The Rise of the Seeing Machine: Computer Vision in Manufacturing Quality Control

In the relentless pursuit of perfection, manufacturers have long sought innovative methods to ensure the highest quality standards. Over the past decade (2010-2019), computer vision has emerged as a transformative force, reshaping quality control processes across diverse industries. From automotive assembly lines to pharmaceutical production, the ability of machines to ‘see’ and interpret images has unlocked unprecedented levels of accuracy, efficiency, and consistency. This article delves into the comprehensive implementation of computer vision for quality control, exploring its challenges, best practices, and real-world impact.

The integration of computer vision quality control marks a significant leap from traditional, often subjective, manual inspection methods. Where human inspectors might miss subtle defects due to fatigue or variations in judgment, automated visual inspection systems powered by machine vision maintain unwavering vigilance. This transition not only enhances defect detection rates but also provides a wealth of data that can be analyzed to optimize manufacturing processes, reducing waste and improving overall product reliability. Consider the automotive industry, where the complexity of modern vehicles demands meticulous quality checks at every stage of production.

Machine vision systems are now routinely used to inspect welds, paint finishes, and component placements with far greater precision than humanly possible. These industrial computer vision applications extend beyond mere defect detection; they also encompass dimensional measurement, ensuring that parts meet exact specifications, and robotic guidance, enabling automated assembly with unparalleled accuracy. Similarly, in electronics manufacturing, where components are becoming increasingly miniaturized, computer vision plays a crucial role in identifying microscopic defects that could compromise the functionality of the final product.

The adoption of AI in manufacturing, particularly machine learning for quality control, has enabled systems to learn from vast datasets of images, continuously improving their ability to detect even the most subtle anomalies. The rise of computer vision in manufacturing is intrinsically linked to advancements in both hardware and software. High-resolution cameras, coupled with powerful processing units, provide the necessary tools for capturing and analyzing detailed images in real-time. Sophisticated algorithms, including those based on deep learning, enable these systems to identify patterns and anomalies that would be imperceptible to the human eye.

Furthermore, the increasing availability of annotated image datasets has accelerated the development and deployment of machine learning models for quality control automation. As computer vision technology continues to evolve, its impact on manufacturing quality control will only become more profound, driving further improvements in efficiency, reliability, and product excellence. The convergence of AI, machine vision systems, and advanced manufacturing techniques promises a future where defects are virtually eliminated, and products are manufactured to the highest possible standards.

Addressing Quality Control Challenges with Computer Vision

Traditional quality control methods often rely on manual inspection, which is susceptible to human error, fatigue, and subjective judgments. Computer vision offers a more objective and reliable approach, capable of identifying defects, measuring dimensions, and verifying assembly with remarkable precision. Specific challenges addressable by computer vision include: Defect Detection: Identifying surface scratches, cracks, dents, and other imperfections. Dimensional Accuracy: Ensuring that parts and products meet specified size and shape requirements. Assembly Verification: Confirming that components are correctly assembled and positioned.

Feature Inspection: Analyzing specific features of a product to ensure they meet quality standards. Surface anomaly detection: The detection of small and subtle defects on a material’s surface. The limitations of human-based quality control become increasingly apparent in high-volume manufacturing environments. Consider the automotive industry, where a single vehicle can contain thousands of individual components. Manually inspecting each weld, panel gap, or paint finish for defects is not only time-consuming but also prone to inconsistencies.

Automated visual inspection powered by machine vision systems provides a consistent and tireless approach, ensuring that products meet stringent quality standards regardless of production volume or inspector fatigue. This transition to quality control automation represents a significant leap forward in manufacturing efficiency and product reliability. Computer vision quality control also excels in applications demanding high precision and repeatability, such as in electronics manufacturing. The placement of microchips on circuit boards, the soldering of tiny components, and the inspection of microscopic features all require accuracy beyond the capabilities of human inspectors.

Industrial computer vision systems, often integrated with robotic arms, can perform these tasks with micron-level precision, significantly reducing defect rates and improving the overall yield of electronic products. Furthermore, machine learning for quality control enables these systems to adapt and improve over time, learning to identify new types of defects and optimize inspection parameters for enhanced performance. This is particularly valuable in dynamic manufacturing environments where product designs and processes are constantly evolving. Moreover, the application of AI in manufacturing, specifically through computer vision, extends beyond simple defect detection.

