How to Build a Local Startup Ecosystem?

As an entrepreneur, you probably have enough work to keep you busy. Whether you’re a successful businessman with several companies under or you’re getting ready to launch your first startup, there will always be essentials to work on.

However, irrespective of your hectic schedule, you should always make time to build your own company. Developing your local startup ecosystem is not as hard as it looks like, it yields a multitude of benefits for you and your startup.

Why You Should Strengthen Your Local Startup Ecosystem

Before you set out to renew your local startup ecosystem, it’s important to evaluate your reasons to do so. Helping out your entrepreneurial community can lead to a multiple benefits for you and your company.

There’s simply no way you can experience first-hand what’s involved with all the different types of startups, marketing approaches or technical challenges, even if I build many different startups throughout my career. Whilst it’s never the same to hear about someone else’s learning than to go through it yourself, by meeting other founders you can be exposed to much more and multiply your experience and knowledge.

It’s an Easy Way to Establish Your Support System

Even if you are anexpert with frequent successful startups, don’t forget that you couldn’t have gotten where you are today on your own. Another benefit of participating in your entrepreneurial community is that you can meet with other founders that can help advance you and your company in the future.

Meeting lots of founders also gives youan intellectual group of people to call on whenever you have a challenge. You might meet an Android developer who needs to chat about struggles of creating a startup, such as validating their idea. If you’re having issues with Android development, you can easily hit them up for help.

Map Out Your Startup Ecosystem

The initial step to building your startup ecosystem is to become accustomed with all of the entrepreneurial activity happening around you. While this may require research, you’ll be able to figure out your next steps much more effectively. This is going to be important with respect to knowing your area, and how to work with it to create startup successes. It will also play a key role in building your network so you can organize the ecosystem in a way that is profitable for your company.

Mapping out your startup ecosystem is also a key step in properly laying out the infrastructure in your area. If your infrastructure is not properly laid out, you are setting your ecosystem up for failure.

We have an open source process for this, called the Startup Ecosystem Canvas, where you can work with the support of your community and the Founder Institute to accurately map your community. 

Follow these steps to get started:

Identify active technology- and entrepreneurship-related meet up groups.

Pinpoint entrepreneurship organizations like the Founder Institute, Startup Weekend, tech conferences, etc. that are currently running programs in your city.

Find out who the angel investors and VC’s are in your area, if any.

Discover local journalists and news outlets that cover startup-related subjects in your area.

Track successful startups currently functioning in your area.

List technology related colleges, universities, and other education institutions operating in your area.

Network with Like-Minded People

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It is no surprise that environment plays an important role on one’s success. This is the reason why aspiring actors move to Hollywood, or why fashion designers opt to live in New York. Similarly, those who want to be successful entrepreneurs flock to areas that increase chances of prosperity. So, what happens to the millions who can’t migrate to Silicon Valley? According to MIT Technology Review, centers for innovation are growing worldwide in areas like Bangalore, Beijing, Skolkovo, and London. In fact, not being in proximity to Silicon Valley allows other hopeful entrepreneurs to eliminate the need to move, and gives them the opportunity to create their own equally successful startup ecosystems.

Below are some key reasons to grow a startup network in your area:

Shared Problems, Shared Solutions

If you’re looking for a network that shares the same vision and goals, chances are you’re not alone. Entrepreneurs understand that people who provide support are a priceless resource. By supporting each other, solutions to problems are discovered more efficiently. In the words of Andrew Carnegie:

Teamwork is the ability to work together toward a common vision. The ability to direct individual accomplishments toward organizational objectives. It is the fuel that allows common people to attain uncommon results.

Accessibility Allows Growth

Building a network also means finding a common place to gather. BorysMusielak, a Mentor of the Warsaw Founder Institute realized his area didn’t have a center for entrepreneurs to converge when over 100 entrepreneurs from all over the country unexpectedly showed up to his housewarming party. His old fashioned house suddenly became a site for founders to flock and develop their ideas.

Succeed and Fail Together

Being surrounded by those who are trying to achieve their dreams means you will get a first hand look into both their accomplishments and mistakes. Rather than learning lessons the hard way, you can gain in-depth knowledge from observation.

Your Network is Just as Important as Net Worth

For Musielak, the growth of one's network also meant the growth of one's success. Since many promising entrepreneurs and new innovative ideas were present at the monthly meetings, foreign investors began attending as well. People who are in pursuit of success attract others on the same path. Put simply by Henry Ford:

Coming together is a beginning. Keeping together is progress. Working together is success.

Click here to read more about BorysMusielak and the startup mansion.

Reach Out to Your Local Government

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Many entrepreneurs question the role of their local government in their startup ecosystem. Does government impact entrepreneurs in a positive way by fueling creativity, or does it hinder the innovation of startups by offering too many barriers of entry? As a startup ecosystem builder, your role is to create processes that build meaningful and prosperous relationships with local government agencies. In an Innovation: America’s Journal of Technology Commercialization article titled, “How State Agencies Are Helping Entrepreneurs,” Casey Short describes the benefits state governments provide to entrepreneurs:

Their legislation focuses on everything from research and development to funding for startups to professional support to the government’s involvement in the process.

