#What are the main challenges companies face when adopting machine learning (ML)

[intelisync]

Overcoming the 60% Struggle with ML Adoption: Key Insights

In the race to stay competitive, companies are turning to machine learning (ML) to unlock new levels of efficiency and innovation. But what does it take to successfully adopt ML?

Machine learning (ML) is a transformative technology offering personalized customer experiences, predictive analytics, operational efficiency, fraud detection, and enhanced decision-making. Despite its potential, many companies struggle with ML adoption due to data quality challenges, a lack of skilled talent, high costs, and resistance to change.

Effective ML implementation requires robust data management practices, investment in training, and a culture that embraces innovation. Intelisync provides comprehensive ML services, including strategy development, model building, deployment, and integration, helping companies overcome these hurdles and leverage ML for success.

Overcoming data quality and availability challenges is crucial for building effective ML models. Implementing robust data management practices, including data cleaning and governance, ensures consistency and accuracy, leading to reliable ML models and better decision-making. Addressing the talent gap through training programs and partnerships with experts like Intelisync can accelerate ML project implementation. Intelisync’s end-to-end ML solutions help businesses navigate the complexities of ML adoption, ensuring seamless integration with existing systems and maximizing efficiency. Fostering a culture of innovation and providing clear communication and leadership support are vital to overcoming resistance and promoting successful ML adoption.

Successful ML adoption involves careful planning, strategic execution, and continuous improvement. Companies must perform detailed cost-benefit analyses, start with manageable pilot projects, and regularly review and optimize their AI processes. Leadership support and clear communication are crucial to fostering a culture that values technological advancement. With Intelisync’s expert guidance, businesses can bridge the talent gap, ensure smooth integration, and unlock the full potential of machine learning for their growth and success. Transform your business with Intelisync’s comprehensive ML services and stay ahead in the competitive Learn more....

[cevioustech]

The Future of Intelligence: Exploring the Transformative Power of Cloud AI

The world is now a global village, and this is where artificial intelligence (AI) comes in; the intelligent tools that run our daily activities from voice commands to recommendation. Yet, there are numerous organisations and individuals using AI and wishing to do it in future experience various problems such as high levels of infrastructure requirements, need to have specialists in this area, and problems with scaling the processes. And that is where Cloud AI enters a game — a new paradigm of AI as a service that doesn’t require the scale of investments like it used to. Well, what is Cloud AI and why is it revolutionising the way we can regard intelligence? Let’s dive in.

Understanding Cloud AI

In its basic definition, cloud AI is the means of obtaining AI solutions and tools through the usage of cloud solutions. This means that Cloud AI doesn’t need dedicated on-site hardware or a group of data scientists to train models and make AI available to everyone with internet access. Some of the readily available powerful AI tools available in the market are Google Cloud AI, Amazon Web Service AI, Microsoft Azure AI and IBM AI which have simpler forms as API that can be integrated to the systems irrespective of the technologist level of the organization.

Why Cloud AI is a Game Changer

There are three key reasons why Cloud AI is transforming the landscape:

Cost-Effectiveness: Classic AI systems are tremendously computationally intensive which in turn requires large investments in hardware. With Cloud AI, one only pays for the service they employ thus making it affordable. Cloud providers take care of all the issues regarding the hardware and software—no need to worry about it, security and system upgrades included.

Scalability: Whether the user is a small, scrappy startup testing the waters with deep learning, or an established enterprise with millions of users to manage, Cloud AI can be easily scaled up or down to meet the particular user’s needs. It enables organizations to introduce products and services to the market with a level of efficiency that does not consider infrastructure bottlenecks.

Accessibility: One of the major challenges that the adoption of AI has faced is that, it has been realized that it requires expertise. Cloud AI provides ways for adopting complex and powerful AI solutions and pre-configured AI solutions for individuals who do not have programming skills.

Key Applications of Cloud AI

The versatility of Cloud AI is vast, touching numerous industries and transforming business processes. Here are some of the most impactful applications:

Machine Learning (ML) Models

Most AI technologies rely on some form of machine learning, yet constructing and training our models is challenging. Various services that work in the cloud have built-in pre-trained AI models that can be returned as necessary. For instance, when using the Google Cloud, AutoML gives clients an opportunity to create new models for different tasks such as image and text classification through interface, and not through coding. This has ensured that new and small businesses seeking to adopt ML for operations such as customer classification, risk management, and recommendation, can easily do so.

Natural Language Processing (NLP)

It involves Text analysis and Speaking and understanding the language of Humans by The machines. At present, cloud AI services provide dependable NLP tools; thus business solutions enable the components for language translation, sentiment analysis, and text summarization. As applied to customer service this is really helpful – one can think of self-learning chatbots that can handle questions or even analyze customer feedback in the hope of enhancing user satisfaction.

Computer Vision

That way, with Cloud AI, companies can take advantage of such technologies, like computer vision, with less expenses in infrastructure. The main areas of use are face identification, object recognition, as well as video analysis at a higher level. For instance, AWS has an AI service named Rekognition that deals with Images and videos to determine objects, text within images among others. Some of the applications of computer vision include in the retail business, the healthcare business, security firms and even in the creation of concepts that make more personalized customers’ touch points.

Speech Recognition

Speech to text has really evolved and thanks to Cloud AI, it is provided as a service which means its more accessible. Speech recognition can make information from voice and audio transcribed and written text which is helpful for the disabled user and new interfaces. Google Cloud Speech-to-Text for instance is popular for converting audio to text enabling many, from voice assistants to a customer care transcribing service.

Data Analysis and Business Insights

The real-time capability of Cloud AI allows data of large quantities to be run through and analyzed. This capability assists businesses to analyze their data in ways that make it easier to know trends, make analyses on the abnormalities, and make concrete decisions based on the outcome. For example, Cognitive Services of the Microsoft Azure offer analytical tools that may help companies to analyze customer actions, define better prices for their goods and services, or avoid possible inefficiencies.

Benefits of Cloud AI for Businesses

Cloud AI bears several significant benefits that many enterprises will find particularly appealing as the world becomes increasingly reliant on technology.

Speed and Agility: Using Cloud AI, organizations can put in place intelligent solutions faster and are able to follow shifting market demands faster than before. That is why, such sectors as retail companies can employ Cloud AI to analyze customer data and to start precise marketing campaigns during several days.

Data Security and Compliance: Cloud providers still use high measures to steer clear of the international laws of the handling of information, it’s safer for companies to handle sensitive details. They also afford methods for the anonymisation and encryption of data, which can also be useful on their own.

Innovation Opportunities: Cloud AI enables companies to test out new business models without high costs of initial investments associated with these ideas. The applied AI allows firms to iron out any problems that it might possess and develop its potential before investing a great deal of money in it.

Enhanced Customer Experiences: Advancement in artificial intelligence works towards making communications personalised and automated hence enhancing the satisfaction of the customer. For example, such features as proper searching and filtering of the required information and the ability to create intelligent and friendly chatbots can help clients to feel unique, and thus are favorable for the brand.

