We bring together engineers, data scientists, and business consultants to build AI solutions that amplify people's business and analytical capabilities. Our first product, HUMAN-Advisor, is a business strategist. She answers colloquial questions about your business with analysis, narrative storylines, actionable recommendations, and implementation roadmaps. Think of HUMAN-Advisor as the star analyst in your team who knows her stuff, listens, communicates clearly, and never stops learning.

Human Ready

Dive into how AI is transforming the investment landscape Remember the early days of robo-advisors? They were like the first smartphones—revolutionary but limited. Now, generative AI is stepping in, offering more nuanced and personalized financial services. For instance, Q.ai offers customers investment kits picked by an AI that considers various data points, from market metrics to social media sentiment. 📊 Performance Metrics While AI's promise is high, its performance is still under scrutiny. ETF Managers Group's AIEQ, which uses IBM's Watson, has returned 4.9% over the last five years, trailing the S&P 500 index fund's 11.78%. It's like a new athlete who shows promise but has yet to win significant titles. 🛠️ Guardrails and Accountability AI isn't given free rein. Companies like Q.ai and AIEQ (see examples below) have human oversight to monitor AI output for biases and errors. Think of it as a self-driving car with a human driver ready to take over in complex situations. 🌐 Accessibility and Inclusion AI is democratizing financial advice. Firms like Fisdom in India use automated tools to offer financial guidance to a broader base of customers. It's like opening up an exclusive club to the general public, making high-quality advice accessible to all. 🤖 The Future: AI in the Back Office Even if AI isn't directly advising clients, it's finding a place in back-office systems. For example, it summarizes meeting transcripts, monitors risky transactions, and generates reports. Examples of startups / smaller companies using AI for investment management: 🤖 Q.ai Location: Jersey City, N.J. About: Q.ai offers no-fee "investment kits" consisting of 4 to 20 securities in a market sector. These kits are selected by an AI that evaluates market metrics, news, Google search trends, and social media sentiment. 📊 ETF Managers Group’s AIEQ Location: Summit, N.J. About: AIEQ uses IBM’s Watson AI platform to analyze millions of data points from various sources like news, social media, and financial statements of over 6,000 U.S. companies. 🌐 Nemo Location: Abu Dhabi About: Nemo uses a ChatGPT model to allow investors to ask questions they might typically ask a human broker. The company emphasizes that they don't let their AI go "off-leash." 📈 Toggle AI Location: Not specified About: Toggle AI aims to implement ChatGPT that will be programmatically constrained to provide reality-grounded answers to investors' questions. 🌟 Fisdom Location: India About: Fisdom uses automated tools to provide financial guidance to a broader base of customers, including individuals with low net-worth. 💼 Betterment Location: New York About: Betterment, one of the first mass-market robo-investing firms, remains cautious about using AI for financial advice. However, they do use AI in their back-office systems for tasks like summarizing meeting transcripts and diagnosing software issues. Examples of incumbent banks using AI for investment management: JPMorgan Chase: This financial giant is leveraging AI to optimize asset management and offer personalized investment solutions. Morgan Stanley is using AI to enhance its asset management strategies. UBS: Known for its wealth management services, UBS is adopting AI to improve its investment strategies and customer engagement. HSBC: This global bank is using AI to bolster compliance and risk management functions and augment and automate data analysis in the investment sector. 🔮 Summary The integration of generative AI into investment management is a significant development, offering more personalized and nuanced services. Performance metrics are crucial for assessing the efficacy of AI-driven investment platforms, and they currently show a mixed picture. The human oversight in AI-driven systems is a necessary safeguard, ensuring that the technology does not go "off-leash." Whether it's automating data analysis, enhancing customer engagement, or optimizing asset management, AI is playing a pivotal role in shaping the future of investment management. Excerpt from One More Thing in AI Newsletter. Get smarter on AI in 5 mins. Carefully curated from hundreds of sources for business leaders and founders. ) Join 1000+ subscribers for free.