There are plenty of valuable resources local governments can provide to small businesses, including guidance on filling out business taxes or advice on applying for business licenses. Many government agencies offer coaching/training programs for entrepreneurs at incubation centers to foster collaborative work environments. In addition, government agencies provide financial initiatives - such as grants, awards, and tax credits - to fund founders throughout their entire entrepreneurial journey.

The amount of influence a government does or does not give itself in the world of technology-based economic development has a large impact on the methods and strategies that a startup should use.

The primary responsibilities of these government agencies is to support growth in industry sectors and increase employment creation within each of their communities. Ultimately, government agencies construct sustainable pipelines for economic development. As a startup ecosystem developer, you should strive to establish strategic partnerships with local government agencies, as they provide the foundation for entrepreneurship in their local communities.

Host Industry Events

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To become a startup ecosystem leader, one must get comfortable with running industry events. When you occupy the position of a host or hostess, you establish yourself as an expert within the startup culture. By bringing authoritative business leaders together, you become a trusted source and important link that unites your community.

Note that consistency is key. The more events you attend or run, the more people you’ll bring into your network. When you load your network with numerous entrepreneurs, mentors, investors, and business experts, you create a collaborative circle that can transform into a powerful business force. The co-founder of the London School of Economics, George Bernard Shaw once quoted,

If you have an apple and I have an apple and we exchange these apples then you and I will still each have one apple. But if you have an idea and I have an idea and we exchange these ideas, then each of us will have two ideas.

Collaboration between entrepreneurs is critical for innovation. When brilliant minds come together, technical skills are exchanged and networks are expanded. Whether you host a large conference or coffee shop Meetup, business events help new founders obtain mentorship from experienced entrepreneurs. By sharing knowledge within a community, more startups have the opportunity to access capital, ultimately reaching success. In a recent study conducted by the Kauffman Foundation, it is stated that:

While the ways startup and experienced entrepreneurs met may seem random, the experienced mentors had specific network ‘circles.’

If your startup community lacks the necessary resources and connections it needs to create flourishing businesses, use the list below to help you get started with running impactful industry events:

Define your event: Assess why you want to host an event. What’s the subject matter? Do you want to spark a discussion? Are you ready to mentor and teach new founders? Do you simply want to expand your network with a mixer? Define goals for your event that either better the community or connect key players.

Decide where it will be held and along with the agenda. Depending on the type of event you plan to host, ensure the venue matches with the agenda. Whether you choose a coffee shop or co-working space, determine how many people you want to invite and what the agenda will entail. If you invite speakers, will they need a projector or computer to give a PowerPoint presentation? Match your event goals with the first draft of your agenda to choose a location.

Evaluate your current network and expand. Who is in your business circle as of now? Can they help? Are they interested in the subject matter of your event? Invite and employ your current network. If it’s a huge event, use applications like Thunderclap to spread the word by tapping into your friend’s social media accounts. Your friends can help market your event, assist with production, or at least attend. You should also be on the look out for possible speakers who may owe you a favor.

Utilize online event invitation software. Use applications like Meetup.com and Eventbrite to collect information from your guests. The Eventbrite app even lets you check-in attendees on the day of the event. Make sure you keep track of the lists you accumulate so that you easily reach out to your network for the next affair.

Create a killer agenda and final draft.. Create an agenda that will help you reach your goals. Employ icebreakers, snacks, activities, breaks, and talks that will keep people's attention from start to finish. Make sure your attendees get the information and resources that were promised upon invitation.

Execute and examine if goals were met. Did you get as many people as you intended to host? Did people leave great reviews on your Meetup account? Analyze the good and bad moments of the event so you know where to optimize next time around.

AI Is Changing the Education Industry heres how

Artificial Intelligence has become an inevitable technology that we use on a daily basis now. AI has been a big help in thinking and reacting like humans when it comes to certain technological processes. We are now using this technology for automatic parking systems, smart sensors for taking spectacular photos, and personal assistance. Similarly, Artificial Intelligence in education is taking a big turn as the traditional methods are changing with the advancements in technology.

The academic world is becoming convenient and personalized thanks to the numerous applications of AI for education. This has changed the way people learn since educational materials are becoming accessible to all through smart devices and computers. Today, students don’t need to attend physical classes to study as long as they have computers and internet connection. AI has eased the automation of administrative tasks, allowing institutions to minimize the time required to complete difficult tasks so that the educators can give quality teaching to their students.

Here are some transformations brought by AI in education.

1. Simplifying Administrative Tasks

AI can automate the expedition of administrative duties for teachers and academic institutions. Educators spend a lot of time on grading exams, assessing homework, and providing valuable responses to their students. But technology can be used to automate the grading tasks where multiple tests are involved. This means that professors would have more time with their students rather than spending long hours grading them. We expect more of this from AI. Actually, software providers are coming up with better ways of grading written answers and normal essays. The other department that is gaining a lot from AI is the school admissions board. Artificial Intelligence is allowing for automation of classification and processing of paperwork.