Challenges and Future of Cloud AI

In the same breath, Cloud AI comes with its own challenges as we will discuss before introducing more information about this AI. Security concerns have not disappeared, and data protection is still a hot issue as more and more businesses collaborate with third parties to store and process their data. Thanks to regulations such as the GDPR coming into play, cloud providers are constantly thinking about compliance, yet, businesses cannot simply rely on such providers to do all the work for them. Another problem is the “lock-in” situation when changing a provider or moving the data becomes painful. To this end, to avoid sticking to one provider, many businesses are using multiple clouds services in different situations. In the next paradigm, the Cloud AI will also have promising growth in the days to come then concepts such as quantum computing, edge IA, and federated learning. These advances will further strengthen Cloud AI and increase its adaptability and security – and open up new opportunities for every industry.

Conclusion

Cloud AI is revolutionizing the capabilities of enterprises and is leading to the mainstream availability of the most sophisticated tools. Cloud AI has a myriad of applications including the training of deep learning models, enhancing customer experience through NLP, among others, that are helping companies deliver technological advancements at higher rates than ever before. This means that as the new generations of AI technologies emerge, the concept of Cloud AI will be instrumental in designing the new world where intellect will be an open resource. To stay relevant and on top of competition, Cloud AI must function as not just a strategy, but an imperative for enterprises. With Cloud AI, companies – no matter if they are a startup or an enterprise – can leverage their data to its full extent and provide their clients with meaningful insights about the world around them. Now is the right time to engage with Cloud AI.

For more information visit = https://cevious.com/

[sjainventuresltd]

Challenges Of Artificial Intelligence In Finance Sector

Problems with algorithmic tradingData availabilityRead:  Best Artificial Intelligence Business Ideas in 2022 Regulatory issuesImpact of AI technology in the short termImpact of AI technology in the long termPredictions for the futureAlso Read: Advantages of Blockchain Technology in Healthcare Sector

Artificial intelligence is revolutionizing the way we live, work, and play in this day and age. It’s affecting every industry, as well as our day-to-day lives, and now, it’s even starting to affect the finance sector! While artificial intelligence can mean a lot of different things, what it boils down to is machines that can act or think as humans do—think robots! Today’s machines are able to learn tasks faster than humans, but how does that affect the finance sector?

 The finance sector has faced many changes over the past few decades, including the rise of artificial intelligence (AI). AI can help make key financial decisions more quickly and accurately than ever before by combining large amounts of structured and unstructured data in real-time, but that doesn’t mean it’s ready to take over financial management just yet. There are still several hurdles preventing companies from fully implementing AI in their finance departments.

 For decades, AI has been the subject of movies and science fiction, but it’s now becoming an important tool in the finance sector in real life as well. However, AI brings with it its own set of challenges that finance managers need to be aware of if they’re going to use AI successfully.

So What Are The Challenges of Artificial Intelligence In Finance Sector?

 One of the challenges of artificial intelligence in finance sector is algorithmic trading. This is when computer programs are used to buy and sell assets in order to make a profit. However, there are several problems with this type of trading. First, creating an algorithm that can beat the market can be difficult. Second, even if an algorithm is successful, it may only be successful for a short time before other traders catch on and start using similar algorithms. Third, algorithms can make mistakes, and these mistakes can cost a lot of money. Fourth, algorithmic trading can increase market volatility. Fifth, it can be difficult to monitor all of the trades that are being made by algorithms. Sixth, algorithmic trading can lead to market manipulation.

 Another main challenges of artificial intelligence in finance sector is data availability. There is a lot of data that is needed to train machine learning models, and it can be difficult to obtain. Another challenge is that financial data is often unstructured, making it difficult to use for predictive modeling. Additionally, financial data can be volatile and change rapidly, making it difficult for models to keep up. Another challenge is that many regulations in the financial sector can restrict what data can be used and how it can be used. Finally, the finance sector is risk-averse, so there may be hesitance to adopt new technologies like artificial intelligence.

The third most affecting challenges of artificial intelligence in the finance sector is regulatory issues. With the rapid pace of technology change, it's hard for regulators to keep up. This can create uncertainty and risk for businesses that are using AI. Additionally, there are concerns about data privacy and security when it comes to AI. due to a lot of sensitive data being stored in financial institutes, which needs to be secured. Another challenge is that AI can be used to manipulate markets or commit fraud. This needs to be protected by taking steps and being aware of these kinds of risks.

 The finance sector has been one of the early adopters of artificial intelligence (AI) and machine learning (ML) technologies. AI can help banks automate processes, identify financial risks, target new customers, and prevent fraud. However, there are also challenges that the finance sector faces when implementing AI. For example, data privacy concerns and the need for explainable AI.

 In the long term, AI will likely have a profound impact on the finance sector. For example, AI could enable real-time monitoring of financial markets, which would provide regulators with better insights into risks. AI could also help banks become more efficient and provide better customer service.

 It is estimated that by 2025, AI will be responsible for $2.9 trillion in cost savings across the finance sector. This is a huge increase from the $232 billion in cost savings that were seen in 2018. With the rapid adoption of AI, the finance sector is expected to see even more cost savings in the years to come. However, there are still some challenges that need to be addressed before AI can reach its full potential in the finance sector.

The development of artificial intelligence is only beginning. The initial deployments typically don't result in significant gains owing to the complexities involved. But it can't be disregarded. The financial industry will undergo a major upheaval due to artificial intelligence. This is the right time to get on board and start the journey of artificial intelligence in the finance sector. Start now!

[mitchellbelstead6675]

Audio and Video Analytics Software Market Investment Opportunity and Projected Huge Growth By 2027

Audio and Video Analytics Software Market: Introduction

Video analytics is an automatic analysis and computerized processing of video content generated, collected, or monitored during video surveillance. Audio analytics is about analyzing and understanding audio signals captured by digital devices.  

Video analytics software is used to analyze video feeds and alert security, whereas audio analytics software is used to detect audio for video and security systems. In most cases, audio analytics software are processed with video analytics software, for surveillance in public areas.

The COVID-19 outbreak has been the main catalyst for the growth and adoption of audio and video analytics software. Organizations, institutions, and governments are now focusing on surveillance across the world. Besides, governments globally have ramped up measures to adopt audio and video analytics software to swiftly respond to the crisis of COVID-19. This is expected to enhance the demand for audio and video analytics software among government agencies for public safety.

Request A Sample Copy Of The Report

https://www.transparencymarketresearch.com/sample/sample.php?flag=S&rep_id=78615

Global Audio and Video Analytics Software Market: Market Dynamics

Video surveillance needs are increasing, and the demand for video and audio analytics is growing in various sectors such as retail, government, and BFSI, among others. This increasing need is facilitating the growth of the audio and video analytics software market.

Another factor which is expected to drive the audio and video analytics software market during the forecast period is the inefficiency of surveillance authorities to physically monitor and recognize suspicious events from various video data, thus leading to the growing need to find actionable observations from a large amount of audio and video data generated.  

Rising concerns about ensuring the protection and safety of the population across developed and developing countries is projected to enhance the growth of the audio and video analytics software market.

An increase in demand for IP-based security cameras is expected to increase the demand for audio and video analytics software.

Growing adoption of Artificial Intelligence (AI), Internet of Things (IoT) and Machine Learning (ML) for security systems is expected to drive the demand for audio and video analytics software across the world.

Increasing investment for smart city development from government bodies and private construction companies  across developed and developing economies is expected to trigger the demand for audio and video analytics software during the forecast period 2020- 2030.