How AI is Transforming the Investment Landscape

Since the introduction of DataStax Astra DB in 2019, we've had the privilege of working with developers who build innovative blockchain applications on Apache Cassandra®. Astra DB is the DBaaS of choice for blockchain applications because of its unique combination of low latency at a global scale, massive data on any cloud, and serverless, pay-as-you-go pricing. Over the past several years, we’ve also seen Web3 developers run into a host of problems managing blockchain data. For single-block queries, reading from the blockchain is as simple as signing for a free node as a service and making a single RPC call with a library like Web3.js. But as you try to run more advanced queries, like aggregations or log analysis, you’re forced to manage your own blockchain data and it gets super complicated quickly. “Node-as-a-Service” gets expensive for applications that require massive amounts of remote procedure calls. You try to run your node, but it’s surprisingly difficult to keep a node in sync. If your node isn’t in sync, you can’t subscribe to new block headers. And you need to decode from hex into human-readable to process data for your application. If your app needs additional related data, you need to either build your own indexing solution or subscribe to an indexing service, which can also get expensive with a ton of API calls. And while you might think the blockchain is immutable, blockchain reorgs happen and require you to build complex indexer logic. Then you have to pick a datastore, but databases struggle with massive amounts of data, as you’ll see when you start working with logs and trace data. If you make it this far, you’ll need to evolve your data models to maintain the latency you need for queries. Introducing Astra Block We built the new service to take these issues off of your plate so that it’s a lot easier to build applications with blockchain data. We’re excited to make it available to developers via invitation access as a free service! Astra Block Astra Block is a real-time blockchain data service that is always in sync with Ethereum and soon for Bitcoin, Polygon, Solana, Binance, and other blockchains. You get decoded blockchain data updates streamed in a clean, human-readable format ready for your app to consume. You don’t need to run nodes or build batch ETL jobs. This new data service is delivered within Astra DB, so there’s no additional setup or cost. And because it’s built on Astra–the serverless, database-as-a-service powered by Cassandra—you can autoscale from zero to massive, global scale while maintaining lightning-fast queries across terabytes of blockchain data. Astra Block is also reorg-aware with a data model designed by Cassandra experts to deliver millisecond latency for advanced blockchain queries. So it’s a lot easier for you to build applications that: Display real-time NFT mints and transfers Enable a block explorer using our now open-source template Alerts for large USDC transfers via a Twitter bot How does Astra Block work? DataStax operates blockchain nodes for you. Whenever a new block is mined, Astra Block detects that event, processes it, enriches it, and saves it to a master copy of the chain that the system uses internally. Your Astra Block account is kept up to date with our “master copy” via change data capture (CDC). Astra Block uses to propagate change events to your Astra Block database for your dApps to work against. And even more, you get access to the same great capabilities of Astra DB, multi-cloud regions, private endpoints, IP access lists, Stargate APIs, Stargate APIs, and more. CDC for Astra DB Use Cases Astra Block gives you a blockchain operational data store for use cases such as: - Rather than taxing the blockchain with reads and writes, you can use Astra Block as an operational database for all blockchain reads. You can continue to use the blockchain directly for writes such as consensus validations and transaction processing. Blockchain processing offload - You can build Bloomberg-style services or APIs for financial applications across multiple blockchains, or run machine-learning models against Astra Block as an operational data store. Multi-chain data analysis -  Your service might need related data such as user, product, or pricing information.  Much of this data makes sense to index into and query from a real-time operational database. Blockchain indexing Try It Out You can request access to use Astra Block right now. You’ll be able to choose between a free or a pay-as-you-go tier. The free tier gives you a clone of the Ethereum blockchain with $25 in monthly free credits that cover up to 40 million read/write operations and 80 GB in storage. The pay-as-you-go plan adds streaming of Ethereum blockchain updates you can use in your applications. As part of the early access program, we can also give you a walkthrough of the data models and show you how you can add to them for additional data you’d like to use. We're excited to help you build and scale Web3 apps on the entire Ethereum data set faster than ever. Don't wait -- ! request your invite now Also published here.

Creating Web3 Apps with Real-Time Blockchain Data

Creación de aplicaciones Web3 en tiempo real con datos de cadena de bloques en tiempo real

Création d'applications Web3 en temps réel avec des données de blockchain en temps réel