2. Smart Content

AI and education go hand in hand. New techniques could be all that is required to ensure that all students attain their ultimate academic success. Smart content is a very hot subject matter today. This technology has already reached a classroom setting. Smart content includes virtual content like video conferencing, video lectures. As you can imagine, textbooks are taking a new turn. AI systems are using traditional syllabuses to create customized textbooks for certain subjects. As a result, textbooks are being digitized, and new learning interfaces are being created to help students of all academic grades and ages.

3. Personalized Learning

Have you checked the type of personalized recommendations on Netflix? The same technology is being utilized in how students are taught at schools. The traditional systems are supposed to cater to the middle but don’t serve pupils sufficiently. But when AI is introduced, teachers are not necessarily replaced, but they are in a position to perform much better by offering personalized recommendations to each pupil. AI customizes in-class assignments as well as final exams, ensuring that students get the best possible assistance. Research indicates that instant feedback is one of the keys to successful tutoring. Through AI-powered apps, students get targeted and customized responses from their teachers. Teachers can condense lessons into smart study guides and flashcards. They can also teach students depending on the challenges they face in studying class materials. Unlike in the past, college students can now access a larger window time for interacting with professors. Thanks to AI, smart tutoring systems can offer quick feedback and work directly with students. Even though these methods are still in their inception stages, they will soon become fully-fledged digital teachers to assist students with any educational needs.

4. Global Learning

Education has no limits, and AI can help to eliminate boundaries. Technology brings drastic transitions by facilitating the learning of any course from anywhere across the globe and at any time. AI-powered education equips students with fundamental IT skills. With more inventions, there will be a wider range of courses available online and with the help of AI, students will be learning from wherever they are.

5. New Efficiencies

AI improves IT processes and unleashes new efficiencies. For instance, town planners could use it to minimize traffic jams and improve the safety of pedestrians. Similarly, schools can determine the appropriate methods of preventing students from getting lost in crowds when they run in corridors. AI can also be used in the modelling of complex data to enable the operations department to create data-driven forecasts. Speaking of which, schools can avoid a lot of wastages caused by over-ordering thereby saving costs. Through new efficiencies, Artificial Intelligence in education can pay for itself. The truth is new technologies come with upfront expenses for installation and training. But eventually, these costs become negligible. Technology gets cheaper over time and so does the hardware and software.

The impact of this technology will be felt from the lowest education levels through higher learning institutions. This will create adaptive learning techniques with customized tools for improving the learning experiences. Artificial Intelligence might inform the students how their career paths look like depending on their goals thus assisting them beyond academics. Only time can tell the ultimate impact of AI in the education industry.

Cyber Security plus Data Science: The Essential Career Path of the Future?

Datascience and cybersecurity, two of the most popular career paths, are on a collision course. The combination of these two skill sets have become highly sought-after in this age of technological advancements. There is a huge talent crunch when it comes to finding IT professionals. If you ask anyone in the IT sector‘whatjob positions are the most difficult to fill?’ you will almost certainly hear "data science" and "cybersecurity." Employees with these skills are hard to find and even harder to retain. What happens when the next generation of jobs will require a combination of these skills?

Many cybersecurity tool providers have been in a frenzy adding data science capabilities to their cybersecurity platforms. This includes factoring behaviour-based analytics and responses into antivirus, firewalls, and traffic analyzers to make their products smart. 

Artificial intelligence and data science can enhance traditional cyber security. However it’s a slow process to use cyber security in data sciences and artificial intelligence. So the next challenge becomes how to secure the black-box algorithms -- products of data science programs -- that learn and grow dynamically.Because these analytics models are so highly valuable to enterprises, cyber security professionals will need to determine standards and methods for protecting these models and ensuring their integrity. To do so, they will need to protect these assets from the outside in and the inside out.

Just as cybersecurity professionals are trained and skilled at managing the limit of the network, they must develop controls around the limit of the black-box algorithms that autonomously make business decisions.This includes two areas of focus.

First, they need to protect the data being fed into the model. The evaluation and assessment of the "goodness" of the data being input into the model is one front that a cyber-security professional can protect from the outside in.Second, they need to protect the model itself. Data scientists are often more like scientists than they are software engineers. Their focus is on research and development and creating new, exciting algorithms and models that can have major business impact. Knowing where a model came from and that it has not been maliciously altered will become the crusade of cybersecurity professionals worldwide as they protect the limit.

Models labelled as AI are, by nature, learning algorithms and are bound in a certain degree of uncertainty. Often, the model does not generate a definitive answer but rather a statistically probable answer. The challenge is that as behaviour evolves over time, the performance of these models will also change. Unlike traditional software engineering that can incorporate unit tests that pass or fail based on expected outputs given a set of inputs, measuring an artificial intelligence model can prove to be more troubling. However, it is still an important area for cyber security professionals to cover as they strive to protect the business.