North America to Account for Major Share of the Global Audio and Video Analytics Software Market

In terms of region, the global audio and video analytics software market can be divided into North America, Europe, Asia Pacific, Middle East & Africa, and South America.

North America is anticipated to lead the global audio and video analytics software market, due to government initiatives and investment in smart city development in the U.S and Canada. This factor accelerates the growth of the audio and video analytics software market in the North America region.

The audio and video analytics software market in Asia Pacific is expected to hold significant share due to rising infrastructure investments by governments for smart cities in developing countries. In addition, the China government has launched an initiative called ‘Made in China 2025,’ to promote industrial growth. In the same way, India launched the ‘Make in India’ initiative, all of which is expected to fuel the growth of the audio and video analytics software market across Asia Pacific.

Request For Covid19 Impact Analysis

https://www.transparencymarketresearch.com/sample/sample.php?flag=covid19&rep_id=78615

Global Audio and Video Analytics Software Market: Competitive Landscape

Key Players Operating in the Global Audio and Video Analytics Software Market

Companies operating in the audio and video analytics software market are increasingly investing in research and development to develop new and innovative techniques to provide audio and video analytics software. The audio and video analytics software market is highly fragmented with the presence of numerous manufacturers in both developed and developing regions. Key players operating in the global audio and video analytics software market are:

Adobe.

Analytics Vidhya

Audio Analytic Ltd.

Avaya Inc.

Aventura Technologies, Inc.

Avigilon Corporation

AxxonSoft.

Bosch Sicherheitssysteme GmbH

Cisco Systems, Inc.

Genesys.

Honeywell Corporation

IBM Corporation

Inflow Technologies

Milestone Systems A/S

NICE Ltd.

SESTEK.

Verint.

Global Audio and Video Analytics Software Market: Research ScopeGlobal Audio and Video Analytics Software Market Segmentation, by Component

Software

Services

Global Audio and Video Analytics Software Market Segmentation, by Deployment

On- premise

Cloud

Global Audio and Video Analytics Software Market Segmentation, by End-user

BFSI

Retail and e-commerce

IT & Telecom

Healthcare

Hospitality

Transportation and Logistics

Education

Manufacturing

Defense

Others (Life Sciences, and Construction)

This study by TMR is all-encompassing framework of the dynamics of the market. It mainly comprises critical assessment of consumers' or customers' journeys, current and emerging avenues, and strategic framework to enable CXOs take effective decisions.

Our key underpinning is the 4-Quadrant Framework EIRS that offers detailed visualization of four elements:

Customer Experience Maps

Insights and Tools based on data-driven research

Actionable Results to meet all the business priorities

Strategic Frameworks to boost the growth journey

The study strives to evaluate the current and future growth prospects, untapped avenues, factors shaping their revenue potential, and demand and consumption patterns in the global market by breaking it into region-wise assessment.

The following regional segments are covered comprehensively:

North America

Asia Pacific

Europe

Latin America

The Middle East and Africa

The EIRS quadrant framework in the report sums up our wide spectrum of data-driven research and advisory for CXOs to help them make better decisions for their businesses and stay as leaders.

Below is a snapshot of these quadrants.

1. Customer Experience Map

The study offers an in-depth assessment of various customers’ journeys pertinent to the market and its segments. It offers various customer impressions about the products and service use. The analysis takes a closer look at their pain points and fears across various customer touchpoints. The consultation and business intelligence solutions will help interested stakeholders, including CXOs, define customer experience maps tailored to their needs. This will help them aim at boosting customer engagement with their brands.

2. Insights and Tools

The various insights in the study are based on elaborate cycles of primary and secondary research the analysts engage with during the course of research. The analysts and expert advisors at TMR adopt industry-wide, quantitative customer insights tools and market projection methodologies to arrive at results, which makes them reliable. The study not just offers estimations and projections, but also an uncluttered evaluation of these figures on the market dynamics. These insights merge data-driven research framework with qualitative consultations for business owners, CXOs, policy makers, and investors. The insights will also help their customers overcome their fears.

3. Actionable Results

The findings presented in this study by TMR are an indispensable guide for meeting all business priorities, including mission-critical ones. The results when implemented have shown tangible benefits to business stakeholders and industry entities to boost their performance. The results are tailored to fit the individual strategic framework. The study also illustrates some of the recent case studies on solving various problems by companies they faced in their consolidation journey.

4. Strategic Frameworks

The study equips businesses and anyone interested in the market to frame broad strategic frameworks. This has become more important than ever, given the current uncertainty due to COVID-19. The study deliberates on consultations to overcome various such past disruptions and foresees new ones to boost the preparedness. The frameworks help businesses plan their strategic alignments for recovery from such disruptive trends. Further, analysts at TMR helps you break down the complex scenario and bring resiliency in uncertain times.

You May Also Like PRNewswire on https://www.prnewswire.com/news-releases/global-saturating-kraft-paper-market-to-grow-as-packaging-sector-endorses-new-materials-and-technologies--transparency-market-research-301066828.html

The report sheds light on various aspects and answers pertinent questions on the market. Some of the important ones are:

1. What can be the best investment choices for venturing into new product and service lines?

2. What value propositions should businesses aim at while making new research and development funding?

3. Which regulations will be most helpful for stakeholders to boost their supply chain network?

4. Which regions might see the demand maturing in certain segments in near future?

5. What are the some of the best cost optimization strategies with vendors that some well-entrenched players have gained success with?

6. Which are the key perspectives that the C-suite are leveraging to move businesses to new growth trajectory?

7. Which government regulations might challenge the status of key regional markets?

8. How will the emerging political and economic scenario affect opportunities in key growth areas?

9. What are some of the value-grab opportunities in various segments?

10. What will be the barrier to entry for new players in the market?

[edwardbailey286]

Audio and Video Analytics Software Market expands with the rise in world population

Audio and Video Analytics Software Market: Introduction

Video analytics is an automatic analysis and computerized processing of video content generated, collected, or monitored during video surveillance. Audio analytics is about analyzing and understanding audio signals captured by digital devices.  

Video analytics software is used to analyze video feeds and alert security, whereas audio analytics software is used to detect audio for video and security systems. In most cases, audio analytics software are processed with video analytics software, for surveillance in public areas.

The COVID-19 outbreak has been the main catalyst for the growth and adoption of audio and video analytics software. Organizations, institutions, and governments are now focusing on surveillance across the world. Besides, governments globally have ramped up measures to adopt audio and video analytics software to swiftly respond to the crisis of COVID-19. This is expected to enhance the demand for audio and video analytics software among government agencies for public safety.

Request A Sample Copy Of The Report

https://www.transparencymarketresearch.com/sample/sample.php?flag=S&rep_id=78615

Global Audio and Video Analytics Software Market: Market Dynamics

Video surveillance needs are increasing, and the demand for video and audio analytics is growing in various sectors such as retail, government, and BFSI, among others. This increasing need is facilitating the growth of the audio and video analytics software market.

Another factor which is expected to drive the audio and video analytics software market during the forecast period is the inefficiency of surveillance authorities to physically monitor and recognize suspicious events from various video data, thus leading to the growing need to find actionable observations from a large amount of audio and video data generated.  

Rising concerns about ensuring the protection and safety of the population across developed and developing countries is projected to enhance the growth of the audio and video analytics software market.

