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Growth marketing uses data-driven approaches and experiments to acquire, onboard, and retain users. The growth funnel from top to bottom includes awareness, acquisition, activation, retention, and referral. I have been researching how Web3 companies tackle their growth marketing and realize that some common strategies and hacks are shared between Web2 and Web3 companies. However, some new nuances need to be addressed differently for Web3 companies. SEO is essential to driving top-of-funnel awareness for Web3 companies SEO works well for complicated products like finance and insurance that require some degree of research before making purchase decisions. Web3 is foreign and somewhat intimidating to many people. Therefore, SEO is vital for many Web3 companies to address users' questions and concerns during the awareness stage. Many Web3 companies succeed by using traditional SEO keyword tactics like what-is, how-to, and learn-to keywords to write relevant content and improve keyword rankings. Web3 companies use content marketing to boost loyalty Web3 provides a new approach to brands and their content and a huge opportunity to enhance brand loyalty. With Web3-style content, companies engage with their fans, creating loyalty and ownership. In addition, companies are creating access and privilege with that content. For example, Web3 companies are increasing the utility of NFT as a token for membership and providing their NFT holders premium content and rewards. With the power of the creator economy in the Web3 world, companies can utilize content creators as part of their monetization channel. Lifecycle marketing will still be driving engagement and retention for Web3 companies In Web2, lifecycle marketing is vital in creating meaningful user segments and driving product engagement strategies via marketing automation platforms. The reason behind segmentation is to serve customers with tailored content and engagement strategies to drive revenue for the companies. In the Web3 world, companies will still need to perform similar practices to drive revenue. For example, I recently watched in the Web3 growth summit by Vahe Baghdasaryan, Sr. growth marketing manager, CoinStats. He explained how he used behavioral science to create a CRM strategy engaging with abandoned users by using personalized push notifications, getting them back to where they dropped off and stating personalized value propositions. this video Paid marketing is not yet the primary growth driver for Web3 companies Due to many restrictions for crypto companies from paid marketing platforms like and , most Web3 companies have not emphasized using paid marketing to drive growth. Web3 gaming companies are not able to use traditional paid marketing as their growth driver either. In the article - , the author Eric Benjamin Seufert described the challenges that Web3 gaming companies face. Google Facebook Growth models for web3 gaming "The current growth playbook for web3 games relies on a patchwork of highly labor-intensive and non-scalable tactics like partnerships, revenue share agreements, and web-based ad campaigns that convert very poorly, given the inherent friction from web click to registration to all of the crypto-related onboarding necessary. Mobile app stores don't currently publish web3 games given the alternative monetization scheme." However, I believe that more and more Web3 companies will utilize Web3 browsers such as Brave browser to advertise their products and services. I wrote an article about and shared my research, experience, and view of the browser. the nuances of the Brave ads platform The nuance of growth in Web3 Community Community is key to Web3. Community members can be part of the workforce, co-owners of the companies, and voting members of projects. Web3 community members have a high , meaning they are more likely to be the company's evangelists and refer their friends and family because they are the coowners of the network. As a result, companies can roll out attractive referral programs to unlock the potential of the community for monetization. K factor Content creators The evolution of Web3 enables creators to build their own creator's economy, which means they can directly monetize the content without going through a 3rd party. Content creators in Web3 are both co-owners and customers of the companies. The article - perfectly describes the role of content creators in the Web3 world. the future of the creator economy in a Web3 world "Web3 will enable creators to not only own their on existing social platforms, but also own a part of the they produce and distribute content on. Content can begin to be creator-owned and platform-agnostic through , which act as proof of ownership and validate the content's authenticity." content platform the use of NFTs Virality Due to the high K factor of community, co-ownership, and scarcity of commodities, Web3 companies can often achieve instant virality. For example, The NFT bored apes went viral when the celebrities Gwyneth Paltrow and Serena Williams shared their customized apes on social media as their PFP. Owning NFT becomes a statement of gaining access to special networks or communities. Some Web3 games also use the "play-to-earn" model to drive virality. Web3 marketers can still utilize growth hacks from Web2 In conclusion, the power of Web2 growth hacks can still be widely used by many Web3 companies. In addition, web3 marketers can still leverage existing Web2 technologies to optimize their marketing tactics and stimulate growth more effectively.

Top 5 Growth Hacks for Web3 

Small commercial enterprise owners face many challenges and risks in the dynamic international of entrepreneurship. From belongings damage and felony liabilities to employee-associated dangers, the course to achievement is fraught with capacity barriers. This is wherein the function of Comprehensive Small Business Insurance Plans will become important, presenting a safety net that may mean the difference between flourishing and floundering in hard times. Understanding the Importance of Small Business Insurance Small companies are particularly prone to economic setbacks arising from unforeseen events. An unmarried lawsuit, natural catastrophe, or workplace twist of fate can result in expenses that many small agencies are unprepared to handle. Insurance is not just a protection degree; it's a fundamental part of your enterprise's economic fitness. Types of Insurance Plans for Small Businesses When thinking about coverage, small business owners ought to be privy to the diverse sorts of coverage available: General Liability Insurance protects your commercial enterprise from well-known prison claims, such as injuries. Property Insurance: If you own or rent space in your commercial enterprise, assets insurance is important to shield your physical belongings against fireplaces, robbery, and other damages. Professional Liability Insurance: Also referred to as Errors and Omissions insurance, it is essential for agencies that provide services or advice to customers. Workers' Compensation Insurance: This is necessary when you have employees. It covers scientific prices and lost wages if an employee gets injured. Product Liability Insurance: If your enterprise manufactures products on the market, this insurance protects against damages that products could cause. Tailoring a Plan to Your Business Needs No groups are alike, and insurance wishes can vary greatly depending on your industry, size, and precise business activities. It's essential to assess your risks and consequently tailor your coverage insurance. Consulting with coverage experts who provide Comprehensive Small Business Insurance Plans can offer insights and alternatives that align with your business requirements. The Cost of Insurance vs. The Cost of Risk While managing expenses concerns small enterprises, skimping coverage may be a steeply-priced mistake. The charges fee is often less than the potential losses from an uninsured event. It's a balancing act between finding less costly insurance and ensuring adequate safety. In conclusion, coverage isn't always just a regulatory requirement; it is a strategy to guard your business's destiny. With the right Comprehensive Small Business Insurance Plans, small commercial enterprise proprietors can shield their tough-earned belongings from the unpredictable nature of the business world. Remember, being organized is not about managing dangers; it is about ensuring the durability and success of your business. This story was distributed as a release by Ascend under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author

