Tag: AI

customer onboarding

How to Improve Customer Onboarding in the Insurance Industry with AI

August 16, 2024

The ongoing digital insurance transformation has unlocked several fascinating possibilities for insurers in terms of enhancing a wide gamut of customer-focused services and functions. For instance, they can now use AI in insurance to boost customer experiences not just during claims processing, but also at the time of insurance customer onboarding. It is this aspect in particular, that is witnessing major innovation across the sector, with more customers inclined towards companies that enable swifter completion of formalities with zero hassles. Let us take a closer look at how insurance companies can improve customer experiences at the time of onboarding with some help from AI. How Generative AI is Playing a Role in Insurance Onboarding From automated risk assessment to seamless claims processing, there are varied insurance applications that are steadily undergoing an AI-led transformation. Onboarding, in particular, is seeing a major revolution of sorts, driven specifically by the power of generative AI. Here’s how: It can then be agreed that AI in insurance onboarding makes the whole process faster, more personalised, and smarter than conventional methods. This matters all the more, since traditional onboarding methods may lead to unwarranted delays and issues for both insurers and clients. This is because they mostly revolve around lengthy and intricate paperwork along with complex verification processes and repetitive tasks. AI can completely transform the whole system into a more customer-centric and effective one, while saving time and effort for all parties in the transaction. Insurance Customer Onboarding with AI- Some Areas that Can Be Transformed So, the big question now is how insurance customer onboarding can undergo a transformation, courtesy of AI. Here are a few core pointers that should be examined in this context. Signing Off Insurance customer onboarding can thus be transformed comprehensively with the help of AI. Self-service KYC and easy document uploading, along with automated risk assessment and underwriting have already become realities. At the same time, verifying identities and documents is also becoming faster and more streamlined. What it does eventually is make the whole process faster and more convenient for customers, while adding a personal touch to the same from the outset. FAQs 1. How can AI speed up the insurance customer onboarding process? AI speeds up insurance customer onboarding through automating risk assessment and underwriting and also document verification. It speeds up the whole process by eliminating lengthy paperwork and verification mechanisms. 2. Does AI onboarding with insurance companies compromise my data security? AI onboarding with insurers should not hinder customer data privacy and security. Most insurance companies make use of advanced technologies like encryption to ensure the safety of customer data. 3. What are the advantages of using AI for insurance customer onboarding? Some of the benefits of using AI for onboarding insurance customers include more personalised experiences, faster processing and risk assessment, seamless underwriting and verification, and of course, automated updates throughout the process. 4. Is AI replacing human interaction in insurance onboarding entirely? AI has the potential to replace human interactions to a large extent in the insurance onboarding process. Virtual assistants and Chatbots can be deployed to offer real-time and 24-7 support to customers along with helpful information and updates. Self-service KYC and automated verification and underwriting can free up insurance personnel to focus on more valuable tasks. 5. What to consider when choosing an insurance company with AI-powered onboarding? When selecting an insurance company that offers AI-based onboarding, there are a few things to consider. These include the overall reputation and presence of the insurer, its claim settlement ratio, the overall security measures undertaken to protect customer data, customer reviews and testimonials regarding the onboarding and claims processes, and of course, the overall ease of onboarding.

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AI-Powered Underwriting- Insurance’s Crucial Game Changer