Modern computer vision systems can analyze complex patterns and correlations in visual data to identify root causes of defects and predict potential quality issues before they arise. For instance, by analyzing images of raw materials, a computer vision system might detect subtle variations in color or texture that indicate a higher risk of defects in the finished product. This proactive approach to quality control allows manufacturers to optimize their processes, reduce waste, and improve overall product quality. By leveraging computer vision for predictive quality control, manufacturers can move from reactive problem-solving to proactive prevention, creating a more efficient and sustainable manufacturing operation.

Building the Vision: Key Components of a Computer Vision System

A computer vision system comprises several key components working in concert. Image Acquisition: High-resolution cameras capture images of the products under inspection. Lighting: Controlled lighting is essential for obtaining clear and consistent images. Processing Hardware: Powerful GPUs (Graphics Processing Units) and CPUs (Central Processing Units) process the images and run the computer vision algorithms. Software: Algorithms, libraries, and frameworks (e.g., OpenCV, TensorFlow, PyTorch) perform image analysis and defect detection. In the past decade, camera technology has advanced significantly, providing higher resolution and faster frame rates, enabling more detailed and efficient inspections.

Furthermore, the development of specialized lighting techniques has improved image quality and reduced the impact of environmental factors. The selection of image acquisition technology is a critical decision point when deploying computer vision in manufacturing. Beyond simple resolution, factors such as sensor type (CCD vs. CMOS), global or rolling shutter, and spectral sensitivity must be carefully considered. For example, inspecting highly reflective surfaces might necessitate specialized polarization filters and dark-field illumination techniques to minimize glare and highlight subtle defects.

The integration of 3D cameras, like stereo vision or structured light scanners, is also gaining traction for applications requiring precise dimensional measurements or surface reconstruction, offering a significant advantage over traditional 2D imaging in complex assembly verification scenarios. This is particularly useful in industries like aerospace and automotive, where precision is paramount. Lighting plays a pivotal role in the efficacy of any machine vision system designed for manufacturing defect detection. Consistent and controlled illumination minimizes shadows and highlights surface imperfections, enabling more robust and reliable algorithm performance.

Advanced lighting solutions, such as LED strobes with precise timing control, can freeze motion and capture high-quality images of rapidly moving parts on an assembly line. Backlighting techniques are useful for detecting shape irregularities and foreign object debris. Careful consideration of the light source’s wavelength is also critical, with UV or infrared lighting sometimes employed to reveal defects invisible to the naked eye. Ultimately, a well-designed lighting system significantly enhances the accuracy and repeatability of automated visual inspection systems.

The computational power underpinning computer vision quality control has seen exponential growth, largely driven by advancements in GPU technology and specialized AI accelerators. Modern industrial computer vision systems often leverage distributed computing architectures, allowing for parallel processing of image data and real-time decision-making. Field-programmable gate arrays (FPGAs) offer a compelling alternative to GPUs in certain applications, providing a balance of performance and power efficiency, especially in edge computing scenarios where processing must occur directly on the factory floor. Furthermore, the rise of cloud-based AI platforms enables manufacturers to offload computationally intensive tasks such as model training and inference, unlocking new possibilities for machine learning for quality control and predictive maintenance.

The Algorithms That See: Computer Vision Techniques in Action

Several computer vision algorithms are commonly employed in quality control applications. Object Detection involves identifying and locating specific objects or components within an image. For example, detecting missing screws on an assembled product. Image Segmentation divides an image into distinct regions to isolate specific areas of interest, such as segmenting a circuit board to inspect individual components. Classification categorizes objects or images based on their characteristics; for example, classifying products as ‘pass’ or ‘fail’ based on the presence of defects.

Deep learning techniques, particularly convolutional neural networks (CNNs), have revolutionized these algorithms, enabling higher accuracy and robustness in complex inspection scenarios. Pre-trained models and transfer learning have also significantly reduced the time and resources required to develop custom solutions. Within the realm of manufacturing defect detection, automated visual inspection systems are increasingly leveraging advanced techniques like anomaly detection. These algorithms learn the characteristics of ‘normal’ products and can flag deviations that might indicate defects, even if those defects weren’t explicitly part of the training dataset.

