Software Development Enters The AI Age

The global AI (Artificial Intelligence) software market is set to explode. The numbers may increase from around 9.5 billion U.S. dollars in 2018 to a whopping 118.6 billion by 2025. In fact, many of the top names in the tech industry are investing in AI-related R and D in a bid to embrace futuristic solutions.

Software Development Enters The AI-age

With AI penetrating almost every space possible, how can software development be left behind? Several firms are looking to accelerate software development and testing functions by using the combined power of AI and ML (Machine Learning).

Here are some of the ways in which Software Development is undergoing a Massive Transformation in the AI-age:

  1. Coding Assistance – Clean code is the way to create stable software that is easy to maintain in the long-term. Both AI and ML make it possible to analyze the code and also, optimize it for better interpretation and performance. A case in point is that of AI-powered tools, which act as coding assistants. These tools come preloaded with learning culled from several thousands of coding rules and help developers fix their code. This cuts down the time for coding, brings to the table the most relevant coding instances, and helps developers. As an added benefit, developers can focus on more creative and intuitive tasks that drive innovation, rather than on repetitive or routine tasks.
  2. Rapid Prototyping – More often than not, coming up with a software product needs tremendous amounts of planning before putting thoughts into action. AI and ML can cut short that duration by offering rapid prototyping capabilities. Think in terms of automated decision-making, optimized development process, early technical validation of ideas and so on. Ergo, developers can easily develop new applications in a shorter time, improve applications quickly and also, deliver enhanced customer experiences.
  3. Bug Fixes – One of the key pillars of the process of software development is bug fixes. AI can completely transform this step. It can detect bugs using intelligent algorithms and without the need for any manual intervention. AI can identify high-risk areas of the code based on historical data or coding patterns. A focus on those areas can help find bugs faster. This also helps reduce the possibilities of bugs being overlooked or ignored by testers under time-pressure.
  4. Software Testing – Like bug fixing, with AI at the forefront, the days of manual testing may be transformed before long. AI can help easily track the common errors and flag them early in the development. AI can also help analyze the system logs to flag down errors and, in the future, the same could be used to make dynamic changes without any human assistance. Then there is the potential impact on test automation. AI can analyze the massive volume of test cases and define which are the best cases for automation based on their priority and “automatability”. It’s only a short stretch from there to AI being able to assemble the scripts needed to automate those test cases on the fly. As the product evolves, AI can also determine how the test cases, and hence the scripts, need to be changed. In essence, AI can automate the creation of test automation!
  5. Product Maintenance – From startups to corporates, a large part of the software development costs is spent on product maintenance. Oftentimes, even the redundant features of a software product are subjected to maintenance. This is costly and time-consuming. In the AI-age, it will be easy to identify such redundant features by scanning massive chunks of data. With intelligent automation, the process will be less complicated and less prone to errors as developers will not need to manually correlate the data from multiple sources. Any unrequired features and functionalities in the software and the associated code can be easily detected and removed if need be.
  6. Bridging the Skills Gap – There’s a lot of hue and cry over AI taking over manual jobs and making certain roles redundant. But then, on the bright side, AI tools and technique can also help speed up the development process by making it easier for developers to develop products using more automated means. They won’t have to learn a new set of skills (at least not immediately). They could use ready components that can put together with little or no coding with AI doing much of the heavy lifting in the background.

For instance, consider Bayou. Bayou generates code snippets for accessing APIs in Java. All the users need to do is “Ask Bayou” for what kind of program would help them address specific tasks in their programming. Bayou then, analyzes the code from the user and the query and delivers the right API idiom. This innovative application of Neural Networks was jointly developed by Rice University and DARPA. Bayou, and others like it, represent the vanguard of the “AI coders” movement that could simplify simple software development tasks.

It seems abundantly clear that AI will become a fundamental business practice offering real business advantage to various industries, including software development. The way we develop software may be set to get more automated, less intuition-driven, and more AI-led.

What is your take on the AI-advantage for software development?

How to Prepare for Enterprise AI Adoption?

The probability is pretty high that you may have been already heard of probable use cases of AI and the potential it holds for the enterprises. According to Gartner AI adoption has increased by 300 % in 2018 alone. The giants like Google are pledging millions of dollars to tackle the AI challenge. It seems clear now that AI can provide robust ROI.