Navigating the Complexities of Small Business Insurance: A Must for Entrepreneurs

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应对小企业保险的复杂性：企业家的必修课

Hype Around Skills Crisis And Waiver Of Formal Education We don’t need no education, we don’t need no thought control… After the World Economic Forum in Davos, everybody is literally baffled by a new problem on the labor market. The annual meeting’s main topic was the dilemma of the outdated current shape of education, which does not change sufficiently over the years and thus unable to support the development of the technological process. Consequently, everyone cherished the idea of abandoning the formal education system, suggesting that we can organize the education process better through short-term courses and special programs. But is this really so, or is it just another hype in the tech world? Does anybody question where will such a radical decision lead us? Let’s analyze the situation soberly. How The Story Began And Who The Instigator Of The Entire Warning? A world’s top business leader and IBM CEO Ginni Rometty announced that in the upcoming tech era, specialists without higher education but with extensive experience will be more in demand. Rometty stated that all private companies should be oriented towards such employees, and the refusal to finance this direction in the near future will provoke a skill crisis. Actually, this is not the first time, as Rometty is well-known for her educational extremism. In her opinion, universities are a waste of time for those who are going to be a data scientist, software developer, computer engineer and more. The IBM-CEO is not just wasting her breath, she revealed her disagreement with the existing system of education also a few years ago. Then she said that IBM is introducing its own training program for future coders, or as she said “new-collars”. The program P-TECH (Pathways in Technology Early College High Schools) is an analog of a technical school, where the study lasts six years. Currently, there are 100 such schools in 13 countries of the world, internships are held in 500 partner organizations. At the forum in Davos, Rometty returned to this issue and was even more categorical than before. “There is a large part of society that does not feel that this is going to be good for their future,” she noted, referring to the problem of finding specialists for technology companies. The only way out of this dilemma for Rometty is the usage of specific programs like P-TECH (promoted by IBM). It’s too early to start jumping to conclusions, but it seems there is a definite connection between this, isn’t it? A Critical View Of The Situation “Leaving Stanford” — is already something like a meme. It seems that every second author of popular books about business, start-ups, personal growth, etc. did it at least once in a lifetime. The choice of university to quit is not random. Not Harvard, not Princeton, not Yale, and not even Berkeley. Stanford is a university directly associated with Silicon Valley, both in geography and biography. The point of view of Rometty is balanced by the point of view of Peter Thiel, one of the “founding fathers” of PayPal, the ideologist of technological libertarianism and the guru of a startup. In 2012, he created a fund to support young tech talents and founded the “20 to 20” program: twenty young people received financial support for the implementation of their projects, provided they discontinue their studies at the university while working on projects. Then, in 2012, Thiel made a speech at his alma mater — Stanford (which for some reason he did not quit!) on the matter of his famous Startup course, which formed the basis of the best-selling book Zero to One. High technologies dictate fashion in the modern world. Undoubtedly, the university has a bunch of superfluous things, but does it mean that a university is not completely needed for the future? Students should be educated in universities to be critical intellectuals, no matter the tooling, but still, those information gaps are incomprehensible. Because to be a critical mastermind you need to be a researcher, hungry for knowledge and capable of finding answers, constantly improving your workflow and bringing some results. Will 17-year-old person do this right after school, is he/she able to properly build this process? What About Famous Technology Leaders And Their Bachelor’s Or Master’s Degrees Bill Gates was a college dropout but no one would say that he is unqualified to run Microsoft. Mark Zuckerberg dropped out of Harvard and ended up founding and running Facebook. But, isn’t it an outdated stroke? It is hard to name them super-geniuses, but they definitely managed to accomplish brilliant projects. Their education is not a key point. It doesn’t necessarily mean all those who left university will equally become successful persons, is not it? By the way, in 2014, Mark Zuckerberg gets a Harvard degree 12 years after dropping out to start Facebook. He returned to the Ivy League school to pick up his degree. Billionaires And Their Doctorate Background The insurance company GoCompare has studied the level of education of hundreds of the richest people in the world according to Forbes magazine over the past 20 years. Although there are billionaires without higher education at all, most of the businessmen on the Forbes 400 list received a bachelor’s degree. According to Forbes, in general, about 84% of the richest people in the United States have a bachelor’s degree and above, while among ordinary Americans, the figure is 33%. By the number of bachelor graduates billionaires are ahead of the US adult population — their rate is higher at 68 percent. Billionaires with a probability of three times more will have a master’s degree or Ph.D. All these means, the university builds an important groud for further education, pushes to self-knowledge and creativity. But most importantly, all success depends on your passion and desire. Here is the main purpose, not the presence or absence of a diploma. Tech Leaders With Diploma Here is a list of people who have received a master’s or bachelor’s degree and cemented their success by dint of it: CEO founder of the American multinational tech and social-media enterprise, which he built as Snapchat Inc. while studying at Stanford. Evan Spiegel. Remarkable businessperson and the CEO of Amazon Web Services (AWS). He received a bachelor’s degree from Harvard College, where he was advertising manager of The Harvard Crimson, accompanied by an MBA from Harvard Business School. Andy Jassy. Is there any other most-talked-about person on Earth than Elon? As a technology entrepreneur, he took main part in creation such labels, as SpaceX, Tesla, PayPal and more. At the age of 17, he moved to Canada to attend Queen’s University, which he left later to study business and physics at the University of Pennsylvania, from the latter one establishment he holds a degree in economics and physics. Elon Musk. Amazon-CEO is an alumnus from Princeton University with a 4.2-grade point average and Bachelor of Science degrees in electrical engineering and computer science. Jeff Bezos. She has been working at IBM since 1981, her first position is a systems engineer. In 2011, she became the first female leader in the story of IBM. She certified with high honors from the Robert R. McCormick School of Engineering and Applied Science at Northwestern University, receiving a bachelor’s degree in computer science and electrical engineering. She has accepted honorary doctoral degrees from Rensselaer Polytechnic Institute and Northwestern University. Ginni Rometty. Is education really does not matter in the formation of these leaders? If it did does, maybe they would not initially go to get doctoral degrees? All these things and more show that not everything that glitters is gold, not everything you touch turns into a footprint. Each of us has different situations, and it is worth going from individual situations. Having done a degree may be helpful in training you to think more broadly and critically and hence, better arrange a career. Of course, this is not a guarantee, but the alternatives to education offered by Mrs. Rometty are also not. It all depends on us and this should be the main focus of attention! Conclusion: To Quit University Or Not? Everything is developing at a record pace demanding workers’ skills enhancement. But this does not mean this problem can be solved by abandoning formal education. A learning system that we have now, of course, has some imperfections. But in general, it has already been tested for centuries. A university, as well as a school, is important for general human development, for the ability to structure tasks and solve them. Even if the university does not give super excellent knowledge in the profession, it is needed in order to help students become full-fledged personalities and finally decide on which way to go and avoid the mistakes of youth. Consequently, we must soberly assess the situation and don’t make hasty conclusions. In the end, we do not know what the consequences of total denial of it might be. Perhaps this will ruin the whole situation even more?

