Tag: Artificial Intelligence

Ways Small Businesses Can Benefit From Artificially Intelligent Systems

It is quite obvious how Artificial Intelligence has become a part of almost every industry verticals. In addition to enhancing intelligence and productivity to operations, AI is widely used by businesses to predict online customer behavior, manage supply chains, automate various difficult and redundant tasks.

According to a recent survey by Vistage, about three-quarters of businesses will invest in software applications. No wonder the AI revolution is also termed as the fourth revolution!

Starting from voice search mobile app development to self-driving cars being able to prompt the driver with possible destinations, most small businesses are jumping into the AI bandwagon. The early adopters of AI implementation have seen a considerable improvement in business turnover and in achieving an increased ROI.

Businesses have improved their turnover and overall performance by making use of data. Small businesses can make use of the AI Revolution to leverage the available data. This can bring a change in their overall business processes. Below are the major benefits that small businesses can reap out of AI:

1. Leveraging Artificially Intelligent Solutions

The fact that any business process can involve AI implementation proves the importance of large chunks of data being generated. To stay competitive, it is essential for businesses to follow the latest trends in the market. This is made possible by deriving insights from data available to predict business outcomes. The possible benefits of AI enabled systems are as follows:

a. Small businesses can use Artificial Intelligence to understand customer segmentation. AI is used by businesses to gather data and then perform market analytics. They can use AI to advertise as AI helps in providing insights to target the right customer base. This helps in determining market-fit customers rather than having to target clients blindly.

b. Small businesses can also make use of AI strategies. For instance, businesses can use third-party AI tools that involve easy interfaces and machine learning algorithms. This will help businesses to couple the critical data and workflows into business intelligence. This helps businesses to achieve practical insights. It also helps in saving time and costs that arise due to data review.

2. Creating Artificially Intelligent Customer Service Solutions

AI-powered chatbots steal the show when it comes to enabling Digital Customer Services. Estimates show that 67 percent of businesses use chatbots for rendering customer services. This number is forecasted to reach 85 percent by 2020.

Voice-search assistants are no exception to customer service solutions. The number of electronic assistants is expected to reach 7 billion by 2020. Chatbots and voice-search assistants can definitely help small businesses increase their productivity and efficiency.

Related Reading: Check out how capitalizing on chatbots will help in redefining your business.

3. Artificial Intelligence For Building A Positive Workplace Culture

Small businesses often find it difficult to hire talents. Artificial Intelligence has made it simpler for the HR Departments of small businesses to recruit talents by eliminating the need for HR managers to manually set and select through the large pile of candidate profiles. AI helps in building a positive workplace culture in the following ways:

a. Hiring is made easier by Machine learning algorithms. The process of hiring is streamlined by AI. The algorithms do so by the process of learning hiring practices of the past. AI applications help in finding good leads by learning an applicant’s history of work and studies and makes the hiring process easier.

b. Sales can be made simpler for small businesses with the help of AI applications. AI-enabled CRM platforms help businesses to derive insights. CRM systems compile data from different customers via phone, email, etc. This is done for automated lead generation. Sales folks of small businesses can make use of AI enabled CRM systems to adjust their leads by analyzing customer ideas through different channels like recording phone calls, emails, phone messages, online behavior, customer reviews, social media posts, etc. This helps the sales team of businesses to personalize their business, according to customer intentions.

c. Small businesses can use AI applications to manage back-end operations cost-effectively. Financial accounting and scheduling daily tasks to employees can be managed more effectively with the help of AI enabled tools. This helps small businesses save time from performing redundant and manual tasks.

Related Reading: Here’s an ultimate guide for you to enhance your existing business application with AI.

4. AI For Effective Data Collection And Competitive Analysis

Identification of competitors is crucial for any business. AI helps in building a competitive analysis that searches for competitors in particular fields. This helps small businesses to collect critical data regarding different competitor strategies.

Businesses learn about the latest trends from the collected data. This is performed via advanced AI methods such as statistical regression analysis. AI has made it cost-effective for small businesses to use sophisticated AI tools that help them in determining how to keep clients interested in their business.

Machine learning algorithms make use of customer sentiments to capture and track customer preferences. This data collection mechanism is made available affordable by AI for small business.

Competitive Intelligence implies being able to react to market trends rapidly and accurately. It is therefore essential for small businesses to be up-to-date about the current market trends. Tracking competitors, changing business processes to suit customer requirements, analyzing cost changes and business metrics is required for a business to succeed. This is made possible by AI-powered competitive intelligence tools.

Related Reading: Read on to know how artificial intelligence can enhance intelligent app ecosystem.

5. AI Enabled Tools To Enhance Marketing Strategies

Any business requires deep know-how in technology to enhance its marketing functions. Small businesses find it difficult and costly when it comes to hiring top-notch professionals to handle large marketing campaigns.

Small businesses can make use of AI enabled tools to manage marketing activities. AI helps in reaching out to a large audience online by making use of advertising platforms. For instance, Facebook and Google have implemented advertising platforms that are AI implemented. This targets specific customers making it easier to collect and analyze data that is critical for lead generation.

CPC (Cost Per Click) can be considerably reduced by implementing AI tools. In addition to being able to find the best marketing strategies, AI enabled tools also analyze consumer engagement via marketing campaigns.

Artificial Intelligence is an effective budget monitoring source for marketing management activities for a business.

The Artificial Intelligence Revolution can jumpstart your small business to its height. Get in touch with our tech-breathing IT consultants now to learn tips on how to start!

You may also be interested in reading Top AI Technology Trends To Watch Out For In 2019

Key Features And Benefits Of An Artificially Intelligent Ecosystem For App Development

Artificial Intelligence is omnipresent in this digital era. Artificial Intelligence and human intelligence work together to deliver numerous solutions at the technological forefront. The global artificial intelligence market size is expected to reach $169,411.8 million in 2025. The leading application development trendsetters such as IBM, Microsoft, and many more global leaders have adopted Artificial Intelligence as an inevitable part of their advancements in bringing up the intelligent app ecosystem.

