Exploring Applied Sensemaking

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Industry Critiques of Cynefin

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Despite its popularity, Cynefin has faced numerous critiques from management theorists and practitioners. Common points of criticism include ambiguity in definitions, oversimplification of reality, and challenges in practical use:

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• Conceptual Ambiguity: Some find the framework “difficult and confusing” with ambiguous terminology. For example, labels like “known, knowable, sense, categorize” can be interpreted in multiple ways, leading to inconsistent understanding. Critics argue Cynefin lacks a rigorous theoretical foundation and covers a limited set of contexts (Cynefin framework - Wikipedia). Essentially, it “boxes” the vast spectrum of situations into a few domains, which may gloss over nuances.
• Oversimplification of Complexity: Business experts like Niels Pflaeging contend that Cynefin’s neat categories oversimplify messy organizational dynamics . Real-world situations often span multiple domains simultaneously, blurring the boundaries that Cynefin draws. Pflaeging argues that complexity is pervasive and cannot be cleanly partitioned – focusing on static domains might underplay the interconnected feedback loops between “simple, complicated and complex” elements in an organization (Rick On the Road: Cynefin Framework versus Stacey Matrix versus network perspectives). In fact, complexity scholar Ralph Stacey (creator of the Stacey Matrix) later asserted that “life is complex all the time” and “there are no ‘levels’ of complexity”, cautioning against the notion that some parts of business are entirely simple or ordered. This viewpoint suggests Cynefin’s domains risk giving a false sense of certainty or separateness, whereas even “ordered” contexts can produce unexpected outcomes.
• Unclear Domain Boundaries: Because real scenarios evolve, it can be hard to discern which domain you’re in until after the fact. Practitioners note that “it’s not so easy to discern the context you’re in when you’re actually in the thick of it.”. Misclassification is a danger: a leader might wrongly treat a complex problem as merely complicated (or vice versa), leading to flawed strategies. For instance, assuming expert analysis can solve a truly complex, uncertain issue may result in “reams of immaculate plans and little action”. On the other hand, treating every challenge as chaos can breed a reactive “firefighting” culture that neglects long-term strategy. These pitfalls highlight that correctly diagnosing the domain is as critical as the response itself.
• Focus on Categorization vs Action: Another critique is that Cynefin emphasizes classifying a situation over providing actionable guidance. Pflaeging notes that identifying a domain (e.g. calling something “complex”) doesn’t automatically tell leaders how to solve it. The framework gives broad response patterns (like “probe–sense–respond”), but translating that into concrete strategy requires skill and judgment. Some AI professionals and agile coaches caution against treating Cynefin as a rigid checklist – the danger is leaders feeling they’ve “done enough” by labeling a problem, without actually adapting their decision-making style. In knowledge management terms, Cynefin is more of a sensemaking model (helping to think about a problem) than a step-by-step method for solving the problem. Joseph Firestone and Mark McElroy, for example, argue that Cynefin is not a full knowledge management process but rather one lens for understanding context.

Business strategists appreciate Cynefin’s insight that different problems require different approaches, but they also warn it can be “confusing” or misapplied. The framework’s simplicity is a double-edged sword: it makes complexity more graspable, yet if taken too literally, it may lull leaders into oversimplifying or misidentifying their challenges. Effective use of Cynefin thus demands training and situational awareness – it’s a guide, not an absolute rule.

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Comparison to Alternative Frameworks

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To better judge Cynefin’s strengths and weaknesses, it helps to compare it with other decision and complexity frameworks. Here we look at three well-known alternatives – the Stacey Matrix, the Wicked Problems approach, and the OODA loop – and how they stack up against Cynefin in concept and use:

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• Stacey Matrix (Agreement vs. Certainty): Developed by Ralph Stacey, this matrix maps decisions along two axes – the level of certainty about cause-and-effect and the level of agreement among stakeholders. It essentially creates a continuum from simple, agreed-upon problems to highly uncertain, conflict-ridden ones (often labeled “anarchy”). In practice, Stacey’s model often gets condensed into zones similar to Cynefin’s domains: simple, complicated, complex, and chaotic. The strength of the Stacey Matrix is its explicit handling of the human dimension (agreement) alongside technical uncertainty – useful for organizational decision-making where consensus matters. It can guide whether to use traditional vs. agile approaches; for example, a project with clear goals and known methods falls in the simple/complicated range (suitable for traditional project management), whereas unclear requirements and novel technology push it toward complex/chaotic (favoring agile experimentation). A noted weakness, however, is that Stacey’s categories were never sharply defined – even Stacey warned that real situations don’t fit neatly on a static grid. In fact, Stacey himself moved away from his early matrix, arguing that “even in the most ordinary of situations, something unexpected might happen…so there are no ‘levels’ of complexity.” This critique mirrors the ambiguity issue in Cynefin. Moreover, the Stacey Matrix doesn’t explicitly account for shifting contexts over time (it’s a snapshot view) and, as some project managers note, it “does not take into account the project environment” or offer crisp boundaries. In comparison, Cynefin is less about plotting coordinates and more about categorical sense-making – it provides five discrete domains with defined decision models. Cynefin’s advantage over Stacey’s framework is that it gives concrete guidance per domain (e.g. use best practices in Clear, use experiments in Complex), whereas Stacey’s matrix is more of a diagnostic map. On the other hand, Stacey’s two-dimensional approach captures nuance (like high agreement + high uncertainty vs. low agreement + high uncertainty) that Cynefin’s single-dimension domains might gloss over. In summary, Stacey’s Matrix and Cynefin share a lineage (indeed, many Stacey Matrix depictions borrow Cynefin’s terminology), but Cynefin evolved as a more action-oriented tool at the expense of simplifying Stacey’s continuum.
• Wicked Problems Framework: The concept of “wicked problems” originates from Rittel and Webber (1973) to describe policy and design challenges that are ill-defined, constantly evolving, and have no clear solution. Classic examples are climate change, urban planning, or in business, things like cultural transformation – issues where every intervention changes the problem itself. In a sense, “wicked vs. tame” is a binary classification: either a problem is wicked (requiring novel, collaborative approaches) or it’s tame/solvable with standard techniques. Cynefin, by contrast, expands the spectrum beyond this binary. It acknowledges varying levels of uncertainty: some problems are truly chaotic (urgent crises with no evident cause/effect), some merely complex (patterns discernible only in hindsight), and others just complicated (difficult but ultimately knowable). A strength of the wicked problems concept is that it brought complexity to the forefront of strategic thinking – it made leaders realize that certain challenges cannot be solved with linear thinking or single-discipline expertise. For example, solving a “wicked” issue often requires iterative experimentation and involvement of many stakeholders, much as Cynefin’s Complex domain prescribes “probe-sense-respond” with safe-to-fail trials. However, a critique of the wicked problem terminology is that it can be counterproductive: by labeling a complex issue as “wicked,” we might inadvertently treat complexity as something aberrant or evil. As one strategist notes, calling a problem ‘wicked’ “gives complexity