Unbiased Analysis Of Hype Around Skills Crisis And Waiver Of Formal Education

Digitization is changing the face of all industries across the globe, and the life insurance space is no exception. By leveraging emerging digital technologies, life insurers can improve their business models, stay ahead of the competition and offer better customer experiences. Let's take a look at how digitalization enables life insurers to enhance their market reach and meet the ever-increasing expectations of modern customers. 1. Digital Customer Onboarding In a traditional insurance setup, the onboarding process is long, complex, and involves a lot of paperwork and back and forths with customers. In comparison, digital customer onboarding helps life insurers optimize time, resources, and costs while offering personalized customer experiences. The adoption of customer-facing digital apps or websites paired with Electronic Know Your Customer (eKYC) solutions for onboarding offers unparalleled agility, and accessibility to customers and streamlines the entire process for insurers. The information provided by the customer is automatically uploaded to business systems such as CRM. This data can be harnessed for personalizing policies, upselling, and cross-selling. Such a seamless onboarding experience is a sure-shot trick to starting off a fulfilling and long-term relationship on the right foot. 2. Omnichannel Customer Experiences The mushrooming of multiple insurer-customer touchpoints is one of the most significant by-products of insurance digitization. As a result, insurance businesses will have to bear the responsibility of managing the various channels of communication. Naturally, it is not something one can conduct manually. As such, a centralized communication hub such as a CRM is a must-have in the life insurer's tech stack. The resulting unification of channels will introduce consistency in the customer experiences regardless of the channel through which they have come across the life insurer. Such omnichannel customer experiences make the entire customer lifecycle customer-friendly. Now, whether a lead is exploring a policy on the landing page or interacting with a PPC ad, whether a policyholder is raising a claim on the website or doing it on the mobile app, whether a customer is registering a complaint over email or through the chatbot - everyone will have the same customer experience. 3. Product and Service Personalization Digitalization helps life insurance businesses personalize products and services to meet the varying needs of individual customers. For example, Artificial Intelligence (AI) and data analytics technologies help insurers analyze customer data across all interaction points of their journeys. The insights extracted from the data allow insurers to identify what the customers want. As a result, insurers can ensure that the customers get tailored products that are in line with their requirements. AI-powered data integration programs also improve the data flow across disparate systems, and helps businesses obtain a single source of truth. Apart from product development, digitization also helps life insurers offer personalized services. Insurers can offer personalized recommendations and guidance to clients based on their requirements and preferences. This paves the way for improved customer satisfaction and helps insurers build an army of loyal brand advocates. 4. Data Analytics Data analytics has been heaven-sent in the field of insurance. It has enabled the elimination of guesswork and formed cold, hard facts as the basis of all policy-related decisions. It performs a cross-sectional analysis of high volumes of customer data sourced from multiple locations, from their profiles to behaviors to preferences, to offer a 360-degree view of their requirements. Such a holistic overview of the customer allows underwriters to perform a detailed evaluation and work out life insurance policies that match the customer's expectations. Naturally, such an approach to creating life insurance policies will enhance customer experience and earn businesses a favorable reputation. Moreover, the application of data analytics far exceeds policy underwriting or risk assessment. It can even act as a window into the customer experience as life insurers can track, measure, and benchmarks customer experience metrics using data analytics. Apart from service delivery, it can come in handy for life insurance product improvement and discovery. All in all, it is an effective ingredient for improving customer experience. 5. Digital Assistants Digital assistants have become ubiquitous in all customer-facing industries, and the same goes for insurance too. Life insurance businesses are turning to digital assistants for carrying out myriad activities - from helping leads find a life insurance policy that suits their needs to accepting payments of premiums and generating receipts to resolving basic customer queries. Chatbots are one of the most popular digital assistants available. These simulate intelligent, human-sounding conversations over text. Depending on the digital maturity of the chatbot, customers can interact through pre-configured menus or interact in their natural language. Some leverage technologies like AI and ML to add a dash of personalization and establish a one-to-one connection. Regardless of the digital assistant type, they can help with end-to-end life insurance business processes and boost customer experience while at it. Conclusion Customer experience is highly dynamic. As such, insurers must be prepared for constant innovation and action. Digital platforms and technologies like life policy administration software allow insurers to transform the customer experience and meet the ever-growing expectations of modern customers. All in all, a cross-organizational digitization strategy will unlock new opportunities and help you build long-lasting customer relationships.