Artificial Intelligence has its key segments in the market based on Technology and they are Machine Learning, Speech Recognition, Image Processing, and Natural Language Processing along with various other industry verticals.

What is an AI application?

Putting it in simple words, AI is the reason why your smartphone when connected to your car’s Bluetooth gives a beep with a message about the traffic conditions and in what time you are likely to reach your destination you often travel to. Artificial Intelligence makes this possible via its pattern recognition skills which it formulates over a period of time, known as machine learning!

For instance, Microsoft has developed a productivity AI application known as ‘Office Graph and Delve’ of Microsoft Office 365. It collects data and shows only relevant content to users according to their priority. Other examples include:

• Voice recognition apps with GPS.
• Search engine applications.
• Chatbots or AI-enabled assistants.
• Personalized shopping applications that make use of Google Analytics.
• Financial applications that are both accurate and efficient in calculations and providing results, such as automated advisors, powered by AI.
• Autonomous vehicles, drones.
• Transport apps powered by AI, Google Maps, etc.
• Social Media applications.
• Smart home devices that make use of data science like smart voice assistants.
• Creative arts such as Watson Beat, yet another powerful AI implemented the app.
• Security apps that implement Facial Recognition and image processing technologies.

Making Mobile Apps Intelligent With AI

The first quarter of 2019 witnessed over 2.1 million apps available in Google’s Play store for Android users. Apple’s Appstore was the second largest to have over 1.8 million apps ready for download.

Related Reading: Read on to learn how voice app can enhance your business. 

With such intense competition, app developers are looking for ways to create personalized applications. The solution to this is AI implementation. The role of AI in an intelligent app ecosystem is to make the applications learn the intention of users. Machine learning and Data analysis features form the Intelligent Ecosystem for these applications.

The AI ecosystem increases the efficiency of programs and machines and also allows users to predict user intentions online like their purchasing trends and other social behaviors when they are online. The following are some of the roles played by Artificial Intelligence in an intelligent app ecosystem:

• Multi-faceted technology that incorporates machine learning algorithms and deep learning.
• Implements Big Data, Neural Networks in its programming.
• Predicts user communication and intention online in real-time.
• Automates functions.
• Can make informed decisions for specific users.
• Can create analytics from a history of searches.
• Interprets tasks using preassigned commands.
• Provides personalization.
• Accurate Insights
• Perfection In App Development Features
• Efficient Ways to Complete Redundant Tasks
• Enhanced User Satisfaction
• Notifies Important Details making operations easier

AI integrated applications help in improving success rates and also increase app performance. Along with helping machines to mimic humans and its problem-solving capacity, AI has become an inevitable part of app development with its ability to learn, perceive and manipulate data.

AI protects an application user’s data. It performs an analysis of the data for identifying user patterns and behavior online in real-time. This data can be used to provide better customer experience and for user engagement.

Role of Artificial Intelligence is also in aiming at generating revenue through efficient user interfaces.

Related Reading: Are you planning on enhancing your business with AI? Here is a guide just right for you.

The Intelligent App Ecosystem: How It Functions

Applications are becoming more intelligent each day. Staying ahead of the game is crucial for app developers with users wanting a personalized experience and much-improved experiences. The following are the latest trends in AI intelligent app ecosystem:

• Wider use of machine learning techniques.
• Wider adoption of microservices for app development.
• A rapid increase of numerous platforms to develop applications.
• A rise in the adoption of various machine learning models.

Various layers of technology are involved in the development of artificially intelligent applications. Artificial Intelligence plays a major role in modeling the market dynamics of businesses to a large extent. In addition to creating new opportunities, be it for startups or even for large industrial giants, artificial intelligence provides numerous implications on the development side including the following key features:

1. Customer Experience with machine learning defines an intelligent app ecosystem

Every business needs high-quality data that will suit their specific application needs. So the machine learning models are required by user data specific companies. For instance, for Google, it was the ‘search’ function, ‘entertainment’ was defined by Netflix, Facebook for the ‘social’ amenities and many more such specific intelligent applications.

Many more categories include healthcare, personal assistants, retail, agriculture, sales, security, automation and many more industry verticals. Machine learning along with user critical data have formed the basis of these applications on the artificial intelligence vertical.

Related Reading: Here’s how you can redefine your business with AI chatbots.

2. Cross-platform services With The Intervention of New Platforms

There are many cross-platform mobile applications developed on cross-platforms such as hybrid and native platforms. Consider Facebook Messenger and Amazon’s Alexa. The added features make it more user-friendly and even beneficial on the sales front. This also becomes simpler for industries to perform when they can deliver their products/ services across such platforms as it is all about adding a new layer of API that connects with the entire existing microservices for authentication and other critical functions. These interfaces, thus revamp the existing applications into macro-services.

3. Machine Learning Models And Intermediate Services For Business-Specific Platforms

Pre-trained machine learning models can be used as plug-and-play for performing functions such as natural language processing, image processing, etc. These are the intermediate services. On the other hand, there are raw intelligence providers that form the building blocks. These two are the major 2 types of industries that provide value to the intelligent app ecosystem.

4. The Big Data Technology Analysis And The Intelligent App Ecosystem

Do you know what Hadoop has contributed to enabling an artificially intelligent ecosystem of app development? The hardware companies that store large chunks of IoT data and other transactional data perform? Well! Big Data Analysis is how businesses keep up with the intelligent app ecosystem.