This is particularly useful in identifying subtle or unexpected flaws that might escape human inspectors or rule-based machine vision systems. Furthermore, the integration of 3D computer vision is enabling more comprehensive inspections, capturing dimensional accuracy and surface irregularities with greater precision. Machine learning for quality control extends beyond simple classification. Generative Adversarial Networks (GANs) are being explored to generate synthetic images of defective parts, augmenting training datasets and improving the robustness of AI in manufacturing.

Reinforcement learning is also finding applications in optimizing inspection parameters and strategies in real-time. For instance, an industrial computer vision system can learn to adjust camera angles, lighting conditions, or inspection routines based on feedback from previous inspections, continuously improving its performance. This adaptive capability is crucial for handling the inherent variability in manufacturing processes. The adoption of computer vision in manufacturing is also driving innovation in hardware. Specialized cameras with higher resolution, faster frame rates, and advanced spectral capabilities are emerging to meet the demands of increasingly complex inspection tasks. Edge computing platforms are becoming more prevalent, enabling real-time processing of images directly on the factory floor, reducing latency and improving responsiveness. The synergy between advanced algorithms, specialized hardware, and robust data strategies is paving the way for a new era of quality control automation, where machine vision systems play a central role in ensuring product excellence.

Fueling the Vision: Data Requirements and Annotation Strategies

The performance of a computer vision system for quality control automation hinges on the quality and quantity of training data. Image Datasets: Large, meticulously curated datasets of images representing both ‘good’ (conforming) and ‘defective’ (non-conforming) products are essential for training the algorithms that power these machine vision systems. The diversity within these datasets must reflect the full spectrum of variations encountered on the manufacturing line, including differences in lighting, orientation, and minor cosmetic inconsistencies that are acceptable.

Without a robust dataset, even the most sophisticated machine learning for quality control algorithms will struggle to generalize and accurately identify defects in real-world scenarios. For instance, in manufacturing defect detection of circuit boards, the dataset should include images of boards with various types of solder defects, component misplacements, and trace damage, captured under different lighting conditions to ensure the computer vision system is robust. Annotation Strategies: Images must be accurately annotated to identify defects, objects, or regions of interest.

This process involves labeling each image with bounding boxes, polygons, or pixel-wise segmentations that delineate the location and type of defect. The accuracy of these annotations directly impacts the performance of the computer vision system. Inconsistent or inaccurate annotations can lead to the algorithm learning incorrect patterns, resulting in false positives or false negatives during automated visual inspection. For example, when inspecting automotive parts for scratches, the annotation must precisely mark the location and extent of each scratch, differentiating between minor surface imperfections and critical defects that compromise the part’s integrity.

The annotation process often requires skilled technicians with expertise in both the product being inspected and the types of defects that can occur. This ensures the annotations are both accurate and consistent, leading to a more reliable computer vision quality control system. Data Augmentation: Techniques such as rotation, scaling, cropping, and color jittering are used to artificially increase the size and diversity of the dataset. Data augmentation is particularly important when dealing with limited data or rare defect types, as it helps the model generalize better to unseen variations.

For example, if a particular type of defect only appears in a small number of images, data augmentation can be used to create synthetic images of that defect under different lighting conditions or orientations, effectively increasing the representation of that defect in the training data. Active learning techniques, where the system intelligently selects the most informative images for annotation, have also gained traction in reducing the annotation effort. These techniques prioritize images that the model is uncertain about, allowing human annotators to focus on the most challenging cases and improve the model’s accuracy more efficiently.

This targeted approach to data annotation can significantly reduce the time and cost associated with building a high-quality training dataset for industrial computer vision applications. Beyond traditional methods, generative adversarial networks (GANs) offer a powerful approach to data augmentation, particularly beneficial in scenarios with limited defect samples. GANs can be trained to generate synthetic images of defects, effectively expanding the dataset and improving the robustness of the AI in manufacturing system. Furthermore, techniques like transfer learning, where a model pre-trained on a large, general-purpose dataset is fine-tuned on a smaller, manufacturing-specific dataset, can significantly reduce the amount of labeled data required to achieve high accuracy. This is especially useful when deploying computer vision in manufacturing environments where acquiring large, labeled datasets can be time-consuming and expensive. The strategic application of these advanced data handling techniques is crucial for maximizing the effectiveness of computer vision systems in quality control.