February 15, 2024

There are no prizes for guessing that enterprises and businesses are seeking to leverage machine learning algorithms, AI tools, and data analytics for streamlining operations, gaining cost and time benefits, enhancing accuracy, taking better decisions, and boosting consumer engagement simultaneously. The same is true for the insurance sector, which is betting big on AI-driven underwriting which could be its biggest game changer yet. Here is a closer glimpse into the same. Underwriting with AI- What It Entails Based on the insurance type and category, the process of underwriting will involve information collection about the personal and financial details of applicants along with driving records, health records, and other crucial data. Post collection, this will be used to determine the calculation of the premium which is fair for the insurance company and the customer alike. Yet, this is not always the best method, although it does save time, cost, and efforts for insurance companies. Underwriting with AI will work in a unique and nonconventional manner, deploying machine learning algorithms to gather and analyze huge data volumes. It will automatically help unearth trends that may otherwise be neglected by conventional methods of underwriting. AI can be applied to gather data from multiple sources, which include public data, social media information, credit reports, and more, thereby enabling insurers to build a more comprehensive risk profile of any customer. AI can undertake data analysis way faster than human beings, while enabling insights that help insurance companies take more accurate and swifter underwriting decisions. As mentioned, several patterns and trends may also be revealed through the usage of AI which may be overlooked otherwise. Advantages of AI-Driven Underwriting AI-powered underwriting comes with several advantages that insurance companies should note. Some of them include the following: Are There Any Challenges of AI-Driven Underwriting? AI-powered underwriting does have a few challenges that insurers need to tackle effectively. There is always a need to ensure data privacy and security while analysing vast amounts of personal details. Preventing unauthorised access or data misuse continues to be a concern for insurers in this regard. Algorithmic biases should also be monitored and tracked, thereby eliminating any unwarranted prejudice against specific social groups or communities.  The higher costs of deploying AI-based underwriting tools are also a challenge for some insurers. Being transparent about AI usage and personal data collection is also the need of the hour for insurance companies in this case. How It All Stacks Up So, does AI-based underwriting make a strong case for itself? The answer is a glaring yes, if you take a closer look at the benefits mentioned above. Predictive analytics will help understand and assess risks better, while generating real-time data for on-demand quotes. Policy customisation will be scaled up for every customer and that too with lower and fairer premiums and more flexibility in terms of coverage limits. Underwriting will be quicker and more accurate, while ensuring greater profitability for insurance companies. Yes, it is a win-win scenario that looks set to be the next big game changer for insurance. At the same time, it is also worth mentioning that AI-powered underwriting plays a vital role in enabling insurance companies to evaluate risks more effectively. This helps them detect frauds or potential issues early on before they snowball into major problems or losses. High-risk customer types/segments can be swiftly identified while insurers can also enhance their risk management and tracking abilities in turn. AI can lower underwriting costs significantly along with cutting out errors and time lags completely. Companies can thus efficiently allocate their resources to other areas of business to scale up their revenues. Of course, customer satisfaction levels also considerably improve with faster underwriting and decision-making. The fact that they get personalised and better pricing also helps immensely. This leads to higher retention volumes and more business growth for insurance companies in turn. So, as they say, a stitch in time saves nine.  With AI-backed underwriting, there could be a lot more than just savings. It can transform most insurance companies into well-oiled machines generating more revenues and catering to customers more effectively. Now who doesn’t want that? The time is ripe for a transition to the AI-backed ecosystem. The time is NOW. FAQs Can AI-powered underwriting adapt to changing market conditions and evolving risk factors? AI-powered underwriting has the capability to evolve in sync with market dynamics and changing conditions along with evolving factors for risks. Adaptability is one of the strongest assets of AI and machine learning algorithms which can keep tracking and adapting to market and borrower behavior changes, while updating their risk assessment models likewise. Which machine learning algorithms are commonly employed in AI-powered underwriting? There are various types of machine learning algorithms that are commonly used for AI-powered underwriting. Some of them include random forests, decision trees, naïve Bayes, and also K-nearest neighbors. Logistic regression is another model that is commonly employed in this case. What are the key benefits of adopting AI-powered underwriting for insurance companies? Some of the biggest benefits for insurance companies adopting AI-powered underwriting include faster and more accurate underwriting, personalised and fair pricing, comprehensive risk assessment and mitigating future problems, and of course, time and cost savings. What is the future outlook for AI-powered underwriting in the insurance sector? AI-driven underwriting will be embraced by more insurance companies for quicker and on-demand quotes for customers which are personalised and come with transparent and fairer pricing. What safeguards are in place to protect customer data in AI-powered underwriting systems? Insurance companies have numerous safeguards for protecting consumer data in the case of AI-powered underwriting. These include data anonymisation, access controls, encryption, regular data breach and security audits, and several other measures.