According to the latest IDC forecast, 75% of the commercial enterprise applications will make use of AI by 2021. This shows that deploying AI for app development will lead your business to have an intense advantage over your competitors in the near future.  To learn more about how you can capitalize AI for your business benefits, get in touch with our custom software development experts today!

According to The Wall Street Journal, consumer spending is the primary driver of economic growth in the U.S. Manufacturers, suppliers and logistics companies are the industries that benefit from retail profits. Retailers now prefer artificial intelligence to protect profits. This improves customer service as well!

Next-Gen Solutions To Solve Retail Profit Shrinkage – Why Would You Choose AritificiaI Intelligence?

In the year 2018, the global AI market was expected to be worth 7,35 billion U.S. dollars. Also, the global AI market is expected to grow from 150 percent from 2016 numbers, reaching a forecast size of 4.8 billion U.S. dollars. These statistics show that AI is a new factor of production that can help skyrocket profitability for retailers!

Related Reading: Check out the latest trends in AI. Find 7 reasons why AI is expected to play out in 2019.

Why would you choose AI to protect retail profits?

By 2035, AI technologies will have the potential to boost productivity by 40% or even more! This means AI will increase economic growth at an average of 1.7% across 16 industries by this period.

These numbers show a straight 59% increase in retail profits alone! For this, advanced analytics are used to design and develop models. These models are then used to fetch possible outcomes from a wide spectrum of data given to a computer to analyze. From these outcomes, future decisions and actions are fed to the system which learns it. The system can now perform without human intervention! It can make decisions in real-time.

Machine learning, deep learning, and natural language processing are a few examples of AI. Marketing, pricing, logistics, risk management, store management, fraud detection are some inevitable areas where the largest retailers have used AI for decades now!!

AI – How it uses advanced analytics to solve a wide spectrum of retail problems

Manufacturers, Logistics companies, and suppliers are the major industries that support retail sales. Consumer spending depends on fluctuation in these sectors. This drives the economic growth in the U.S! Since these sectors benefit from strong retail profits, “retailers are turning to artificial intelligence to help protect profits” says a report from Forbes!

AI replaces redundant and individual-driven analysis. This is a more convenient method and ensures consistency across the retail chain. Thus AI can replace 1000 people performing the same tasks by answering spontaneously with just the same analysis!

AI can also enhance customer experience and drive sales. Online retailers use chatbots and product suggestions, while stores can enjoy real-time, targeted marketing messages. Retailers have the need to generate chunks of data on a daily basis. With predictive analytics, this data can predict trends and thus reduce the chance of failures by determining necessary changes to improve profitability. Thus the employees can have proper information faster and deliver more consistent results!

Related Reading: AI and Robotics carry the power to enhance customer experience. Here’s a CTO Guide to it.

Solving Retail Problems With AI – Steps to Overcome Challenges And Improve Efficiency To Boost Sales

All retail companies (physical and online), face four fundamental challenges. These can be solved with predictive analytics and an effective data production plan. They include:

• Siloed and Static Customer Views

Retailers can adopt a complete, real-time strategy by combining traditional data sources with the non-traditional like social media or other external data sources to create valuable insight, resulting in robust fraud detection systems, more effective marketing campaigns, more accurate and targeted churn prediction, and better customer service. This helps them encounter the problems faced by siloed data, where transaction data are separated from web pages, which is again separate from CRM data.

• Time Consuming Vendor and Supply Chain Management

Retailers can adopt real-time analytics and unstructured data sets to combine structured and unstructured data to create more accurate forecasts or automatic reordering, resulting in optimized pricing strategies and more efficient inventory management.

• Analysis Based on Historical Data

Retailers can use prediction and machine learning in real time to create predictions based on current behaviors and trends. This helps them predict the client’s next move. Thus AI helps to adapt automatically to customer behaviors.

• Single-Time Data Projects

It is high time retailers turned towards automated and scalable data workflows. This helps them improve their overall efficiency. This is made possible with predictive analytics using AI.

Related Reading: Enrich customer experience at your retail store. Read along to reveal five secret to win your retail customers.

How AI can Generate Additional Revenue

IDC analysts predict that by 2019, 40 percent of retailers will have developed a customer experience architecture supported by an AI layer! In a nutshell, the major ways in which AI can generate additional revenue and help avoid additional overheads and losses are the following:

• AI can replace redundant and individual-driven analysis for a retailer. This process is far more efficient and it ensures consistency across the retailer’s stores.
• Additionally, employee theft and paperwork errors contribute a high figure of shrink to retail profits. For instance, the National Retail Federation conducted a survey and it turned out that the reason for more than 50% of retail shrink, ie, (the difference between the real ‘on-hand’ inventory and the inventory level recorded in the computer system) is a result of employee theft and manual errors! AI models yield better and efficient results.
• Also, retailers can use AI to predict solutions benefits and functionalities prior to buying it. This way, AI can help reduce risks in retail profit shrinking.
• Choosing a consultant that can address the needs of your company can be another major benefit to reap out of AI to reduce retail profit risks. The following concerns can be addressed with the consultant:

1. How to address constraints like budget, time and personnel? Ask your consultant to provide explanations for the outcomes.
2. Consider the consultant’s skill sets from their previous projects and work experience before you decide to hand over the responsibilities and contract

Related Reading: Does AI have the potential to drive business value across industries? Read through to find how AI is revolutionizing various industries.

AI adopts next-generation solutions that use predictive analytics to capitalize on their data and knowing that 80% of this data is untapped and unstructured is the winning solution! Want to know more reasons why you can undoubtedly bet your company on AI?

Stay tuned to our latest articles and blogs to learn how AI solutions have managed to perform a quantum shift in computing and revenue generation for successful retail companies!

Most of us, technology geeks or not, are eager to stay first in line to catch up with the latest game-changing technology trends. Here we are to know which technologies will thrive in future!

