Why Hyperautomation is the Next Big Leap for Your Business

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Hyperautomation has rapidly moved from a tech buzzword to what Gartner calls a “condition of survival” for modern enterprises. In simple terms, hyperautomation refers to the end-to-end automation of business processes using a combination of advanced technologies – from AI and machine learning to robotic process automation (RPA) and analytics. Organizations worldwide are embracing this trend at an unprecedented scale. Gartner estimated that the global market for hyperautomation-enabling technologies reached $596 billion in 2022, up from $481 billion in 2020. This massive investment signals how critical automation at scale has become for staying competitive in a digital-first economy.

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Across industries and regions, companies are making automation a strategic priority. The World Economic Forum states that in 2023, 85% of organizations identified increased adoption of new technologies and broadening digital access as the trends most likely to transform their business. The trend continues and will continue – in our days, a large majority of companies have adopted some form of automation. Europe is no exception – in fact, awareness of hyperautomation is especially high in some European countries. For example, European countries lead globally in search interest for “hyperautomation”, reflecting strong regional appetite for advanced automation solutions. As businesses emerge from the disruptions of recent years, they are accelerating digital transformation efforts and turning to hyperautomation as a key lever for efficiency and resiliency in the face of skill shortages and economic pressures.

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What Does Hyperautomation Mean?

Source: Freepik

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Hyperautomation is often defined in lofty terms, but simply put it means automating as many processes as possible using an arsenal of advanced technologies. It’s an expansion of automation’s scope and scale across the enterprise. Gartner defines hyperautomation as “a business-driven, disciplined approach that organizations use to rapidly identify, vet and automate as many business and IT processes as possible”. In practice, this involves orchestrating multiple tools and platforms – artificial intelligence (AI), machine learning, event-driven software, RPA, business process management (BPM) suites, integration platforms, low-code apps, and other automation tools – together towards the goal of end-to-end process automation. In essence, hyperautomation is not a single technology, but a holistic approach to automation.

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Hyperautomation strives to “automate everything that can be automated” within an organization. This means going beyond individual tasks to automate entire workflows and even complex decision-making processes. For example, a hyperautomation initiative might combine an AI model that understands documents, an RPA bot that executes transactions, and a workflow engine that coordinates tasks – all working in concert. The outcome is intelligent, self-driving processes that require minimal human intervention. It’s important to note that hyperautomation is an ongoing journey; organizations continuously identify new automation opportunities and expand their automation footprint over time.

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Automation vs. Hyperautomation: Key Differences

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How is hyperautomation different from the traditional automation that businesses have been pursuing for years? The distinction comes down to scale, breadth of technologies, and level of autonomy:

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Scope of Automation

Traditional automation usually targets individual tasks or a specific process – for example, writing a script or using RPA to automate data entry in one department. Hyperautomation seeks to automate entire end-to-end processes and even multiple interrelated processes across the organization. It’s a comprehensive, enterprise-wide approach. A basic automation might handle task A, B, or C; hyperautomation aims to automate the entire workflow A→B→C and beyond.

Technologies Used

Conventional automation might rely on a single technology (e.g., a simple macro, an RPA bot, or a basic workflow tool). Hyperautomation orchestrates many tools together – AI, ML, RPA, BPM/workflow, analytics, etc. – to achieve a more powerful result. This “stack” of technologies allows hyperautomation to tackle processes that are too complex for any one tool. For example: a basic automation flow might input data into a system; hyperautomation might use computer vision to read data from a scanned document, an RPA bot to input it, and an AI model to verify accuracy, all coordinated by a workflow platform.

Intelligence and Adaptability

Traditional automation is often rule-based and static – it does what it’s programmed to do. Hyperautomation involves intelligent automation capabilities (AI/ML models, cognitive services) that enable the automation to handle variability and make simple decisions. Hyperautomation systems can adapt to changing inputs or learn from data over time, whereas basic automation typically cannot. In other words, hyperautomation can be smarter and more resilient to change. It’s not limited to rote tasks; it can automate tasks that require judgment (to an extent) by leveraging AI.

Ultimate Goal

The goal of basic automation is often efficiency in a specific task or workflow (e.g., reduce manual effort or errors). The goal of hyperautomation is transformative productivity across the enterprise – it seeks broad productivity gains, cost reduction, and agility by automating wherever possible in a holistic manner. Hyperautomation initiatives often start by identifying dozens or hundreds of candidate processes for automation and continuously expanding the automation portfolio. It’s essentially automation on steroids, guided by a strategic vision.

