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The Role of AI in Revolutionizing Business Processes In today's digital age, artificial intelligence (AI) is no longer just a buzzword—it is a powerful catalyst for change. From automating routine tasks to optimizing complex decision-making, AI is revolutionizing business processes across every industry. Whether you're in manufacturing, logistics, retail, or services, AI-driven solutions are redefining operational efficiency, cutting costs, and driving innovation. How AI Transforms Business Processes 1. Enhanced Operational Efficiency AI-driven technologies automate repetitive tasks and provide real-time insights into your business operations. This leads to: Automated Decision-Making: AI can quickly analyze vast amounts of data and offer actionable recommendations. Resource Optimization: By dynamically allocating resources based on real-time demand and constraints, AI minimizes downtime and maximizes productivity. Process Automation: Routine tasks—from data entry to workflow management—are streamlined, freeing up employees to focus on strategic initiatives. 2. Improved Scheduling and Planning Effective scheduling is at the heart of operational excellence. AI-powered planning solutions use advanced algorithms to: Optimize Timetables: Allocate tasks and resources efficiently, ensuring that every minute counts. Adapt to Change: Quickly adjust schedules in real time to accommodate disruptions, ensuring continuous operations. Reduce Waste: Minimize idle time and material waste by aligning production schedules with actual demand. 3. Data-Driven Decision Making AI transforms raw data into strategic insights, allowing businesses to: Forecast Demand Accurately: Advanced analytics predict future demand, helping to plan production and inventory levels more effectively. Enhance Customer Satisfaction: With predictive analytics and real-time monitoring, businesses can proactively address issues before they impact customers. Support Continuous Improvement: Ongoing data analysis enables companies to refine processes and drive long-term growth. The Power of Advanced Planning with Timefold/OptaPlanner One of the most impactful applications of AI in business process optimization is found in advanced planning and scheduling. Timefold (formerly OptaPlanner) is a lightweight, open-source constraint solver that is transforming how companies approach complex scheduling problems. How Timefold/OptaPlanner Empowers Your Business: Constraint-Based Optimization: Timefold uses sophisticated algorithms to handle both hard constraints (such as resource capacities and deadlines) and soft constraints (like employee preferences and cost minimization), ensuring the best possible schedule. Real-Time Adjustments: With its ability to process real-time data, Timefold can quickly adapt to unexpected changes—minimizing downtime and keeping operations running smoothly. Scalability and Flexibility: Whether you're managing workforce scheduling, production planning, or logistics, Timefold scales to meet your needs and can be customized to suit unique business processes. Increased Efficiency: By automating the scheduling process, Timefold reduces manual errors and improves the overall allocation of resources, leading to significant cost savings and productivity gains. Real-World Impact of AI-Driven Schedule Planning Businesses across various sectors are reaping the benefits of AI-driven schedule planning: Manufacturing: Optimized production schedules reduce material waste and energy consumption. Logistics: Improved vehicle routing and delivery schedules result in lower fuel costs and better service levels. Retail: Smart inventory management and employee scheduling enhance customer satisfaction and reduce operational costs. Services: Automated appointment scheduling and resource allocation lead to higher efficiency and better utilization of workforce skills. How Oodles ERP Can Help as an Official Timefold Implementation Partner Implementing an advanced planning solution like Timefold can be transformative, but it also requires expertise to integrate seamlessly with your existing systems. This is where Oodles ERP comes in. As an official Timefold implementation partner, Oodles ERP offers comprehensive services to help you harness the power of AI-driven scheduling and planning. Our Value Proposition: Custom Solution Development: We tailor Timefold's planning models to align perfectly with your specific business needs, ensuring optimal resource allocation and operational efficiency. Seamless Integration: Our team ensures that Timefold is integrated smoothly with your ERP, SCM, and MES systems, enabling real-time data exchange and enhanced visibility across your operations. Expert Support and Maintenance: With ongoing support and proactive maintenance, we help you keep your planning solution running at peak performance. Driving Sustainable Growth: By optimizing schedules and reducing waste, our Timefold implementations contribute directly to lower costs, improved productivity, and enhanced sustainability. Conclusion AI is revolutionizing the way businesses operate—driving efficiency, reducing waste, and enabling data-driven decision-making. Advanced planning tools like Timefold (formerly OptaPlanner) are at the forefront of this transformation, offering powerful, constraint-based optimization that can dramatically improve scheduling and resource allocation. Whether you're looking to optimize production, streamline workforce management, or enhance supply chain operations, effective schedule planning powered by AI is key to staying competitive. Ready to revolutionize your operations? Contact Oodles ERP today to learn how our expertise as an official Timefold implementation partner can help you leverage AI-driven scheduling solutions and transform your business processes for a more efficient, sustainable future.

