Pursuing new opportunities, knowledge, or capabilities without the pressure to resolve conflicting strategies prematurely.
Divergence: Maintaining multiple, often contradictory paths or ideas to explore different strategic options.
Example: A tech company manages two competing innovation streams: one focused on traditional improvements to existing products, and another exploring radical new technologies. By preserving the strategic tension, the firm adapts dynamically based on market feedback and technological advances.
2. Attributes and Dimensions
Adaptive Capacity:
Syntelic: Adapts by aligning and converging diverse inputs toward a single goal. As new data, perspectives, or challenges arise, syntelic systems synthesize them into the overarching objective, providing a clear direction for the organization.
Syncretic: Adapts by preserving tensions between conflicting strategies and allowing for parallel exploration. It enables organizations to maintain flexibility by holding multiple strategies in balance until one becomes most viable based on emerging conditions.
Tension:
Syntelic: Resolves tensions by aligning divergent perspectives and strategies into a cohesive whole, reducing complexity and conflict within the organization.
Syncretic: Intentionally preserves tensions and contradictions, using them to foster creativity and innovation. These systems maintain multiple strategies without resolving conflicts prematurely.
Cognitive Complexity:
Syntelic: Manages lower cognitive complexity by synthesizing diverse strategies into a streamlined framework. Decision-making becomes focused on aligning inputs toward the overarching goal, reducing cognitive burden on leadership.
Syncretic: Involves high cognitive load as it requires managing multiple, often contradictory strategies simultaneously. This approach requires advanced decision-making frameworks to track parallel paths without convergence.
Outcome Orientation:
Syntelic: Optimizes for efficiency, alignment, and operational coherence by driving all resources toward a clearly defined goal. This approach is ideal for stability and consistency.
Syncretic: Optimizes for innovation by maintaining multiple conflicting strategies, allowing for creative tension to generate novel solutions or breakthroughs.
Application:
Syntelic: Ideal for environments where standardization, scalability, and operational efficiency are critical, such as in highly regulated industries.
Syncretic: Suited for industries requiring agility and dynamic adaptation, where parallel exploration of multiple strategies can lead to breakthrough innovations.
3. Contextual Factors
Adaptive Organizational Structure:
Syntelic: Works best in hierarchical or matrix organizations that require alignment and unified direction. These structures ensure that the organization's diverse teams converge on common goals.
Syncretic: Thrives in cross-functional or agile organizations that emphasize flexibility, allowing for parallel explorations and the coexistence of multiple perspectives. This structure is better suited to environments where innovation and agility are paramount.
Market Conditions:
Syntelic: Effective in stable or regulated environments where consistency, scalability, and operational coherence are necessary. Organizations in these conditions benefit from adapting strategies into a unified direction.
Syncretic: More suited to volatile or fast-changing markets, where rapid iteration and the exploration of multiple strategies are essential for staying competitive. The adaptive capacity of syncretic ontologies allows for continuous adjustment based on shifting market dynamics.
4. Personal Ontologies in Syncretic Systems
Definition of Personal Ontologies: Personal ontologies refer to the unique cognitive approaches, mental models, and experiences that individual team members bring to an organization – a person’s cognitive fingerprint. In syncretic systems, personal ontologies are essential for maintaining cognitive diversity and driving innovation through the preservation of competing perspectives.
Role of Personal Ontologies in Syncretic Systems:
Cognitive Diversity: Personal ontologies contribute to the richness and complexity of a syncretic system by introducing varied problem-solving methods and viewpoints. This diversity is crucial for generating productive tension within the organization.
Innovation Catalyst: By preserving these diverse perspectives and personal ontologies, syncretic systems create an environment where creative friction can lead to innovative solutions.
Example: In an innovation lab, engineers, designers, and business analysts each bring distinct mental models to the table. The syncretic ontology maintains their differing perspectives, allowing for adaptive flexibility as they explore different paths to innovation.
5. Practical Applications of Adaptive Syntelic and Syncretic Ontologies
Syntelic Application Example
Scenario: A global automotive company uses a syntelic ontology to align its production, marketing, and regulatory strategies across continents. As the company faces varying regulations in different markets, the syntelic framework enables it to adapt by integrating these diverse requirements into a cohesive global expansion plan.
Outcome: The syntelic ontology allows the company to maintain operational efficiency and consistency while meeting diverse regulatory challenges in different regions.
Syncretic Application Example
Scenario: A biotechnology firm is working on multiple R&D projects focused on gene therapy and traditional drug development. A syncretic ontology allows these parallel strategies to coexist, maintaining tension between the exploration of radical new methods and established approaches.