In summary, if automation is like using power tools to make a single task easier, hyperautomation is like building an entire smart factory – multiple automated processes linked together, monitored, and optimized as a whole. One insightful comparison is that AI and point automations are parts of the broader hyperautomation framework, contributing to the strategy but not encompassing the whole process landscape on their own. Hyperautomation combines all these parts to automate processes from start to finish, at scale.

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Core Components and Technologies used in Hyperautomation

Hyperautomation isn’t about any one product – it’s about deploying a suite of complementary technologies. At its core, hyperautomation utilizes building blocks that include both well-established automation tools and cutting-edge AI capabilities. Hyperautomation can use a wide variety of technologies and tools, but let’s look at two of the central technology components: Robotic Process Automation (RPA) and Generative AI, and how each plays a role in hyperautomation.

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Source: turian

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Hyperautomation with Generative AI

One of the most exciting developments propelling hyperautomation forward is the rise of Generative AI – AI systems (such as GPT-4 and other large language models (LLM)) that can generate human-like text, images, or other content. Generative AI is proving to be a powerful complement to automation because it can handle tasks that were traditionally very hard to automate: understanding natural language, creating written content, conversing with users, and even writing software code. 

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Generative AI in hyperautomation can serve as the “brain” and “voice” of automated processes. For example, hyperautomation platforms integrate LLMs to read and interpret an incoming customer email, take action or draft a response, summarize documents or reports, convert instructions into workflow actions, and even write code for simple applications. According to McKinsey, generative AI could potentially add $2.6 – $4.4 trillion in economic value annually across industries, highlighting how impactful it could be when woven into enterprise processes.

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turian’s solution is a real-world example – it uses advanced AI (including generative models) to analyze emails and attachments, extract data, update systems, and draft customized email responses to clients or suppliers. By leveraging such AI, hyperautomation platforms can handle unstructured data (text, images, voice) which opens up areas like customer service, marketing, and legal document processing to automation.

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Generative AI also aids in decision-making automation. Generative AI models (or related large AI models) can analyze context and suggest decisions or predictions (for example, flagging a transaction as likely fraudulent or recommending a specific course of action in a workflow). These can then be incorporated into the automated process. The result is greater autonomy: hyperautomation systems that not only do tasks but also figure out what needs to be done next in complex scenarios. 

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Hyperautomation with Generative AI supercharges your organization by enabling automation of creative, communicative, and cognitive tasks. It makes hyperautomation systems more conversational (think AI chatbots handling customer interactions as part of a larger process) and more adaptive. 

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Hyperautomation and RPA

At the heart of many hyperautomation programs is Robotic Process Automation, the technology that uses software “bots” to emulate human actions for rule-based tasks. RPA has been a game-changer in the last decade, allowing companies to automate repetitive tasks in user interfaces (clicking buttons, copying data between systems, etc.) without needing to change underlying systems. However, RPA alone is usually limited to structured, rule-based tasks. Hyperautomation RPA takes a step further by integrating it with AI and other tools to handle more complex work and orchestrate entire workflows.

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In a hyperautomation initiative, RPA often serves as the “hands” of the operation, executing tasks quickly and tirelessly. For instance, an RPA bot might log into multiple applications to move files or enter data, but an AI computer vision component might first read information from an invoice for the bot to input. By combining RPA with AI, process mining, and decision management, hyperautomation can automate processes that RPA alone could not. In fact, hyperautomation has been described as “the convergence of RPA with AI, Machine Learning (ML), and process mining” to create self-driving processes. The RPA provides the execution capability, while AI/ML provides understanding and learning, and process mining helps identify what to automate and how to optimize it.

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RPA provides the robotic workforce for hyperautomation, and when enhanced with AI and integrated via intelligent platforms, it becomes far more potent. Hyperautomation would typically involve dozens or hundreds of RPA bots coordinated together. It also involves analyzing processes (using tools like process mining to discover inefficiencies or bottlenecks) and reengineering them for optimal automation. The core idea is to use RPA not just for low-hanging fruit, but as part of a larger automated machine that can handle complex, multi-step jobs under supervision.