One of the fundamentals of data science is the need to monitor a model's performance once deployed into production. Traditionally this has been a point of operational effectiveness so the data science team knows when the behaviour has evolved past initial constraints and needs updating to accurately represent new business conditions. Cyber security professionals can catch trends in the model's output and performance and thus detect and prevent danger to the enterprise.

Another mechanism to protect these models from the inside out is to establish results thresholds. Regardless of the model outcome, if these thresholds are exceeded, the transaction can be put into a holding area until it can be reviewed for legitimacy. An example of this could be limits on stock trades associated with algorithmic trading. The model itself is generating decisions about when to trade, when to sell, and at what price. Ceiling or floor price thresholds at which price a stock is sold would prevent hackers from tampering with the model constraints in order to manipulate the stock market.

The convergence of cyber security and artificial intelligence is dignified to be one of the hottest areas of IT growth in the coming years. With a talent crunch today for both cyber security and data science professionals, be aware that this up-and-coming job role of artificial intelligence cyber security is on the near-term horizon. 

Blockchain in Healthcare: Opportunities, Challenges, and Applications.

Growing businesses demands the need to bring revolutionary changes in all the aspects of their businesses as time and technology progress. When it comes to the field of health care, the urgency of growth escalates to higher levels. Quality healthcare services backed up with the latest technology is the need for today. Moreover, the healthcare system landscape is shifting towards a more patient-centric approach which focuses on two major elements namely, affordable treatment and apt healthcare facilities at all times.

Focusing on quality health care services means ensuring patient health management at a superior level at all times. However, federal rules and regulations are making processes even more tedious and lengthy. Due to this, keeping the processes intact and providing effective patient care is not feasible in many cases.

In the healthcare sector, critical patient data and information remains scattered across different departments and systems. Crucial data is not accessible and handily available in times of need. The existing healthcare ecosystem cannot be considered complete as multiple players in the system do not have a system in place for smooth process management. Moreover, it is also termed as inadequate for handling the exchange of information and requires certain major changes.

Many healthcare facilities today are dependent on outdated systems for keeping patient records. These systems hold the functionality of keeping local records of the patient data. This can make it difficult for the doctor to diagnose which is time-consuming for the doctor and tedious for the patients too. Due to this, the cost of maintaining a patient-oriented business is increased considerably. Patients don’t have any control over their data, the chances of identity thefts, financial data crimes and spamming are increasing every day.

Despite having gadgets like computers and mobile phones at every healthcare facility these days, we’re still not able to collect, analyze, secure and exchange data seamlessly. Therefore, the healthcare system today not only needs an advance system rather it also needs a system that is smooth, transparent, economically efficient and easily operable.

However, Blockchain is one of the most disruptive technologies that has taken the world by storm these days. A blockchain is nothing but a register that keeps track of transactions and activities happening throughout the network. The most unique factor of a blockchain is that once a piece of information is added to the distributed ledger, no one can alter it. The information stored on a blockchain is absolutely secure in its entirety. In order for anyone to make a change in one block, it is mandatory to make changes to all the subsequent blocks after it.

Blockchain has the power to bring out a massive breakthrough in the healthcare ecosystem as it can easily bring specific changes in the healthcare management of the patient. With the aid of this technology, the power will come back to people’s hands. Meaning that individuals will be responsible for handling their own records thus, getting the overall control of their own data.

The technology holds the ability to successfully improve patient care quality while maintaining the funds at a reasonable rate. All the challenges and hindrances that occur in multiple level authentication can be eliminated through blockchain. With the increasing adoption rate, Blockchain has made its way to the healthcare sector. Even in its beginning stage, the technology is being positively accepted by people in the healthcare ecosystem.

The comprehensive vision for blockchain to disrupt the healthcare sector in the coming times would be to resolve issues that afflict the current system. Imagine a healthcare system where all the information is easily accessible by doctors, patients and pharmacists at any given time. Blockchain allows the creation and sharing of a single common database of health information.

This system would be accessible by all the entities involved in the process no matter which electronic medical system they use. This offers higher security and transparency while allowing doctors find more time to spend on patient care and their treatment. Moreover, it will also enable better sharing of statistics of researches which, in turn, would facilitate clinical trials and treatment therapies for any rare disease.

In a healthcare system, smooth data sharing between healthcare solution providers can lead to accuracy in diagnosis, effective treatments, and cost-effective ecosystem. The day-to-day growth of patient data requires proper utilization of resources in order to make the most effective utilization of the insights discovered through it.

The potential of blockchain technologies for healthcare highly depends on the acceptance of the new technology within the healthcare ecosystem in order to create technical infrastructure. Though there are certain concerns and speculations regarding Blockchain’s integration with current healthcare systems and its cultural adoption, the technology is still popular in the healthcare sector.With so many potential uses, and possibilities, blockchain is sure to disrupt the healthcare landscape for good.