रीयल-टाइम ब्लॉकचैन डेटा के साथ रीयल-टाइम वेब3 ऐप्स बनाना

リアルタイム ブロックチェーン データを使用したリアルタイム Web3 アプリの作成

Criando aplicativos Web3 em tempo real com dados Blockchain em tempo real

Tạo ứng dụng Web3 thời gian thực với dữ liệu chuỗi khối thời gian thực

使用实时区块链数据创建实时 Web3 应用程序

Are you trying to get some media exposure for yourself and your startup but have no idea where to start? Worry not, because that's pretty common. And yes, you have now landed in the right place. The reality is that there are plenty of ways to get press attention. And while I mainly work with fintech startups, what’s covered here, also applies to other verticals. Let's go! Here are a few tips for you to get some media exposure: Ready? Build human relationships 😊 Spray and pray don't make any sense here. You will not get any coverage by spamming a list of journalists with a generic message. On the contrary, it can be detrimental. Remember, you are sending your email to other humans who receive tons of meaningless press releases every day. Don't be just another «Dear [First name]» fool. Remember that journalists are humans, too. Connect with relevant journalists on LinkedIn and Twitter, follow their work, and comment when appropriate. Attend networking events, connect with local media and tell them your story one-on-one. It's all about building relationships and trust. And in the long run, it always pays out to be nice (in many different ways!). Craft a great pitch 🎯 When pitching your story to the media, think of it from the journalists’ point of view: is it really something their readers will care about? Is there a social impact, or does it offer a new perspective on an existing topic? You need to capture attention fast, so make sure your email subject line stands out and clearly states why what you have is newsworthy and why it matters for their specific audience. In the body of your email, be sure to introduce yourself and your startup quickly (e.g. in one sentence). Keep it short and sweet while avoiding jargon while including all the links and info needed for editors and reporters to check your story. Also, if you are sending out a press release, clearly indicate whether or not it is an exclusive story and if there is an embargo, including the date and time of when the announcement will be made. And if you are looking for a quick call, be courteous in your request and explain why you want to talk. Remember, journalists like founders have no time to waste, so make sure to provide all the key info. Offer something newsworthy 📰 This may be the most crucial tip. If your story is not newsworthy, it probably won’t get picked up – no matter how well-crafted and accurate the pitch is. To ensure your message grabs journalists’ attention, try to come up with something fresh. So, you may now think, ok, cool, but what can be considered newsworthy? Here are some examples: Startup and product launches Easier to get some attention when your startup is the first to offer a specific service, you’re offering something totally new in your industry, or you have some well-known co-founders and backers. Major company milestones Whether hitting your first 10k users or reaching $100k MRR, these are milestones worth sharing (and celebrating!). Awards, recognitions, and certifications can also be newsworthy. New customers or partnerships This could be a partnership announcement with a well-known company, the fact that you got your product into a prominent retailer, or a case study with a customer you worked with. Here it's essential to have some evidence and data that can be shared with the media. Industry insights and reports Do you have data on the state of the industry? Is there something major happening that's affecting your sector? Write about it on your company blog and newsletter, as well as a guest author on external publications. Having a proper content strategy in place can do wonders in this sense. Data-driven stories, especially when some nice visuals are included, work pretty well for getting some media coverage. You need to offer concrete facts, not just opinions. Fundraising round Did you just get funding? If the amount is significant or involves some notable VC firms or angel investors, it will be easier to get some press. Still, you must provide all the relevant details. Be patient and persistent ✨ Yes, because getting press coverage does not happen overnight. You need to be consistent with your efforts and put yourself out there, making sure that people can easily find you and your startup - do yourself a favor and keep info in directories such as Crunchbase and on social media pages always up-to-date. Be ready to respond quickly if a journalist or editor reaches out with questions – it's common to work under tight deadlines, so try to help out with research and inquiries as soon as possible. Bonus tip: That's all for now!  I hope this brief guide will help you get the press attention your startup deserves. Good luck, and don't forget to be nice, keep your pitch short and sweet, get in love with the process, and always keep going. Consistency will get you there 💪