An increase in demand for IP-based security cameras is expected to increase the demand for audio and video analytics software.

Growing adoption of Artificial Intelligence (AI), Internet of Things (IoT) and Machine Learning (ML) for security systems is expected to drive the demand for audio and video analytics software across the world.

Increasing investment for smart city development from government bodies and private construction companies  across developed and developing economies is expected to trigger the demand for audio and video analytics software during the forecast period 2020- 2030.

North America to Account for Major Share of the Global Audio and Video Analytics Software Market

In terms of region, the global audio and video analytics software market can be divided into North America, Europe, Asia Pacific, Middle East & Africa, and South America.

North America is anticipated to lead the global audio and video analytics software market, due to government initiatives and investment in smart city development in the U.S and Canada. This factor accelerates the growth of the audio and video analytics software market in the North America region.

The audio and video analytics software market in Asia Pacific is expected to hold significant share due to rising infrastructure investments by governments for smart cities in developing countries. In addition, the China government has launched an initiative called ‘Made in China 2025,’ to promote industrial growth. In the same way, India launched the ‘Make in India’ initiative, all of which is expected to fuel the growth of the audio and video analytics software market across Asia Pacific.

Global Audio and Video Analytics Software Market: Competitive Landscape

Key Players Operating in the Global Audio and Video Analytics Software Market

Companies operating in the audio and video analytics software market are increasingly investing in research and development to develop new and innovative techniques to provide audio and video analytics software. The audio and video analytics software market is highly fragmented with the presence of numerous manufacturers in both developed and developing regions. Key players operating in the global audio and video analytics software market are:

Adobe.

Analytics Vidhya

Audio Analytic Ltd.

Avaya Inc.

Aventura Technologies, Inc.

Avigilon Corporation

AxxonSoft.

Bosch Sicherheitssysteme GmbH

Cisco Systems, Inc.

Genesys.

Honeywell Corporation

IBM Corporation

Inflow Technologies

Milestone Systems A/S

NICE Ltd.

SESTEK.

Verint.

Global Audio and Video Analytics Software Market: Research ScopeGlobal Audio and Video Analytics Software Market Segmentation, by Component

Software

Services

Global Audio and Video Analytics Software Market Segmentation, by Deployment

On- premise

Cloud

Global Audio and Video Analytics Software Market Segmentation, by End-user

BFSI

Retail and e-commerce

IT & Telecom

Healthcare

Hospitality

Transportation and Logistics

Education

Manufacturing

Defense

Others (Life Sciences, and Construction)

This study by TMR is all-encompassing framework of the dynamics of the market. It mainly comprises critical assessment of consumers' or customers' journeys, current and emerging avenues, and strategic framework to enable CXOs take effective decisions.

Our key underpinning is the 4-Quadrant Framework EIRS that offers detailed visualization of four elements:

Customer Experience Maps

Insights and Tools based on data-driven research

Actionable Results to meet all the business priorities

Strategic Frameworks to boost the growth journey

The study strives to evaluate the current and future growth prospects, untapped avenues, factors shaping their revenue potential, and demand and consumption patterns in the global market by breaking it into region-wise assessment.

The following regional segments are covered comprehensively:

North America

Asia Pacific

Europe

Latin America

The Middle East and Africa

The EIRS quadrant framework in the report sums up our wide spectrum of data-driven research and advisory for CXOs to help them make better decisions for their businesses and stay as leaders.

Request For Covid19 Impact Analysis

https://www.transparencymarketresearch.com/sample/sample.php?flag=covid19&rep_id=78615

Below is a snapshot of these quadrants.

1. Customer Experience Map

The study offers an in-depth assessment of various customers’ journeys pertinent to the market and its segments. It offers various customer impressions about the products and service use. The analysis takes a closer look at their pain points and fears across various customer touchpoints. The consultation and business intelligence solutions will help interested stakeholders, including CXOs, define customer experience maps tailored to their needs. This will help them aim at boosting customer engagement with their brands.

2. Insights and Tools

The various insights in the study are based on elaborate cycles of primary and secondary research the analysts engage with during the course of research. The analysts and expert advisors at TMR adopt industry-wide, quantitative customer insights tools and market projection methodologies to arrive at results, which makes them reliable. The study not just offers estimations and projections, but also an uncluttered evaluation of these figures on the market dynamics. These insights merge data-driven research framework with qualitative consultations for business owners, CXOs, policy makers, and investors. The insights will also help their customers overcome their fears.

3. Actionable Results

The findings presented in this study by TMR are an indispensable guide for meeting all business priorities, including mission-critical ones. The results when implemented have shown tangible benefits to business stakeholders and industry entities to boost their performance. The results are tailored to fit the individual strategic framework. The study also illustrates some of the recent case studies on solving various problems by companies they faced in their consolidation journey.

4. Strategic Frameworks

The study equips businesses and anyone interested in the market to frame broad strategic frameworks. This has become more important than ever, given the current uncertainty due to COVID-19. The study deliberates on consultations to overcome various such past disruptions and foresees new ones to boost the preparedness. The frameworks help businesses plan their strategic alignments for recovery from such disruptive trends. Further, analysts at TMR helps you break down the complex scenario and bring resiliency in uncertain times.

You May Also Like PRNewswire on https://www.prnewswire.com/news-releases/sake-brewery-industry-to-play-a-positive-role-in-cubitainers-market-growth-from-2020-to-2028-transparency-market-research-301104740.html

The report sheds light on various aspects and answers pertinent questions on the market. Some of the important ones are:

1. What can be the best investment choices for venturing into new product and service lines?

2. What value propositions should businesses aim at while making new research and development funding?

3. Which regulations will be most helpful for stakeholders to boost their supply chain network?

4. Which regions might see the demand maturing in certain segments in near future?

5. What are the some of the best cost optimization strategies with vendors that some well-entrenched players have gained success with?

6. Which are the key perspectives that the C-suite are leveraging to move businesses to new growth trajectory?

7. Which government regulations might challenge the status of key regional markets?

8. How will the emerging political and economic scenario affect opportunities in key growth areas?

9. What are some of the value-grab opportunities in various segments?

10. What will be the barrier to entry for new players in the market?

[impactqa]

How IoT and Machine Learning is changing the World?

IoT and Machine Learning are getting smarter. Companies are incorporating artificial intelligence (AI)-in specific, machine learning into their IoT apps. From smart thermostats to wireless sensors, IoT devices are gradually but definitely garnering mainstream adoption. Besides, virtual assistants (like Siri, Alexa, and Cortana,) are only making this technology easy to adopt.

The core purpose behind advancement in the IoT space is to help information move between parties smoothly and seamlessly. For as much as we condemn technology, we can all recall a moment when the right message has appeared at the right time, with perfect user experience.

The truth of IoT and artificial intelligence – specifically machine learning – is far less sinister. Besides, it’s not something of the far-off future. It’s completely simplifying and shaping the way we live, travel, work, and communicate. In reality, it’s shaping our lives smartly and the decisions we make. Though, it is even how you came across this blog.

The proliferation of smart IoT devices is shaping the future and gives instant access to the information world. Let’s have a glance at these burning IoT statistics:

There are about 17 billion inter-connected devices in the globe as of 2018. With more than 7 Billion of these IoT (internet of things) devices. (Source- IoT Analytics)

According to McKinsey Global Institute, each second, 127 new IoT devices connect to the net.