Blockchain Explained: What Is Blockchain?

Blockchain is a technology that allows individuals and companies to make instantaneous Digital Transformation companies on a network without any middlemen like banks. Transactions made on blockchain are completely secure, and, by a function of blockchain technology, are kept as a record of what happened. Strong computer codes ensure that no record of a transaction on a blockchain can be altered after the fact.

Here’s a more in-depth look at what a blockchain looks like.

 Blockchain Explained: A Visualization of How It Works

Funnily enough, you can explain a blockchain as literally a chain of blocks. Those blocks represent data, held all together in a specific order. You can also imagine it as a ledger—because that’s essentially how most blockchains function. Each block of data represents some new transaction on the ledger, whether that means a contract or a sale or whatever else you’d use a ledger for.

A blockchain is a record of transactions. Using blockchain, companies or people can both make and verify these transactions. That’s two very important concepts lumped together, so let’s take a closer look. Blockchain allows companies to make transactions.

Blockchain allows companies to verify transactions. Adding a transaction to a blockchain involves getting it verified. Whatever your “network” is, everyone will have agreed to rules that determine which transactions are valid and which are not.

This “democratic” system of security is one of the biggest reasons why so many people are flocking to blockchain right now. No one can change the records, so blockchain is a trustworthy and fair source of information that anyone can verify.

Blockchain is instantaneous. Transactions on a blockchain get processed and verified much more quickly than the alternative systems. This might seem counterintuitive because the lemonade example makes it sound like everyone has to copy everything that happens to the chain. But in actuality, these transactions get processed by computers in milliseconds. The reason why blockchain is much faster than the alternative is that it’s decentralized, so let’s talk about that to finish off the definition.

Blockchain Is decentralized. Blockchain operates with no central authority. This is the kicker. Blockchain lets people or companies add and verify their transactions, without a single governing body making sure everything is okay.

Blockchain technology essentially cut out the middleman. When one copy of the blockchain ledger gets changed, they all verify that transaction before adding it to their ledgers. And blockchain is faster than the alternative because everybody involved doesn’t have to wait on a single, slow-moving source for verification. It all happens simultaneously. By now, you’ve got the run-down on blockchain explained. It’s not too complicated, even though it sounds convoluted.

So far, we’ve got that a blockchain is a digital ledger shared between a network of people. Each participant can manipulate that ledger, recording new blocks of data onto the chain, but with each transaction, the entire chain gets analyzed by everyone to make sure it’s still accurate. In other words, everyone has their copy of the ledger. But nobody can make a change without everyone agreeing to it. It’s a democratic system.

That’s Blockchain Explained, But Who Uses It?

Here’s some industries looking to incorporate blockchain technology:

Banks and financial services are using blockchain to cut out the middlemen and a lot of time, money, and risk when dealing with monetary transactions. Industries that are high risk for fraud are starting to use blockchain to verify their software. A few companies are implementing blockchain to prevent the false certification or sale of blood diamonds and stolen art, for example.

Digital content like music, movies, and online ads could use blockchain to prevent piracy. By using new file formats that can play the media and encode blockchain data that reflects intellectual property and payment history, musicians and filmmakers wouldn’t be losing out on millions.

In medicine, blockchain technology can be used to prevent the theft of pills through the supply chain and give medical history ownership back to patients (who can distribute it to their doctors, for certain amounts of time, as they want or need).

In the food and drink industry, farmers could use blockchain to monitor their crops—and trace where and when food recalls occur.

Insurance could be dramatically changed. Imagine a world where you can get the insurance that lasts for a few hours, like if you’re doing some extreme sport. Or where Uber drivers can bypass insurance companies by combining their money on a blockchain and creating a safety net for themselves.

Blockchain is a development that lends itself to creativity, so we’ll likely see some very interesting uses in the future. Whether you’re an individual or a multinational company selling and buying, each transaction you add to the blockchain is checked against everyone else’s blockchain ledgers. This system prevents anyone from using the same bitcoin more than once—which was the biggest problem with all-digital currencies before bitcoin came along.

Since bitcoin is a purely person-to-person digital currency model, anyone using bitcoin can make fast, secure, low-fee transactions whenever they want, to anyone in the world. It’s a universal currency.

Hopefully, now you’ve got a solid grasp on what blockchain is, why everyone’s talking about it, and how it can impact your small business. Whether or not you decide to accept bitcoins as a form of payment depends on you and your small business needs.

How artificial intelligence is changing the face of banking in India?

In this age of machines, traditional banks find themselves competing with tech-savvy players, moving to adopt newer technologies like artificial intelligence to keep up. Artificial intelligence will empower banking organisations to completely redefine how they operate, establish innovative products and services, and most importantly impact customer experience interventions. In this second machine age, banks will find themselves competing with upstart firms leveraging advanced technologies that augment or even replace human workers with sophisticated algorithms. To maintain a sharp competitive edge, banking corporations will need to embrace AI and weave it into their business strategy.AI’s potential can be looked at through multiple lenses in the banking sector, particularly its implications across the operating landscape of banking. Focusing on some of the key artificial intelligence technology systems like: robotics, computer vision, language, virtual agents, and machine learning that underline many recent advances made in this sector.