How to Get Press Coverage for Your Startup in 2023

AMANDA

Technologies like Artificial Intelligence, Big Data, Machine Learning, Telemedicine, Virtual Reality, Augmented Reality, and the Internet of Things play a vital role in shaping the future of Health Tech. The goal is to make it easy for humans to take care of themselves and their overall health. In this article, we’ll discuss some of the ways AI, Telemedicine, AR, VR, IoT, and 3D technologies are improving healthcare and have become the driving forces of some medical technologies. Artificial Intelligence in Health Tech One of the top technologies causing a radical change in health tech is Artificial Intelligence. AI is the backbone of all modern emerging technologies. For the healthcare industry, AI-enabled solutions can assist medical research and help with new product development. With Machine Learning, the most common form of AI, it has become possible for researchers now to reach conclusions easily and with better precision. Big Data, which goes hand in hand with ML, is to analyze enormous amounts of patient data and detect the patterns of diseases. It includes diagnosing diseases to discovering links between genetic codes and robots assisting surgeries. Altogether, it can lead to better outcomes and patient engagement with immediate returns through cost reduction. used A deep neural network, called the , is developed by researchers at Penn State University. The new ML model would predict any future health conditions of a patient by scanning and analyzing the electronic health records of the patients. LSAN Image: Fraunhofer FOKUS AI in Cancer Care The integration of AI technology in cancer care is one area that can make a breakthrough impact on humanity. Cancer screening today is inconvenient and invasive. The detection of two common cancers, colon and breast, require screening technologies developed 50 years ago. Cancer patients have a 90% chance of survival if cancer is detected at stage I versus only a 5% chance at stage IV, so early detection is a critical means of improving patient outcomes. Helio Health is an AI-driven healthcare startup focused on developing and commercializing early cancer detection tests from a simple blood draw. The company's mission is to simplify cancer screening so lives can be saved by detecting cancer earlier. Helio Health has secured in venture funding and currently in clinical trials for its lead liver cancer detection test, the HelioLiver Test. Helio's development program is focused on liver, colon, breast, and lung cancer, and actively with top national cancer centers. $86 million currently collaborating Telemedicine in Health Tech Telemedicine technologies have been making a huge impact. Telemedicine boomed during the COVID-19 pandemic and I believe the trend is going to stay. There are plenty of reasons for that, but the real reason is as more and more gadgets, gears, and wearable devices - like Ring, FitBit, or Embr Wave - are becoming part of health tech. Apple announced a breakthrough ECG app - approved by the FDA - empowering patients to maintain a log of their electrocardiogram anytime. Our smartphones can now also pair with third-party health devices like glucometers, heart monitors, body scales, toothbrushes, and spirometers for other important and vital metrics. Wearable Devices Wearable and mobile devices are becoming popular as they are providing more accurate results than before. One of the causes of death worldwide is hypertension. But your smartphone can measure your blood pressure as well. OptiBP, by grants from The Bill & Melinda Gates Foundation, is a smartphone app that measures your blood pressure anytime. The app is very , and aided by telemedicine, can really make a difference to fight the global hypertension crisis even in low-income countries. leading Biospectal backed accurate Image: CNET Health tech devices can even transmit data automatically from such devices to telemedicine service providers. This growing number of health devices and their convenience is not only just helping researchers with day-to-day data but also opening a new era of at-home telehealth. Eye Exams Can Now be Done Online Another example of telemedicine is , a leading eye health provider. During the pandemic, they started to offer all eye care as part of their telemedicine initiative. The optical company offered patients to receive a customized eye care treatment plan and prescription through a virtual video screen with a local Ophthalmologist (MD) or Optometrist. It allowed patients to receive eye care during a pandemic when many eye care providers are turning away patients within the safety of their homes. Stanton Optical Many other startups like DoctorSpring and Second Opinions are telemedicine services. allows you to do a medical consultancy from board-certified doctors 24x7. Second Opinions also let you submit a medical questionnaire before scheduling an online meeting with a board-certified doctor. providing DoctorSpring Health tech is making it possible for the healthcare industry like diagnosing and treating cancer, diabetes, hypertension and helping patients suffering from mental health issues. to manage major diseases Neural Interfaces Can Also Improve Health Tech Elon Musk's neural technology company, , is working on a brain chip that will be implanted into the human skull and connected directly to the brain. The goal of the Neuralink chip is to provide an interface to communicate with the brain. It can help to improve mental health and cure brain disorders like Alzheimer's, Parkinson's disease, and spinal cord injuries. It would initially focus on curing and treating major traumas and brain injuries, and can also be used to . The chip electrodes can read signals from the brain and write them down and will be helpful in curing paralysis as well. Neuralink restore eyesight and hearing Screengrab: Neuralink YouTube The brain chip designed by Neuralink is still in the phases of trials. It was first implanted in the brain of a pig for initial trials. The chip was recently implanted in a monkey, and a video showed it playing a video game by using its brain only. Elon Musk that his company and the chip will be ready for human trials later this year. However, he made a similar claim in 2019 that the chip would be tested on humans by the end of 2020. claimed in a tweet Facebook made a formal entry into the neural world when it CTRL Labs, a startup co-founded by Internet Explorer creator and neuroscientist Thomas Reardon, in 2019. CTRL Labs is working on a similar brain-machine interface, but unlike brain implanted chips by Neuralink, CTRL Labs' mainstream product is their wristband which they to transmit electrical signals from the brain into computer input. acquired demonstrated CTRL Labs wristband is part of Facebook's AR/VR research group. Facebook plans to use the neural interface technology of CTRL Lab's wristband that connects to their AR/VR devices more naturally and intuitively. Neural technology development is a clear indication that we will see a breakthrough in health tech very soon. Medical VR/AR Solutions Virtual Reality (VR) and Augmented Reality (AR) have proved to be significant and useful visual technologies. They have already advanced so much in the healthcare sector that medical practitioners can now 3D images of human autonomy and their CT scans for better examination and to precisely locate the blood vessels, bones, and muscles. render , a startup based in Palo Alto, has raised $14 million in September to build a virtual reality surgical training and assessment platform. This can help surgeons in training to repeat steps many times virtually. Orthopedic residency programs using Osso VR include Columbia University, David Geffen School of Medicine at UCLA, Harvard Medical School, and more. Osso VR Image: Osso VR Organovo has already printed human liver cells and tissues in 3D. Their is helping the pharmaceutical and the healthcare industry in testing the conditions of the human liver. ExVive3D Liver Tissue The technology is advancing quickly enough that we could soon see surgeons and medical staff frequently use VR or AR glasses during critical surgeries, and it was that the individuals who take help from this health tech perform surgeries quicker and with better precision. Surgeries are quicker and more precise now with the help of AR and VR. found Final Thoughts on the Current State of Health Tech Healthcare has always been of immense importance to human beings. Health tech is constantly improving the healthcare sector, and the ways of providing basic healthcare to humans have become easier and more effective. Do you think I missed an important health tech development? Share your views in the . HackerNoon Community Want to keep up with all the latest health topics? Subscribe to our newsletter in the footer below.