The global IoT market is expected to be worth $1.7T in 2019. (Source: CBI Insights)

What is Machine Learning?

ML is one of the critical components (driving force) of AI, where a computer is programmed with the ability to improve its performance. In short, Machine Learning is all about analyzing big data- the automatic extraction of information & using it to make predictions. Besides this, decipher whether the prediction was correct and if wrong, try to make a correct prediction.

Netflix, Amazon, Google, and other gigantic E-commerce platforms use it to bring semantic outcome. It is based on algorithms that analyze a user’s hunt, buying, and viewing history to predict what they are likely to want.

Machine learning is being gradually more integrated into all verticals and every aspect of our time. Through the automation of physical labor, improving our connectivity and shaping the future of AI and the IoT.

What is the Industrial Internet of Things (IIoT)?

The Industrial IoT or Industry 4.0 or the 4th industrial revolution is all names given to the use of the Internet of Things technology in a business setting. The concept is similar to the consumer IoT; to use wireless networks, a mix of sensors, big data and analytics to optimize industrial processes.

The Internet of Things devices help provides information, control, and analytics to connect a world of hardware devices and high-speed internet.

We can separate the Industrial IoT into two main categories:

Industrial IoT- Where the local network is derived from any of different technologies. The Internet of Things device will send out data over the global Internet.

Commercial IoT- Where local communication is either Ethernet (wired or wireless) or Bluetooth. The Internet of Things device will normally communicate only with local devices.

How IoT (Internet of Things) and Machine Learning changing the world?

The Internet of Things and ML are enhancing the way we live and communicate our lives. Exponential growth and advancements are being made in mind-reading technology. For instance, the AlterEgo headset easily responds to our brainwaves to control appliances. Besides, Alexa and Amazon’s Echo enables the voice-activated control of your high-tech smart-house.

This amalgamation of IoT and machine learning is changing various industries and the relationships that companies have with their clients. Businesses can easily gather and transform data into valuable information with IoT.

IoT is also transforming business models by aiding companies to move from concentrating on products & services to companies that give the best outcomes. By impacting organizations’ business models, the blend of IoT-enabled devices & sensors with ML creates a collaborative world that aligns itself around results & innovation.

Challenges- IoT and Machine Learning

It is no wonder that enterprises are inundated with data that comes from IoT devices & is seeking AI to help manage the devices and gain insight. Yet, it is tough to manage and extract crucial information from these systems than we might expect.

There are aspects to IoT like data storage, connectivity, security, app development, system integration, and even processes that are changing in this space. Another layer of complexity with the Internet of Things has to do with functionality level.

Critical challenges companies face with IoT and ML are with the application, ease of access, and analysis of IoT data. If you have a set of data from varied sources, you can run some statistical analysis with that data. However, if you want to be proactive in predicting events to take future actions, a business needs to learn how to use these technologies.

Many firms are turning to the main cloud platform provides — for instance, Google, Amazon, Microsoft, Alibaba Cloud, or IBM. These companies offer a range of services to store IoT data and prepare it for data analytics, plus to train and run machine-learning models. Besides creating graphs, dashboards, and other simple-to-grasp layouts to visualize the information these models generate. Overall, IoT and machine learning are combined to provide high visibility and control of the wide range of sensors and devices connected to the Internet.

Wrap Up

Futurists say ML (Machine Learning) and the Internet of Things (IoT) will transform business profoundly than the digital and industrial revolutions combined.

Are there some kinds of risks? Yes, as with any new technology, we have to accept both the profit and risks that come with mainstream adoption. We can do this with the confidence only when these technologies are tested against several odds. One of the innovative solutions for seamless operation flow is IoT testing. There will be several other types of testing that require to be considered to cover the comprehensive functionality of IoT devices.

As part of ImpactQA’s Advisory Services, we also provide an implementation plan to help our clients improve time-to-market while keeping their business goals in mind. We use our assessment frameworks, based on industry best standards, focusing on processes, tools, and infrastructure.

Collaborate with our specialists to improve all QA areas–people, processes, tools, and infrastructure across the delivery life-cycle.

If you enjoyed this post, please leave a comment below!!

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4 Key Digital Transformation Trends in Hospitality

As new technologies evolve and market disruptors reach their critical mass, every industry faces the need for a core transformation. The hospitality industry is no exception. Modern guests expect to have a customer-centric hotel experience starting as early as choosing a destination and learning more about accommodations and property. Guests also want more comfort, features, convenience, faster customer service, new experiences, and the list is growing.

Most of the customer-focused features offered by most large and small hotel operators and various chains are quite rudimentary vs. the possibilities brought by cutting-edge technologies. Therefore, hotel operators need to be aware of the looming offerings of competitors and play catch-up, and/or pioneer some of the newest features their clients will welcome and perceive as premium. Given this need for a rapid change to sustain the pace of the fourth industrial revolution that is upon us, demand for technologies that help address the challenges in an effective and meaningful way may seem overwhelming.

As Intellectsoft has been working with leading hotel operators, helping them implement the latest technologies, we share our expertise on what to expect in the hotel industry's technology landscape in future. From Internet of Things (IoT) based smart room features allowing guests to control all amenities, to autonomous artificial intelligence based chatbot solutions enhancing response time and automating basic hotel experience, let's explore four main technologies underpinning digital transformation trends in the hospitality industry — IoT, artificial intelligence, augmented and virtual reality, and mobile.

Internet of Things (IoT): Smart Rooms, Beacons & Tablets

As in any other business today, customers expect seamless experience shifts between their home, car, airplane, and chosen hospitality amenities. The level of technology adoption at each instance should be more advanced — or at minimum on par with what customers have at home, i.e. the ability to stream subscription-based content or control devices like air conditioners without moving about. Guests expect to extract maximum value from what they paid for — a demanding target in the age of market disruptors like Airbnb. Enabled by the combination of IoT and mobile, hotel rooms are already adopting smart features to partially address those expectations of their end customers.

A smart room enables guests to control amenities and order any guest services via a hotel's mobile app or voice assistant application based on Google Home or Amazon Alexa. In smart rooms, air conditioners, media sets, lights, window shades, and other amenities are all supplied with ultra-compact IoT hardware and embedded software that has the ability to communicate with the hotel app and speech recognition driven voice assistants, allowing guests to control key room elements easily. The application serves as a universal remote where everything is just a few clicks away. On top of that, such an app can include additional features, like ordering in-room services, chatting with staff, and accessing important information (i.e, local flight schedule and hotel food and entertainment options).

If a hotel offers multi-bedroom suites or villas, smart room apps are adaptable to a variety of layouts and accommodation types, ensuring users can control different amenities in different rooms in an easy and intuitive way. In villas, for example, beacon technology needs to supplement other technologies that are present at smaller premises. Beacons are small devices that can send messages to mobile devices, providing navigation and location-based tips. For example, beacons can remind a hotel app a real-time location of the guest to pinpoint the exact room in which a user attempts to control the amenities.

Smart rooms provide a foundation for another hospitality sub-trend — hyper-personalization. The data gathered by the entire device ecosystem will allow hotel operators to fine-tune their guest experiences and address (or even anticipate) specific demands of each guest at every corner — an invaluable capability in the time when hotels need to forecast sudden fluctuations in customer demand.