Technology: GENERATIVE AI Category: ERP Solutions

Rahul Sharma

26 Feb 2025

Revolutionizing the Insurance Industry with Generative AI In today's rapidly evolving digital landscape, the insurance industry is undergoing a revolutionary transformation. Generative AI is emerging as a game-changing technology that will reshape everything from claims processing to policy underwriting. As insurance companies face increased competition and rising customer expectations, harnessing the power of generative artificial intelligence is proving essential for operational efficiency, risk management, and customer-centric innovation. The Role of Generative AI in Insurance Automating Claims Processing Generative AI is revolutionizing claims processing by automating data extraction from documents, images, and even handwritten notes. With advanced optical character recognition (OCR) and natural language processing (NLP) capabilities, AI can quickly analyze claim forms and supporting documents to determine validity, estimate repair costs, and flag potential fraud. This automation not only speeds up claim settlements but also significantly reduces processing errors and administrative overhead. Also, Read Transforming ERP Systems Using Generative AI Enhancing Underwriting and Risk Assessment Insurance underwriting has traditionally been a manual, time-consuming process. Generative AI now enables insurers to leverage vast amounts of data for more accurate risk assessments. By integrating historical claims data, customer demographics, and even social media insights, AI models can predict risk more accurately and customize policy pricing. This leads to more competitive premiums and better risk mitigation strategies. Personalized Customer Engagement Today's customers expect tailored solutions and immediate responses. Generative AI facilitates hyper-personalized customer interactions through chatbots, virtual assistants, and automated policy recommendations. These intelligent tools analyze customer data in real time to provide customized advice, streamline the application process, and enhance overall customer service—building stronger relationships and improving retention rates. Fraud Detection and Prevention Insurance fraud costs the industry billions every year. Generative AI is now at the forefront of fraud detection, capable of analyzing transaction patterns and behavioral data to spot anomalies that human analysts might miss. With continuous learning algorithms, AI systems evolve to detect sophisticated fraudulent schemes, thereby protecting the insurer's bottom line and ensuring regulatory compliance. Also, Read Accelerating Supply Chain Processes with Next-gen Technologies Benefits of Integrating Generative AI in the Insurance Industry Improved Efficiency and Reduced Costs Generative AI reduces operational costs and speeds up service delivery by automating routine tasks and optimizing decision-making processes. Insurers can reallocate resources to higher-value activities, ensuring faster claim settlements and more efficient customer service. Enhanced Data-Driven Decision Making Generative AI offers advanced analytics and real-time insights, empowering insurers to make informed decisions quickly. Whether it's setting premiums or adjusting risk models, having access to comprehensive data analytics transforms traditional decision-making processes into agile, strategic initiatives. Streamlined Compliance and Risk Management With ever-evolving regulatory demands, generative AI assists insurers in staying compliant by automating regulatory reporting and monitoring changes in the compliance landscape. This not only minimizes the risk of penalties but also ensures that internal processes align with industry standards. Also, Read Transforming Supply Chain Management with Artificial Intelligence (AI) Elevated Customer Experience AI-driven personalization and quick response times lead to improved customer satisfaction. Enhanced digital interactions, from chatbots to personalized policy recommendations, ensure that customers feel valued and understood—key factors in building loyalty and trust. Key Use Cases of Generative AI in Insurance Virtual Claims Adjusters: AI-powered chatbots and virtual assistants that guide customers through the claims process, reducing turnaround time. Automated Policy Generation: Systems that create tailored policies by analyzing customer data and historical trends, ensuring competitive pricing. Predictive Analytics: Tools that forecast future risks and market trends, enabling proactive adjustments in risk management. Document Digitization: Technologies that convert paper-based forms into digital formats, making data readily accessible and reducing storage costs. Fraud Monitoring Systems: Continuous monitoring of transactions and customer behavior to identify and flag suspicious activities instantly. Challenges and Considerations While the benefits of generative AI are immense, insurance companies must also address challenges such as data privacy concerns, the risk of algorithmic bias, and the need for robust cybersecurity measures. Ensuring transparency in AI decision-making and maintaining regulatory compliance are crucial for successful implementation. The Future of Insurance: A Digital Transformation The integration of generative AI into insurance is not just a technological upgrade—it is a paradigm shift toward a more agile, efficient, and customer-focused industry. As digital transformation accelerates, insurers that invest in AI-driven solutions will be better positioned to navigate regulatory complexities, optimize risk management, and deliver exceptional customer experiences. Also, Read Business Intelligence Software Solutions For Effective Retail Management How Oodles ERP Can Help At Oodles ERP, we understand that revolutionizing the insurance industry requires more than just advanced technology—it demands a tailored approach that fits the unique needs of your business. Our custom ERP solutions integrate seamlessly with generative AI tools to provide: Tailored Automation: We design ERP systems that automate claims processing, underwriting, and risk assessments specifically for the insurance sector. Scalability and Flexibility: Whether you're a boutique insurer or a large financial institution, our ERP systems are scalable and customizable to grow your business. Partner with Oodles ERP to harness the power of generative AI and transform your insurance operations. Let us help you drive digital transformation, reduce costs, and enhance customer satisfaction through our innovative, custom ERP solutions. Ready to revolutionize your insurance operations with generative AI and custom ERP solutions? Contact Oodles ERP today to learn how we can empower your business to achieve greater efficiency, compliance, and customer engagement. Oodles ERP—your trusted partner for transforming the insurance industry through innovative ERP solutions and advanced AI technologies.