Outcome: The company retains its innovation capacity, with the flexibility to adapt based on scientific breakthroughs or market demands. The syncretic ontology ensures that neither approach is prematurely abandoned, preserving the potential for breakthrough discoveries.
6. Integration with AI Systems
Executable Adaptive Ontologies in AI Systems
Syntelic Mode: AI systems enhance the execution of syntelic ontologies by synthesizing data from diverse strategies into a unified, goal-directed approach. These systems continuously evaluate incoming data, helping human decision-makers align their strategies based on real-time conditions, ensuring operational coherence.
Syncretic Mode: AI is crucial for managing the high cognitive load required to maintain syncretic ontologies. AI systems are used to track the parallel strategies and conflicting paths within the organization, providing real-time recommendations on when and how to maintain or resolve strategic tensions. This allows syncretic ontologies to dynamically adjust to evolving circumstances without overwhelming human decision-makers.
Syncretic Ontologies and Cognitive Load Management
Cognitive Overload in Human Teams: Managing syncretic ontologies introduces significant cognitive complexity, requiring human teams to balance multiple, often conflicting strategies simultaneously. This can lead to slower decision-making, mental fatigue, and inefficiency, particularly when rapid adaptation is required.
AI as a Cognitive Load Manager: AI systems are uniquely capable of handling multi-threaded complexity without experiencing the cognitive fatigue that humans do. They can:
Track the progress of multiple conflicting strategies simultaneously.
Analyze which tensions are productive for innovation and should be maintained.
Provide real-time data-driven insights on when to converge or diverge strategies.
Feasibility through AI: Without AI, managing the complexity and cognitive demands of syncretic ontologies would be challenging for human decision-makers. AI systems ensure the feasibility and scalability of syncretic approaches by processing large volumes of data and allowing for continuous strategic adaptation.
Example: In a technology firm, AI tracks competing innovation projects and suggests when strategic tensions should be maintained for maximum innovation and when it is advantageous to converge efforts into a single strategic direction. This enables the firm to adapt quickly without overburdening its leadership team.
Summary of AI's Role in Syncretic Ontologies:
Syncretic ontologies thrive on creative tension and the simultaneous exploration of multiple strategies, but this inherently creates a high cognitive burden for human teams.
AI systems are critical for managing this complexity by handling the disparate cognitive load without fatigue, allowing for dynamic adaptation and ensuring that the innovation process moves forward efficiently. AI's ability to process multiple inputs at scale makes it a powerful enabler of syncretic strategies.
7. Conclusion
Syntelic and Syncretic Ontologies as Adaptive Systems: Both syntelic and syncretic ontologies serve as adaptive frameworks that organizations can leverage to dynamically balance the need for operational alignment (syntelic) and innovation-driven exploration (syncretic). Syntelic ontologies provide the stability needed for coherent strategic execution, while syncretic ontologies enable the preservation of creative tension necessary for breakthroughs in innovation.
AI's Role in Adaptive Ontologies: AI is indispensable for managing the cognitive load associated with syncretic ontologies. Without AI, the complexity of maintaining multiple, often conflicting strategies would overwhelm human decision-makers. By dynamically tracking, evaluating, and recommending actions based on real-time data, AI systems ensure that adaptive ontologies can respond quickly to market shifts and strategic opportunities. In syntelic ontologies, AI helps streamline decision-making, ensuring that all components of the organization remain aligned toward a common goal.
Final Outlook: The integration of adaptive ontologies—syntelic for alignment and syncretic for innovation—allows organizations to maintain a balance between efficiency and agility. In today's fast-paced, ever-changing environment, this flexibility is critical for sustained competitive advantage. With AI as a key enabler, businesses can navigate complexity and strategic contradictions with confidence, ensuring that they remain both operationally efficientand innovatively agile.
Grounding Syntelic vs. Syncretic in Practical Examples
Rather than following a strict RDF or OWL format, this approach will provide verbalized, practical examples that illustrate how both syntelic and syncretic ontologies can function in real-world scenarios. These examples will highlight how each ontology framework is applied in organizational settings, offering clear contrast between their objectives and processes.
Syntelic Ontology - Practical Example
Scenario: Global Retail Expansion
Company Context:
A global retail company is planning an expansion into emerging markets in Southeast Asia, South America, and Africa. The goal is to ensure that the company's global brand, operational consistency, and supply chain processes are applied uniformly across these new regions. At the same time, the company needs to adapt to local market demands, legal regulations, and cultural preferences.