a normative (and negative) connotation…instead of simply part of reality”, which can “stand in the way of an effective new approach”. In other words, if everything challenging is branded wicked, organizations may feel these issues are almost unsolvable “exceptions,” rather than acknowledging complexity as a normal part of business to be continuously managed. Cynefin avoids the value judgment of “wicked” and instead normalizes complexity as just one domain (indeed, Cynefin’s name itself means “habitat” – suggesting each domain is a natural place at times). Another difference: wicked problem frameworks don’t delineate ordered problems in detail – they simply contrast them with wicked ones – whereas Cynefin gives equal weight to simple and complicated domains where clear best practices or expert analysis apply. In practice, many organizations find the Cynefin framework more actionable: it not only identifies when you’re facing a wicked/complex scenario, but also points to how to tackle it (for instance, by probing and allowing solutions to emerge, rather than trying to impose a plan). The wicked problems concept remains highly influential (especially in public policy and design thinking), but it is often used in tandem with models like Cynefin. In fact, Cynefin can be seen as refining the “tame vs. wicked” continuum into more categories – providing a toolkit for different shades of complexity.
• OODA Loop (Observe–Orient–Decide–Act): The OODA loop, developed by military strategist Col. John Boyd, is not a categorization framework but a process model for rapid decision-making. It directs decision-makers to continuously Observe the environment, Orient by analyzing information and context, Decide on a course of action, and Act, then repeat quickly. The key idea is getting inside the opposition’s decision cycle – in business terms, adapting faster than competitors or fast-changing conditions. The strength of OODA is its focus on agility and feedback: it’s very useful in chaotic or high-volatility situations where quick reactions are essential. It has been widely adopted beyond the military – for instance, in cybersecurity, aviation, and even AI development, teams use OODA for fast iterative response. Many tech and business leaders appreciate that OODA explicitly incorporates Orient, meaning one must continuously re-assess assumptions and context before deciding. Compared to Cynefin, OODA is action-oriented and dynamic, but it doesn’t explicitly tell you what kind of situation you’re dealing with. In fact, Cynefin and OODA are often seen as complementary: Cynefin helps you sense “what kind of problem is this?”, and OODA drives “how we iterate towards a solution.” Dave Snowden has noted that both frameworks work in the “decision support arena” but on different aspects. A potential weakness of OODA is that if one applies it blindly, there’s a risk of over-emphasizing speed at the expense of understanding. For simple or complicated problems, an OODA-type fast cycle might be overkill, whereas for truly complex problems, deciding and acting too quickly (without sufficient probing) could lead to chaos. Cynefin would suggest that in a Complex domain you sometimes need to slow down and sense (e.g. run experiments and learn) before deciding – whereas OODA’s bias is always toward faster cycling. That said, seasoned practitioners combine the insights of both: for example, NATO and U.S. Special Operations teams have used both Cynefin and OODA in planning, seeing Cynefin’s domains as informing the orientation phase of OODA. OODA is excellent for operational tempo in uncertain environments, while Cynefin excels at contextual clarity. Where Cynefin asks “What kind of situation are we in, and therefore what approach is appropriate?”, OODA asks “Are we observing and adapting fast enough?”. Many business leaders facing AI-era rapid change find value in both – using Cynefin to avoid methods mismatch (e.g. don’t apply rigid analysis in a complex scenario) and OODA to foster a nimble, learning-oriented culture.