The Potential Technology Trends You Need To Explore In 2025

Have you ever looked up at the sky and clapped your eyelids on a bat? This is commonplace. But what if it was a drone. Or would it be a flying fleet? Since we don’t belong to the Jetsons family, the latter is not expected but we are close to it! This year is definitely a transformative year for technological innovation!

According to Gartner, the Top 10 Strategic Technology Trends for coming years are Block chain, Artificial Intelligence, Empowered Edge, Privacy and Digital Ethics, Quantum Computing, Immersive Experiences, Augmented Analytics, Autonomous Things, and Digital Twins!

This is just the tip of the iceberg. Following are the emerging technology trends and catalyzing technical innovation that we can expect to see more of in future!!

Related Reading: Find how digital innovation is transforming today’s business world.

1. Blockchain Technology – The ‘New Internet’

Some call Blockchain technology the ‘New Internet’. The blockchain is the brainchild of a person or group of people known by the pseudonym, Satoshi Nakamoto. It permits digital information to be distributed but not duplicated.

It was first devised for the digital currency, Bitcoin.  It is also called the “digital gold”. To this day, the total value of the currency is nearly $112 billion US!

“Blockchain solves the manipulation problem”, says Vitalik Buterin, inventor of Ethereum.

2. Artificial Intelligence (AI)

Apart from AI-powered chatbots, This year will witness chip manufacturers such as Intel, NVIDIA, AMD, ARM, and Qualcomm shipping specialized chips that speed up the execution of AI-enabled applications.

This year will also be the year for hyperscale infrastructure companies like Amazon, Microsoft, Google, and Facebook.

Related Reading: Check out the top AI trends

3. Cloud-independent edge computing

The study from IDC illustrates that 45 percent of the entire data created by IoT devices will be stored, processed, analyzed and acted upon close to or at the edge of a network by 2024! Edge computing is a mesh network of data centers that process and store data locally before being sent to a centralized storage center or cloud.

4. Privacy and Digital Ethics

Facebook, recently witnessed the biggest security breach in which 50 million accounts were compromised. Facebook, later clarified that data of 30 million accounts were stolen.

People are becoming more nervous about how organizations and third-parties are using their personal data.

5. Quantum Computing

The world is behind building the first fully-functional quantum computer. Also called the supercomputer, this is expected to be a cloud service rather than an on-prem service. IBM is already offering cloud-based quantum computing services. For instance, the automotive, financial, insurance, pharmaceuticals, military, and research industries have the most to gain from the advancements in Quantum Computing.

6. Immersive Experiences

Conversational platforms are changing the way in which people communicate with the digital world. Virtual reality (VR), augmented reality (AR) and mixed reality (MR) are changing their approaches to know more about people’s perception.

7. Augmented Analytics

Augmented analytics relies on augmented intelligence. This uses machine learning (ML) to transform how analytics content can be developed, consumed and shared.

“Through 2020, the number of data scientists will grow five times faster than the number of experts”, says David Cearley!

8. Autonomous Things

Autonomous things, such as robots, drones, and autonomous fleet, use Artificial Intelligence techniques to automate their functions that were previously performed by humans.

9. Digital Twins

A digital twin is a digital representation of real-world items that are interlinked. Cearley states that there can be digital twins of people, processes, and things!

A DTO is an aspect of the Digital Twin evolution that is a dynamic software model that relies on operational or other data. DTOs help drive efficiencies in business processes.

Apart from these, there are other key technology trends that organizations need to explore in future. These include:

10. Cybersecurity and Risk Management

According to the estimates from the firm Gemalto, the data breaches were 4.5 billion in the first half of 2018! The University of Maryland study found that hackers attack computers every 39 seconds.

In coming years we will be facing a more sophisticated array of physical security and cybersecurity challenges.

Cybersecurity is thus the digital glue that has held IoT, Smart Cities, and the world of converged machines, sensors, applications, and algorithms operational throughout!

11. Smart Spaces

A smart space is a physical or digital environment in which humans and technology-enabled systems interact in an increasingly open, connected, coordinated and intelligent ecosystems, according to Gartner! The world of technology is to enter accelerated delivery of smart spaces in 2019.

 12. Self-powered data centers

Data centers grow every minute with the implementation of virtual servers and storage, energy-efficient buildings. In coming years, the data centers are expected to run on its own self-contained power plants!

13. IoT integration

This year will witness more IoT implementation. An International Forrester IT survey that said among a recent group study, 82% of respondents were unable to identify all of the devices connected to their networks. Of this lot, 54% were nervous about device security, and 55% were concerned about integration!

Related Reading: Find the role of Data Analytics in Internet of Things (IoT)

14. More self-service IT kiosks for business users

This year will be a year of IT innovation designed to build better communication between IT and end users. The self-service IT kiosks to be set up would enable users to log on and choose what they want for the apps that they build.

15. The Internet of Things and Smart Cities

50 billion equipment, including smartphones, and others are expected by the IoT to be wirelessly connected via a network of sensors to the internetin future. 

The term “Smart City” means creating a public/private infrastructure to conduct activities that protect and secure citizens. It integrates communications (5-G), transportation, energy, water resources, waste collections, smart-building technologies, and security technologies and services!

To upgrade your business with the latest technology trends on the table, contact the experts at Fingent today! Also, read through our latest blogs to learn more about accelerated technological development!!

The Experts Speak: The Adoption of AI and ML in Software Development

Artificial Intelligence (AI) and Machine Learning (ML) is transforming nearly every industry. In our previous blogs, we discussed how it is transforming finance, customer service, supply chain & logistics, and other industries. In this blog, we will talk about how AI and ML are radicalizing the software industry. We will look at the benefits of the adoption of AI and ML in software development and what industry experts have to say about it.