Application of Syntelic Ontology:
The company uses a syntelic ontology to synthesize global and regional strategies into a cohesive framework. Here's how it works:
Convergence of Global and Local Strategies: The company has global brand guidelines that dictate how its stores should be designed, how marketing campaigns should look, and how products should be sourced and distributed. In emerging markets, however, there are specific customer preferences and legal requirements. Using a syntelic approach, the company creates a single strategic framework that integrates local needs into the global strategy while maintaining brand coherence.
Operational Efficiency through Alignment: The supply chain processes, store layouts, and customer service protocols are adjusted slightly for each market but are largely standardized to maintain efficiency. The syntelic ontology ensures that all departments—marketing, operations, product development—work under a unified set of goals and guidelines that allow for smooth scaling and brand consistency.
Example of Unified Execution: In Brazil, the company's marketing team incorporates local cultural elements into advertisements, but the overall messaging follows the global narrative. The retail stores in Vietnam adapt their inventory to reflect local preferences for certain product categories, but the layout, customer service standards, and pricing structures align with the company's global model.
Outcome:
The use of a syntelic ontology ensures that the company maintains its global brand identity, scales efficiently into new markets, and operates within a coherent framework while still adapting to local conditions.
Syncretic Ontology - Practical Example
Scenario: Technology Company R&D
Company Context:
A technology company specializing in smart devices has an R&D division tasked with exploring both incremental improvements to its existing smartphone line and radical innovations involving wearable technology and smart home devices. The company must explore both paths without forcing premature convergence between the two, as both avenues hold potential for long-term success.
Application of Syncretic Ontology:
The company adopts a syncretic ontology to preserve tensions between these two strategic pathways, maintaining a state of creative friction to fuel innovation.
Maintaining Parallel Strategies: The company's incremental innovation team focuses on refining existing smartphone features, such as battery life, screen technology, and software optimization. Simultaneously, a disruptive innovation team works on smart glasses that can integrate seamlessly with the smart home ecosystem. Rather than converging these two strategies early on, the syncretic ontology supports the simultaneous exploration of both, allowing the company to benefit from the diverse outcomes of each strategy.
Strategic Tensions: The smartphone improvement team wants to stay close to the company's current core competency, arguing for continued investment in a product that has an existing customer base and market share. The smart glasses team, on the other hand, is focused on a radical, future-oriented vision that could disrupt the current product lines. The syncretic ontology keeps these tensions intact, recognizing that both strategies have merit and could lead to breakthrough innovations.
Dynamic Adaptation through AI: The company uses AI systems to track the progress of both innovation paths. AI evaluates the market potential for the incremental smartphone improvements, analyzing customer feedback, technical feasibility, and competitor activities. Simultaneously, it monitors the progress of the smart glasses project, assessing trends in wearable technology and smart home adoption. The AI helps the leadership decide when to preserve tensions between these strategies and when to allow some form of convergence (e.g., integrating smart glasses technology into future smartphones or smart home devices).
Outcome:
The syncretic ontology enables the company to stay competitive in the current market with incremental smartphone improvements while also exploring new horizons with smart glasses. By maintaining strategic contradictions, the company maximizes its potential for long-term innovation without forcing early decisions that could limit creative outcomes.
Key Distinctions between Syntelic and Syncretic in Practice
Syntelic: In the global retail expansion example, the syntelic ontology focuses on convergence and alignment. It synthesizes diverse regional needs and global goals into a cohesive strategy, ensuring that the company can scale efficiently across new markets. This ontology works best where operational efficiency and brand coherence are priorities.
Syncretic: In the tech R&D example, the syncretic ontology preserves the divergence between conflicting innovation paths. It maintains tensions to fuel creative exploration, understanding that both incremental and radical innovations are valuable. This ontology is ideal for organizations that need to embrace complexity and allow for multiple outcomes before choosing a path forward.
Verbalization Summary
Syntelic Example: A global retail company aligning regional needs with global branding, ensuring efficient scaling and brand consistency across diverse markets.
Syncretic Example: A technology company maintaining tensions between smartphone improvements and smart glasses innovations, using AI to manage the complexity and strategically preserve both paths for maximum innovation potential.
These examples verbalize how syntelic and syncretic ontologies function in real-world organizational contexts, providing clear illustrations of how each approach manages alignment and tension to achieve different strategic objectives.
Glossary of Key Terms
1. Adaptive Ontologies
Definition: Adaptive ontologies refer to strategic frameworks that allow for dynamic adjustment in response to changing internal or external conditions. This means organizations can shift between different modes of strategy (e.g., syntelic or syncretic) depending on market trends, technological advancements, or organizational needs.
Context in the Ontology Card: Both syntelic and syncretic ontologies are considered adaptive because they are designed to respond flexibly to various organizational demands—converging when alignment is necessary or diverging when exploration is needed.