Plus, both beacons and tablets provide another avenue to drive sales of guest services with personalized offers. For example, Fontainebleau Miami uses its beacon data and property management systems to generate early guest check-in and late-stay personalized promotional offers. While capturing and constantly accessing guests' data, every hotel needs to ensure this data is secure by creating comprehensive privacy policies and giving guests the ability to delete most of the data at check-out.

A leading luxury Asian hotel chain operator is transforming its business model with the help of the latest technologies as we speak. One of their top-tier properties has a slick tablet in each guest room, enabling guests to control room amenities (air conditioning, lights, windows, media centers), chat with staff, order room service, access hotel information, and more. This cloud-based solution that integrates with other systems has a number of additional benefits, including driving sales of services; solidifying its luxury brand and speed of service through a stunning UI & UX feature design and well-built software; allowing the hotel to learn more about their guests, and other.

Artificial Intelligence (AI) in the Guest Experience

AI solutions with machine learning algorithms analyze big data to provide precise estimates across various important industry and risk management metrics, enabling businesses to significantly improve their decision-making capabilities.

The simplest example of how AI is gaining traction in the hospitality industry is the rapidly increasing usage of chatbots, which aim at improving guests' stay at every step. Hotel chatbots analyze data from a wide array of sources (interactions with guests in a hotel app, purchase history, food preferences, stored payment options, spa and amenity usage, etc.) to provide a deeply personalized experience. The more data available to a chatbot's algorithms to learn from, the better is the delivered outcome and chatbot's suggestions. Furthermore, AI-driven chatbots have a very quick response time: guests can receive answers to their queries almost immediately, as if they speak with the knowledgeable person facing them.

Chatbots are positioned to dramatically alter the operational backbone of the hospitality industry, starting with something as simple as the booking process, and proceeding to streamlining workflows at call centers and other hotel support units. Machine Learning (ML) algorithms in chatbots will be trained utilizing historical calls with customers and their booking behavior on a hotel website, offering them the most relevant booking options, the ones they are most likely to use.

Augmented & Virtual Reality (AR / VR)

For better or worse, not all photos of a hotel's exteriors and rooms tell the full story about the hotel to its potential customers while they are booking a room online. Purchasing a right to use property should be treated as any other product purchase online. Ultimately, guests want and should be able to see exactly what they are buying, more so if the hotel is expensive and far from their home.

With AR and VR hotels can offer virtual tours of rooms and all property amenities. These tours should be simple to navigate in the device-agnostic environment: from a smartphone, laptop, through inexpensive glasses like Google Daydream, or sophisticated headsets like Oculus for more comprehensive and immersive tours. The latter option, to be offered by luxury hotels, would require clients to visit an office or have a headset kit delivered to a place of their choice.

Airbnb already prototyped similar experiences. The company showed a VR prototype that lets users explore properties from their homes with a smartphone or VR headset. As for AR, the company detailed a system that allows hosts to leave guiding notes in AR to provide useful information to guests, who will access them by scanning the property with a smartphone.

There are other exciting ways to use these technologies in hotels. For example, a few years ago Marriott surprised their guests with VR postcards, immersing them into headset-driven 3-D travels stories. If a hotel has a certain theme, it can use the concept of Pokemon Go, creating a hotel-themed AR quest for kids where they would explore the hotel by way of discovering items.

Mobile Remains Front & Center

Mobile will continue to be the backbone in the process of improving the technology behind the next-generation hotel experience. A branded hotel mobile app allows for two-way communication between guests and the property: guests can access any hotel service and other information anytime (for example, order room service dinner while they are still in the spa), while the hotel can use the application to get in touch with guests at the right moment, sending important notifications, updates, offers, and alerts.

A hotel app can offer:

Booking options

Remote check-in/check-out

Restaurant booking with in-app menus

Chat with staff

Guest services (in-room dining, laundry, etc.)

Hotel map

Other timely l information (flight schedules, hotel entertainment)

Room key functionality

Today's smart room apps represent only the first iteration on the way to next-generation hotel rooms. So, there is still enough space for innovation, and any hotel can now help shape the future of the industry with fresh ideas.

Final Thoughts

Whether it is creating a smart room or implementing AI-driven algorithms, innovation is never easy, more so if core aspects of the hotel experience are in question. To help hotels make solid first steps in their transformation efforts, we have gathered our innovation leaders for a webinar to share unique insights and real-life case studies of Digital Transformation (DT) in the hospitality industry.

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How Artificial Intelligence (AI) can be leveraged by Small businesses

“Along with Facebook, Microsoft, and Apple, these companies are in a race to become our 'personal assistant.' They want to wake us in the morning, have their artificial intelligence software guide us through our days, and never quite leave our sides.”- Franklin Foer (former editor at The New York Republic)

Artificial Intelligence has taken the world and our industries by storm. All the tech giants that Franklin listed are striving to incorporate artificial intelligence in their systems, products, and every other service they provide to deliver bespoke solutions to their users. The impact of AI in industries is quite extensive. However, when we dig deep into the impact zone, we’ll find that its impact has been severely restricted.

According to several researches and project so far, it’s predominantly the large corporates that have reaped the benefit from modern and advanced technologies such as Artificial Intelligence and Machine Learning.

These results and implementation force us to ponder on the following subjects:

●  Is AI technology confined to fancy and big corporations only?

●  Has it not facilitated any benefits to small businesses or MSME in any way? If yes, then

How can AI be leveraged by small businesses to grow?

If you’re inquisitive about learning the answers to these questions, read on to know how Artificial Intelligence can be employed by SMEs to transform their business approach and customer experience.

Current Relationship of Small Businesses & AI

It may seem a daunting task to implement artificial intelligence in the operations of SME. For most small business orthodox owners, AI is still a slightly alien tool that can only be understood by experts and computer scientists at big tech organization, but this is a mistaken belief.

It’s apparently no surprise that only 11% of small businesses currently use AI and 41% feel that it’s exceedingly complicated for their requirements. But, you may be astonished to know that 51% of SME owners think that AI is one of the most prominent technologies that must be adopted for their business growth. This proves AI is no longer prerogative to large businesses.

In reality, every small business despite its niche can take advantage of AI technology RIGHT NOW.

In fact, small businesses must not wait for adopting AI, as their competitors aren’t waiting. More and more companies are embracing the technology, increasing the competition and adoption rates and making this disruptive technology accessible for a much broader range of businesses.

How can AI influence the problems experienced by Small Businesses?

Deploy AI for enhancing customer experience

Small businesses normally serve their products and services to a wider range of customers. And there’s a constant war among themselves to lure customers by meeting their fluctuating expectations.

After the explosion in the number of communication channels available online, small companies not employing AI, are struggling to meet the expectations of consumers who demand far more engagement from the brand than ever before. Consequently, it’s crucial for companies to find new ways to quickly address the concerns of their customers.

Small businesses can prosper in the market with exceptional benefits of chatbots and other forms of AI-based communication. AI-powered chatbots can be a blessing for the firms which can't afford to maintain an in-house team for providing 24*7 customer service or operators to answer customers emails and calls. It would help them instantly answer customers queries to increase the chances of them becoming a potential lead.

Note:  In the US, small businesses account for more than 40% of GDP and  more than half of net job creation.