The Fundamental Shift in Software Development

At its very core, software development involves writing reams of rule-based code. Traditionally, developers had to specify every detail to let the system know what to do, and then customize the features of each piece of technology. With the adoption of Artificial Intelligence and Machine Learning, this process becomes much easier.

Author, scientist and Google research engineer Pete Warden puts it well:

“The pattern is that there’s an existing software project doing data processing using explicit programming logic, and the team charged with maintaining it find they can replace it with a deep-learning-based solution … What I see is that almost any data processing system with non-trivial logic can be improved significantly by applying modern machine learning. This might sound less than dramatic when put in those terms, but it’s a radical change in how we build software. Instead of writing and maintaining intricate, layered tangles of logic, the developer has to become a teacher, a curator of training data and an analyst of results.”

Practically, this means a revolutionary change in the very essence of software development.

AI and ML in action

“Modern IT environments are incredibly (and increasingly) complex and ever-changing, leading to large amounts of time and resources devoted to monitoring, troubleshooting, and course correcting,” says Phil Tee, Cofounder, and CEO of Moogsoft Inc. “It’s a reactive position for most companies, but when teams use AIOps technology they can leverage change-tolerant algorithms and access indexed information. This allows them to spend more time focused on proactive, meaningful work rather than fixing the same problems repeatedly or spending time managing rules and filters.”

Artificial Intelligence and Machine Learning can transform the entire Software Development Life Cycle (SDLC). Three ways in which it does this are:

• Synthesis of large volumes of data to predict the success or failure and business value of a project.
• Predicts accurate project delivery timelines, delivers project status updates and creates project schedules.
• Automatic diagnosis and rectification of problems in the project.

Let us consider the details of how this is achieved.

1. Turn Idea into Code Quickly and Effectively

The process from the inception of an idea to its actual execution into workable code is time-consuming and complex. Traditionally, developers must go through a long process of trial and error to get the basic code in place. Obtaining funding approvals for the project is complicated as well and requires getting the project to a prototype level even before requesting for funds. All this can be made easy with AI and ML. In the future, the system itself could learn to process ideas from natural language and suggest machine-executable code without human intervention.  AI and ML are making this possible even now by teaching systems to suggest code completion. This way systems will slowly learn to generate code through predefined modules.

Considering the viability of a project also becomes easier and faster with AI and ML. Machine learning can help developers identify and prioritize the effects of the project based on business risk. This way, time and effort on unnecessary regression testing and rectification can be eliminated. A sound decision on whether the project is feasible or not can thus be made in the earlier stages.

Related Reading: How to accelerate your business growth with Robotic Process Automation

2. Effective Project Management

Through pattern detection, AI and ML can be trained to use details of past projects to provide accurate estimates of the current project. Historical project details like bugs, test phases, actuals, and estimated values can be fed into the system and used to fine-tune the level of accuracy. Through this, the system will learn to predict accurate delivery schedules and create work schedules for individual members of the team. Risk Management, as well as Resource Management, can be better executed this way.

3. Automation of Testing and Error Detection

Pattern Detection is one of the most valued benefits of machine learning. This can be used effectively to automate the testing and debugging process in software development. One of the challenges of software testing is to come up with a list of most likely cases and scenarios that could affect the program’s performance. Through pattern detection, systems can look at past logs and generate a test case list automatically. They can also identify and classify error types and in time learn to automatically fix these errors.

Joe Colantonio, an expert on software automation and performance testing discusses what is possible with AI and ML.  “Wouldn’t it be great if you could answer the classic testing question, “If I’ve made a change in this piece of code, what’s the minimum number of tests I should be able to run in order to figure out whether or not this change is good or bad?” he says. “Many companies are using AI tools that do just that. Using ML, they can tell you with precision what the smallest number of tests is to test the piece of changed code. The tools can also analyze your current test coverage and flag areas that have little coverage, or point out areas in your application that are at risk.”

Revolutionizing IT

Apart from making a big difference in the software development process, Artificial Intelligence and Machine Learning will also change the way applications are made. With the power of AI and ML, developers will be able to design apps, which will be able to listen, think, speak, reason and make decisions. Vision Recognition technologies, Optical character recognition, and many more AI-powered capabilities will help developers create faster, smarter and better apps in the future. At Fingent, we endeavor to put this into action every day. There is no area that is immune to the changes that AI and ML can bring and we are determined to stay on top of it.

Related Reading: Top Artificial Intelligence Trends to Watch Out for In 2019

AI continues to positively disrupt businesses around the world by empowering them with automation and data-driven insights.

In this video, Deepu Prakash, Head of Process and Technology Innovation at Fingent shares his expertise on deploying AI within the context of the modern business environment. He puts forth five crucial steps that you can begin right now to shift your company into an AI-driven model systematically.

The past decade has been a game-changer for the way businesses operate in the realm of retail. The advent of e-commerce and its subsequent boom has compelled brick and mortar outlets to undertake a paradigm shift from a profits-first to a consumer-centric approach. Failure in conforming to new consumer demands fueled the retail apocalypse that toppled the brick and mortar landscape. Thus, we see retail giants like Bon-Ton Stores Inc., Sears and Macy’s filing for bankruptcy and liquidate their holdings.

Implementation of targeted mobile promotions, loyalty benefits, e-payment gateways is just some of the strategies adopted by retail outlets to maintain a competitive advantage in the face of fierce technological overhauls. With disruptive innovation gaining a strong footing now more than ever, the need to constantly reinvent and augment is more pressing than before. Here are five key disruptive technology trends that you need to sync your business model with, to offer consumers retail experience par excellence:

Related Reading: 5 Ways to Enrich Customer Experience at Your Retail Store

1. The advent of Artificial Intelligence

Robots and AI bots capable of not just learning but also executing real smartness are the new focus in tech innovations. Retail giants are already experimenting with ways to implement these AI bots in their business operations. A strong case in point would be Amazon’s no-checkout cashier-less convenience stores, Amazon Go being tested across different states in North America.