How Health Tech is Shaping the Future of Healthcare

Business owners rely on a lot of metrics to evaluate whether their business is growing or taking a toll down the hill. Monthly active users, month-by-month growth rate, user retention rate and what not? But at the end of the day, what matters is how your overall revenue is fairing against the cost of acquiring customers. , the pioneer of Lean Startup movement, coined a term for all these secondary metrics — Vanity metrics. If you are not filling in your pockets by the end of the day; the number of active users, number of leads and other Vanity metrics can serve as a false pretense of growth. Eric Ries (If you have not read The Lean Startup by Eric Ries, I would highly recommend you do so, especially if you are running a startup or managing a team) I am not saying that these metrics should not be tracked and be optimized by your team’s effort. Tracking and optimizing these metrics is absolutely necessary. But they should be aligned with the end goal of your business — revenue. Suppose your user base is growing 100% month on month, the right question to ask would be how much revenue do this 100% month on month growth is bringing. What matters is how you optimize your efforts which in turn will help you grow your revenue, eventually growing the overall business. With more revenue, you can push back money in to accelerate your marketing efforts, hire quality engineers and sustain an accountable growth. Marketing, sales and customer support are integral pathways which weave path for revenue growth. A modern marketer is accountable for revenue(which is primarily a sales metric) and a modern sales guy has to be market and technology aware. Similarly, the support team should have basic qualities of tech, marketing, and sales. Though usually far seated; marketing, sales, and support need to work together for the greater good of the business. A strong alignment of these three parts will help you formulate and act upon your end goals more clearly. In the end, the revenue growth will happen if you keep prospects aware of your platform (Marketing), make them customers (Sales) and retain them to keep on adding revenue (Support). Marketing and Sales Coordination “The new reality is that sales and marketing are continuously and increasingly integrated. Marketing needs to know more about sales, sales needs to know more about marketing, and we all need to know more about our customers.” , Chief Growth Officer, Marketo Jill Rowley Marketing and sales are the customer-facing sides of your business. Marketing brings in potential customers which sales team can turn in to paying customers. It is an utmost necessity that these teams collaborate to meet end goals of the company — the revenue target. The marketing team present the company to the outside world and are responsible for building the desired perception of your brand or product. How they do it, is not the topic of this post. The job is to get leads which salespeople can then convert into customers. In a lot of business vertical, and especially SaaS, a good chunk of leads coming in are junk. The number increases more if your business runs on a freemium model. Hence, it is important for the marketing team to bring quality leads so that your sales teams do not waste precious time on engaging with the wrong ones. This is done through lead scoring. There are majorly two types of lead that marketing qualifies: An MQL is a lead which is nurtured by the marketing team to make it ready for human interaction. The basic criteria for a lead to be qualified as MQL are to match the demographics, job/role description, business type, company size of your ideal customer persona. Marketing Qualified Leads (MQL) An SQL is a lead which is ready for human interaction and is passed on to the sales team. Marketing team generally uses drip email campaigns to convert an MQL to SQL. As per your website navigation and business type, you can decide a par score for MQL to be considered as SQL. Sales Qualified Leads(SQL) Please note that, before putting efforts in building a framework for lead scoring, you need to decide whether you actually need lead scoring or not. A great article about this decision making can be found here . On the other hand, the sales team needs to give feedback to marketing on the quality of leads. Sales team should find trends and hypothesis about the quality of leads. The sales team needs to answer question such which leads converted fast and are high paying? They belong to which demographic, company size or product use case? Our sales peers at Applozic constantly collate this data to give feedback to the marketing team. Based on the data provided by the sales team, the marketing team can optimize their efforts to bring in similar quality leads and build a better user persona to target effectively. These activities can lower the cost of customer acquisition by concentrating on getting leads which have a higher chance of conversion. Depending on the sales team’s feedback marketing team can improve a number of things such as messaging, content strategy, landing pages, use cases etc. The feedback can even help in bringing ideas for product roadmap if a sufficient number of customers are asking for a similar feature. This is a virtuous circle of sales-marketing coordination, which becomes more efficient with every iteration. Sales and Support Coordination Sales and support team have to often work very closely for customer success and service. In many small teams, where there is no dedicated support team, it often drops down to sales (or marketing) to handle customer support. As quality customer support can be the prime reason for customer retention, it is important to have sales and support team to collaborate for making it better. Bonus: If you are looking for an intelligent customer support software for improving your customer support efficiency, we can help . Once a