Leveraging Digitization to Drive Customer Experiences in Life Insurance

Will you be content if you hire and pay four people but only three show up to actually do the work? And yet, many employers do just that. According to McKinsey, an average worker spends 1.8 hours daily gathering and aggregating data, a task that is redundant and doesn’t have a direct impact on business success, and, above all, can be easily automated. Is your insurance company looking for ways to relieve your employees from this routine burden while cutting costs and minimizing errors? If so, you can consult a to build or customize an RPA solution specific to your needs. robotic process automation company But before that, where can you use RPA in insurance? And how to prepare for its implementation — either in specific processes or company wide? What is RPA, and how can it serve the insurance sector? In the insurance industry, robotic process automation (RPA) refers to rule-based software bots that can perform repetitive manual tasks, such as extracting claims data, aggregating client information from various internal and external sources, and performing background checks. If we look at the statistics of RPA implementation, the insurance sector, along with retail, are the main beneficiaries of this technology. With this popularity, Juniper Research predicts that almost a half of the insurance sector . And McKinsey believes that those who have already adopted the technology will be able to by 2025. will invest in RPA by 2024 automate 25% of their manual processes Deploying RPA in the insurance sector brings about the following benefits: . RPA can serve as a bridge between modern software, such as an ERP solution, and the existing legacy programs and devices that are still important for business. Sometimes deploying RPA in insurance can replace legacy systems altogether. For instance, a North American insurer Security Benefit in one year by automating legacy processes. Facilitates integration with legacy systems saved 40,000 labor hours . There are many real-life examples of when adopting RPA in insurance took the excessive load off employees’ backs and gave them more time for higher-value tasks. For instance, Encova Insurance saved its workers by automating their customer retention process, while Australian Unity of manual labor over the course of eight months thanks to RPA. Reduces manual work 25 hours a week spared its employees 22,493 hours . When employees are not overwhelmed with tedious routine tasks, their job satisfaction increases. A large insurance group based in Hong Kong used to delegate routine clerical work to junior staff, who made mistakes resulting in high employee turnover. The company after automating 80% of these processes. Lowers attrition rates reported lower attrition rates . Deploying RPA in insurance does require initial investment, but it helps companies to save money in the long run. For instance, a global insurance provider after deploying just 13 RPA robots. Cuts costs saved £140,000 in six months Top 7 robotic process automation insurance use cases Underwriting Underwriters must analyze information from numerous sources to assess risks and find the best rates and policy options for their clients. In the life insurance niche, for instance, this task can take up to four weeks. Underwriters of their time on data gathering and entry. spend on average 40% Bots can aggregate and process data from internal and external sites and display it on a dashboard for faster and more convenient decision making. According to McKinsey, using RPA in insurance can . Moreover, enhanced with AI, bots can perform the following tasks: reduce data processing time by 34% Populate corresponding fields in the internal system Analyze clients’ claim history and suggest pricing Verify if the person already has an existing policy Assess loss runs Identify health risks, such as checking if the person smokes Extract the client’s credit rating from third-party resources, such as Experian When RPA takes over the trivial tasks, human underwriters can focus their efforts to streamline more complex cases. A Tier 1 US insurer from different policy systems and provide underwriters with a 360-degree of the client and their risks. Prior to this collaboration, underwriters wasted a lot of time to perform this task manually. And now they could use this time to better serve the customer. turned to Accenture to automate data extraction Business process improvement The insurance sector is full of paper-intensive processes, which makes it challenging to assess operational efficiency and identify areas for improvement. When automation bots take over, it becomes possible to track their workflow and record each step. Afterwards, during audits, companies can review the logs and measure parameters, such as processing speed and the number of manual interventions required, to identify candidates for process optimization. Even by simply automating a process, the efficiency increases in terms of speed and error rate. To achieve this, many organizations are incorporating insurance RPA into their workflow. Cattolica Assicurazioni, an Italian insurance firm, turned to UiPath to automate their processes. They investigated the process of financial reconciliation and found that it contained very few exceptions and could be almost fully digitized. They deployed RPA bots to perform tedious tasks, such as matching 20.000 lines of numbers. The financial department allocated six months to complete this project. With RPA automating 90% of the process, the company with a zero error rate. accomplished this task in merely two months Claims management and fraud detection McKinsey reports that automation . reduce the amount of manual work by 80% As you can see from the stats, this is a major robotic process automation insurance use case. When claim registration is done manually, it results in errors, slow service, and unpleasant customer experience. Incorporating RPA in insurance can streamline the whole process from First Notice of Loss (FNOL) to settlement: Integrate claims data from multiple sources, such as medical records for RPA in health insurance, photos of damaged vehicles, etc. Process scanned paper claims by classifying them and entering into the system correctly Assist in claims verification and sport fraudulent claims Identify any missing information and bring this issue to the responsible officer’s attention The Canadian claims management provider SCM Insurance Services fully automated data entry for FNOL, which allowed claims . And a large US property and casualty insurer, EXL, worker compensation claim handling time by 60% within the first four months of deploying RPA in insurance claims processing. to be completed 80% faster could decrease Policy administration and cancellation Even though current software packages for policy administration save employees a lot of labor, they still include complex navigations through multiple applications, causing inefficiency and opening room for error. Insurance robotic process automation solutions, backed by and natural language processing, can receive emails from policy holders, extract data, make the required changes, such as bank mandate and address alteration, and send confirmations. The technology can handle policy rating, issuing, endorsing, renewing, and cancellation, among other tasks. machine learning Zurich Insurance Group built by Capgemini based on BluePrism software. It involves RPA robots early in policy handling as they enter policy details into the system, issue invoices, and draft policy documents that officers can review. Both Zurich clients and the insurer benefit greatly from this approach. Raffaele Nutricati, Head of Robotic Process Automation at Zurich Commercial Insurance, said, deployed an automation solution “Not only are they receiving policies of higher quality than those processed in the traditional manner, there is a significant decrease in the number of emails and phone calls to clarify information with our support desk.” Regulatory compliance The insurance sector relies on several compliance standards, such as tax law and HIPAA privacy rules, to guide documentation and processes. Officers are expected to monitor and act upon any changes in regulations. They need to align claim types with the corresponding jurisdiction as fast as possible, risking a regulatory breach. Automation bots can take over this responsibility by monitoring regulations, validating customer data accordingly, keeping a log of changes, and generating regulatory reports. For example, one popular compliance use case is Name Screening Alert Review for Sanctions on politically exposed persons (PEP). Screening systems can generate thousands of alerts every day, and it would be exhausting to verify any false positives manually. RPA in insurance can pre-process these alerts, significantly limiting the number of false positives remaining for manual verification. Customer service As we established above, deploying RPA in insurance speeds up client-facing services and almost eliminates error rates, resulting in a better customer experience. With the abundance of insurance companies, clients always have an option to leave one provider for another. According to Deloitte’s survey, after experiencing poor customer service. 41% of respondents have left their insurer Moreover, automation enables companies to perform advanced analytics to gain a better understanding of customer expectations and provide personalized offerings. For example, a client has posted on social media about his upcoming trip. An RPA assistant takes notice of this information, and when the client contacts an insurance agent about, let’s say, home insurance, the smart assistant will notify the agent about the vacation, enabling the agent to produce a customized offer covering all of the client’s needs. Such an approach will give you a competitive advantage as about the lack of personalization in insurance products. 88% of clients complain Query processing The insurance sector receives large volumes of broker and customer queries that demand fast resolution. Manual query processing can be error prone and exhausting for employees, while deploying RPA insurance bots will guarantee faster and more accurate results. Hollard Group managed to facilitate broker communication through . As a result, they reduced execution time by 600%, saving 2,000 hours of employee time per month. automating 98% of email handling processes The path to successful adoption of robotic process automation in insurance Here are the five steps that you can take to prepare yourself and your company for the upcoming automation project: . Opt for processes that are well-structured, rule-based, and don’t have many exceptions. It is also a good practice to choose frequently used processes as they will amplify the benefits and speed up return on investment. According to Gartner, the should not include more than 20 steps or involve over three applications. Identify candidate processes for automation ideal RPA candidate . Analyze the process first and eliminate any unessential steps. Also, even though every employee performs the task differently, come up with a standardized process version for automation. Optimize processes before automation . This will help people appreciate RPA benefits, give improvement suggestions, and will make them generally motivated to use the new system. Involve your employees from the beginning . There are four well-established RPA vendors, namely , , , and . Select your RPA platform UiPath Blue Prism Automation Anywhere WorkFusion . Most likely, you will still need to customize your preferred RPA solution to cover all your needs and fit into your workflows. An experienced robotic process automation company will ensure a seamless integration. Find a vendor who will help you incorporate the selected RPA insurance tools into your existing system You can learn more about in our recent article. RPA adoption challenges and how to overcome them What’s next for RPA in insurance? Deploying robotic process automation in the insurance industry can speed up operations, bring down expenses, and increase customer satisfaction. But this is just the beginning of the road towards intelligent process automation (IPA), which will empower RPA bots to take over tasks typically reserved for humans. With the help of cognitive technologies, such as , , and , RPA can maximize its contribution across the insurance value chain. AI emotion recognition optical character recognition You can learn more about intelligent automation from our recent article that . We also composed a comprehensive guide on and . Finally, you can discover more on by visiting our blog. compares IPA to RPA how to start with enterprise automation how much it will cost to implement an RPA solution RPA in finance Want to increase efficiency and cut costs by automating some of your manual insurance processes? ! Our RPA experts will help you select the right automation platform and will customize the solution to cover your unique needs. Drop us a line