Then there are self-driving grocery stores and automated trucks making home deliveries that are still undergoing trials. Of course, that is not to say that AI dominating retail operations will become the new normal tomorrow, but it is in the offing. Businesses that are in the retail sector for the long haul will stand to gain from their preparedness to embrace this change.

2. The Internet of Things

The ability of devices to interact with humans, understand commands and execute them is passé. The Internet of Things (IoT) puts the limelight on the ability of machines to interact with one another. The slow but consistent development of IoT is shaping up a new ecosystem where our gadgets will be able to operate without human intervention. Besides, the global market size for IoT in retail is expected to grow around 94.44 billion by 2025.

The emergence of IoT will inevitably alter the dynamics of the way consumers interact with retail business and the way businesses interact with distribution networks and supply chain partners. More importantly, it will usher in a connected customer model by relying on smart-store applications like smart shelves, beacons, and customer service robots. Making room for these swift connections powered by the internet will help you build a business model that is future ready.

3. Striking the Online-Offline Balance

It is the age of digital customers where the lines between online and offline existences are forever blurring. Brick and mortar businesses need an online extension to sustain themselves. Now, the spotlight is on understanding the dynamics of virtual and augmented reality and creating a marketing strategy that caters to the customers’ dual persona – considering their social media image and real identity – to encourage continued interactions and conversions.

The result – a complete overhaul of the shopping experience by bringing in a consistent omnichannel approach built around a convenient digital backend. For instance, Oasis, the UK-based fashion retailer is closing the gap between in-store and online purchasing by merging shopping experiences across its mobile app, website, and brick-and-mortar stores.

4. Personalized Shopping Experience

Take a look at how e-commerce websites function – bringing customers exactly what they need, every time, on every device, without fail. This carefully curated shopping experience eliminates the need for buyers to browse through the inventory of online stores to find what they need. Over time, this approach toward shopping has been normalized to an extent that customers expect the same out of their retail shopping experience too. Installing smart screens, tablets etc. is one way of using technology to recreate the same sense of personalization in your retail business.

5. Banking on Data

Big data is the next big thing in terms of business operations. Multinational corporations are pumping in billions of dollars to assimilate and organize this seamless information to create the right kind of marketing strategies. While big data may be out of your reach as a standalone business entity, you can create your own pool of data and use it to offer improved retail experiences for your customers.

Fun quizzes, for instances, are a great way to gather insights into your customers’ buying preferences, which can then be used to offer personalized product recommendations. You can take it a step further by tracking these recommendations to know if they are appealing to your customers and tweak them accordingly.

Related Reading: How Big Data and Analytics are Evolving Customer Experience in Retail

[Courtesy : European Bank for Reconstruction and Development]

Meanwhile, other technologies like virtual and augmented reality will continue to grow in popularity and efficiency. As a retailer, the onus of using these disruptive innovations to offer a seamless customer experience falls on you. Pairing with the right technology partner is the first step. Get in touch with our experts today to uplift your retail experience with cutting-edge software solutions.

Artificial Intelligence (AI), the next big thing in the technology space, is all set to unleash big scale disruptions. The potential impact is more profound in the financial sector, which lives and breathes data.

Artificial Intelligence (AI) powered “smart” machines do not just crunch data. It indulges in self-learning to solve cognitive tasks, until now the forte of the human brain. A traditional analytical engine or software robot requires someone to feed in clear set of rules set in advance. AI-endowed self-learning machines create the rules and frame the logic by itself, as it crunches through millions and millions of rows of historic data, identifying. It learns to perform the required task based on the decisions humans have made previously. AI powered algorithms also learn from mistakes, meaning the predictive analytics in spews becomes more and more accurate with every recorded transaction.

In the financial sector, such AI-powered systems delve into many years of banking, insurance, mortgages and financial trading history, apply deep learning principles, and self-construct algorithms to automate routine tasks, unlocks insights, improve decisions, mitigate risk, and prevent frauds. AI’s natural language processing system can read through regulations, reassembling words into a set of computer-understandable rules.

The CBInsights Conference on the Future of Fintech predicts a rapid pace of disruption in the financial sector, requiring incumbent stakeholders to adapt or risk being submerged in the coming tidal wave of predictive analytics. Apart from conventional tools, Artificial Intelligence-inspired technologies such as blockchains, insurance tech, robo-advising, and other latest cutting-edge innovative tools promise to take analytics to a whole new level.

The following are the potential applications of Artificial Intelligence, which would take over financial services in the near future:

• Improved efficiency through greater automation and better insights
• New models for several traditional functions, especially in stock analysis and wealth management advisory services
• Customization and personalization of financial products, leveraging the services of analytics-driven recommendation engines;
• Improved cyber-security monitoring and responsive systems, and automated fraud detection

Improved Efficiency

AI and robotics powered automated solutions are on the verge of subverting the traditional business models of banks and other financial institutions.

AI-based algorithms automate much of the routine tasks that require extensive workforce now, leading to considerable savings on overheads, accelerated processes, and overall improved efficiency. Being lean and mean is the mantra for success in today’s highly competitive environment, and AI will help financial institutions discover new meaning to lean and mean.

AI-based technology simplifies and automates a host of routine tasks related to money management processes, such as identity authentication, know-your-customer checks, sanctions list monitoring, billing fraud oversight, anti-money laundering monitoring, risk control processes, and more.

AI facilitates several tasks, such as bot messaging, document discovery and more which require extensive manual work now.

Such efficiency improvement interventions have the potential to offer huge ROI for the financial service industry. Banks adopting ROI already report about 40% increase in productivity.