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Cynefin in AI-Related Decision-Making

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With the rise of artificial intelligence and automation, organizations have been applying the Cynefin framework to tech strategy and AI project management. AI initiatives often involve a mix of complicated technical work and complex adaptive challenges, making Cynefin a helpful lens to decide how to tackle different aspects. Here we highlight how some organizations leverage Cynefin in AI contexts and what insights it provides for AI strategy:

• Guiding AI Strategy and Implementation: Tech consultancies are explicitly using Cynefin to shape AI projects. For example, Thoughtworks describes Cynefin as a critical tool for framing AI-related problems. In a recent analysis, Thoughtworks noted that many companies initially tried to apply traditional predictive AI models to complex business decisions – only to find unpredictability still prevailing . By using Cynefin, leaders can separate complicated parts of an AI project from truly complex elements. Complicated parts (where cause and effect are knowable with expertise) might include building the AI model or refining algorithms – these benefit from analysis, expert knowledge, and “good practices”. In contrast, Complex elements include how an AI system interacts with human behaviors, market responses, or organizational culture – where outcomes are uncertain and emergent. Cynefin encourages a different approach for the complex aspects: experimentation and learning rather than upfront certainty. Thoughtworks highlights this with a retail example: predicting what product a customer will buy next is a complicated problem (you can analyze purchase data and get an answer), but understanding why customers buy (their motivations, how needs might change) is a complex problem . A successful AI strategy should handle both – optimize the predictable parts and explore the ambiguous parts. As Thoughtworks put it, “while complex systems aren’t predictable, we can run experiments to discover valuable cause-and-effect relationships… This is where AI comes back in. Not only can we use it to help find these relationships, we can use AI to identify which experiments will yield the most instructive ones.” In practice, this meant designing analytical systems not just to maximize a metric, but to enable “safe-to-fail” probes – for instance, using AI to simulate scenarios or detect patterns that guide pilot interventions. This approach informs AI strategy by ensuring teams don’t solely chase prediction accuracy (a complicated-domain goal) but also invest in sense-making for unexpected outcomes (a complex-domain necessity).
• Real-World Adopters: A number of high-profile organizations have applied Cynefin in their decision-making processes, including those in technology and defense where AI plays a role. Snowden has reported that NATO and the U.S. Department of Defense (e.g. U.S. Special Operations Command) have used the Cynefin framework to help leaders make sense of rapidly changing operating environments. In these contexts, AI and data analytics are increasingly used for situational awareness – Cynefin provides a way to contextualize AI outputs. For example, an AI system might flag certain cybersecurity threats; Cynefin can help categorize which are routine (Clear – handle via automated response), which are complicated (delegate to an expert team for deep analysis), and which are complex or chaotic (novel attack patterns requiring experimentation or an immediate containment action). Outside of defense, the World Health Organization (WHO) has also utilized Cynefin for navigating health crises . During events like disease outbreaks (where AI models might be used to project spread), Cynefin reminds decision-makers that such models are only as good as the data and assumptions – the situation may lie in the Complex domain, calling for continual re-assessment and local experiments (e.g. community-specific interventions) rather than a fixed global plan. Another example is the UK National Health Service (NHS): researchers applied Cynefin to understand the complexity of care delivery. This indirectly relates to AI because healthcare systems are adopting AI for diagnostics and logistics; using Cynefin, NHS managers identified which parts of care are predictable vs. which are complex human systems, guiding where AI can be safely applied and where human judgment must remain central. On the corporate side, consultancies like Pariveda Solutions have written about using Cynefin to craft generative AI strategies, noting that it’s especially useful for the “complex and chaotic aspects” of deploying generative AI. For instance, when rolling out an AI chatbot, the technical building might be complicated (needing expert developers), but user interactions and social acceptance are complex – a Cynefin approach would urge piloting the chatbot in a controlled setting (a probe) and learning from emergent behavior before scaling. Such case studies show Cynefin working as a bridge between AI technology and business decision-making: it helps organizations decide when to rely on data/algorithms and when to experiment, involve diverse perspectives, or exercise gut intuition.
• Informing AI Governance and Risk: Another AI-related application is in ethical and strategic governance. Because AI deployment carries uncertainty and risk (bias, unexpected outcomes, etc.), some AI ethics teams use Cynefin to categorize issues. Known risks (e.g. well-understood failure modes) fall in the Complicated domain, to be handled with expert checklists and testing. Unknown or emergent risks (like an AI behaving in an unanticipated way in the wild) fall in the Complex domain, suggesting the need for continuous monitoring, scenario simulations, and adaptive policies. In 2017, the RAND Corporation explicitly cited Cynefin in a discussion of decision models for risk assessment, and the European Commission published a field guide using Cynefin to navigate crises. In these publications, the message is that leaders should sense which context they are in and adapt accordingly – a principle highly relevant as organizations grapple with AI’s fast-evolving impact. For example, if an AI system is introduced in a financial market (a complex adaptive system), regulators might use a Cynefin mindset to remain humble about predictions (avoid assuming it’s a Complicated domain problem) and instead set up “safety nets” (buffers, rapid response teams) as they would for a Chaotic context where quick action might be needed. We also see Cynefin’s influence in AI project management: teams break down their work streams by domain – orderly tasks like data cleaning use defined processes (Clear domain), while ambiguous research questions (like figuring out why an algorithm is making certain recommendations) are tackled with exploratory analysis and cross-disciplinary input (Complex domain). This domain-based allocation helps AI teams balance efficiency with innovation.

In essence, Cynefin is proving valuable in AI strategy by ensuring that organizations don’t treat AI projects monolithically. Instead, they parse what can be planned versus what must be discovered. Business and technology leaders report that this leads to more resilient AI implementations – for example, avoiding costly over-engineering on problems that didn’t need it (a risk when everything is seen as Complicated) and, conversely, avoiding complacency in truly complex scenarios (a risk when leaders try to force certainty). By aligning decision approach with problem nature, Cynefin helps AI efforts stay agile and context-aware. As one AI consultant observed, it prevents extremes like “applying the framework too rigidly and oversimplifying complex problems”, or blindly chasing every new AI trend without a framework (chaotic approach). Instead, it brings a structured yet flexible mindset that complements AI’s analytical power with human judgment.