deal is closed and the customer starts paying, sales team should clearly lay out what kind of support is been promised to the customer. The best way to do it is to make tiered support programs that resonate with your pricing plans. The sales team should clearly handover the customer to support team with the designated level of support promised. This helps support team to optimize their efforts accordingly. Sales and support coordination is also required while the customer is integrating your product into theirs. This is a major uphold in SaaS sales cycles. Multiple sales calls, delayed integration and unsatisfied trial might result in longer sales cycles. Which in-turn can hamper the revenue goals. The best way is to have the sales and support team collaborate efficiently to help the customer make most out of the product during the trial period. The support team can be a medium for sales team to upsell or cross-sell the product to customers. If a customer is overall happy with your product or services (This can be done by evaluating Net Promoter Score(NPS) of each customer), the support team can pitch in ideas to customers about additional features and upgradable plans. Marketing and Support Coordination Marketing and support teams, at times, are regarded as miles apart with less frequent interaction. Since major marketing involvement happens at the first touchpoint of the customer and the support team comes in when they already have been using the product for some time. Believe me, a good coordination of the two will help in making both their jobs easier. Marketing can help build an efficient self-serve content bundle to take the load off the customer support. The customer support team can collate the data such as what kind of technical and generic issues customers are facing and what is the frequency of each such question. The marketing team can then act upon the aforementioned data. The marketing team can include repetitive tech queries received by customer support teams in their documentation or product use instructions page. They can make self-help, support articles, and Frequently Asked Questions (FAQs). This will help in reducing the workload on customer support team so that they can efficiently invest their time in handling more complex queries. Also, once a prospect becomes a customer, the customer support team becomes the point of contact. Hence, they can easily collect information such as feedback on the product and feature requirements. The customer support teams can thus forward this data to marketing and product teams to work upon. Customer support teams help in referral marketing as well. As mentioned, support team becomes the point of contact for customers, they can essentially help in getting more customer through existing customer’s referral. The customer support team can help the marketing team in defining the NPS for each customer and marketing team can ask this customer to refer the product to their network depending upon the NPS of that particular customer. Three is the charm! Efficiently aligning your marketing, sales, and support can help your company tremendously. You can always find more ways for these teams to collaborate depending on your business model. The ideas of collaboration might be different for a SaaS business in comparison with a social media application or an on-demand services company. But in the end, it boils down to a impeccable coordination between marketing, sales, and support teams. These collaborations should be done keeping the end goal in mind, which is the revenue you get. I just got off a call with a leading entrepreneur who told me if we are not meeting our revenue goals perpetually, we should shut down the company. Harsh, but true. There are companies who are excellent in marketing, there are the ones with charming salespeople who make you believe in their product and the ones with fall-in-love customer support. But the winning ones are where these three teams perform really well together. Have you heard them saying jack of all trades and master of none? This doesn’t mean being brilliant in any aspect of the business is not beneficial for you. It certainly is. But, for the home run, you need to make sure that all the three customer-facing team collaborate exceptionally. How to facilitate these collaborations? For handling collaboration between teams, you need to put efforts in making a transparent framework which is easily understood by all the stakeholders involved. You can use a number of tools to facilitate these collaborations. For marketing and sales collaboration and lead scoring, we use (a marketing automation tool) and (a sales CRM). ActiveCampaign Pipedrive We use our own product for customer support — which handles our support team’s collaboration with sales and marketing team. Using Kommunicate, the support team can make FAQs to facilitate a self-serve platform. Kommunicate Also, the marketing team can see customer queries and understand if there is a need for change in product messaging or positioning. Similarly, using Kommunicate, the sales team can collaborate with support teams to assist customers in during trial and integration period to reduce the length of sales cycle and fasten the onboarding process. Some would argue to build their own systems from ground zero. I would certainly not recommend this. Putting efforts in building these tools might hamper the progress of your own product. SaaS businesses are thriving miraculously because they help businesses reduce workload in building tools to support their main business. You may choose a number of tools which help you achieve these effective collaborations. In the end, it matters the most to have marketing, sales and support teams collaborating efficiently with each other for the end goal of the company — revenue growth. I work for Kommunicate , an intelligent customer support platform. This article was originally published here .