From Lab to Line: Implementing Computer Vision in Manufacturing

Implementing computer vision for quality control involves a structured process, a carefully orchestrated symphony of hardware, software, and human expertise. The initial setup phase is critical, demanding a meticulous definition of inspection requirements. This involves not only identifying the types of defects to be detected – scratches, dents, missing components – but also quantifying acceptable tolerances and setting performance benchmarks for the automated visual inspection system. Selecting the appropriate hardware and software is equally crucial; high-resolution cameras with suitable lenses, lighting systems that ensure consistent illumination, and powerful processing units capable of handling the computational demands of the computer vision algorithms are all essential.

The imaging environment itself must be carefully controlled to minimize external factors that could affect image quality, such as ambient light variations or vibrations. This stage lays the foundation for the entire project, ensuring that the system is designed to meet the specific needs of the manufacturing process. Model training forms the core of any successful computer vision quality control implementation. This involves feeding the system a large, meticulously annotated image dataset containing examples of both acceptable products and products exhibiting various types of defects.

The annotations, which may involve bounding boxes around defects, segmentation masks isolating specific regions of interest, or labels indicating the presence or absence of certain features, provide the ground truth that the machine learning algorithms use to learn the characteristics of each defect. The choice of algorithm – whether it’s a convolutional neural network (CNN) for image classification, a region-based CNN (R-CNN) for object detection, or a semantic segmentation model for pixel-level analysis – depends on the specific inspection task.

Careful attention must be paid to data augmentation techniques, which artificially increase the size and diversity of the training dataset by applying transformations such as rotations, scaling, and changes in brightness and contrast, to improve the model’s robustness and generalization ability. Validation and testing are essential to ensure the trained model performs reliably in real-world conditions. This involves evaluating the model’s performance on a separate, held-out dataset that was not used during training. Metrics such as precision, recall, and F1-score are used to quantify the model’s accuracy in identifying defects while minimizing false positives and false negatives.

Robustness is also assessed by testing the model’s ability to handle variations in lighting, product orientation, and other environmental factors. Only after rigorous validation and testing can the model be confidently deployed in the manufacturing environment. Deployment involves integrating the computer vision system with existing manufacturing equipment, such as robotic arms, conveyor belts, and programmable logic controllers (PLCs), to automate the inspection process. This integration often requires custom software development to ensure seamless communication and data exchange between the different components of the system.

Continuous improvement is paramount for maintaining the long-term effectiveness of the computer vision quality control system. This involves continuously monitoring the system’s performance, tracking defect rates, and identifying areas for improvement. As product designs change, new types of defects may emerge, or the manufacturing process may evolve, requiring the model to be retrained with new data. Regular audits of the image dataset are also necessary to ensure the annotations remain accurate and consistent. Furthermore, advancements in computer vision algorithms and hardware may offer opportunities to further enhance the system’s performance and capabilities. The implementation process thrives on collaboration between computer vision experts, manufacturing engineers, and quality control personnel, fostering a shared understanding of the system’s capabilities and limitations and ensuring its seamless integration into the manufacturing workflow. This multidisciplinary approach is key to unlocking the full potential of computer vision in manufacturing defect detection and quality control automation, driving efficiency gains, reducing waste, and improving product quality.

Overcoming Challenges: Integrating Computer Vision into Existing Workflows

Integrating computer vision into existing manufacturing workflows can present several challenges. Lighting Variations: Fluctuations in ambient lighting can affect image quality and system performance, hindering accurate manufacturing defect detection. Part Variability: Variations in product appearance or orientation can confuse the algorithms, leading to false positives or missed defects in automated visual inspection. Integration Complexity: Integrating the computer vision system with existing equipment and software systems can be complex, requiring specialized expertise and careful planning. To overcome these challenges, manufacturers should carefully plan the integration process, use robust algorithms that are less sensitive to variations, and implement proper lighting control.

Best practices include: Thorough testing and validation before deployment. Regular monitoring and maintenance of the system. Training personnel on the operation and maintenance of the system. The effective deployment of machine vision systems within a quality control automation framework demands a nuanced understanding of both hardware and software considerations. Selecting appropriate cameras, lenses, and lighting solutions tailored to the specific inspection task is paramount. Furthermore, the choice of processing hardware, such as GPUs optimized for industrial computer vision, directly impacts the speed and accuracy of defect detection.