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Decision-Making Frameworks in the AI Era

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Looking ahead, decision-making frameworks like Cynefin are evolving to meet the challenges of an AI-driven, fast-changing world. Several trends indicate how business and strategy tools may adapt in the future:

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• Integration of AI into Frameworks: Rather than treating AI as just another domain input, new ideas are emerging to embed AI within sense-making frameworks. For example, Erich R. Bühler’s “AI Bubbles” concept proposes augmenting the Cynefin framework with AI by creating localized AI-supported sense-making zones in the Complex domain (AI Bubbles: Augmenting Cynefin with AI for Enhanced Decision-Making | by Erich R. Bühler | Enterprise Agility Magazine). The idea is that AI can help leaders detect patterns and simulate outcomes in areas of high uncertainty – essentially forming “pockets of enhanced sense-making” without eliminating the inherent complexity. This points to a future where frameworks are more data-driven and continuous. Leaders might use real-time analytics dashboards that automatically suggest which Cynefin domain a situation might be in (e.g. by detecting signal patterns of chaos vs. complexity) and even recommend interventions. We already see early signs: some organizations use machine learning to analyze narrative data from the field (a technique pioneered by Snowden’s own company via the SenseMaker® tool) to augment human judgment in complex situations. As AI advances, it could become an “advisor” in the decision framework – for instance, an AI system could run through thousands of scenarios (micro OODA loops) and highlight a few actionable insights for humans to consider. The caveat is that AI itself can fail to distinguish context; thus human oversight remains crucial. But overall, expect decision frameworks to become more tech-enabled, using AI to handle the heavy data crunching in Complicated domains and to aid sensing in Complex ones, while humans focus on creative and ethical dimensions. Dave Snowden has noted that as complexity rises, tools that can capture weak signals and provide decision support will be invaluable – we can foresee Cynefin-like models tightly coupled with AI assistants for executives.
• Automation of Simple Decisions, Human Focus on Complex Decisions: As automation and AI take over more routine tasks, leaders will increasingly be making higher-level complex and strategic decisions. This shift will likely reinforce the need for frameworks that guide when to rely on automation vs. when to apply human judgment. Research already shows that AI excels at handling “structured, predictable patterns” – for example, optimizing logistics or detecting minor fraud – but “processes requiring empathy, ethical reasoning, or strategic vision resist full automation because they depend on human interpretation and judgment.”. In the future, many Clear or Complicated domain decisions (e.g. scheduling, basic data analysis, even some straightforward financial decisions) will be made by AI or decision engines. Business and technology leaders will then be freed to concentrate on Complex and Chaotic domain challenges – those that involve high uncertainty, rapid change, or significant value judgments. Frameworks like Cynefin might thus become even more pertinent as a training tool: tomorrow’s leaders may need to be adept at recognizing a brewing complex scenario and shifting into an exploratory decision mode, since they won’t be bogged down in operational details that AI can handle. We may also see new hybrid frameworks to manage human–AI decision teaming. For example, a future decision model might incorporate a step for “AI output evaluation” (where AI provides an analysis if the problem is in a predictable range) and a step for “human sense-making” (if the problem is novel or conflicting). Companies are already adopting a “hybrid decision-making model, where AI handles routine tasks while humans oversee complex, strategic, and ethical decisions.”. This aligns perfectly with Cynefin’s philosophy of matching the method to the domain – with AI as a method for the ordered domains and human-led approaches for the unordered domains. The future challenge will be creating seamless collaboration: ensuring that when an AI flags an anomaly (potential chaos) or a trend change (emergent complexity), human decision-makers respond appropriately, guided by frameworks that anticipate such handoffs.