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Business Process Management and Integration Platforms

Another key component in hyperautomation is Business Process Management (BPM) or workflow orchestration. In hyperautomation, you often have an orchestration layer (like an intelligent BPM suite) that sequences tasks – assigning some to RPA bots, some to AI services, and some to humans as needed. This ensures end-to-end process flow and handles exceptions or approvals. Additionally, integration platforms (iPaaS) are often used to connect systems on the back-end, so that automations can trigger actions in various applications via APIs rather than the front-end. This kind of tight integration means the automated process is seamless and doesn’t break even if user interfaces change, because it leverages stable system APIs when possible.

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Hyperautomation Benefits for Businesses

Hyperautomation benefits are many. Hyperautomation is ultimately a means to a business end: working faster, smarter, and at lower cost. By knitting together automation and AI across the organization, companies can unlock significant performance improvements. Let’s break down some of the practical benefits enterprises are seeing (or expecting) from hyperautomation:

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Source: Freepik

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Increased Productivity and Efficiency

One of the clearest wins is the sheer amount of work that can be accomplished with automation handling repetitive tasks 24/7. Employees save time and can refocus on higher-value activities like strategy, innovation, or relationship-building. For example, if sales staff spend less time inputting data and more time engaging clients, revenue can increase. Process throughput often accelerates dramatically – the U.S. Department of Veterans Affairs automated business processes using advanced technology like RPA bots, seeing a turnaround time drop by 90%, from weeks to days or hours.

Cost Savings and Operational Improvements

By automating manual work, organizations can achieve substantial cost reductions. Fewer repetitive tasks for people means lower labor costs or the ability to redeploy staff to value-generating roles. Automation also reduces errors and rework, which can be costly. Gartner has stated that companies combining hyperautomation technologies with redesigned processes will cut operational costs by 30%. A McKinsey analysis similarly found hyperautomation can reduce supply chain costs by up to 30% through efficiencies. Additionally, hyperautomation often reveals process improvements – by examining workflows in detail, companies find unnecessary steps to eliminate, leading to leaner, more cost-effective operations overall.

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Source: created by turian based on data from Gartner and McKinsey

Higher Accuracy and Quality

Automated processes, especially when governed well, tend to be far more consistent and less error-prone than manual processes. Removing human error in data entry, calculations, or rule-following can vastly improve quality. Fewer mistakes mean better outcomes – customers get correct information and bills, regulators get accurate reports, products have fewer defects, etc. Hyperautomation can embed compliance checks into automated workflows, ensuring every transaction or activity adheres to policy (something humans might overlook when fatigued). This leads to better risk management and auditability. Improved process quality also enhances internal decision-making because data is more reliable.

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Scalability and Agility

Hyperautomation gives organizations a scalable digital workforce. Once a process is automated, increasing capacity is often as simple as running more bot instances or allocating more computing power. This means, for example, that a company can handle growth or spikes in volume without scrambling. During the COVID-19 pandemic, for instance, many companies that had automated key processes were able to transition to remote work and handle surges in online transactions more gracefully than those reliant on manual workflows. Additionally, hyperautomation can make the business more agile by shortening process cycle times – enabling quicker launch of new products (because internal processes like setup, testing, deployment can be partly automated) and faster adaptation to regulatory changes (just update the automation rules centrally). Organizations essentially become leaner and more responsive. Deloitte research found that enterprises integrating automation widely report higher improvements in revenue and business outcomes than those with siloed automations, indicating that broad automation capability translates into better business performance.

In summary, hyperautomation is a means to tangible business results: more output with the same or fewer resources, lower operating costs, faster and higher-quality service, and improved ability to scale and compete. As one executive put it, hyperautomation allows the organization to “do more with less” – more work completed in less time, with less errors and often less expense. These benefits are why over 56% of organizations surveyed by Gartner already had four or more concurrent hyperautomation initiatives underway – the returns are compelling. Of course, realizing these benefits requires the right strategy and execution, but when done right, hyperautomation can significantly boost an enterprise’s efficiency and bottom line.

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Real-World Hyperautomation Examples

Seeing hyperautomation in action across different sectors helps illustrate its value. Here are a few current hyperautomation examples and use cases that show how organizations are leveraging hyperautomation technologies:

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1. Finance (Banking)

Large banks have been pioneers in hyperautomation to improve customer service and back-office efficiency. For instance, Bank of America implemented a hyperautomation strategy combining AI chatbots on the front-end with RPA bots on the back-end to transform its customer service operations. The AI-driven chatbots handle routine customer inquiries in natural language (answering questions, helping with simple transactions), while RPA bots automatically execute the necessary updates in banking systems (such as transaction processing or account changes) without human intervention. This end-to-end automation led to faster response times for customers and reduced workload on call center staff, improving both efficiency and customer satisfaction. Bank of America reported notable gains: lower operational costs from handling high volumes of inquiries with fewer human agents, and a boost in customer experience evidenced by higher satisfaction scores. This case shows how hyperautomation (AI + RPA) can scale customer support to millions of users while maintaining quality.