RPA in Insurance: Your Complete Guide

We'll start off this article by discussing the history of drones. Upon the creation of the Royal Air Force in 1918, the UK . Target practice entailed towing gliders behind crewed aircraft. However, that method failed to provide a realistic simulation for engaging enemy fighters in live combat. So in 1935, the De Havilland DH.82B Queen Bee aircraft came into existence as a low-cost radio-controlled drone developed for aerial target practice. The Queen Bee was the first modern drone. needed effective methods for training pilots 3D printing and affordable consumer models significantly lowered the financial bar of owning a drone. Since 1935, , primarily for military reconnaissance and in niche hobby scenes. In the 2000s, drones had a breakout moment as cultures, content, and open-source collaboration moved online, powering the consumer drone hobbyist scene. 3D printing and affordable consumer models significantly lowered the financial bar of owning a drone. 3D printing and open-source design allowed for the everyday consumer to manufacture replacement parts easily and affordably from the comfort of their own homes creating a thriving hobbyist community. drones have existed as specialty tools This led to the cost of manufacturing sensors decreasing dramatically. These new sensors opened up new applications for drones in both consumer and professional applications. Affordable sensors not only decreased the final price point but enabled experimentation. Whereas before a drone may cost in the hundreds of thousands, a drone that cost a few thousand was expendable. This expendability and ubiquity are powering the future of the drone economy. Consumer drone technology in 2021 Drones Today Outside the well-documented (and potentially infamous) applications of military drones, they have a variety of applications in both the commercial and the insurance space. . More are created every day Among the most prevalent are companies that use drones to survey land and property to build catastrophic flood, fire, and wind risk and other property-related risk maps. Drones are also seeing use to assess property claims in unstable or hazardous areas post-catastrophe. A real-world example is the failed Fukushima Daiichi nuclear power plant. Drones are being developed that can autonomously navigate in a confined, GPS-denied environment, avoiding obstacles along the flight path, and surviving the radiation levels expected during a mission inside the collapsing power plant. Other use cases include: Powered by AI and computer vision, drones have also adopted the role of mobile security and surveillance. In agriculture, farmers are using drones to assess crop viability over large pieces of land. Universities are experimenting with using drones to for pest and disease control. distribute vectors Drones will soon proliferate Drones Tomorrow Especially for traversing large distances, it is still more time and cost effective to collect data and deliver packages, using fossil fuel powered vehicles. Artificial intelligence is enabling the use of drones to deliver packages and people as the technology matures. The path to adoption for these future applications of drones is riddled with practical and legislative obstacles. In the US, regulators require licensed drone operators to maintain a clear line of sight of their drones. This additional redundancy significantly reduces the financial viability of commercial drone applications due to labor costs. However, the Federal Aviation Administration recently gave permission in January 2021 for a private company to conduct autonomous commercial drone flights. This momentous decision will open up autonomous commercial flights for other companies, leading to a proliferation of drone technology and usage. Technological challenges for drones center on the current limitations of weight to battery capacity. Especially for traversing large distances, it is still more time and cost-effective to collect data and deliver packages, using fossil fuel-powered vehicles. As battery technology continues to develop, the applicability of drones will increase exponentially. The urban landscape will also change to accommodate the use of drones. From communal drone landing pads and universal charging stations, cities will build ‘drone friendly’ features which will increase the utility of drones. Drones present a new opportunity for insurance professionals What does this mean for Insurance? Imagine a scenario where you got in a car accident. Rather than calling 911 for potential injuries and waiting for 60-90 minutes for an ambulance and law enforcement to assess a scene, your phone or onboard system will send out a distress message. This will trigger a drone to fly out in less than 10 minutes to assess the scene of the accident, the health of the drivers, and damage to the vehicles. They relay this data in real-time to emergency services so they show up prepared for the situation and to your insurance company to give a more accurate representation of the accident and expedite the claims process. Assuming there were no injuries sustained in the accident, the insurance company now has the data to send both a tow truck to transport your vehicle to an adjuster and body shop and a rental car to the site of the accident so you could continue your day as if nothing happened — saving hours of time, paperwork and providing unbeatable customer service. To prepare for this opportunity, insurance brokers should understand how their insureds use drones - in a personal or commercial capacity. This is simply one of the amazing future use cases for drones. On the insurance side today, drone applications are still undergoing pilots (pun intended). Many insurance companies today cannot receive, handle, and effectively use the vast quantity and variety of data drones offer at scale. These siloed legacy systems will continue to buckle under technological advancements, and nonstop challenges will continue to emerge for startups until the industry creates data and API standards to connect their systems. To prepare for this opportunity, insurance brokers should understand how their insureds use drones - in a personal or commercial capacity. It is extremely important to educate commercial clients about the risk that drones possess so that when they purchase one, their insurance agent is the next call they make. Wholesalers should position themselves so that they are working with the handful of forward-thinking carriers that currently offer drone-related policies. Last, carriers should dedicate time and resources to explore personal and commercial drone offerings. A key piece of this preliminary research is triggered that would signal to the carrier that it is time to launch the policy. It is never too early to be thinking about the future. And if you are thinking about the future, think about drones. Special thank you to the co-author of the article, Elisha Cheng . Elisha is the COO of ReFocus AI , an enterprise AI-powered sales plugin for insurance professionals. As a recent graduate, he co-founded EPort an international import export company out of Southern China which he sold to East West Trading Co. He holds a bachelor's degree from University of California, Riverside in International Political Economy. Elisha currently resides in Sacramento, California. Also published on: https://blog.refocusai.com/drones-and-insurance/