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AI-Powered Customer Support: A Win-Win for Insurers and Policyholders

AI-Powered Customer Support: A Win-Win for Insurers and Policyholders

November 13, 2023

AI-powered customer support is a major catalyst for change in the global insurance sector, enabling numerous benefits for insurers and policyholders alike. The insurance industry has already shifted focus towards offering top-notch customer service with a more customer-oriented model. As per several reports, in 2021, more than 40% of CIOs scaled up their budgets for implementing AI for their pilot projects in the insurance sector with an aim to enhance customer service.  The customer base in the insurance and financial services sector now desires more personalized solutions along with quicker processing for inquiries, and enhanced security for personal information, among other customer-focused aspects. Here are some advantages enabled by AI-powered customer support.  Insurer Customer Service- Benefits of AI in Insurance AI in insurance has been a game-changer for the industry, offering several advantages for both companies and their policyholders in terms of insurer customer services and support. Here’s looking at some of them in a nutshell:  How Else Can AI-Powered Customer Support Help?  AI-powered customer support is already revolutionizing the sector rapidly. Companies like Edelweiss General Insurance (EGI) have already set the ball rolling by introducing AI-based voice bots for enabling registrations of motor claims. This helps customers initiate their claims swiftly and seamlessly. This AI bot can easily interact with customers in English, Hindi, and even Hinglish. It also enables registration of claims in real-time on a 24-7 basis while offering round-the-clock support alongside.  These AI-backed insurer-policyholder interactions go a long way towards simplifying customer support and service, answering questions, offering guidance and information, registering claims, offering support around the clock and streamlining claims management. Bots will also enable swifter resolution of claims along with enhancing customer satisfaction and operational efficiency levels considerably. Interactions can be easily automated while robotic interpretation of voicemails helps save time and enhance overall accuracy levels before calls reach human representatives. AI also enables automated customer support throughout multiple channels, which ensures better responsiveness and availability. It can also augment interactions with customers through augmented email tagging and messaging alike.  AI in insurance can also analyze big data sets while suggesting relevant content based on customer location and behaviour among other aspects. AI can evaluate customer behaviour and sentiment to improve responses in the long run. It also builds personalized customer experiences, making it easier to bypass issues before they crop up. AI can help customer service representatives take care of their follow-up duties in time. Agents get help with writing in real-time along with insights from customer data. Generative AI can hugely boost customer segmentation in analytics. It can easily identify customer similarities and patterns by evaluating huge amounts of customer information. This includes psychographic, demographic, and behavioural information. It helps in segmenting customers more effectively. Insurers can benefit from features like automated feature engineering, personalized recommendations, customer clustering, predictive modelling, and sentiment analysis.  To sign off, AI-powered customer support helps insurance companies greatly in terms of enhancing their customer support and service functions with better guidance, quicker responses, and timely assistance. It is certainly the way forward for the industry, going by the recent trends.  FAQs How does AI-powered customer support enhance the insurance experience for policyholders? AI-powered customer support boosts the overall experience for policyholders greatly. They can get instant and timely resolutions to their queries along with better engagement and understanding of their concerns. At the same time, they also benefit from faster claims registration, management, and handling along with quicker and more hassle-free onboarding and claims processing.  What specific tasks and processes in the insurance industry can AI-driven customer support streamline and improve? AI-driven customer support can not only improve, but also support various processes and tasks including customer communication and notifications, answering questions, providing relevant content, registering claims, archiving requests, following up with requisite channels, managing claims, and more.  What are the potential cost-saving benefits for insurers when implementing AI-powered customer support? Insurance companies can save time and money considerably on customer support and service processes by automating diverse tasks. From eliminating the need to manually store, archive, and gather data to doing away with paper-based systems, the cost savings are huge for insurance companies.  How can insurers ensure the security and privacy of policyholder data while using AI in customer support interactions? Insurance companies can ensure more privacy and security of policyholder information while deploying AI for their customer support-based interactions. This is possible with steps like data encryption, fraud detection mechanisms, identification of suspicious customer behaviour patterns with relevant algorithms, and more. 