It is due to this acceptance that AI is slated to grow by 270 % more in the coming years.

enterprise AI adoption

Enterprise AI Adoption Guidelines:

In fact, AI could transform how businesses operate. But for this complete makeover to be realized, the enterprise has to adhere to certain basic guiding principles to reap the full benefits.

  1. The first step is to get a clear understanding of what is AI?AI is much larger than the chatbots all of us have experienced on the phone and home devices. Business decision-makers must acquire a clear sense of the potential of AI to come up with a relevant use case that is right for their business.
    AI is an umbrella term which covers, data analysis, collection, applying machine learning or deep learning, and coming up with relevant applications. Once the stakeholders can see the lay of the land it will be easier for them to come up with a use case that can be aligned with their vision.
  2. An important next step would be to carry out an extensive competitor analysis. Every industry has multiple segments and a variety of players. These competitors maybe at different levels of maturity with regards to AI adoption. Analyzing the moves the competition is making will help you understand the possible areas you could focus on.

These two steps will help you zero in on the likely use cases for AI. This will depend upon your specific needs and on the maturity of the organization. Choosing the right use case is of primary importance as that will have a massive impact on the ROI. AI use cases will vary according to the industry.

For the automobile industry, it might be predictive maintenance whereas for an e-commerce company a recommendation engine might be the need of the hour. Similarly, a chatbot guiding the customer through self-service options might be another potential application.

That done, mandatory due diligence or a current state assessment must follow.  This assessment will identify the current state of the data, how it is being used, the gaps, the best possible ways to go ahead and design the collection, management, and how to effectively and economically achieve the analysis of data. This assessment will also help you decide the tools and technologies that can be used to make the organization AI-ready.

This is an important stage in driving the strategy. Based on the gaps and the requirements that emerge, the company must analyze whether they have the capability in the house or will have to onboard an outside partner with the required skill set necessary for creating the AI. Obviously, this step has tremendous implications on budgets and business strategy. Selecting the right partner becomes an important decision point in the AI journey.

Once done with the make or buy decision and after having on-boarded the right partner, the groundwork must begin. The primary task at this stage is to build a scalable data foundation, whether on-premise or in the cloud. The data collation pipeline has to be set up. Tools and technologies have got to be aligned. A robust data foundation is a mandatory first step for setting forth on the AI/ML journey.

It is great to think big but start small. This means that first carrying out a small POC to see whether you get the desired result and whether the system is effective or not. Look for limited applications at this stage. For eg. apply the recommendation engine for a particular category rather than going full throttle.

Similarly, monitor one aspect of the engine health of an automobile, and see the system’s efficacy in spotting anomalies. Once the kinks are ironed out over the course of the limited scope pilot, the AI implementation can spread to different areas.

It’s clear that that the effectiveness of an AI / ML system improves as more and more data is fed into the system. Interestingly, this effectiveness does not hit a plateau but improves exponentially as it gets new data to train on. This suggests that it is imperative to ensure the system undergoes continuous improvements and updates.

Last but not least, think about the organization. Most organizations are reluctant to change. Hence it becomes important to drive AI adoption as a top-down approach. The push from the top management and their committed involvement becomes mandatory.

This is a critical aspect as it is this commitment that will convince the rest of the workforce to align with a new digital culture powered by AI.

What you must consider for your Enterprise AI strategy?

A chatbot is the most in-your-face use case of AI, but it’s easy to underestimate the opportunities that AI can help us realize. By some estimates, by 2023 around 40% of all internal operations teams in Enterprises will be AI-enabled. The flip side is that even though the growth opportunities are huge, it will take time, effort, and a concerted strategy to realize the true potential.

Enterprise AI Strategy
Let us look at the key considerations to factor in while embarking on the AI journey.

  1. Definite Use Cases:

    It is imperative to have a definite use case in mind before one thinks of implementing AI in your Enterprise. Many implementations fail simply because they are implemented with no thought about the end goal to be achieved. To avail a great ROI, it is extremely important that one has a clear definition of the specific business goals to shoot for. For instance, a customer service operation may want to reduce the number of customer service calls by a factor of 50%. Chatbot-enabled engines could help -and after a defined period you can establish clearly if the initiative was a success.