Aligning Sales, Marketing, and Support for Revenue Growth

There is no doubt; the word bumping inside my head in the last year was . Automate everything; being obsessed with repeating tasks over time in the IT Operation context is crucial in having success when building a stable and well-thought infrastructure. “Automation” But, what is behind process automation, or simply managing IT Operations and configurations? A real-world episode: think about a toy robot singing your baby’s favorite nursery rhyme, instead of yourself or your partner. Of course, the song would sound perfect rhythm and exact text, but after a few weeks your baby would recognize it and would get bored soon. That is the time to learn a new song or invent another one, or at least, modify it in a whole new fashion! That’s why some toy robots are capable of performing so many tasks today (they sing, move, dance, cry, “eat” and “drink”, interact in similar ways we humans do!). The same issue has been experienced with automated material handling and processing equipment industries. Capabilities just increased in number and complexity. Automating one single process is not enough! And that is the reason why we find next to the term “automation” also terms like “orchestration” and “configuration management”. The following is one of the numerous definitions of word I found in WEB: “automation” Automation: “The technique of making an apparatus, a process, or a system operate automatically” There is no surprise about the worries on the future and rising AI in some industries (especially manufacturing); the impact will be transformative and will cause a drastic shift (let’s think about truck drivers who one day would not have to drive anymore “thanks to” driverless cars!). However, what seems fundamental, and surprising to me, are the actions to take in order to know how to deal with this game changer. I understand it is certainly a tough challenge. And time to think about it has come! Of course, the resulting minor labor for people (blue-collar job positions according to recent USA researches) would be threatening if nothing is done in the meantime. The strategies are numerous: from starting to train high-skill profiles to managing new technologies to immediately introduce new plans for future businesses, future services and products. The point is: Start now, experiment as soon as possible. Organizations need to embrace as soon as possible the automation of business processes. Enabling the assets (it could be software or hardware or a combination of both) to take some well-defined actions, would put us in a very privileged position. Mostly because of the following rising facts: Obtain a status of when designing architectures that are more effective and reliable. high-level responsibility The chance to preserve a kind of (a lot of discussion also going on about and ) when building artifacts through automation. social ethics human relevance social consciousness Preserving a attitude, I always used to remember myself human-centric “Do not automate ignoring usability and values”. Short time in responding to business needs (and so, more wealth and lower expenses). Releasing dedicated employees from certain IT Operation actions, we gain time to think about other stuff. creatively Treat your infrastructure as code. Even if you do not have experience in Configuration Management in the Cloud (though I strongly suggest to try by yourself one of the available tools: , , , , … AWS CloudFormation Cloudreach Spectre Red Hat Ansible HashiCorp Packer Chief The idea of something that records, that captures in the background, all your IT Operations and development tasks, in a traceable and secure way, fascinates all of technology admirers. Managing resources from a simple text-based editor and a human-and-machine-ready language (JSON or YAML) in a versioned fashion is disruptive. What to automate first? The trick is starting from a small set of applications (examples are: automate SQL queries, storage backup, services restart, log monitoring, alerting, freeing up disk space, provisioning resources in the public Cloud, customizing snapshots/images when launching VM, installing most common services on servers…) and then increase complexity when adding whole frameworks and correlate them together. Finally, automate the traceability! The surprising outcome is that bringing all these features together would create a perfect habitat for orchestration and you have all the instruments to manage systems lifecycle! In the past, having to write docs and notify teammates about changes in the Cloud environment, customizing and developing code, sharing content about provisioning (and de-provisioning) Cloud Computing resources is accompanied with confusion, synchronizing problems, repeatable actions (vulnerable to human errors), lots of emails and of course time-consuming. Thinking well about automating, putting down the necessary steps with the tools you prefer and then making it independent brings unpredictable success. It lets you focus on other arguments (multi-cloud deployment, increasing efficiency, training staff, think ). creatively No easy job out there: to automate you should bring qualities from a wide range of technical areas, but also soft skills to think 360-degrees about building autonomous systems. It should not be the final purpose of the mechanism and it should always look for optimization and extension. As I mentioned above, automation asks for responsibility and social consciousness. It also boosts from cross-functional professionals with creative mindsets. And there is no surprise: they get obsessed with automation, they look for shapes of digital perfection when orchestrating IT resources. In addition, making different things work well together and independently seems likely an artistic approach.