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2. Sales

Hyperautomation is delivering big wins in sales, where there are many repetitive, document-heavy processes. A great example is Unigloves UK, a manufacturer and distributor of gloves, which faced a flood of sales orders especially during the COVID-19 pandemic. They turned to turian’s hyperautomation solution to automate sales order processing from emails. Incoming email orders (often containing large PDF attachments with hundreds of line items) are now processed by an AI that extracts the order data and inputs it into the ERP system automatically, instead of an employee typing each line. The results were dramatic – Unigloves reduced manual order processing time by 68% and now saves about 84 hours per month in order entry effort. The AI-driven system achieved an 85% full automation rate on orders (meaning only 15% of cases need any human touch at all). This hyperautomation of sales order management not only sped up fulfillment but also allowed the small team to handle a larger volume of orders without adding headcount. By integrating seamlessly with the company’s Outlook email and ERP (SAP) environment, turian’s solution shows how hyperautomation can streamline supply chain transactions end-to-end – from reading an email, to updating systems, to even sending confirmation emails back to customers, all done automatically. This example underscores significant operational improvements: faster cycle times, fewer errors, and employees freed to focus on exceptions or strategic supplier relationships rather than data processing.

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Source: turian

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3. Customer Service and Support

Many companies in e-commerce go down the hyperautomation path. Amazon, for instance, famously uses extensive automation in its customer service – from automated package tracking updates, to AI-driven self-service returns, all coordinated in workflows that only escalate to humans when needed. The result is customers get immediate answers and actions on their issues at any hour, contributing to Amazon’s high service ratings. These examples highlight hyperautomation’s role in customer experience transformation. By handling routine requests automatically and ensuring information flows through all necessary systems (CRM, billing, notification) without delay, companies can provide faster, more consistent support. Human agents remain vital for complex or sensitive cases, but even then they are supported by AI-driven suggestions and summarized customer info (another aspect of hyperautomation) to serve the customer better.

These cases from different sectors all share a common theme: hyperautomation marries AI’s cognitive abilities with automation’s efficiency to deliver outcomes that weren’t possible before. Whether it’s processing an order, a claim, or a customer request, the hyperautomation approach is enabling end-to-end handling with speed and accuracy at scale.As these examples show, hyperautomation is not theoretical – it’s happening now, and it’s delivering measurable business value across industries.

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How Hyperautomation Works: A Roadmap to Implementation 

Understanding how hyperautomation works in an organization helps demystify what implementing it entails. At its core, hyperautomation works through a systematic process of identifying opportunities, automating them with the right mix of tools, and continually orchestrating and optimizing the results. Here’s an overview of the typical steps involved in hyperautomation:

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1. Assess and Plan (Organizational Readiness for Hyperautomation)

Start by evaluating your organization’s readiness for hyperautomation. This means understanding your current state: What processes do you have, and how efficient are they? What existing automation or AI capabilities are in place? It’s crucial to identify areas with high automation potential and also areas that may pose challenges, and prioritize process for automation accordingly. Assess the culture and skills of your workforce – do you have people who can champion automation projects, or will you need to bring in/train talent? Also, align on your objectives: are you aiming to cut costs by 20% in two years, improve turnaround times by X, etc.? Getting executive buy-in at this stage is key; hyperautomation should be tied to strategic business goals. This planning phase should result in a clear roadmap of priority processes to automate (often determined by a combination of potential benefit and feasibility) and a commitment of resources (budget, team, and technology) to get started. 

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2. Select the Appropriate Tools and Technologies

Choosing the technology tools is a critical early decision. Once opportunities are identified, evaluate hyperautomation platforms or a mix of tools that fit the process. Tool selection is guided by the nature of the task: use AI/ML where decision-making or unstructured data is involved, use RPA for structured, repetitive actions, use business rules engines for enforcing policies, etc. Many hyperautomation platforms (like turian) provide a suite of these capabilities under one roof, making it easier to integrate them. Other key factors to consider when selecting the tools for hyperautomation include ease of integration with your systems, scalability, security features, analytics, and user-friendliness. 