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AI vs Metaverse: Understanding the Fundamental Differences

AI vs Metaverse: Understanding the Fundamental Differences

November 7, 2023

The AI vs. Metaverse debate is worth looking at closely. Both these concepts have the potential to rapidly transform the world as we know it. Yet, knowing more about the Metaverse and artificial intelligence differences is a must. Here’s looking at the same more closely in this article.  Understanding Artificial Intelligence AI and virtual reality have completely changed the game across diverse business sectors. AI or artificial intelligence is a specialized field which emphasises developing intelligent machines. They are equipped with special algorithms and sizable computational abilities to execute tasks which normally necessitate human intelligence. These include reasoning, learning, decision-making, and problem-solving. Here are some points worth noting in this regard:  Understanding Metaverse Technology  Before getting into the artificial intelligence and Metaverse fundamental distinctions, here is a closer look at Metaverse technology and what it entails.  AI vs. Metaverse- Key Differences Here are some of the Metaverse and artificial intelligence differences that should be noted closely:  The Metaverse, conversely, focuses more on immersive interactions that are within digital/virtual environments. Users have avatars to explore and navigate these environments while interacting with the same and other users too. It is just like being in a virtual world.  The AI vs. Metaverse story is thus clear. They are both complementary yet distinctively different technologies. AI enables better decision-making while the Metaverse offers immersive experiences and activities. Both these technologies will be future game-changers for the world, especially as they continue evolving rapidly over the years.  FAQs How does AI contribute to the development and functionality of the Metaverse, and what role does it play within virtual environments? AI tools can enable better social analytics in the Metaverse. This will help users understand their connections and interactions better. Insights can be leveraged from AI-based data analysis to boost user engagement and build better relationships. AI will also contribute towards better process automation, user experiences, and the creation of more intelligent virtual environments.  What challenges and ethical considerations arise when implementing AI in the Metaverse, and how are they distinct from AI in the real world? There are a few challenges arising from the implementation of AI in the Metaverse. They are also different from real-world use cases of AI at times. These include deepfake technology risks, lack of transparency in AI-based decision-making, ethical issues related to using digital twins, and the effect of bias in AI and virtual reality (VR).  In what ways can AI and the Metaverse collectively shape the future of technology and human interaction? Both AI and the Metaverse can collectively reshape technology and human interactions in the future. From more intelligent digital personas to analyzing vast information swiftly, there are several use cases that will be seen over the years. Some other game-changers include swift facial recognition for avatars, digital humans and NPCs, immersive education and training, insight-driven engagement, and multilingual accessibility and interactions. 

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10 Ways to Use AI to Get Ahead in the Insurance Business