Technology: GENERATIVE AI Category: ERP Solutions

Rahul Sharma

21 Feb 2025

Exploring the Role of Generative AI for Efficient VR Content Creation In the dynamic landscape of Virtual Reality (VR), content creation stands as a critical pillar, shaping the immersive experiences that captivate users and drive engagement. However, the process of generating high-quality VR content can be intricate and time-consuming, often requiring significant resources and expertise. Generative AI, a subset of artificial intelligence, holds the promise of automating and accelerating various aspects of VR content creation. Through advanced algorithms and machine learning techniques, Generative AI systems can analyze vast datasets of existing VR content, extrapolate patterns, and generate novel assets autonomously. This capability not only expedites the content creation process but also alleviates the burden on creators, allowing them to focus on higher-level tasks such as conceptualization and storytelling. This blog delves into how generative AI is playing an important role in virtual reality development by enabling the creation of highly realistic and dynamic virtual environments.How Generative AI Enhances Virtual Reality Development Processes1) Virtual World Generation: Generative models have the capability to learn the patterns and characteristics of diverse environments from data, enabling them to procedurally generate completely new, coherent, and realistic virtual worlds. This includes generating detailed landscapes, cityscapes, interiors, and other elements that make up immersive virtual environments. The models learn to create environments that exhibit realistic properties, adhering to the laws of physics and exhibiting natural phenomena.2) Asset Creation: Generative models can learn the underlying data distributions of various types of assets, allowing them to generate new, unique instances that resemble the training data. This encompasses a wide range of assets crucial to virtual reality, including 3D models, textures, materials, and even animations. By learning from data, these models can produce highly realistic and diverse assets in a scalable manner, streamlining the content creation process.3) Integrating Multimodal Data: Generative models can combine and learn from multiple modalities of data, such as text, images, audio, and sensor data, to generate comprehensive virtual experiences. This allows for the creation of virtual environments that seamlessly integrate various aspects, such as visual elements, audio cues, and even physical simulations, resulting in a more immersive and cohesive experience.4) Personalized Content: Generative models can analyze user data, such as preferences, interests, and behavioral patterns, to create personalized virtual experiences tailored to individual users. This can include generating customized environments, narratives, or objects that cater to the specific tastes and goals of each user, enhancing engagement and relevance.5) Compact Representations: Generative models can learn to encode complex data into compact representations or latent spaces. These compact representations can then be used to efficiently store, transmit, and generate new instances of the original data. This is particularly useful in the context of virtual reality, where large amounts of data need to be processed and rendered in real-time.Technological Framework For Expanding Role of Generative AI in Virtual Reality Development1. Generative Adversarial Networks (GANs): GANs are a type of deep learning architecture that consists of two neural networks: a generator and a discriminator. The generator network is trained to produce synthetic data (e.g., images, 3D models) that resemble the real data from the training set. The discriminator network is trained to distinguish between the real data and the synthetic data generated by the generator. These two networks are trained in an adversarial manner, with the generator trying to fool the discriminator, and the discriminator trying to correctly identify the real and synthetic data. Over time, the generator learns to produce increasingly realistic and diverse outputs. In the context of VR, GANs can be used to generate highly detailed and realistic 3D models, textures, and environments, reducing the need for manual modeling and asset creation. GANs can also be used to generate realistic character animations, facial expressions, and other dynamic elements within virtual environments.2. Variational Autoencoders (VAEs): VAEs are a type of generative model that combines aspects of autoencoders (used for dimensionality reduction) and variational inference (a technique for approximating complex probability distributions). VAEs consist of an encoder network that maps input data (e.g., images, 3D models) to a lower-dimensional latent space, and a decoder network that reconstructs the original data from the latent representation. In case of VR, VAEs can be used to learn compact representations of 3D models, environments, and other virtual assets. These compact representations can then be used to generate new variations of the original data, enabling the creation of diverse and unique virtual content. VAEs can also be used for tasks like style transfer, where the visual style of one asset is applied to another.3. Diffusion Models: Diffusion models are a class of generative models that work by gradually adding noise to an input (e.g., an image or a 3D model) and then learning to reverse the process, effectively removing the noise to generate a new output. These models are trained on large datasets and can generate high-quality outputs from text prompts or input images. In the context of virtual reality, diffusion models can be used to generate photorealistic 3D models, environments, and