New Trading Models

AI powered predictive analysis has the potential to create entirely new business models in equity, forex and other trades.

Algorithms are already in widespread use to manage risk and exposure. With AI getting better, the scope of intervention will change from refining existing models to becoming the bedrock of newer innovative models. AI-powered algorithms offer brokers and investors access to charts and trends at a much more sophisticated level than before, enabling them to chase short-lived opportunities cutting across venues, asset classes, and geographies. Trading algorithms assess the best liquidity providers during execution. The possibilities are endless.

Thomson Reuters predict algorithmic trading systems to handle 75% of the global trade volumes in the near future. Almost all hedge fund in the world already have a huge data-science team, and deploy sophisticated filters to screen investment ideas.

Tightening regulation mandating extensive record keeping and tracking of all trades, and the need for increased speed in correlating multiple variables, to keep pace with a fast paced and highly competitive environment plays into the eventual dominance of AI.

Deep Personalization and Customization

One of the innovative possibilities with AI in the financial sector is in the realm of marketing. Already, marketers are analyzing behavioral data captured from online activities in a big way, to customize and personalize offerings based on spending habits, social-demographic trends, location, and other preferences.

With AI capable of understanding human language and emotion, marketers of financial products and services would soon take personalization to a whole new level. For instance, AI-powered marketing database could suggest language that elicits certain emotional responses to advertising and email subject lines, and trigger cultural sensitivities to certain words and timing of campaigns. Biggies such as Citi and AmEx already use such tools to good effect, to fine tune their social media and marketing engagement.

Security and Fraud Prevention

Cyber-attacks have brought down many high-profile financial institutions, and continue to be a nightmare for the industry stakeholders. AI promises a fresh breath of hope.

For instance,

• AI based analytic engine capture multivariate time series patterns, to predict anomalies.
• AI based predictive analytics make explicit customer behavior, to flag potential fraud or breach in real-time
• AI can easily correlate and link multivariate transaction data, facilitating easy recovery of layers of bank documentation and data to meet regulatory policies, and establish a money trail to bust financial frauds.
• Sophisticated machine learning and algorithm that monitor market movements in real time allow regulators to prevent major accidental market movements.

Regulations influencing financial markets, such as the European Union Markets in Financial Instruments Directive II (MiFID II) push for greater automation of trades, further boost the prospects of AI in the niche.

Experts estimate AI to surpass human intelligence by 2040. However, the financial services sector need not wait that long for predictive analysis to take over and relegate human brains to a role of supporting AI functions. It nevertheless takes great skill in developing actionable apps and products that leverage AI to unlock the many possibilities on offer. Your best bet is to partner with us and leverage our highly talented and experienced skill set for the task. We are in the thick of AI inspired things, having access to the latest technology and developments, and offer you the unique value proposition of technical competency with a considerable track record in every industry to understand your specific needs and develop highly customized products.

B2B ecosystems constitute the backbone for several critical functions in the enterprise including supply chain optimization, sales, marketing, product development, knowledge management, and even innovation. Such B2B ecosystems thrive on the exchange of information and uninterrupted collaboration.

The growth in popularity of mobile devices is all-pervasive. It is not just retail customers who prefer mobile devices for shopping, but also internal enterprise users, who prefer to get things done through the convenience of their smartphones. The proliferation of the BYOD culture, where employees access the corporate network through apps downloaded on their mobile devices and get the job done even when on the move, has further fueled the growth of mobile services in the enterprise.

In such a state of affairs, the business-to-business (B2B) segment is also seeing a surge in mobile services, side-by-side with the much-publicized business-to-customer (B2C) segment. With mobility now becoming central in the scheme of things for B2B ecosystems, here are key trends in the future of B2B enterprise mobile services.

Mobility Solution Management to Mature from EMM to Seamless Integrated Experience

Enterprises are moving to enterprise mobility management (EMM) solutions to gain control over their ever expanding mobility ecosystem. EMM facilitates laying down policies and structures for mobility management, offering a unified, integrated place for the management of devices, apps, and content, and also to enforce security.

In today’s fast paced tech world, organisations have started moving beyond such traditional EMM functionality as well. Enterprise apps, dealing with distinct processes such as procurement, sales, and more, are still independent to one another, existing in silos, without any functional integration between them, and not factoring in conditions relevant to the user. This is changing, with mobile devices now slowly but surely becoming a central node in a connected ecosystem. In such a changed scenario, B2B app users transition from a broken to integrated frictionless experience, with the mobile experiences evolving in terms of channel, context, and construction.

In response to the changing scenario, EMM is evolving from simple device management, to incorporate advanced features such as user credential distribution, single sign-on (SSO), access control, and more, deeply integrating itself into the core IT of the enterprise. Advanced EMM also offers software development kits (SDKs), enabling app developers to offer content control, encryption, data loss prevention policies, and other security-enhancing features.

Artificial Intelligence to Become Well-Entrenched

All the hype surrounding artificial intelligence and automation notwithstanding, most mobile devices still rely on direct user instructions, forcing business users to move in and out of apps to get things done. However, the spread of AI technologies means many tasks now done manually, even with the convenience of apps, will soon be automated, requiring no human intervention, or at best only validation. The proliferation of new AI inspired technology such as Google Glass and more would also decrease the dominance of apps in the scheme of things, offering users more touch points to get things done in a much easier way. The key to such next generation B2B enabling devices is present and upcoming technology innovations.

Rise of Rapid Application Development and Containers

The latest trends in mobile app development, such as the use of low-code and no-code option for rapid application development, the use of containers, and other trends will apply equally in B2B space, just as it shakes up the B2C space. Mobile app development is increasingly becoming agile as well, with greater thrust on design reuse, modular approach, and more.