10 Ways to Use AI to Get Ahead in the Insurance Business

October 17, 2023

Artificial intelligence (AI) is rapidly transforming the insurance industry. Insurers and brokers are using AI to automate repetitive tasks, improve decision-making, provide more personalised service, and reduce costs. In this blog post, we will introduce the top 10 tools for LLM/AI in the insurance business. These tools provide a variety of capabilities that can be used by insurance companies to improve their operations. 1. Google AI Platform Google AI Platform is a suite of cloud-based AI services that can be used to build, train, and deploy AI models. It includes a variety of services, such as Cloud TPUs, Cloud AutoML, and Cloud Vision. Google AI Platform can be used by insurance companies to: Link: Google AI Platform: https://cloud.google.com/ai-platform/  2. AWS AI Services AWS AI Services is a suite of cloud-based AI services that can be used to build, train, and deploy AI models. It includes a variety of services, such as Amazon SageMaker, Amazon Rekognition, and Amazon Polly. AWS AI Services can be used by insurance companies to: Link: AWS AI Services: https://aws.amazon.com/ai/  3. Microsoft Azure AI Microsoft Azure AI is a suite of cloud-based AI services that can be used to build, train, and deploy AI models. It includes a variety of services, such as Azure Machine Learning Studio, Azure Cognitive Services, and Azure Bot Service. Microsoft Azure AI can be used by insurance companies to: Link: Microsoft Azure AI: https://azure.microsoft.com/en-us/services/cognitive-services/  4. IBM Watson IBM Watson is a cloud-based AI platform that can be used to build, train, and deploy AI models. It includes a variety of services, such as Watson Assistant, Watson Visual Recognition, and Watson Discovery. IBM Watson can be used by insurance companies to: Link: IBM Watson: https://www.ibm.com/watson/  5. Salesforce Einstein Salesforce Einstein is a cloud-based AI platform that can be used to build, train, and deploy AI models. It includes a variety of services, such as Einstein Sales Cloud, Einstein Analytics, and Einstein Service Cloud. Salesforce Einstein can be used by insurance companies to: Link: Salesforce Einstein: https://www.salesforce.com/products/einstein/  6. AlphaChat AlphaChat is a no-code conversational AI platform that can be used to build chatbots for insurance companies. It includes features such as natural language understanding, live chat, and authentication. AlphaChat can be used by insurance companies to: Link: AlphaChat: https://alphachat.ai/  7. Chatfuel Chatfuel is a no-code chatbot development platform for Facebook, Instagram, and WhatsApp. It can be used to build chatbots for insurance companies to provide customer support, answer questions, and generate leads. Chatfuel can be used by insurance companies to: Link: Chatfuel: https://chatfuel.com/  8. PolicyGenius PolicyGenius is an online insurance marketplace that uses AI to help customers find the best insurance policies for their needs. PolicyGenius uses AI to analyse customer data and recommend the best policies from a variety of insurers. PolicyGenius can be used by insurance companies to: Link: PolicyGenius: https://www.policygenius.com/  9. Lemonade Lemonade is a digital insurance company that uses AI to automate the underwriting process and speed up claims processing. Lemonade uses AI to analyse customer data and assess risk more accurately. This allows Lemonade to offer lower rates and faster payouts to its customers. Lemonade can be used by insurance companies to: Link: Lemonade: https://www.lemonade.com/  10. Gabi Gabi is an online insurance marketplace that uses AI to help customers compare insurance rates and find the best policies for their needs. Gabi uses AI to analyse customer data and recommend the best policies from a variety of insurers. Gabi can be used by insurance companies to: Link: Gabi: https://www.gabi.com/  Way Ahead AI is rapidly transforming the insurance industry. Insurers are using AI to automate tasks, improve efficiency, and provide a better customer experience. Additional Benefits of Using AI in the Insurance Business In addition to the benefits listed above, using AI in the insurance business can also lead to: How to Get Started with AI in the Insurance Business If you are an insurance company and you are interested in getting started with AI, there are a few things you can do: AI is a powerful tool that can help insurance companies improve their operations, reduce costs, increase revenue, and improve customer satisfaction. If you are an insurance company, you should consider using AI to stay ahead of the competition and provide the best possible service to your customers.

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Navigating the Future of BFSI: Insights from Vymo on Market Positioning, AI Impact, Gamification, and Talent Retention

Navigating the Future of BFSI: Insights from Vymo on Market Positioning, AI Impact, Gamification, and Talent Retention