other virtual assets based on textual descriptions or rough sketches. This allows for more intuitive and natural content creation, as developers and artists can describe their desired virtual elements using natural language, and the diffusion model will generate the corresponding 3D assets.4. Neural Radiance Fields (NeRFs): NeRFs are a technique for representing and rendering complex 3D scenes using neural networks. Instead of explicitly modeling the geometry and materials of a scene, NeRFs encode the scene as a continuous function that maps spatial coordinates and viewing directions to radiance values (color and density). This function is represented by a neural network that is optimized to recreate the scene from a set of input images or data. NeRFs can be used to generate highly realistic and detailed virtual environments from a collection of images or 3D scans. NeRFs can capture complex lighting, materials, and geometric details, enabling the creation of immersive and photorealistic virtual worlds. Additionally, NeRFs can be used to render novel views of a scene, allowing for dynamic camera perspectives within the virtual environment.5. Motion Capture and Retargeting: Motion capture (mocap) is the process of recording the movements of real actors or objects and translating that data into digital form. Motion capture data can be used to drive the animations of virtual characters and objects within a VR environment. Retargeting is the process of adapting captured motion data to different character rigs or skeletal structures, allowing for the reuse of motion data across various virtual characters. In the context of generative AI for VR, motion capture data can be used as input to generative models, such as GANs or VAEs, to generate new character animations or movements that are not present in the original data. These generative models can learn the underlying patterns and styles of motion from the captured data and produce novel, realistic animations. Retargeting techniques can then be used to apply these generated animations to different virtual characters or avatars within the VR experience.6. Natural Language Processing (NLP): NLP is a branch of artificial intelligence that deals with the interaction between computers and human language. NLP models can be used to interpret and generate natural language text, enabling more intuitive and conversational user interfaces within virtual environments. NLP can be integrated with generative models to allow users to interact with and modify the virtual world using natural language commands or descriptions. For example, a user could describe a desired change to the virtual environment, and the NLP model, in combination with generative models like GANs or diffusion models, could generate the corresponding modifications to the 3D assets and environments in real-time. NLP can also be used for natural language-based narratives and interactions with virtual characters or agents within the VR experience.7. Reinforcement Learning: Reinforcement learning (RL) is a type of machine learning technique that involves training an agent (e.g., a neural network) to make decisions and take actions within an environment to maximize a reward signal. RL algorithms can be used to train generative models to create virtual environments and agents that adapt and evolve based on user interactions and feedback. For virtual reality, RL can be used to train generative models to create dynamic and responsive virtual worlds that change and evolve based on the user's actions and behaviors. The generative model acts as the agent, and the virtual environment serves as the environment in which the agent operates. By providing rewards for desirable outcomes (e.g., engaging and immersive experiences), the generative model can learn to modify and generate virtual content in a way that maximizes user satisfaction and engagement.8. Game Engines and Rendering Pipelines: Game engines like Unity and Unreal Engine, along with their associated rendering pipelines, provide the foundation for creating and deploying virtual reality experiences. These engines handle tasks such as rendering 3D graphics, simulating physics, and managing user interactions. In the context of generative AI for VR, game engines and rendering pipelines serve as the platform for integrating and leveraging generative models. Developers can create custom plugins, scripts, or tools that interface with the generative models and enable real-time content generation and rendering within the virtual environment. Game engines also provide tools for optimizing and enhancing the generated content, such as level-of-detail (LOD) systems, lighting and material editors, and post-processing effects. *Please Note*: It's important to note that many of these technologies are often used in combination or integrated with each other to unlock their full potential in creating dynamic, realistic, and personalized virtual reality experiences.Final ThoughtsWhile generative AI for VR is still an emerging field with ongoing research and development, its potential to streamline content creation, enable dynamic and personalized experiences, and push the boundaries of realism and immersion is significant. As the technology matures, it is expected to play a central role in shaping the future of virtual reality across various domains, including gaming, education, training, and more. Our expertise in VR development ensures seamless integration of VR solutions tailored to your specific business needs, providing a competitive edge in today's dynamic market. Contact us here, to start your transformative VR retail journey today.Check out our YouTube channel to dive deeper into our expertise by exploring our insightful videos: https://www.youtube.com/watch?v=2MwZKvdDx-o