Banks entering the intelligence age are under intense pressure on multiple fronts. Rapid advances in 

AI are coming at a time of widespread technological and digital disruption. To manage this impact, many changes are being triggered. Leading banks are aggressively hiring Chief AI Officers while investing in AI labs and incubators.  AI-powered banking bots are being used on the customer experience front. Intelligent personal investment products are available at scale.Multiple banks are moving towards custom in-house solutions that leverage sophisticated ontologies, natural language processing, machine learning, pattern recognition, and probabilistic reasoning algorithms to aid skilled employees and robots with complex decisions.

Banks today are struggling to reduce costs, meet margins, and exceed customer expectations through personal experience. To enable this, implementing AI is particularly important. And banks have started embracing AI and related technologies worldwide. According to a survey by the National Business Research Institute, over 32% of financial institutions use AI through voice recognition and predictive analysis. The dawn of mobile technology, data availability and the explosion of open-source software provides artificial intelligence huge playing field in the banking sector. The changing dynamics of an app-driven world is enabling the banking sector to leverage AI and integrate it tightly with the business imperatives.

Digital personal assistants and chatbots have transformed customer experience and communication. They are powerful enablers in freeing routine daily tasks and ensuring a personalised experience for customers. Virtual assistants and chatbots have many applications. Automated AI-powered customer service is gaining strong traction. Using data gathered from user’s devices, AI-based relay information using machine learning by redirecting users to the source. AI-related features also enable services, offers, and insights in line with the user’s behaviour and requirements. The cognitive machine is trained to advise and communicate by analysing users’ data. Online wealth management services and other services are powered by integrating AI advancements to the app by capturing relevant data.

Personalised wealth planning is revolutionised using AI. For example, if a customer is looking to buy a new car, the app will provide guidance for the proposed outlay and loan approval limits based on current expenditure and income. Chatbots too can be “employed” to act as customer service agents and serve customers continuously throughout a day. The tested example of answering simple questions that the users have and redirecting them to the relevant resource has proven successful. Routine and basic operations i.e. opening or closing the account, transfer of funds, can be enabled with the help of chatbots.

Online fraud is an area of massive concern for businesses as they happen on a large scale. Risk management at internet scale cannot be managed manually or by using legacy information systems. Most banks are looking to position machines or deep learning and predictive analytics to examine all transactions in real-time. Machine learning can play an extremely critical role in the bank’s middle office.The primary uses include moderating fraud by scanning transactions for suspicious patterns in real-time, measuring clients for creditworthiness, and enabling risk analysts with right recommendations for curbing risk.

Lending is a critical business for banks, which directly and indirectly touches almost all parts of the economy. At its core, lending can be seen as a big data problem. This makes it an effective case for machine learning. One of the critical aspects is the validation of creditworthiness of individuals or businesses seeking such loans. The more data available about the borrower, the better you can assess their creditworthiness.Banks are increasingly relying on machine learning to make smarter, real-time investment decisions on behalf of their investors and clients.These Algorithms can progress across distinct ways. Databecome an integral part of their decision-making tree, this enables them to experiment with different strategies and to broaden their focus to consider a more diverse range of assets. But one thing everyone in the Indian banking sector can agree on is that we need to utilize artificial intelligence to make banking functions easy and efficient.

Advanced analytics applications in IoT

The new age of technology Internet of things is all over the place. The real benefit of IoT comes with inter-connecting more and more devices and our ability to harness this data to take quicker decisions leading to accurate and timely actions by leveraging Advanced Analytics.Advanced Analytics has become a key driving factor to define the success of a company irrespective of the business.

Imagine the days of no online shopping or the convenience of calling a cab via a mobile app on a rainy day or unable to check the weather for the day online or find the route to drive home. Internet of things has advanced in endless areas of our lives like manufacturing, healthcare, social media, e-commerce etc., more so in areas that impact our personal and social lives like weather, connected homes and soon connected cars and even connected elevators. After all, what will we do without our smart phones on our side with its array of sensors to wirelessly connect us to this world? Our smart phones have gotten smarter while we are getting dumber by being so reliant on them.

We have a huge gap when it comes to our capability to harness this potential. Businesses sit on top of piles of digital transformation data with no way to extract a strategic plan that works to be profitable. The challenges faced include how to effectively apply advanced analytics on this overwhelming data and also how to identify and prioritize their application areas.

IoT implementation in applications are far too many and impacts all areas starting from data inception, consumption to mining foresights from the data.Business intelligence systems and analytics systemsare used interchangeably these days because of the poor understanding of its true meaning.Objectives of business intelligence systems are to churn out management reports in the form of structured reports that can be consumed by management operations. Reports will also include Push or pull notifications like SMS, email etc. On the contrary, Analytics starts where business intelligence ends with a key distinguishing factor being complexity of calculations performed by leveraging mathematical based models and their outcomes.