• Emphasis on Adaptability and Continuous Learning: In fast-moving, “never normal” business conditions, leaders are prioritizing adaptability over rigid planning. Decision frameworks are trending in the same direction – becoming more iterative, experimental, and fluid. We see a convergence of ideas from agile, design thinking, and complexity science: terms like “emergent strategy, continuous iteration, probe-learn-adapt” are becoming commonplace in boardrooms. Future frameworks will likely incorporate feedback loops explicitly. Cynefin itself is not static; Snowden and colleagues have continued refining it (adding the central Disorder domain, describing “liminal” states between domains for transitions, etc.). Over 20+ years, Cynefin has evolved through multiple iterations and is expected to keep evolving to address new contexts. Business and tech leaders often blend frameworks in practice – for instance, using Cynefin to understand context, Design Thinking to ideate solutions in complex spaces, and OODA loops to implement and adjust rapidly. The future of decision-making might not belong to any single framework but to an ecosystem of approaches. Leaders may draw on a toolbox of Cynefin, Stacey, OODA, Systems Thinking, and more, depending on the challenge. In response, thought leaders are working on meta-frameworks or guides for when to use which tool. We also see new constructs like VUCA and BANI for describing the environment (Volatile, Uncertain, Complex, Ambiguous → Brittle, Anxious, Nonlinear, Incomprehensible) – these aren’t decision models per se, but they influence how leaders frame situations. The common theme is recognizing the limits of prediction and embracing flexibility. Even strategic planning processes are shifting from static yearly plans to rolling, scenario-based plans that can pivot – essentially applying complexity thinking. In this landscape, Cynefin’s core message of contextual decision-making is more relevant than ever, but it may be delivered in new ways. We might imagine an AI-driven dashboard that continuously updates a leader on what domain different parts of their business are in (say, operations in Clear, R&D in Complex, a PR crisis in Chaotic), accompanied by recommended playbooks for each. Some enterprises are already experimenting with such “sense-making centers” that combine human analysts, AI insights, and frameworks like Cynefin to steer strategy in real time.
• Addressing Framework Limitations: Lastly, the future will likely address current limitations by combining the strengths of multiple frameworks. For example, one could integrate the Wicked problem lens (to ensure we consider societal and ethical complexity) with Cynefin’s domains. Or use Stacey’s agreement axis to factor in stakeholder alignment when using Cynefin (e.g. a complex problem with low stakeholder agreement might be handled differently than one with collaborative stakeholders). The trend in leadership thought is toward “both/and” rather than “either/or.” We see this in the way consultants talk about Cynefin now: not as a be-all-end-all, but as part of a portfolio of sense-making tools. There is also a push to make these frameworks more accessible. One critique of Cynefin was that it could be “difficult” for newcomers  – future iterations may simplify language (indeed “Simple” was renamed “Clear” to reduce confusion) and include richer examples or even simulations. Gamification and experiential learning might help leaders internalize the concepts (some workshops already use interactive simulations to teach Cynefin by throwing participants into each domain scenario). In the age of AI, another limitation to tackle is bias and blind spots. Frameworks are products of human design and can inherit biases (for instance, an overemphasis on one type of logic). With AI and big data, there’s an opportunity to validate and refine frameworks empirically – e.g. analyzing hundreds of decision case studies to see if outcomes indeed aligned with Cynefin’s prescribed approaches. This could either strengthen confidence in the framework or suggest adjustments. We might discover, for example, new sub-domains or factors (some practitioners have suggested an additional domain for “complicated but with high uncertainty due to external volatility” – essentially a blend). The openness of Cynefin’s community (now formalized in the Cynefin Co) means it can adapt as new findings emerge. Thought leaders like Snowden advocate an “open ecosystem” for decision frameworks – much like open-source software – where ideas from academia (complexity science, cognitive psychology) and industry practice continuously improve the tools leaders useThe ultimate direction is clear: frameworks must keep pace with a world where AI, globalization, and societal change make contexts more dynamic than ever. The ones that survive and thrive will be those that enhance human decision-making without constraining it – providing guidance but also encouraging creativity, ethical reflection, and adaptability. Or as one article put it, “AI should serve as an augmentation tool – enhancing capabilities, reducing cognitive overload, and providing data-driven recommendations” for humans, not replacing them. Decision frameworks of the future will embody this principle, combining the best of human strategic thinking with intelligent support systems.

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