Technology: GENERATIVE AI Category: Metaverse

Arpita Pal

03 Jun 2024

AI Agents and the Agent Design Pattern Introduction: In the world of artificial intelligence (AI) and software development, the concept of agents has gained significant attention. AI agents are autonomous entities that perceive their environment and make decisions to achieve specific goals. The Agent Design Pattern is a software design pattern that provides a structured approach to designing and implementing AI agents. In this knowledge sharing post, we will explore AI agents and the Agent Design Pattern in detail.What are AI Agents? An AI agent is a software component or system that operates autonomously in an environment to achieve predefined objectives. These agents possess the following key characteristics:Autonomy: AI agents can operate independently without direct human intervention.Perception: They have the ability to perceive and interpret their environment through sensors or input data.Decision-making: AI agents can make decisions based on their perceptions and knowledge to determine the best course of action.Goal-oriented: They are designed to achieve specific goals or optimize certain performance measures.AI agents can be classified into different types based on their capabilities and the environment they operate in. Some common types include reactive agents, deliberative agents, learning agents, and multi-agent systems.The Agent Design Pattern: The Agent Design Pattern provides a structured approach to designing AI agents. It defines a set of components and their interactions to create autonomous and goal-oriented agents. The key components of the Agent Design Pattern are:Agent: The central component that encapsulates the agent's behavior, knowledge, and decision-making capabilities.Sensor: Responsible for perceiving the environment and providing input to the agent.Effector: Allows the agent to interact with the environment and execute actions based on its decisions.Environment: Represents the domain or context in which the agent operates.Knowledge Base: Stores the agent's knowledge, beliefs, and information about the environment.Reasoning Engine: Enables the agent to make decisions and generate plans based on its knowledge and goals.The interaction between these components follows a specific flow:The sensor perceives the environment and provides input to the agent.The agent processes the input using its knowledge base and reasoning engine.Based on its goals and the current state of the environment, the agent makes decisions.The effector executes the chosen actions, influencing the environment.The environment may change as a result of the agent's actions or external factors.The agent continuously repeats this cycle, adapting its behavior based on new perceptions and knowledge.Benefits of the Agent Design Pattern: Applying the Agent Design Pattern offers several benefits in AI agent development:Modularity: The pattern promotes a modular design, allowing for the separation of concerns and easier maintenance.Reusability: Components such as sensors, effectors, and reasoning engines can be reused across different agent implementations.Flexibility: The pattern accommodates various types of agents and can be adapted to different environments and goals.Scalability: Multi-agent systems can be built by composing multiple agents based on the pattern.Testability: The modular design facilitates testing and debugging of individual components.Conclusion: AI agents and the Agent Design Pattern provide a powerful approach to designing autonomous and intelligent systems. By understanding the concepts of AI agents and applying the Agent Design Pattern, developers can create robust and adaptable agents that perceive, reason, and act in their environment to achieve specific goals. Embracing this pattern enables the development of modular, reusable, and scalable AI agent systems.As AI continues to advance, the Agent Design Pattern will play a crucial role in shaping the future of intelligent software systems. By leveraging this pattern, organizations can build sophisticated AI agents that drive innovation, automate processes, and solve complex problems across various domains.Video Link