Why do I see creativity in the future of IT Automation

The concept of (AVs) has long sparked debate as to the ethics of transferred decision-making autonomy on roads. The Society of Automotive Engineers sets out 6 Levels of autonomy used officially across the industry to differentiate AV capability[1]. autonomous vehicles Levels 0-2 AV already exist in commercial markets. Level 3 is the first significant jump in capability. It describes vehicles that can self-drive for short periods, but require a human driver to be ready to intervene if the system requests it. Levels 4-5 then go beyond environmental detection. These encompass cutting-edge technologies which obviate human override altogether[2]. Level 4 AVs can complete an entire journey without human intervention under specific conditions. Level 5 can complete entire journeys under any circumstances. Level 5 would be associated with vehicles that don’t even need steering wheels or pedals, for example. The moral and ethical dilemmas that emerge surrounding these two higher levels of autonomy arise from the loss of almost all direct decision-making power. Correct functioning of core technologies, ability to value human life and principles, trade-offs, and accountability then all become issues under both ethical and legal frameworks. We will explore these, starting with the infamous Trolley Problem. The Trolley Problem The Trolley Problem is a thought experiment created within the branch of philosophy called virtue ethics and discusses how foreseeable consequences compare to intended consequences on a moral level. The main variation, devised by British philosopher Philippa Foot (1967)[3], is as follows: A trolley is running along a set of tracks, out of control and unable to break. 5 people are tied onto these tracks though, and the trolley is fast approaching them. You are stood off the tracks next to a lever, which if pulled would divert the trajectory of the trolley to a different set of tracks. This alternative track has only one person tied to it, so the trolley will currently kill 5 people, but this could be reduced to just one if you act. Do you pull the lever? Ethical Frameworks in Decision-Making The Trolley Problem can be viewed under many ethical frameworks. Consequentialists would argue it’s better to reduce overall harm in the outcome by any means necessary. Deontologists would argue that the act of pulling the lever and actively killing one person is more morally wrong than letting the trolley continue its due course. Utilitarians would argue that the most ethical choice creates the greatest amount of good for the greatest number of people. Rawlsians would argue that all lives are equal, and to achieve justice and act most fairly one must prevent the greater harm. Rights-based ethics would argue that the right to life is absolute and should not be violated or sacrificed for any trade-off. Whichever ideology, our duty to minimize harm to others directly conflicts with our duty to choose the morally correct action. It’s the ability to value decisions and trade-offs like these that many question in autonomous vehicles[4]. For example, if an AV was about to crash, should passengers of the vehicle be prioritized over pedestrians/other vehicles? It isn’t just the ability to make tough decisions that must be considered in the ethics of autonomous vehicles though. When humans ourselves cannot agree on which ethical framework would best answer the Trolley Problem, how are we meant to program self-driving cars to weigh up trade-offs like these under one ideology? What basic values and principles should we be programming into AI? Should we want it to prioritise positive duties: the number of lives saved, or negative duties: minimising active harm done? Case Study In 2018, Uber tested Level 3 AV in Arizona, resulting in a tragic pedestrian fatality – the first caused by AV ever[5]. Being Level 3, there was a backup driver present in the vehicle, but it wasn’t enough. With the environmental detection system struggling to correctly identify the obstacle – here a pedestrian with a bike, the possibility of harm was not recognized by the car’s alert systems fast enough. By the time the backup driver was finally alerted to take control, the vehicle was already 0.2 seconds to impact, and traveling at 39mph[6]. This example does not necessarily discuss the trade-off of direct harm to AV passengers versus pedestrians external to the vehicle, as the backup driver was never at risk of harm herself. However, it does bring to light whether we can and should be relying on AI sensory detection over our own and whether manual override is a feasible backup in such high-pressure, pressed-for-time scenarios. It also highlights the issue of transferring autonomy even temporarily to an AV, via the lack of a moral agent culpable for the killing. In this case, Uber retracted more than 90 other Level 3 AVs it had been testing in Arizona and settled with the victim’s family. The backup driver on the other hand was charged with negligent homicide[7]. Was blame correctly placed on her or should it have been the vehicle – is the latter even possible? Ethical AI UNESCO outlines that AI ethical frameworks should prioritise avoiding harm and respecting human rights[8]. Safety and non-discrimination should underpin machine learning principles. Human oversight, control, and accountability should also be considered essential alongside responsible AI. Additional concepts of fairness, and ‘for the greater good’, suggest that we want AI to use a utilitarian ideology for decision-making. On the other hand, ‘respecting human rights’ plays into the moral rightness of actions themselves, i.e. deontology. Transparency will of course also be paramount in understanding how decisions end up being calculated by AVs. For evaluating harm caused or prevented in the case of an AV accident, we will need to understand how and why the underlying AI technology reaches a certain conclusion. Public trust in AVs will require understanding accountability, and making sure the right frameworks are being adhered to. Ethical Automated Decision-Making The European Parliamentary Research Service recognizes the ethical, legal, and economic concerns which must be addressed in developing and deploying automated decision-making AI[9]. This includes research into how to develop ethical principles in the underlying algorithms, and how to bring global policy and regulations up to speed with the exponential rate of AI innovation. In terms of human rights, human agency is also being prioritized, with research bodies wanting to protect the ‘right of end users not to be subject to a decision based solely on automated processing’[9]. On the technology side, cybersecurity standards will become more important to ensure secure and reliable systems. Ethical AI requires trustworthy software. Conclusion While we do not currently have the general public using Level 3+ AVs on roads in the UK, or any such vehicles available in domestic markets yet[10], major players in the industry like BMW, Tesla, and Mercedes aim to launch these by 2025 using technologies like Traffic Jam Pilot to do so[11]. If AVs get the ethics of decision-making right, there are great benefits to be seen. Some estimates predict a 90% reduction in traffic-related accidents with them on the roads[5]. Still, it is clear that we do not yet have quantifiable ethical and legal frameworks outlining how decisions should be made and trade-offs prioritised when it comes to the technologies that underpin AVs. AV players will therefore need to further outline what ‘minimise harm’ means, and which ethical ideology should dictate decision-making. As we saw with Uber’s accident in 2018[7], accountability and agency will also have to be clarified. All of these, how they are handled, and which direction we progress in, will have long-term ethical implications for society. References Navigating Ethical Dilemmas in Autonomous Vehicles: A Case Study | by Helenjoy | Medium The Current State Of Play In Autonomous Cars | Hackaday The Trolley Problem — Origins. The Trolley Problem is a thought… | by Sara Bizarro | Medium Uber AV road fatality poses questions | News | Insurance Times Ethical Decision Making in Autonomous Vehicles: The AV Ethics Project | Science and Engineering Ethics (springer.com) ‘I’m the Operator’: The Aftermath of a Self-Driving Tragedy | WIRED Navigating Autonomous Vehicles Levels: The Vasquez Case and the Debate Between Level 3 and Level 4 Autonomy – Arizona State Law Journal AI Ethical Framework (rootstrap.com) EU guidelines on ethics in artificial intelligence: Context and implementation (europa.eu) Electric and Autonomous Vehicles: The Future Is Now | Bain & Company The future of autonomous vehicles (AV) | McKinsey

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