A basic application of analytics in IoT where in there is a predetermined relationship between a set of parametric values are known to indicate a specific outcome of another
parameter within the system. These parametric values are nothing but data read from sensors mounted on various IoT subsystems like temperature, pressure etc. These rules are knowledge acquired by subject matter experts about the system which is translated and embedded as software code for raising an alarm when the conditions are met.

Anomaly detection is a step up where in using statistical techniques, a deviation from the normal operating ranges of an equipment is identified and alerts are raised. Anomaly can be due to a single parameter or a combination of parametric values. Next higher in the order of complexity is change pointdetection wherein a statistically driven algorithm detects a change in performance of the system. Change point detection is often confused for rule based deviation detection. The primary distinction is that change point detects any permanent departure in the performance of a system while a rule based change detection detects a point in time change which might fall back to normal sooner or later.

One of the advanced applications of analytics in IoT is Predicting systems behaviour. These can be either forecasting future state of a parameter using time series models and multivariate event driven predictive models. The target for prediction can be linear values like binary or conditions like yes or no. One of the most complex areas to implement advance analytics predictive model is real time control of automated processes in manufacturing like welding, spray painting etc.

While advanced features like edge analytics may sound glamorous from commitment to modernizing an organization perspective, it is recommended to consider a very detailed evaluation to justify the need for such disposition.The closer one moves towards the edge layer, more tighter coupling between the software and hardware platforms is needed. Thus, any dispositions on the edge require an overhaul of the hardware controlling the equipment, and hardwiring them to analytics capable software systems.To what extent a decision and an action translation can be allowed in an automated system without human intervention to judge validity of such machine generated decisions?

The pros and cons of AI

When we talk about Artificial Intelligence we are basically talking about giving a machine the ability and power to react or think like a human and act on it with a relevant response. People have feared AIever since it was invented. Hollywood in particular has done a masterful job of stoking those fears with movies like "2001: A Space Odyssey," "Terminator" and "The Matrix" all making AI systems into demonic forces.

There is no inherent good or evil of AI, it's about how it's used and implemented. People are just projecting their own misuse onto the technology. Once you look past the foolish fears, what you have is a technology that is hard to create, easy to control, and more of a threat to certain jobs than our overall existence.

There are some Pro’s to Artificial Intelligence

Humans get bored and tired as they work or especially when they do mundane tasks, machines don't. Which is why they are the best to do routine jobs. A.I. allows for more intricate process automation, which increases productivity of resources and takes repetitive, boring labour off the shoulders of humans. They can focus on creative tasks instead.

A.I. and cognitive technologies help in making faster actions and decisions. Areas like automated fraud detection, planning and scheduling further demonstrate this benefit. Big Data means datasets in the petabytes, far too much for a human to sift through. AI can chew through that data as fast as the Xeon processors in the servers can go and derive insights from the data much faster than any human could. A lot of the big data processing and analysis being attributed to AI is really just the work of machine learning. True AI would need to take things so much further; toward genuine self-learning using artificial neural networks that emulate the structure and functions of neural networks in human brains.

Humans make errors. Computers don't. AI processing will insure error-free processing of data, no matter how large the dataset. Judgement calls, however, are a different matter. AI-powered machines are doing jobs humans either can't do or would have to do very carefully. Space exploration is one of them. The Mars rover Curiosity is an example. It is freely roaming Mars because it examines the landscape as it explores and determines the best path to take. The result is that Curiosity is learning to think for itself.

And here are some Cons of Artificial Intelligence

People lose out to machines and there is no way around it, AI will cost lesser-skilled people their jobs. Robots have already taken many jobs on assembly lines and as AI gets better at doing complex tasks, even more low-skill jobs will be taken. Driverless cars is one obvious singular tech that will displace millions of human drivers fairly quickly, although the recent fatality involving a Tesla car on auto-drive may have set the whole effort back a bit.

The changes will be subliminally felt and not overt. A tax accountant won’t one day receive a pink slip and meet the robot that is now going to sit at her desk. Rather, the next time the tax accountant applies for a job, it will be a bit harder to find a job. Intelligence is a fine balance of emotions and skill that is constantly developing. Today, shades of grey exist when we make judgements. Our behaviour is an outcome of the world around us – the more artificial it becomes, the more our definitions are subject to deciding on simply right or wrong, rather than the quick mid-course corrections that make us human. Replacing adaptive human behaviour with rigid, artificial intelligence could cause irrational behaviour within ecosystems of people and things.

AI can be programmed with a benign goal but implement it in a perverse manner just because the solution is logical and efficient. So if there is a problem with the food supply, an AI's solution may be to reduce the population by any means available rather than find ways to increase food production or decrease food waste.

Eventually when it comes to us to decide if the pros weigh more than the cons or vice-versa. We should keep in mind that we humans have limitations that can be met by intelligentmachines. It would be smart of us to make use of these machines where there is a need for it and avoid it in places that would lead these machines to harm us eventually or worse take over.