  2. Think Big Start Small:

    It is best to have lofty goals while aiming for a transformation with AI but start with a small test or a pilot project. It’s always prudent to test the waters before taking the plunge. Chose one particular LOB, or a small department to test AI and its viability for this particular endeavor. This will throw up the problems one can encounter while undergoing a transformation. And at the same time, you will also identify the challenges resident within the ecosystem that may have to be addressed for achieving a seamless transformation.

  3. Creation of a Knowledge Repository:

    The success of an AI implementation is dependent on how robust the underlying knowledge base is. This requires data, lots of it. The AI will learn as it goes along -but even at the stage of training the AI, vast amounts of data is needed. The idea is to have the AI system define how a problem can be solved and be driven by the relevant insights the AI provides. By having a highly mature algorithm driven by a robust database you can improve the quality of the insights available. The primary difference between a normal knowledge repository and a Knowledge repository for AI is in the structure and the content. For AI, an interface along with highly structured data which can be queried is necessary.

  4. Build or Buy and choosing the Correct Partner:

    AI may be necessary for every organization but not every organization will have the requisite resources to implement it on their own. You could build the expertise, or you may have to work with a partner.Picking the right partner is a crucial decision. The selection should be driven by considerations like the availability of skilled human resources, successful past implementation,understanding of your business challenges, and their future roadmap.

  5. Data Quality:

    For AI data quantity is not enough, data quality is paramount. AI is driven by Data Science and statistical algorithms. These algorithms become trustworthy if the data quality of the data set on which the system is being trained and implemented is pure and pristine. That is the reason why there should be a state-of-the-art data quality monitoring system. You may have to fix the data duplication issues and weed out the corrupt and broken data.

  6. Cloud or On-Premise:

    Once put into place, the knowledge repository will increase in size at an exponential rate. A tsunami of streaming data will fill up the data storage really fast. Hence many organizations consider the cloud for storing the data. The answer to the question of whether to go for cloud or stay on-premise will be driven by factors like the security and compliance requirements, apart from the cost and storage volume needed.

  7. Right Resource Pool:

    Irrespective of the decision to build or buy it’s true that there are not many trained and experienced human resources out there. It is common to underestimate the demands AI will make on the business. This is not just about the technical resources needed to implement the systems. AI strategies sometimes fall apart because the Enterprise didn’t train or develop their functional resources to cater to the new ways of working. Business processes will change, agility will increase, and responsibilities will shift -your people will have to be ready.

  8. Top Management Buy-in:

    Like any other strategic initiative, the involvement of the top management is a key factor for the success of any AI implementation. Many Enterprises still work top-down. With top management throwing its weight behind a project, the probability of its success increases exponentially. The organization starts treating the implementation with the required seriousness. Resources get allocated, Results get tracked.

Conclusion:

As you can see, there are quite a few factors to bake into the implementation of your Enterprise AI initiative. Knowing these factors and staying hyper-focused will help you stay on track with your AI initiative. And implementing a robust AI strategy that has the greatest chance of delivering business impact is what it’s all about -isn’t it?

How AI can transform Enterprises?

Artificial Intelligence, more popularly known as AI, might no longer be the new technology on the block, but it is ‘the’ technology that everyone is talking about. Facial recognition, digital assistants, autopilots etc. are examples of the existing AI around us. AI is emerging as that disruptive technology that will change the way we live and work. While AI has been seen often in a consumer-centric world, the enterprise too is warming up to this technology.

2018 witnessed widespread adoption of AI in different industries as organizations realized the value AI brought to the table – be it in improving operations, assisting the data analytics drive, boosting innovation, and improving customer experience amongst other things. Owing to the immense value AI brings to the table, the global AI market size is expected to reach $169,411.8 million in 2025, from $4,065 million in 2016 growing at a CAGR of 55.6% from 2018 to 2025 according to MarketWatch.

So, what transformative value does AI bring for the enterprise? Here’s a look at how AI will transform enterprises and change the future of work.