Drones Will Change The Insurance Industry

Welcome to the dawn of the self-driving car era, and a subsequent emerging challenge: how to insure them. For the last two decades, improvements to sensor technology & its gradual adoption into both commercial and domestic sectors has drastically improved the affordability of manufacturing. This cheaper technology eventually made its way into the hands of automakers, and is the genesis of self-driving technology. To understand this and how to insure its additional risks, we must first look at the technology itself. Technology & Viability Advanced technology provide the 'brains' for self-driving vehicles Originally, the largest technical limitation to self-driving cars was the significant processing power required to synthesize the data from so many sensors. However, recent advancements in cloud systems, computer chip design, and AI has enabled much smaller computers to monitor real-time data, take action, and provide services autonomously. This data gold rush has driven companies like Tesla, Waymo, and Luminar into a race to create commercially viable self-driving vehicle systems. In contrast to the cloud systems and AI, many sensor technologies are mature in their development if not application. Many of these companies are making the push to create self-driving systems that use an array of sensors to feed data into a computer that autonomously decides on behalf of the driver. These sensors include cameras (which detect and identify objects via an AI called computer vision), ultrasonic, radar, and Lidar (which is better at detecting and mapping short and long range distance). In contrast to the cloud systems and AI, many sensor technologies are mature in their development if not application. For example, the vast majority of Fortune 500 companies are still undergoing digital transformation incorporating cloud and AI technologies into their operating models. Whereas sensor tech like Lidar and radar has been used for decades in military and commercial applications. What this means is that vehicles like Tesla which rely heavily on cameras/computer vision, radar, and ultrasonic sensors are still years from maturing their technology and having fully autonomous self-driving capabilities. Companies like Luminar (which provides the sensors) with proven lidar systems are beyond the price range of the average consumer, making them uneconomical for self-driving applications. Unless industry makes drastic advancements on cheaper lidar versions, these systems will not be viable at scale except perhaps in commercial applications like driverless delivery. What does this mean for insurance? When fully autonomous vehicles reach saturation, the reduction in risk will drive the price of auto policies down. Yet, the increase in value of autonomous vehicles will require owners to purchase more coverage. Over time, this will create a major problem for state insurance regulators. The high purchase price of fully autonomous vehicles will exclude many low income auto owners from adequately insuring their vehicles, potentially resulting in an overall increased cost of coverage. State regulators will also have to set new minimums on auto coverage to address the increased value of self-driving systems included in the overall cost of the average vehicle. Ultimately, state legislatures will have to be cautious that they do not create a system that imposes more costs on the less financially abled. The intersection between vehicles with greater connectivity and cyber security will become increasingly important over the next decade. Cyber crimes will continue, and it is only a matter of time before cyber criminals hack into these systems/vehicles. The purpose of these hacks may vary, but the use of ransomware, disabling the vehicle until the owner pays the ransom, will make up a significant number of these cyber security incidents. This will lead to insurers either including, or offering supplemental coverage options for cyber to consumers as a part of auto insurance. Self-driving cars will require changes to not just auto insurance, but the new infrastructure being built to support them. Self-driving vehicles will pose unique opportunities and challenges for claims professionals. On one hand, the plethora of sensor data will make it easier to determine the accident timeline and which driver(s) are at fault. On the other insurance companies will have to develop the capacity to analyze this sensor data, thus imposing an additional expense on the insurer. The new reliance on vehicle sensor data will see the claims field shift to a heavier IT emphasis, with new software augmenting much of what a claim professional does. Eventually, determining payouts and accident faults will be entirely automated under the belief that ‘data does not lie.’ As claims are on the tail-end of the insurance life-cycle, self-driving technology will have little to no effect in the immediate future. Within the next decade, though, claims will undergo a radical shift because of this new technology. Self-driving cars will require changes to not just auto insurance, but the new infrastructure being built to support them. In commercial applications like increasingly popular last mile delivery services, driverless delivery will dramatically cut labor expenses. The cost for insurance however will increase in the short run. Without a driver involved, there is an ambiguity of who handles goods delivered. The retailer, the storage facility, the delivery service, or the customer? This increases the liability for lost, damaged, or stolen packages. Storefronts and storage facilities being repurposed for last mile delivery are especially exposed to extra risks like theft, property damage, spoilage of food products, and fire. Thinking about insurance holistically (auto, property, general liability, etc.) will become increasingly important in the era of the self-driving vehicles. Self-driving vehicles are coming into existence because consumers want even greater convenience. And to stay relevant, insurance professionals will need to match that level of convenience. On the technology side, regardless of the flavor of the sensor, companies on the cutting edge of technology and manufacturing processes are still 3-10 years away from making a commercially viable fully autonomous vehicle, plus an additional 4-10 years after commercial viability before enough companies and segments of the population adopts these vehicles to make a significant impact on insurers. Whether through embedded insurance, better sales technologies, or new product offerings like auto-cyber, the next decade will see major innovation within the insurance industry. Special thank you to the co-author of the article, Elisha Cheng . Elisha is the COO of ReFocus AI , an enterprise AI-powered sales plugin for insurance professionals. As a recent graduate, he co-founded EPort an international import export company out of Southern China which he sold to East West Trading Co. He holds a bachelor's degree from University of California, Riverside in International Political Economy. Elisha currently resides in Sacramento, California. Also published on: https://blog.refocusai.com/insurance-and-self-driving-cars/