Example: In a tech company, an adaptive ontology would allow the organization to pivot between aligning teams around a unified product launch (syntelic) and maintaining multiple conflicting innovation paths (syncretic) when developing next-generation technologies.
2. Syntelic Ontology
Definition: A syntelic ontology refers to a strategic framework that synthesizes and aligns various strategies or perspectives into a unified, goal-directed structure. The term "syntelic" is derived from Greek roots syn- (together) and telos (goal), emphasizing that the purpose is to converge diverse inputs toward a shared objective.
Context in the Ontology Card: Syntelic ontologies are used when organizations need operational coherence and efficiency, especially in stable or regulated environments.
Example: A healthcare company uses a syntelic ontology to integrate its regional marketing, R&D, and regulatory efforts into a global expansion plan that ensures operational consistency.
3. Syncretic Ontology
Definition: A syncretic ontology allows for the preservation of tensions, contradictions, and conflicting strategies within an organization. The goal is to maintain these contradictions to stimulate innovation and enable the exploration of multiple strategic pathways.
Context in the Ontology Card: Syncretic ontologies are particularly valuable in dynamic or fast-moving environments, where the ability to maintain parallel explorations is crucial for discovering new solutions.
Example: An R&D division in a biotechnology company preserves tensions between incremental improvements in drug formulations and radical innovations in gene therapy, allowing for parallel exploration without forcing premature convergence.
4. Cognitive Load
Definition: Cognitive load refers to the mental effort required to process complex information or manage multiple tasks simultaneously. In the context of syncretic ontologies, cognitive load is especially high because decision-makers must maintain multiple conflicting strategies at once.
Context in the Ontology Card: Syncretic ontologies inherently impose a higher cognitive load on human teams due to their complexity. AI systems are essential for managing this cognitive load by processing and organizing parallel strategies, ensuring that decision-makers aren't overwhelmed.
Example: In a financial services firm exploring both blockchain innovations and traditional banking services, the cognitive load for human decision-makers would be excessive. AI assists by managing and assessing each strategic pathway, offloading the complexity from human teams.
5. AI-Driven Executable Ontologies
Definition: Executable ontologies are enhanced with both domain knowledge (about the specific area) and procedural knowledge (about how to act on that knowledge). In AI systems, these ontologies allow for dynamic decision-making, helping organizations shift between syntelic and syncretic modes based on real-time data.
Context in the Ontology Card: AI is crucial in making syncretic ontologies feasible by handling complex, parallel strategies without experiencing cognitive overload. Executable ontologies enable AI to continuously adapt strategies, making real-time adjustments and ensuring the organization maintains operational agility.
Example: In a tech company, AI systems analyze data from multiple R&D projects to decide when tensions should be maintained for innovation and when teams should converge toward a unified product strategy.
6. Convergence
Definition: Convergence refers to the process of aligning multiple strategies, inputs, or perspectives into a single, coherent direction. It involves reducing internal contradictions to achieve a unified goal.
Context in the Ontology Card: Convergence is a key feature of syntelic ontologies, where the focus is on ensuring that all organizational actions drive toward a single, clear objective.
Example: A global automotive company converges its marketing, engineering, and sales strategies into a single product launch roadmap for consistency across international markets.
7. Divergence
Definition: Divergence refers to the intentional preservation of multiple conflicting strategies or pathways, allowing them to coexist and evolve independently until a resolution is required.
Context in the Ontology Card: Divergence is central to syncretic ontologies, where the focus is on maintaining parallel explorations to allow for innovation and the discovery of new opportunities.
Example: A software development team maintains divergent paths, working on both a legacy product and a radically new technology, waiting for market feedback before deciding which direction to converge on.
8. Exploitation
Definition: Exploitation refers to the strategy of maximizing returns from existing resources, knowledge, and capabilities. It is focused on improving efficiency, consistency, and scalability.
Context in the Ontology Card: In syntelic ontologies, exploitation is often the goal, as the organization seeks to optimize current processes and capabilities toward a unified goal.
Example: A manufacturing company focuses on improving its existing production processes to reduce costs and increase scalability, using a syntelic ontology to align all departments toward this goal.
9. Exploration
Definition: Exploration involves the pursuit of new opportunities, capabilities, and innovations. It is characterized by a willingness to take risks and maintain multiple possibilities simultaneously.
Context in the Ontology Card: Syncretic ontologies are designed to facilitate exploration by allowing conflicting strategies to coexist, fostering innovation through tension and parallel discovery.
Example: A pharmaceutical company exploring both gene editing and traditional therapies maintains an exploratory approach, using a syncretic ontology to explore both options until it becomes clear which strategy to pursue.