AI tranform enterprises

                                                     

  1. The New age of Automation: AI is going to give automation the boost that it needs. As enterprises look towards technologies such as Robotic Process Automation (RPA), with AI we shall be moving into the world of Intelligent Process Automation. IPA combines process automation with Robotic Process Automation (RPA) and Machine learning (ML) and creates choreographic connections between people, processes, and systems. IPA will not only automate structured tasks but also generate intelligence from process execution.

    IPA is all set to increase the level of transparency in business processes, optimizing back-office operations, increasing process efficiency and customer experience, and improving workforce productivity considerably. Along with this, IPA also holds the promise of reducing costs and risks and promises more effective fraud detection. Owing to these benefits, the IPA market is expected to be worth $13.75 billion by 2023.

  2. The Rise and Rise of Chatbots: The friendly chatbot has already made some inroads into the enterprise. With AI, the chatbot invasion is going to become more pervasive in the enterprise of the future. Customer-facing industries such as retail, healthcare, banking, and financial services shall witness the rise of AI-powered voice assistants such as Alexa or Siri to create interactive experiences for the customer without pushing the load of delivering exceptional customer experiences on the staff alone.

    Chatbots will also become the norm to service the internal customers of the organizations, the employees. Enterprise chatbots will be powered by AI technologies such as NLP (Natural Language Processing), semantic search, and voice recognition. They will enhance search capabilities and deliver a new way for employees to interact with corporate data to improve their productivity.

  3. AI and the UX Impact: The focus on User Experience or UX is only going to keep increasing. With AI, the user experience will not be driven by guesswork but by faster analysis of the right data, by the enterprises in the future.  User experiences with software products, even within the enterprise, have to mimic consumer-grade experiences.

    Fluid, intuitive, efficient, and highly-personalized user experiences are going to be the norm. UX is also going to be the defining factor in product success and acceptance. Enterprises will look at the insights provided by AI by intelligent information gathering and identifying patterns to deliver greater value to the end-user. This will make the user experience of products highly intuitive and intelligent as well.

  4. Greater Intelligent Customization Capabilities: As we move deeper into the age of personalization, enterprises will have to look towards technologies such as AI to develop intelligent customization capabilities. Data is already improving the customization capabilities of enterprises.

    With cognitive technologies such as AI, they will be able to further improve their customization capabilities and create products that individual users will love. Leveraging user data and faster data-processing capabilities, AI can speed up interactions and provide intelligent insights to develop products and solutions that can be highly customized to meet user demands.

  5. Cutting Edge Analysis To Bolster Data-Driven DecisionsAI will be leveraged in the enterprise to perform advanced data investigation in less time to improve business process, product, and service efficiencies. AI technologies have the capability to analyze usage patterns and then deliver deep insights that will take data-driven decision making to the next level.

    Whether it is for predictive maintenance or predictive analytics for product development, or risk management or planning, the AI impact will make the enterprise smarter and more proactive in its decision-making.

  6. AI In Software Development and TestingSoftware Development and Testing will also feel the AI impact as this technology gets more pervasive. To respond to the market need for robust, reliable, and high-quality software that is delivered faster, AI technologies will get ingrained into the development and testing lifecycle.

    With self-learning algorithms that are designed to self-improve, enterprises will be looking at improving the efficiency of the process of software development. They will leverage automated code-generation, among other things, and achieve a shorter time to market with greater confidence.

While AI has met with a certain resistance in the past, the coming years will see this technology achieve greater maturity. Given the immense value that AI can deliver, it is only a matter of time before AI will become a necessity for the enterprises that wish to remain relevant in this ever-evolving and competitive marketplace.

The Role of AI In Software Testing

According to Gartner, by 2020, AI technologies will be pervasive in almost every new product and service and will also be a top investment priority for CIO’s. 2018 really was all about Artificial Intelligence. Tech giants such as Microsoft, Facebook, Google, Amazon and the like spent billions on their AI initiatives. We started noticing the rise of AI as an enterprise technology. It’s now clear how AI brings new intelligence to everything it touches by exploiting the vast sea of data at hand. Influential voices also started talking about the paradigm shift that this technology would bring to the world of software development. Of course, software testing too has not remained immune to the charms of AI.