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3. Test, Pilot, and Refine

Before fully rolling out an automated process, organizations usually conduct pilots or simulations. The organization would test the bots and AI in a controlled environment or on historical data to ensure they work as expected. It’s common to run the new automation in parallel with the old process initially, validating that outcomes match and quality is maintained. Any errors or exceptions encountered are analyzed and the automation is adjusted (for example, improving an AI model’s accuracy or adding a rule to handle a scenario). This iterative tuning is important for building trust in the system. Feedback from employees who interact with the new system should also be gathered to improve usability. Think of this as the training wheels phase – ensuring that when the automation goes live enterprise-wide, it will operate smoothly. Some organizations also perform “shadow testing” where the bot does the work and a human monitors, only stepping in if necessary, until confidence is high. This stage helps iron out integration glitches, security issues, or performance lags in the automation workflow.

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4. Implement in Phases, Scale, and Integrate

As mentioned, rather than attempting a big bang rollout, it’s highly recommended to start with one or two pilot processes to automate. With pilots done and initial processes automated, the next step is scaling up hyperautomation across the enterprise and expanding to additional use cases. This means going back to your backlog of automation opportunities and executing more projects. A common strategy is to use the momentum to tackle a cluster of related processes next (for example, after automating invoice processing in accounts payable, next tackle purchase order creation in procurement). This phased approach helps manage risk and change: it’s easier to train staff and adapt gradually than to flip a switch on dozens of processes at once. Each phase can incorporate feedback from the last, improving your frameworks and models. Also, early wins help make the case for further investment and keep leadership enthusiastic.
Additionally, integration is key. Integration into existing business systems is critical: the automation needs to fetch and push information to ERPs, CRMs, databases, websites, etc. This is achieved either by having bots mimic user actions or, more robustly, through direct integrations/APIs.

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5. Deploy, Monitor, and Continuously Improve

After successful pilots, the hyperautomation solution is deployed fully into production. Hyperautomation workflows now handle real transactions at scale. But the journey doesn’t end at go-live. A hallmark of hyperautomation is continuous monitoring and improvement. Organizations can set up dashboards to track KPIs like processing time, error rates, volume of transactions automated, etc. Any exceptions that the system can’t handle (which get referred to humans) are logged – these provide insight into how the automation can be improved to cover more cases over time. For example, if the AI fails to read certain invoice formats, those examples are used to retrain the model for better accuracy. Hyperautomation efforts often adopt an agile, ongoing enhancement approach: adding new features, expanding the automation to adjacent processes, and updating the logic as business rules or systems change. Regular audits might be done to ensure compliance and that controls are functioning. Also, governance is put in place to manage this digital workforce – much like employees – ensuring bots have proper access rights, and any changes to processes are documented and approved. Human workers and the automated systems begin to work in tandem, with clear hand-off points (for instance, if an approval is needed or an anomaly is detected, the system alerts a person). 

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Importantly, hyperautomation works within existing business systems and enhances them; it doesn’t necessarily replace core IT systems, nor employees. It’s a layer of intelligence and automation over your current applications. The key is having a well-defined process, methodology, and strong collaboration between business analysts (who know the processes), developers/engineers (who build the automation), and IT (who ensure integration, security, and governance). When all these pieces work together, hyperautomation becomes a powerful engine continuously driving efficiency through the organization.

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Ready to Bring Hyperautomation to Life in Your Organization?

Hyperautomation is no longer a future concept—it’s a strategic response to today’s business demands. Organizations across industries are embracing it to automate entire processes using a powerful mix of technologies like RPA, AI, and generative AI models. This shift delivers measurable gains: faster operations, fewer errors, lower costs, and greater agility.

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As real-world examples show, companies that implement hyperautomation thoughtfully – starting with a clear strategy, selecting the right tools, and scaling in phases – are seeing major returns. Those that delay risk falling behind. Hyperautomation is not just about doing things faster. It’s about rethinking how work gets done—at scale, with intelligence, and for long-term growth.

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Ready to bring hyperautomation to life in your organization? turian’s AI-powered platform combines intelligent agents, RPA, and generative AI to automate complex business workflows – end to end. Whether you need to process sales orders, extract data from documents, or connect systems across departments, turian helps you reduce manual work, speed up operations, and scale with confidence.

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