On the software side, employing sophisticated algorithms capable of handling variations in lighting, orientation, and surface texture is crucial for reliable performance. This often involves leveraging machine learning for quality control to train robust models that can generalize well to unseen data. Addressing the data requirements for training computer vision in manufacturing applications is another critical aspect of successful integration. The performance of AI in manufacturing for quality control is directly proportional to the size and quality of the training dataset.

Acquiring and annotating a sufficient number of images representing both ‘good’ and ‘defective’ parts can be a significant undertaking. Strategies for data augmentation, such as rotating, scaling, and adding noise to existing images, can help to artificially expand the dataset and improve the robustness of the trained models. Furthermore, active learning techniques can be employed to strategically select the most informative images for annotation, thereby maximizing the efficiency of the data collection process. Beyond the technical hurdles, organizational factors also play a significant role in the successful adoption of computer vision quality control.

Securing buy-in from key stakeholders across different departments, including engineering, operations, and quality assurance, is essential. Clear communication of the benefits of computer vision, such as improved accuracy, reduced costs, and increased throughput, can help to overcome resistance to change. Furthermore, investing in training programs to upskill existing employees on the operation and maintenance of the new system is crucial for ensuring its long-term success. By addressing both the technical and organizational challenges, manufacturers can unlock the full potential of computer vision to transform their quality control processes.

Seeing is Believing: Real-World Case Studies of Computer Vision Success

Several companies have successfully implemented computer vision for quality control, achieving significant improvements in efficiency and accuracy, marking a paradigm shift in manufacturing processes. Automotive Industry: A major automotive manufacturer implemented computer vision to inspect welds on car bodies, reducing defect rates by 50% and increasing production throughput by 20%. This deployment of industrial computer vision not only enhanced the structural integrity of vehicles but also demonstrated the potential of quality control automation to optimize production lines.

Electronics Industry: An electronics manufacturer used computer vision to inspect circuit boards for defects, reducing inspection time by 75% and improving accuracy by 90%. The integration of machine vision systems allowed for the rapid identification of micro-defects, a task nearly impossible for human inspectors, thereby significantly enhancing product reliability. Pharmaceutical Industry: A pharmaceutical company implemented computer vision to inspect vials for cracks and contamination, ensuring product safety and regulatory compliance. This application of computer vision in manufacturing highlights the technology’s crucial role in maintaining stringent quality standards in highly regulated industries.

Beyond these specific examples, the adoption of computer vision quality control is transforming entire manufacturing ecosystems. Consider the textile industry, where automated visual inspection systems are now capable of detecting subtle fabric flaws that would previously go unnoticed, leading to significant reductions in material waste and improved product quality. Similarly, in the food and beverage sector, computer vision is being used to assess the size, shape, and color of produce, ensuring consistency and meeting consumer expectations.

These applications illustrate the versatility of computer vision in addressing diverse quality control challenges across various manufacturing domains. The rise of machine learning for quality control further amplifies these capabilities, enabling systems to learn from data and continuously improve their defect detection accuracy. Moreover, the benefits of computer vision extend beyond mere defect detection. These systems provide valuable data insights that can be used to optimize manufacturing processes and prevent defects from occurring in the first place.

By analyzing patterns in defect data, manufacturers can identify root causes and implement corrective actions, leading to continuous improvement and enhanced operational efficiency. This proactive approach to quality control, facilitated by AI in manufacturing, is a key differentiator for companies seeking to gain a competitive edge in today’s demanding marketplace. The integration of computer vision also enables real-time monitoring and adaptive control, allowing manufacturers to respond quickly to changing conditions and maintain consistent quality standards throughout the production cycle.

This capability is particularly valuable in industries with high product variability or complex manufacturing processes. The success of these implementations hinges on careful planning, robust data management, and a deep understanding of the specific manufacturing context. Selecting the right computer vision components, developing effective annotation strategies, and ensuring seamless integration with existing workflows are all critical factors. As the cost of computer vision technology continues to decline and the capabilities of machine learning algorithms continue to advance, we can expect to see even wider adoption of these systems across all sectors of manufacturing. The future of quality control is undoubtedly visual, with computer vision playing an increasingly central role in ensuring product excellence and driving operational efficiency.