Deploy AI for Data analysis and collection

AI technology is recognized for handling repetitive tasks with more efficiency and ease. In fact, according to a study conducted by Qualtrics, in near future, AI is expected to handle the repetitive tasks such as data cleaning and statistical analysis.

You must be wondering that this usage is beneficial for large enterprises only as they are the ones with a staggering amount of data. But, that’s not the case, even small scale industries can put AI to use for drawing meaningful insights from the modest amount of data they hold about their consumers.

Due to limited budgets, small businesses were not able to use statistical regression analysis and many other advanced techniques for data analysis. But, AI is making it intuitive and affordable for them. By using AI-based data analysis tools, SMEs would learn more about their customers and find new ones.

Apart from analysis, every business is prone to leverage powerful data gathering mechanisms with AI. It can also assist small businesses in assembling a notable amount of data. From machine learning (ML) algorithms to sentiment analysis, SMEs too can track their consumer's preferences and habits.

Intelligent Hiring with AI

Finding the right talent and recruiting the top one is amongst the unique challenges that several SMEs face. The large enterprises definitely sustain a huge network, recognition and more resources to search the right talent for their organizations, but what about small businesses?

To meet the level of such talent-gobbling machines, SMEs can use AI as their equalizer. Artificial Intelligence turns the manual process of screening through huge piles of CVs and resumes into a more streamlined one. In case you’re doubting the abilities of AI for recruiting purpose, here’s a brief explanation of what it can be leveraged for:

● The AI-driven tool can inform the best possible type of communication that would appeal to the candidates of a specific industry.

●  Machine learning algorithms can share the most effective hiring strategies that were used in the past. It may include sharing the strategy used to approach candidates or the platform where the candidates were looked for.

●  AI-based applications can also help SMEs to discover solid leads in surprising places

●  It can also help recruiters to perceive the details of applicants’ past work history.

Developing an AI-based Marketing Platform

AI is revolutionizing marketing strategies. A survey conducted by http://Inc.com found that around 93% of marketing experts think that AI possesses an opportunity for their industry. When it comes to marketing, SMEs can leverage AI just as any other large organization

With affordability as the main issue, small businesses have always seen a crunch in the marketing channels they can use for putting ads. But, with AI, SMEs can now enjoy the facility to reach a broad audience online. In fact, they can also use AI-based advertising platforms that are developed to target particular consumers. Moreover, SMEs can also gather and analyze user data from multiple channels. And all this is possible without an army of marketers on a mission!

No wonder that’s the reason a McKinsey report in April 2018 stated that the impact of AI in the field of marketing and sales would be most substantial.

The above listed four ways are just a few among the numerous other uses of AI. Small business must begin integrating AI now if they desire to increase their reach to a spectrum of potential consumers ready to engage with them.

Undoubtedly, the technology behind Alexa can also help small businesses. If you’re running an SME and are interested in incorporating AI to your business, here’s a list of recommendations on how can AI be implemented by small businesses:

Recommendation 1: Incorporate AI to gain insights into your competitor’s business process

If you want to thrive in the brutal industry of ever-evolving market trends, then be competitive. Utilize AI-based analytics software that can present you with some relevant and vital insights into the business of your competitors.

Recommendation 2: Remodel your marketing approach with AI.

AI is at the verge of radically transforming marketing processes from the roots. SMEs must commence the integration of AI into their system to reach their potential customers by leveraging AI-based advertising platforms.

Recommendation 3: Integrate AI to offer unparalleled customer service

Nurturing every customer is one of the most essential processes for any business. By leveraging AI-enabled support services, the anomalies in customer support can be addressed. Moreover, the risk of human error is entirely eradicated too.

Recommendation 4: Get exceptional data solutions with AI

With AI, small businesses can not only get better insights into the process but can also benefit from better suggestions on solutions for their problems. SMEs can deploy AI tools into literally every element of their business workflow, which deals with data.

Conclusion

“AI is not going to become self-aware, rise up, and destroy humanity.” So it’s probably time to stop doomsday prepping and learn how to manipulate AI to keep your job, not save your life. (Huffington Post)

Artificial intelligence (AI) is focusing on every narrow task available in the industry, but these narrow AI-DRIVEN tasks combined are transforming the way businesses work. SMEs by utilizing deep learning can end up saving a lot of money which they would spend over hiring and maintaining extra resources for their projects.

The gob of hats wearer in small businesses must make a decision on how to leverage AI for their business. If your business deals with repetitive or time-consuming work processes, then you must leverage the expertise of AI-based tools or applications to execute the work more

efficiently. This would free up your employees from unnecessary tasks, and they can focus on performing some creative work, that’ll help your SME grow.

Here’s a brief Infographic on the same:

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AI Isn’t an IT Project; Rather a Business Strategy

Despite the momentum, corporate adoption of artificial Intelligence (AI) technologies is still lagging. Indeed, enterprises are recognizing AI’s business implications, but being nascent in business settings, it becomes challenging for them to profitably employ it. In a recent interaction with TTM, Balakrishna D R, Head of AI and Automation, Infosys shares his perspectives on how businesses can embrace AI for business growth and innovation and the unprecedented value AI can bring to the enterprise.

TTM: From an industry point of view, how do businesses know if they are ready for AI?

Balakrishna D R: The only prerequisite for a business venturing into AI is to be prepared for change. AI has applicability in almost every industry. Earlier, one of the biggest barriers was lack of data. However, with the emergence of techniques such as transfer learning and meta-learning, the need for high volume data has reduced. Aspects like explainability of AI, elimination of bias and ensuring AI is used ethically are becoming mainstream, thereby encouraging enterprises to adopt AI more widely. Additionally, today we can automate the steps and processes involved in the life cycle of creating, deploying, managing, and operating AI models. In turn, this can help scale AI more widely into the enterprise.

TTM: Talent crunch is a key challenge in the AI space. How are industry leaders like Infosys solving this problem?

Balakrishna D R: Lack of talent in AI related skills has been found to be one of the top three barriers in almost all surveys. Fortunately, across the globe large investments are being made on training to build AI skills. As the entire experience of corporate training is evolving with the rise of digitization, innovation and the demand for lifelong learning, enterprises are being compelled to invest in next-gen learning environments, digital platforms and innovative learning experiences. When designing learning programs, organizations should ask themselves three main questions:

What skills are core to working with AI?

What progression do they seek from today to tomorrow?

What do they need to be ready for the future?

At Infosys, we have answered the above questions with respect to all the technologies we work with. We have sourced and created in-house material on our learning platform (Lex) that is open to all employees. We have made learning convenient and relevant with real-life, best-in-class curated content in safe practice environments.

We have accelerated our talent transformation journey by categorizing all skills into three horizons. Horizon 1 includes all core services with previous skills that are increasingly being replaced by extreme automation. Horizon 2 includes skills that meet today’s need for new services. Horizon 3 is the skills of the future that underpin our engines of growth. Then, we designed training programs that allow our employees to up skill from Horizon 1 to 2 and 3. These higher skills pertain to not only AI but all innovative technologies such as data science, machine learning, autonomous technologies, big data and analytics, cloud technologies, agile, and DevOps.