4 Digital Transformation Strategy Examples and What You Can Learn from Them

Leading brands are continuing to pursue digital innovation and are disrupting established industries in the process. Amazon, for instance, started in e-commerce but is now entering the retail grocery business with a fully digital checkout process. Moves like this have led companies of all sizes to focus on digital transformation the acceleration of activities, processes, and competencies to fully take advantage of digital technologies.

Companies are investing a lot of money and resources into digital transformation strategy, but to win, they’ll need a strategic approach. Digital transformation, after all, is not a sprint, it’s a marathon. To get the most out of their digital strategy, companies need to get a pulse on the changing market and adjust their approach accordingly.

We identified four ways innovative companies are shaping their digital transformation strategy. Following these examples will help business leaders create a thoughtful approach that drives change in the entire organization.

1. Look past your competitors

The impetus behind many digital transformation initiatives is fear, fear that a competitor will beat you to the punch, or that a disruptor will leave you behind. There is a good reason to fear. The average lifespan of S&P 500 companies has declined from 61 years in 1958 to about 20 years now, indicating that disruption rather than longevity is the new norm. 

Disruption can come from anywhere, so companies can’t afford to just analyze what their closest competitors are up to. They need to look upstream to the changes in other industries that will inevitably permeate their own as well.

General Electric (GE) was able to successfully make a digital leap, in part because it looked outside of its industry for inspiration and insight. Instead of just watching its direct competitors, the company studied innovative companies in industries like tech. It hired people from outside the industry, including Bill Ruh, who had experience developing advanced solutions from Cisco. GE also teamed up with various incubation labs to gain experience working with start-ups.

By expanding its point of view, GE was able to launch the Predix platform, an Internet of Things platform that allows industrial machines to be monitored and optimized digitally. Analyzing trends in other industries put GE ahead of its competitors today, pulling off an impressive initiative that’s already landed them the business of companies like Pitney Bowes.

2. Ask your customers

Without understanding your target audience, your digital strategy will only ever be an educated guess. Saudi Telecom Company (STC), a $12 billion telecom company, sensed something had shifted in its target audience and understood that it needs to launch new digital initiatives. Instead of crafting a generic digital strategy, STC hired a team of researchers to study the habits and lifestyles of millennials in the country. Specifically, the company was interested in understanding the pain points of young consumers.

According to CEO Subhra Das, “We then had to rely on creativity, great design and cutting-edge tech to address the pain points and their needs using the power of digital.” The result was a business unit called Jawwy that is successfully reaching Saudi’s digital natives. Among other things, Jawwy offers new ways of managing mobile plans and support functions such as billing and charging—activities that millennial customers are looking for.

Jawwy had a simple mandate: understand the pain points of the company’s target customers, and work backward to design a digital solution. For Jawwy, customer insight was not an afterthought; it was the fundamental cornerstone of the digital strategy.

3. Use executive influence

Some executive teams in large organizations are launching innovation hubs to do digital thinking for them. This strategy can be effective when there’s a specific plan to test digital initiatives. In 2015, for instance, Scotiabank launched Digital Factory, a tech accelerator unit that helps identify areas for improvement in the company’s processes. Tech experts in this hub examine the customer experience and aim to offer solutions to the most pressing pain points.

A typical problem with these digital hubs, however, is that they lack appropriate executive leadership. As a recent Harvard Business Review article pointed out, “Only the CEO has the power to provide this kind of [digital] direction across the entire enterprise.” For a thorough digital transformation to take place, nothing less than an executive’s direction will do.

Consider the example from Alan Mulally, former CEO of Ford, who led the company through a digital transformation after the 2008 recession. He created a business plan and shared it with his executive team in a weekly meeting. That meeting was mirrored at every level of the organization, all the way down to front-line employees. Managers then closed the feedback loop by sharing employee insight with the executive team.

Ultimately, Mulally set the high-level agenda while also considering feedback from the entire organization. Your digital strategy could fail to get off the ground without executive support, but it may also have large organizational gaps without input from the right stakeholders. Finding the right balance is key to a successful digital transformation.

4. Align your company culture

Peter Drucker, the renowned management expert, famously said, “Culture eats strategy for breakfast.” Even the most informed digital strategy with executive support will lack staying power without the right culture to sustain it. For example, when Adobe decided to make the transition from physical software to a cloud-based model, it knew it needed to shift its employees’ focus towards the needs of the customer. To achieve this, it created a staff Experience-a-thon, where employees could test and provide feedback on Adobe products, not from their viewpoint as employees, but as users. 

Employee engagement was a key strategy during Adobe’s shift to becoming a cloud company.

While each company faces unique challenges, a digital initiative has to be accompanied by a meaningful cultural change that will sustain the transformation into the future. Start with the customer experience. A deep understanding of your customers’ motivations and intentions is a critical starting point for any digital transformation initiative.

In this new technological age making smart use of digital transformation technologies in your business can pave the way to success for your firm.