In the quest for efficiency, the thrust of application development will not just be on creating new apps and solutions, but also on ensuring online workflows work seamlessly with offline mobile contexts. There will be a greater focus on innovation as well, by applying in-vogue and emerging concepts such as mechanisation, machine learning, natural language,  immersive analytics, and more. There will be increased integration of voice, video, and images in B2B apps as well. The application of MVP (model-view-presenter) architecture will improve user experience significantly, and drive the application of new technology to spur innovation.

B2B Enterprise Seek Increasing Value from Mobility

In an interconnected ecosystem, the real value of mobility goes beyond the value of a particular function or app. The true value realizes when the app completes a piece of the jig-saw, or enabling well-timed business moments that enables the continuation of a relationship, and or opens up additional value. In such a scenario, the focus shifts from data models to service models, where customer or client experience is the overriding concern as opposed to the data or asset the enterprise holds. What the enterprise has is no longer relevant. Rather, what they do with the available resources makes all the difference.

Enterprises are now forging a tighter integration, and even merging mobility with IT team, to ensure the agility brought about by mobility rubs off on the rest of the enterprise.

In the quest for operational excellence, improved engagement with customers and clients, and also a competitive advantage in an increasingly tough business environment, B2B enterprise mobile services are embracing big changes. Those who miss out stand to lose out big time. For instance, mobility-lagging organizations face the risk of key employees becoming frustrated with the lack of productivity enhancing apps, and resigning.

In such a scenario, it pays to rope in a strategic partner who is competent in the mobility space and has solid experience helping enterprises roll out highly successful mobility campaigns and implementations. We are the right partners for you, as evident from our list of satisfied customers, cutting across sectors.

About 71% of enterprises regard mobility as a top priority, and Nasscom estimates the global enterprise mobility market to be worth $140 billion a year. Such widespread adoption of enterprise mobility solutions come as no surprise, considering an employee gains 240 hours a year on average, in terms of productivity, if they use cutting edge mobility solutions at work.

However, mobility is still an emerging technology and an extremely fluid space. New paradigms emerge by the day, and there is no standardized way to apply or govern technologies. Success requires a lot of trailblazing, adopting the latest trends, and tackling challenges head-on. Here are the latest trends to follow, and the challenges to overcome in future.

1. The Rise of Companion Apps

Enterprises are scrambling to develop enterprise apps for both their internal functions and for their customers. Rather than generic apps, there is now an increasing shift to companion apps, or apps aimed at making a specific thing or function easy, rather than try and replicate every single feature of a desktop app.  

Research major Gartner estimates the demand for enterprise mobile apps outstripping available development capacity by five times. The pressing challenge before enterprises is to solve the grave talent crunch, and develop the required mobile apps, or run the risk of losing out big time in productivity and new possibilities. Enterprises also face a far daunting challenge of unlocking data silos to ensure mobility solutions work optimally.

2. BYOD Becomes Much More than a Trend

The Bring-Your-Own-Devices (BYOD) culture is already well-entrenched in the corporate world. The tremendous productivity and efficiency benefits it brings out, coupled with the rising popularity of mobility apps have graduated BYOD to much more than a trend. BYOD is fast becoming the established norm in many enterprises.

BYOD however brings new challenges in its wake. Employee devices rarely feature the required enterprise-grade security features, and the enterprise IT team remains hard-pressed to manage the wide range of BYOD devices that operate within the corporate network. Mobile device management (MDM) solutions, implemented to ease out potential pain points often hinder the productivity or fluidity of the work carried out on the devices.

Enterprises need to devise effective mobility policies that secure employee-owned devices. Centralizing the management of mobile devices, akin to remote management of PC and notebook desktops enable enterprises to have control over the devices in use.  The trick is to roll out such policies without compromising the privacy of employees.

3. Accelerated Migration to the Cloud

The increased maturity of cloud services, the easy availability of affordable and reliable cloud services, and the game-changing possibilities with big data has accelerated migration of enterprise solutions to the cloud.

The increased popularity of the cloud notwithstanding, security remains a pressing challenge. Enterprises seeking to reconcile the security issue increasingly opt for hybrid solutions, hosting mission critical apps on premises and other apps on the cloud. The increasing stakes of security mean regulatory security controls may not be too far away.

4. Personal Assistant-Friendly Features Go Mainstream

Artificial Intelligence (AI) technology has already made its mark in computing. More and more enterprise apps are now becoming “smart,” leveraging features such as those offered by Siri, Google Now, Cortana, and other smart personal assistants. There is a boom of “smart apps” that allow users to control lighting, heating and security systems using Siri.

As AI makes the enterprise more and more dependent on technology, security becomes even more important than before, and security breaches run the risk of bringing down the entire enterprise. The onus is on the enterprise to guarantee effective security.

A key focus of enterprise network security now is effective threat management, or identifying potential vulnerabilities and operating system-specific threats. A multilayered approach that authenticates enterprise applications, reinforced with agile device management solution is the way to go for most enterprises.

5. The Rise of Citizen Developers

The growing demand for enterprise apps has resulted in the rise and popularity of cross-platform low-code and no-code app development platforms, which offer easy drag and drop options and accelerate the app development timeline greatly. Side by side with such new app development technologies is the rise of citizen developers, who can leverage such platforms to develop apps on the fly, with little or no coding knowledge.

Accelerated app development and the rise of citizen developers present its own challenge of half-baked implementation and serious vulnerabilities in the developed software. Native apps, developed for the platform, are generally more feature-rich and deliver a better user experience, over the HTML5 based cross-platform app development. Native apps also allow administrators more control.
Your best bet to overcome such challenges, while tapping into the latest trends at the same time, is to partner with a sound professional developer like us. Partnering with us allow you to leverage our wealth of experience and expertise in rolling out highly intuitive enterprise mobility solution that meets all your objectives, in a cost effective manner.