Presently, most people have a certain depth and breadth of skills, represented by the figure ‘T’. In future, this will shift to a Z-shaped skill model that will combine business and digital literacy along with five Cs, namely, collaboration, critical thinking, communication, cultural fluency, and change management. We are working with various academic institutions such as Rhode Island School of Design, Purdue university, Trinity College, Hartford, Cornell University and the University of North Carolina to reskill our employee in various digital skills.

TTM: What are some of the other concerns faced by businesses around AI technologies in the country?

Balakrishna D R: We already spoke about the lack of data and skills shortage. Some of the other challenges are lack of a clear strategy and functional silos within the organization. As per a Mckinsey study, just 17 percent of respondents said their companies have mapped out where all potential AI opportunities across the organization lie. Also, only 18 percent have a clear strategy in place for sourcing the data that enables AI work. There is also a reluctance to accept the changes that AI technologies bring across job functions. To adopt AI seamlessly, organizations need to take additional measures to ensure better security, governance, and change management.

TTM: What are the possible business use cases of AI-ML you foresee in the next 3-4 years?

Balakrishna D R: As enterprises can mitigate the challenges around AI adoption, we can see a plethora of new applications and use-cases opening up across industries. Let’s highlight some industry-specific use-cases:

In the financial services industry, AI can play a role in data extraction, data validation, breach detection, and customer risk profiling. In banking, AI finds application in areas such as fraud detection, anti-money laundering, regulatory reporting, document extraction, payment reminder follow-ups, real-time user authentication. Likewise, the insurance industry can benefit greatly from AI, especially in areas such as claim data extraction, claim management, regulatory compliance, risk evaluation, adjudication, match to issued policy.

Again, AI can help streamline distributed marketplaces, food auditing, inventory control, loyalty programs, procurement optimization, and drive supply chain traceability.

On the media and telecom front, AI can help significantly enhance network operations and improve fraud detection, predictive maintenance, and customer service. In the services and utilities industries, AI can help achieve better load forecasting, demand management, predictive maintenance, energy trading, consumption insights, and analysis.

In addition to these industry-specific applications, there are plenty of use cases such as customer service, finance and accounting, HR, marketing and sales, and procurement. For instance, AI can help streamline customer enquiry routing, offer customer self-service support in the form of chatbots or voice assistants, and run customer feedback and surveys. AI can support the HR team through resume screening, candidate profiling, performance management, and employee virtual assistant.

The marketing and sales function can benefit from AI in areas such as price optimization, shelf audits, social media marketing, lead management, and customer data management. In procurement, AI can enable better demand forecasting, payment processing, goods receipt and confirmation, e-auctions, and contract management.

AI Generative Algorithms will transform several creative domains like art, ad design, creating recipes, music generation etc. over the next few years. Doctors powered by AI can diagnose diseases like cancer much faster, virtual nurse assistants can help the elderly. Precision medicine driven by personal genomes and analytics will transform healthcare dramatically. AI based Self-driving trucks, Intelligent warehousing and smart traffic management will transform logistics. The list is just endless.

TTM: What’s your view on who should lead the AI initiatives in an organization – the CEO or the CIO?

Balakrishna D R: I believe an AI initiative should not be seen as a technology upgrade or an IT program but rather as a business strategy. Therefore, it should not be limited to the function of a title. Anyone who has the vision to understand the business value that AI brings and has the necessary command to drive change can lead an AI initiative. The key is to visualize the power of technology and have the conviction that its disrupting. Without such conviction, AI will remain yet another IT project.

The post AI Isn’t an IT Project; Rather a Business Strategy appeared first on TTM.com.

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Breaking The Buzz - 2018 Will Be All About These Technologies

We are well into the new year and bid adieu to 2017, which was marked by a great many technological trends. Be it the much-acclaimed fame of Blockchain, the blink-and-you-missed-it popularity of Pokemon Go (the widely accepted VR/AR gaming app), the rise of Bots and AI, or the hue and cry over cyber-security, the year was a complete disruptive one for Technology.

While it’s all in the past now, but 2017 has shaped much of what we can expect in 2018. here are some technological trends for this year, that will cause much more disruption in the way industries have been functioning since long.

Blockchain more than a Buzzword – Businesses have begun realizing the reliability, security, and efficiency the Blockchain technology is all for. Developers this year will be developing exciting and challenging use cases for the financial services sector and manufacturing supply chains. The immutable, trusted, and efficient transactions that Blockchain promises, the integrity of data and ledger-management that is its core, are all the reasons why the technology will see the face of production this year.

Chatbots will get sophisticated – While the last year saw a rise in the usage of chatbots by every organization big and small, regardless of the industry and sector, this year will be more about the quality than the quantity. Chatbots will be expected to do more than say hello and hand over the case to a human for assistance. Chatbots are expected to meet the AI standards that they were originally built for. According to a prediction, efficient interaction by chatbots will increase from just 20 percent in 2017 to a whopping 93 percent by 2022.

IoT will accelerate Edge computing – The Internet of Things (do we need to expand IoT still?) devices have become a part and parcel of many homes and businesses. As the number of devices connected to the Internet increase, there will be an analogous increase in the exchange of data over the network. That will give the pace to Edge computing. This means a faster exchange of data between devices, without the need of connecting to a cloud. Edge computing is a concept wherein the data collection and delivery points are kept close to the processor. Manufacturers are realizing the increase in the number of IoT-enabled devices, and are striving to make their offerings better, that is, considering edge computing!

Cybersecurity will continue to lurk from behind – With the rapid rise in devices connecting to the Internet only to exchange personal information, has not made matters any easier for cybersecurity experts and professionals. According to statistics, damage costs due to cyber crimes will hit $6 trillion per year by 2021. There have been considerable investments in ensuring security over the Internet in 2017, and the investments are predicted to increase by $1 trillion by the year 2021.

Machine Learning will take use cases that are practical – Machine learning will step into mainstream application development. The two main reasons that will drive the growth in ML this year are- pre-built modules for ML development are available in leading platforms and ML is essential when analytics have to be applied to the data stored in large datasets. Machine Learning development and ML testing are becoming increasingly popular amongst the younger generation, who are thrilled to learn these concepts to build real-world applications. Generating recommendations, predicting outcomes, and making automated decisions according to historical scenarios, are the use cases that will require ML applications.

Serverless architectures – The concept of a serverless architecture is quite appealing to organizations. When a demand arises for the execution of a piece of code on a certain event, instantiate the infrastructure, deploy and execute the code, and charge me according to the time consumed in this complete process. The need for flexibility and scalability with cost-effective solutions has given rise to serverless architectures. While debugging and development challenges are lined up on the path towards adoption of serverless architectures, companies are still considering to invest big in the idea.

Leveraging services rather than products – An increase in the adoption of cloud has resulted in organizations eyeing towards services more than products. Without increasing their capital costs and internal support needs, online cloud-based services are becoming the preferred ways of developing and deploying solutions. For an example, why will an organization invest in buying a new mobile device for its testing purposes, when for a small monthly amount, it can access all simulation environments for all device sizes and operating systems over the cloud.

While the predictions and trends are numerous, it is imperative to realize how far we have travelled as a society towards technology and innovation. While 2017 was a year of innovation and analysis, this year could witness some real-world applications exploiting the most complex and challenging technologies.

So, the least you can do is just get in touch with the top outsourcing software development company and share your idea to turn that into reality.