Technology: GENERATIVE AI

Maneesh Madan

30 Mar 2024

Is AI Going To Put Writers Out Of Business Is AI Going To Put Writers Out Of Business?Is AI a threat to authors?Yes, but only for those who aren'tgood at writing.I've been writing content for nearly 5 years, and I've seen many changes, the latest being the use of AI for content creation.As a writer for digital needs, I couldn't be happier.Here's why 👇Skilled writers don't have to offer their work for less value.With the introduction of AI, every client is now aware that there can be two types of content – generic, and expert-written content.Earlier, clients used to treat content like ticking a box from their checklist, but now they value it more.Now, everyone involved in the research, generation, and publishing of content is aware that quality content makes a difference at multiple levels – search rankings, lead generation, and conversion of clients.Verdict: The clients have to pay the good writers more, as these writers can understand the client's brand from inside and out, and generate masterpieces accordingly.Research time is slashed to half (or less!).Earlier, for a well-equipped piece of content that details every aspect of the topic, a writer had to spend hours. For example, they had to search the entire web, refer to news articles, scramble for research papers, and find books that could help with the topic.While writers may still have to do this, AI has reduced the amount of time and effort spent on this process.For starters, it can suggest the websites that one needs to visit related to their topic, the news articles that might be relevant, and it often accurately quotes the research papers with their links.On top of that, given the right prompt, it will also provide me the list of subtopics I can cover, and a detailed draft of the entire blog.Verdict: When a writer's time on research is reduced, they are better able to deliver user-friendly content and something that the readers are exactly interested in!Writers can tweak/edit/use AI content better.As a writer, before starting with my topic-related research, I get the following questions answered –What tone is the client looking for?Where will it be published?Is there any pre-decided presentation style?Besides, I also check out any document/ information that I might have been provided beforehand. With a detailed conversation around these questions with the client, I write the prompt and edit the content generated by AI, combined with my research.When you don't invest the time to write and re-write the prompts, you will get generic content. The prompts can ask the tool to change tone (professional, lively, simple, etc.), add more topic-related content, list the pros and cons, and more.Verdict: One cannot rely entirely on AI. To use it well, one still needs to have a content writer who can use the tool well.Fact-checking made easy (and faster!).I earlier said that AI saves time on research by quoting the resources relevant to my content.It does the other way around as well. For example, I may have landed a research finding, or want to check whether a particular day is observed or not for the cause that I have mentioned in my content.How will I use AI here?I can simply put my content with the prompt, and ask an AI tool to fact-check whatever I've written. It will find out whether I've cited a wrong resource, or whether I have used a fact partially.Verdict: AI can safeguard you as a writer and publisher from posting anything that might be false news/ statement. It promotes responsible writing.Don't succumb to irritation due to writer's block (ANY MORE!).How do I say this differently?👆That's a question that I often ask AI when I am out of options (although I am aware of the impact I want to have on my readers). It's difficult to put something in the right words.That's writer's block for you.AI can not only suggest alternative ways to write something, but it will also find suggestions, prompts, and different phrases that you can use instead of what you had in mind originally.As a writer, when I suffer from writer's block, I have to seek help from a fellow writer. Sometimes, I have to throw the entire content away and start afresh.Verdict: With AI, this isn't the case anymore. A writer will save time on the content, and the help will be just a few clicks away!Here's an analogy!In essence, AI is likely to become a supportive technology for writers, rather than a complete replacement.Similar to how calculators haven't replaced mathematicians, AI writing assistants will likely not replace human writers.These are powerful tools that enhance a writer's capabilities. However, they cannot replicate the creativity, critical thinking, and human touch that are essential for high-quality writing.These tools will only wipe out the menial task with little to no use of creativity. For me, it means a heavy weight is lifted off my shoulders. I can put more time into what I like doing as a writer… writing more creatively!Author's Bio:Aleeza is a versatile content writer, crafting engaging and informative written materials across various platforms. Her expertise spans SEO writing, technical writing, blogs, PRs, and social media.Aleeza has a proven ability to adapt her writing style to diverse audiences and niches, tackling both technical and non-technical subjects with equal proficiency. In her free time, she likes to read books and play badminton.

Technology: CHATGPT , AI more Category: Digital Marketing

Aleeza Siddiqui

15 Mar 2024

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