How Self-Driving Cars Will Change the Auto Insurance Industry

Executive Summary An in-depth analysis of probable challenges of Artificial intelligence is critical to the successful application of the system. Organizations should carefully evaluate replacement decisions and address any technology gap that may affect the consumer experience. The system heavily relies on data, which is useful in determining customer satisfaction and preference. The accuracy of data collected influences the outcome of the expected results. Organizations should improve the methods of data collection to increase the chances of obtaining accurate predictions. Wearable devices enable motor insurance firms to acquire information that can be used to provide tailored services and products and to enhance the pricing strategies based on customer requirements. Before the implementation of AI systems, organizations should also consider critical success factors which have the potential to derail the implementation process. This research uses a troubled, though potentially successful, insurance company as an example to show on practice how artificial intelligence can be implemented by similar companies in the industry and beyond. Successful implementation of artificial intelligence (AI) in any insurance company requires an in-depth evaluation of critical challenges the organization confronts while executing its obligations. These issues often influence the profitability of the system (Mehr, Ash and Fellow 2017). AI systems affect the most profit-influencing spheres of activities. The replacement of systems to pave the way for new AI programs may face a myriad of challenges. However, a proper understanding of the likely outcomes enables the management to identify the problems that need to be solved and the mechanisms to address the challenges. Therefore, it is paramount for the organization to consider the effect of AI on stakeholders' perceptions. AI implementation often faces the resistance force of the system. It is essential in the firm's profitability. Consumer experience is one of the most competitive drivers of growth when successful. However, it may be the most significant source of risk when handled inappropriately. Therefore, the implementation process starts with the collection of enough and appropriate information to understand the difficulties the company faces when addressing customer products and insurance claims. AI allows companies to identify the needs of customers and establish the impact of any changes on consumer loyalty. These abilities address the current unstructured data and improve the organization's predictive analysis in identifying and managing risk promptly. Consumers expect a unified experience and may get disappointed when the company fails to deliver as expected. Therefore, customer journey analytics and AI are the essential components required to join all the organization's departments. The application of AI in customer experience requires the company's capabilities in data unification and real-time insights product delivery. The client's dissatisfaction leads to losses. Additionally, adopting AI systems by the company is critical in making effective decisions regarding suited for each customer's needs. Thus, the system enhances product delivery, saves time and costs, and promotes customer satisfaction. The most significant impact of AI involves the transformation of customer service delivery to make it fast and reduce unnecessary inconveniences. Description and Justification of Information Architecture The AI system aids decision making, which is critical in service delivery and profitability of the organization. Problems. Current database system, mainly unstructured, required replacement; Current storage of records makes the predictive analysis rather complicated; The system cannot adequately determine customer preferences; The system cannot deal with required marketing initiatives, and product pricing strategies; The current system requires modification to promote consumer satisfaction and predict future implications based on previous trends; The information provided as of now is complex, which makes it difficult to breakdown the available data and to subsequently determine significant implications; Inaccurate or wrong information leads to wrong interpretation and subsequently increases the risk of getting incorrect predictions. . AI systems streamline flow and filter relevant information to determine any change in customer needs. An offered decision AI can analyse large amounts of data to detect unfavourable patterns. This system does evaluate data more accurately and in real-time. It shall enable the company to monitor different applications, such as shifts in consumer demand, pricing and marketing development, unsettled claims, and fraud (Castro and New, 2016). Moreover, AI can extract valuable information, which can be used to make decisions through simulation. This process is efficient in uncovering abstract patterns that the current system does not detect. Processing and conveying the required information enhances communication, which improves customer loyalty. Thus, the firm shall benefit from improved predictive outcomes and customer satisfaction. Outcome. Technology Architecture AI changes the way the sector is run through telematics and wearable sensors, which collect information about the insured products or items. For instance, installing such detectors in a car gathers the required information on how the customer drives. This information helps the company to rate its customers. Based on statistics from different clients, the insurance products and services can be tailored to suit individuals. The devices help the company to make accurate predictions based on the client’s record. Therefore, AI utilizes real-time and current information to enhance the decision-making process. Additionally, it provides precise information and saves money, which may be incurred from regular assessments and audits. The insurance company in question needs to develop devices that can provide the required information compared to the current technology in use. Thus, wearables play a central role in data collection, analysis, and interpretation. The architecture of wearables involves the use of several components: data, physical energy, social properties, etc. geographical locations, social interactions, videos, photos, biomedical data. The physical aspects of the wearables cover elements related to and . These elements shall enable the company to enhance direct communication between wearable devices. In addition, the devices require power supply based on the consumption of each element, which often varies depending on the functioning rate and the frequency with which the device is used. This information enables the management to device effective marketing and pricing strategies. hardware software applications Social interaction is a critical element of the wearable components. The devices are manufactured considering the functionality of the wearable based on customer requirements and expectations. The manufacturing of the element also examines the market demand since the organization's financial performance is fundamental to its sustainability. Description and justification of the Governance and Methodologies Applied in the Project The ICT department should manage multiple AI activities within the organization, and here is why: The department, under its current constitution, is sufficiently staffed to handle AI issues. The department’s value chain, which comprises planning, building, delivering, and running are crucial streams that produce the desired results. The ICT team drives critical decisions that are essential in providing appropriate products within the established resource constraints. The team drives the AI implementation schedule, defines business requirements, the budget, and the project’s application functionality. The ICT department drives core decisions, which require integration across all departments of the organization. Since the introduction of AI includes increasing the company’s productivity, the department should determine significant tradeoffs between cost, schedule, and deliverable. While discharging the required duties, the ICT team should prepare a detailed work plan and a timeline, which integrates all activities. Also, the team should identify mitigation measures, quality assurance plans, develop a comprehensive system design document, and status reports. The team should also manage the transition from the current system to avoid inconveniences or gaps that may compromise customer delivery and prevent losses. Furthermore, the systematically-defined AI frameworks extend beyond knowledge inquiry and organization. Structured subdivisions promote understanding, dissemination, and