Ethical Considerations and the Future of Computer Vision in Manufacturing

The integration of computer vision quality control into manufacturing processes brings not only unprecedented efficiency but also significant ethical considerations that demand careful attention. One primary concern revolves around bias inadvertently embedded within training data sets. These biases can manifest as algorithmic discrimination, where machine vision systems exhibit varying levels of accuracy across different product types, materials, or even subtle variations in manufacturing defect detection. For instance, a system trained predominantly on images of defects occurring in one type of metal might underperform when inspecting components made of a different alloy.

Addressing this requires meticulous curation of training data, ensuring diversity and representativeness across all potential product variations and defect modes. Furthermore, employing techniques like adversarial training can help mitigate bias and improve the robustness of AI in manufacturing. Transparency and explainability represent another critical ethical dimension of automated visual inspection. Unlike human inspectors who can often articulate the rationale behind their decisions, complex machine learning models can function as ‘black boxes,’ making it difficult to understand why a particular product was flagged as defective.

This lack of transparency can erode trust in the system and hinder efforts to improve manufacturing processes. To counter this, researchers are actively developing explainable AI (XAI) techniques that provide insights into the decision-making processes of computer vision systems. These techniques can highlight the specific features or image regions that contributed most to a defect detection, enabling manufacturers to identify root causes and implement corrective actions more effectively. Explainability also fosters greater accountability, ensuring that industrial computer vision systems are used responsibly and ethically.

The implementation of quality control automation driven by machine vision systems also raises legitimate concerns about potential job displacement within the manufacturing sector. While computer vision in manufacturing undoubtedly enhances efficiency and reduces human error, it may also lead to the automation of tasks previously performed by human inspectors. To mitigate the social and economic impact of this shift, proactive measures are essential. Manufacturers should invest in retraining programs that equip workers with the skills needed to operate and maintain these advanced systems. Furthermore, new roles may emerge in areas such as data annotation, model validation, and AI system management, creating new employment opportunities within the evolving manufacturing landscape. By prioritizing workforce development and embracing a human-centered approach to automation, manufacturers can ensure that the benefits of computer vision are shared broadly.

The Future is Visual: Embracing Computer Vision for Manufacturing Excellence

Computer vision has revolutionized quality control in manufacturing, offering unprecedented levels of accuracy, efficiency, and consistency. By addressing specific quality control challenges, leveraging advanced algorithms and hardware, and implementing a structured approach, manufacturers can unlock the full potential of this transformative technology. As computer vision continues to evolve, it will play an increasingly critical role in ensuring product quality, reducing costs, and enhancing competitiveness in the global marketplace. The future of manufacturing is undoubtedly visual, with computer vision leading the way towards a new era of quality and efficiency.

Looking ahead, the integration of AI in manufacturing, particularly machine learning for quality control, promises even more sophisticated automated visual inspection capabilities. Imagine machine vision systems not only identifying defects but also predicting potential failures based on subtle anomalies undetectable to the human eye or even traditional algorithms. This predictive quality control, powered by advanced machine learning models, allows for proactive intervention, minimizing waste and maximizing uptime. Companies like Cognex and Keyence are already pushing the boundaries, developing industrial computer vision solutions that learn and adapt, offering unparalleled precision in manufacturing defect detection.

Furthermore, the convergence of computer vision quality control with other Industry 4.0 technologies, such as IoT and cloud computing, will create a seamlessly connected quality ecosystem. Real-time data from sensors and machines across the manufacturing floor can be fed into computer vision systems, providing a holistic view of the production process. This enables manufacturers to identify and address quality issues at their source, optimizing processes and preventing defects from occurring in the first place. The ability to remotely monitor and manage quality control processes through cloud-based platforms also enhances flexibility and scalability, allowing manufacturers to adapt quickly to changing market demands.

This interconnected approach solidifies computer vision’s position as a cornerstone of modern manufacturing. Ultimately, embracing computer vision in manufacturing is not just about improving quality; it’s about fostering a culture of continuous improvement and innovation. By leveraging the power of computer vision, manufacturers can gain deeper insights into their processes, identify areas for optimization, and empower their workforce to make data-driven decisions. As the technology becomes more accessible and affordable, even smaller manufacturers can benefit from the transformative potential of computer vision, leveling the playing field and driving a new wave of manufacturing excellence. The journey towards a fully visual manufacturing landscape is underway, and those who embrace this technology will be best positioned to thrive in the future.