October 12, 2023

The future of the banking and financial services (BFSI) industry hinges on various factors that leverage advanced technology towards enabling higher progress. Here are some insights from the Managing Director-Asia Pacific at Vymo, Rajesh Sabhlok. Vymo is one of the quickest-growing SaaS entities in the market at present with a novel positioning based on its focus on the financial industry.  This narrowed-down emphasis on the ideal client profile has enabled a deeper understanding of the challenges faced by the sector and sales teams, while ensuring that core solutions cover their requirements and spur higher adoption of users. Let us now look at some valuable insights that deserve closer attention.  Key Insights for the BFSI Industry Here are a few pointers worth highlighting in this space:  Let us now take a closer look at some other valuable insights that should matter to BFSI companies.  Additional BFSI Trends Worth Noting Here are a few more trends and insights from Vymo that should merit closer attention from banking and financial services institutions.  Here are some more emerging trends as per Vymo’s forecasts.  Emerging BFSI Trends According to Vymo Here are a few more emerging trends that are crucial for the banking and financial services (BFSI) sector.  Vymo feels that it has created a unique ecosystem with its digital sales engagement and enablement solutions for sales teams, along with higher user adoption through strategic approaches. It has created a customer-focused post-sales target operating model which has a novel tool kit that ensures better engagement and user adoption levels. The entity is also investing in ML and AI models to enable better user experiences and skill development simultaneously. Customers now have increasing access to reviews, price comparisons, product comparisons, and other information online. Their buying behaviour will naturally be influenced by all these factors. Hence, sales teams and agents should rapidly transform their processes while being digitally enabled to meet the customer shift. Hyper-personalized solutions for customers are the need of the hour throughout multiple touch points. There is a higher demand for more agile approaches that are tailored to individual requirements along with digital-first interactions through portals and apps. Customers now want digital onboarding journeys and automated onboarding procedures. Sales professionals are increasingly looking for more support from AI-based tools to streamline and quicken procedures while lowering their overall workloads. Flexible and usage-based insurance will also be necessary for customers along with pay-as-you-go systems. Insurance companies will also have to increasingly sync their operations with ethical and sustainable practices to connect better with their customers and their specific requirements. These are the core trends that the BFSI industry should witness over the coming decade.

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AI and life sciences: Navigating risks and challenges

AI and Life Sciences: Navigating Risks and Challenges

September 21, 2023

With the increasing penetration of artificial intelligence (AI) in life sciences, there has been a barrage of questions regarding the risks and challenges involved in this integration. While AI has already started its transformative journey throughout multiple other industries, the life sciences sector has recently woken up to the potential of the same.  Some factors that are key moot points in this regard include the role played by AI in developing COVID-19 vaccines in quicker time (less than one year as opposed to a decade in most cases), AI-driven drug discovery where a novel drug candidate was found for liver cancer in only 30 days, and more. Even Google Cloud has unveiled new AI-backed tools that facilitate quicker drug discovery. Many other technology companies are coming up with tools for automating processes that were manual and time-consuming in nature earlier.  How it stacks up  Life sciences and healthcare AI have already reached a watershed point where there are challenges and disruptions to contend with, but the speed and scale of adoption continue unhindered. Here are some points worth noting in this regard:  Yet, ethics, data privacy, regulatory aspects, and other challenges must be tackled with care to ensure widespread benefits from integrating artificial intelligence (AI) in life sciences. Let us first look at the range of its applications in this space.  Applications of AI in life sciences and healthcare Here are a few points that should be noted in this context:  Now that the benefits of AI are clearly visible, let us take a closer look at the challenges mentioned above and the ways to navigate them for swifter progress in the domain.  Major challenges of AI in life sciences Here are the risks that still remain while deploying artificial intelligence (AI) in life sciences.  Signing off, it can be said that the AI-enabled transformation drive is now in the second phase, i.e. completing patterns and going beyond the initial brief of recognizing them. The life sciences sector will greatly benefit from this current AI stage, provided it can counter the challenges mentioned above.  FAQs AI has a vital role to play in the life sciences industry, enabling faster drug discovery and development along with boosting clinical trial design and data-gathering. It helps analyze vast data sets and generate better insights from the same.  2. What are the key challenges and risks associated with implementing AI in healthcare and life sciences? There are a few challenges and risks that companies have to face while implementing AI in the life sciences and healthcare industry. These include the lack of skilled talent, regulatory compliance hurdles, ensuring data privacy and patient confidentiality, and steering clear of biases in AI algorithms. 3. How can data privacy and security concerns be effectively addressed when using AI in life sciences? Data security and privacy concerns can be tackled effectively with a few proactive steps while using AI in the life sciences sector. These include dedicated patient confidentiality and privacy approaches along with an increased focus on secure data transmission and usage. Governance and data security protocols should be established as per regulatory standards for secure storage, processing, and collection of patient data.  4. What ethical considerations should be taken into account when deploying AI in medical decision-making? The biggest ethical consideration that should be kept in mind while AI is being used for medical decision-making, is the elimination of biases. While training AI models based on real-world data and inputs, there are unconscious biases that get embedded into the same. This may have negative consequences for patients if they are not tackled at the outset.