measurable implementation by providing information and arguments in manageable proportions (Succar, 2009). It is essential to develop a framework that positions AI as an integrated product and modeling process as opposed to disparate technologies. Work Breakdown and Work Package Decomposition The work breakdown structure (WBS) of AI is divided into packages that can be easily handled by different sections of the ICT department. The breakdown. The WBS constitutes essential stages, which eliminate errors and risks during the implementation process. Moreover, an appropriate WBS makes the process easier to monitor and manage. Step-by-step approach. A successful AI implementation system should be integrated into the organization's culture. Therefore, culture becomes a significant driver of the designed AI program. Corporate culture integration. It also serves as a guide that relates project activities to specific objectives. For instance, the insurance’s primary goals are to reduce costs to improve the product delivery process and increase the level of customer satisfaction. WBS represents all the activities linking objectives (Iranmanesh and Madadi, 2009). Also, it shows tasks given to each section of the ICT department to save time and avoid overlapping activities. Activities are, therefore, attached to different departments or phases. Determination of the project’s total scope. Moreover, the AI WBS structure assists the management in estimating project costs, developing the schedule, obtaining necessary resource information, which is essential in planning and controlling framework. Proven management assistance. Identification of Critical success Factors (CSF) The popularity of wearable devices continues to expand globally. However, the success of these gadgets in motor insurance depends on the ability of organizations to eliminate or minimize identified barriers and obstacles (Nah and Siau, 2018). Therefore, it is critical to address fundamental factors, which influence the adoption of wearable devices in the motor insurance industry. Additionally, factors inhibiting the application of these devices affect the success of their application in the industry. The implementation process of the wearable gadgets involves the analysis of numerous critical : success factors the perceived case of use and usefulness affects the application of wearable gadgets; the degree of effectiveness of devices as perceived by prospective consumers is critical to the success of the AI system; the success of AI systems is based on technological functions, the attractiveness of the design (Nah and Siau, 2018); The potential : risks are the perceived usefulness includes the customer’s subjective probability regarding the effect of the device on his life; ICT staff may also feel threatened by the device since it is expected to perform the tasks of many workers. Thus, the fear of losing jobs may endanger the success of the AI systems; those clients with focus on technology find it harder to embrace and apply devices compared to those with fashion perceptive. The former focus on the functional benefits while the later prioritize social benefits and risks associated with infringing other peoples’ privacy. These factors affect the adoption of wearable devices by both ICT employees and customers. In addition, the price of the wearable device is critical to the success in the success of AI systems. Implementing the technology requires massive investment, which hinders the adoption of the wearable devices. Impact of the Implementation on the Organisation The adoption of AI shall fundamentally shift the organization's ICT department. The system will dramatically enhance the performance of communications, content, applications, e-commerce. These changes may trigger the organization to create and merge different ICT sections to avoid overlap of tasks and to accommodate additional responsibilities. Additionally, the changes associated with AI may require the company to adopt new business models, which impact the ICT department directly or indirectly. For instance, the organization may change the advertising and marketing strategies which may be driven by the ICT department. It implies that the ICT department will be required to perform tasks previously done by other departments (Makridakis, 2017). The department will also be tasked with regular changing and creation of user interfaces to accommodate emerging trends. Furthermore, the evolution of ICT and digital technologies will transform other industries. AI requires the use of technology in every department compared to the current system. It implies that the entire ICT department should be restructured to accommodate these changes. For instance, the marketing and advertising department shall require an ICT manager to oversee the technical aspects compared to the current system, which does not need such an employee. Consequently, the company may need to employ more ICT staff and offer further training to enhance their ability to understand the components and other new aspects of their work (Frank et al., 2019). The organization is likely to increase the pay of the ICT employees who may be compelled to undergo further training. An increase in the share of job roles in the ICT requires high levels of formal education at the expense of those in other departments (Royal Society and British Academy, 2018). Recommendations The introduction of AI requires massive resource allocation, which may affect the general performance of the organization. As a result, the organization should take appropriate precautionary measures to prevent or mitigate probable risks. The company should conduct a survey before the implementation of the program to obtain first-hand information on the consumers expectations. The ICT department should attend training programs and seminars. It will prepare them and enhance their understanding of the implemented system. Due to rapid changes in technology, regular training is necessary to keep employees updated on the new developments. Wearable devices should be tailored to suit different tastes and fashion to avoid negative perceptions which may hinder the success of the project. Conclusion AI implementation requires the evaluation of numerous aspects to determine the viability of the project. The application of wearable devices in the insurance industry helps organizations to gather critical information, which assists management in establishing new market and pricing strategies. The collected data can be applied to offer tailor-made products and services based on consumers’ requirements. The entire AI process should be undertaken carefully to avoid gaps which may compromise customer satisfaction. Moreover, factors affecting the adoption of AI may vary depending on different perceptions exhibited by individuals. Social influence is critical in the adoption of AI systems. Information privacy is one of the highest privacy concerns linked to social factors. Ultimately, AI can effectively increase the organization's productivity by reducing wastage and increasing sales. References Balasubramanian, R., Libarikian, A. and McElhaney, D., 2018. Insurance 2030—The impact of AI on the future of insurance. McKinsey & Company, New York, NY, USA, Apr. Castro, D. and New, J., 2016. The promise of artificial intelligence. Center for Data Innovation, pp. 1-44. Davenport, T.H., 2018. The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. MIT Press. Frank, M.R., Autor, D., Bessen, J.E., Brynjolfsson, E., Cebrian, M., Deming, D.J., Feldman, M., Groh, M., Lobo, J., Moro, E. and Wang, D., 2019. Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences, p.201900949. Iranmanesh, H. and Madadi, M., 2009. Framework of intelligent systems to support project scope. In Recent Advances in Technologies. IntechOpen Lamberton, C., Brigo, D. and Hoy, D., 2017. Impact of Robotics, RPA and AI on the insurance industry: challenges and opportunities. Journal of Financial Perspectives, 4(1), pp. 8-20. Makridakis, S., 2017. The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, pp.46-60. Mehr, H., Ash, H. and Fellow, D., 2017. Artificial intelligence for citizen services and government. Ash Cent. Democr. Gov. Innov. Harvard Kennedy Sch., no. August, pp.1-12. Nah, F.F.H., Hall, R.H., Siau, K., 2018. Factors influencing the adoption of smart wearable devices. International Journal of Human–Computer Interaction, 34(5), pp.399-409. Royal Society and British Academy, 2018. The impact of artificial intelligence on work. pp. 1-69. Succar, B., 2009. Building information modelling framework: A research and delivery foundation for industry stakeholders. Automation in construction, 18(3), pp.357-375. doi:10.1016/j.autcon.2008.10.003.

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