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Manufacturing Demand with Advanced Analytics

Optimising Manufacturing Demand with Advanced Analytics

August 10, 2023

Manufacturing efficiency and supply chain enhancement are key goals for most industrial enterprises today. In this scenario, the optimisation of manufacturing demand with advanced analytics is deemed essential for driving higher productivity, lowering wastage, and enabling higher operational efficiencies for companies. Based on data-driven insights, enterprises can deploy analytics for setting goals and empowering self-management of performance at the operators’ end. They can identify the best practices for achieving growth while designing their formal working procedures accordingly.  They can also enable the empowerment of front-line personnel with regard to enhancing overall training, collaboration, and communication, especially by using varied productivity tools for this purpose. Manufacturing entities can use advanced analytics to generate higher efficiency in production along with enabling more agile production lines, assets and supply chains with data-based support and decision-making. Real-time and historical operational data can be leveraged for improving processes and ensuring higher standardisation to save both time and costs. At the same time, this will also help personnel detect potential risk sand variability, thereby bypassing errors that can prove costly in the long run.  The Importance of Demand Data Demand data is crucial for manufacturing enterprises at multiple levels. Here are some points worth noting in this regard.  Hence, as can be seen, demand data is the lifeblood for leveraging advanced analytics towards taking better business decisions. Let us now take a closer look at data integration and why it matters.  What role does data integration play in manufacturing optimisation? If manufacturing enterprises have to rely on data-driven insights and use analytics, then data integration is a must. Here are some pointers on the same.  Using robust data platforms and focusing on data integration are crucial tasks for manufacturing entities. It is essential for ensuring the accuracy and quality of data that is being used for analysis and insight-gathering. Advanced analytics can be used effectively to forecast demand for manufacturers. Here’s examining the same in more detail. Advanced Analytics to Forecast Demand Using advanced analytics for demand forecasting comes with its fair share of benefits for manufacturers. Here are some of them:  Manufacturing demand can be suitably optimised by leveraging advanced analytics. At the same time, manufacturing enterprises can manage inventory better and streamline product planning with the help of these insights.  FAQs Manufacturing analytics can be adapted to different manufacturing environments. They may enhance the quality of the end product of the company while also enabling data-based product optimisation, identifying defects or errors, and also evaluating consumer feedback and other buying trends.  Optimising manufacturing demand comes with several advantages, including enhancing overall equipment efficacy, lowering delays in production, enhancing the effectiveness of equipment, lowering the chances of breakdowns, and also tracking equipment performance and availability. It may also help in scaling up overall quality.  Optimisation goes hand in hand with just-in-time (JIT) manufacturing. This is sometimes known as lean production/manufacturing. Instead of supplying consumers and producing goods from stocks, JIT systems emphasise on the production of the exact amount required when consumers need the same.  Analytics can greatly enhance relationships with suppliers. Some of the advantages include easy tracking of supplier performance and tackling issues along with boosting trust and communication throughout multiple channels. This is enabled through easier demand forecasting, avoiding overproduction and streamlining timelines and supply chains accordingly.

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Computer Program Reads 2.5 Million Scholarly Papers

November 15, 2016

A Seattle-based company has announced that its computer program ‘Semantic Scholar’ has mapped the citations of 2.5 million neuroscience articles after reading them, and also ranked each author’s influence. The company plans to use Semantic Scholar to read 20 million papers by next year. This is artificial intelligence in its scholarly avatar. #vr #ai

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YouTube Announces VR App

November 14, 2016

Google unveiled its new Daydream View VR headset recently and to coincide with that, YouTube announced its VR app. This VR app will be available on Daydream first and will likely make 360 degree videos more realistic than ever. #vr #ai

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