Role: AI In Software Testing.

Role of AI In Software Testing

But first, Why do we Need AI for Software Testing?

It seems like we have only just firmly established the role of test automation in the software testing landscape and we must start preparing for further disruptions promised by AI! The rise of test automation was driven by development methodologies such as Agile and the need to ship bug and error-free, robust software products into the market faster. From there we have progressed into the era of daily deployments with the rise of DevOps. DevOps is pushing organizations to accelerate the QA cycle even further, to reduce test overheads, and to enable superior governance. Automating test requirement traceability and versioning are also factors that now need careful consideration in this new development environment.

The “surface area” of testing has also increased considerably. As applications interact with one another through API’s leveraging legacy systems, the complexity tends to increase as the code suites keep growing. As the software economy grows and enterprises push towards digital transformation, businesses now demand real-time risk assessment across the different stages of the software delivery cycle.

The use of AI in software testing could emerge as a response to these changing times and environments. AI could help in developing failsafe applications and to enable greater automation in testing to meet these expanded expectations from testing.

How will AI work in Software Testing?

As we move deeper into the age of digital disruption, the traditional ways of developing and delivering software are inadequate to fuel innovation. Delivery timelines are reducing but the technical complexity is rising. With Continuous Testing gradually becoming the norm, organizations are trying to further accelerate the testing process to bridge the chasm between development, testing, and operations in the DevOps environment.

  1. AI helps organizations achieve this pace of accelerated testing and helps them test smarter and not harder. AI has been called, “A field of study that gives computers the ability to learn without being explicitly programmed”. This being the case, organizations can leverage AI to drive automaton by leveraging both supervised and unsupervised methods.
  2. An AI-powered testing platform can easily recognize changed controls promptly. The constant updates in the algorithms will ensure that even the slightest changes can be identified easily.
  3. AI in test automation can be employed for object application categorizations for all user interfaces very effectively. Upon observing the hierarchy of controls, testers can create AI enabled technical maps that look at the graphical user interface (GUI) and easily obtain the labels for different controls.
  4. AI can also be employed effectively to conduct exploratory testing within the testing suite. Risk preferences can be assigned, monitored, and categorized easily with AI. It can help testers in creating the right heat maps to identify bottlenecks in processes and help in increasing test accuracy.
  5. AI can be leveraged effectively to identify behavioral patterns in application testing, defect analysis, non-functional analytics, analysis data from social media, estimation, and efficiency analysis. Machine Learning, a part of AI, algorithms can be employed to test programs and to generate robust test data and deep insights, making the testing process more in-depth and accurate.
  6. AI can also increase the overall test coverage and the depth and the scope of the tests as well. AI algorithms in software testing can be put to work for test suite optimization, enhancing UI testing, traceability, defect analysis, predicting the next test for queuing, determine pass/fail outcomes for complex and subjective tests, rapid impact analysis etc. Since 80% of all tests are repetitive, AI can free up the tester’s time and helps them focus on the more creative side of testing.

Conclusion:

Perhaps the ultimate objective of using AI in software testing is to aim for a world where the software will be able to test, diagnose, and self-correct. This could enable quality engineering and could further reduce the testing time from days to mere hours. There are signs that the use of AI in software testing can save time, money, and resources and help the testers focus their attention on doing the one thing that matters – release great software.

Why we Expanded our Technology Portfolio?

The ThinkSys growth story is known to a few already. For the longest time, we were known as a QA-focused organization. Over time we added a strong Test Automation thread to that story. Adding new skills and technology areas, the company grew organically and now our several highly-talented engineers provide impeccable service in the field of custom software development, web and mobile app development, Cloud, and a multitude of other software services. As technology continues to become a driver of business transformation, we at ThinkSys strive to meet the end-to-end software development and testing needs of our current client as well as future clients. This meant an expansion of the areas we work in. Here’s what drove out thinking.

The Inclusion of Big Data, IoT, and AI:

For many years, big data, IoT, and AI have been impacting organizations across several industries and applications. Although they have all contributed to businesses in unimaginable ways, it is the convergence of these three powerful technologies that can drive next-generation innovation and transformation: from smart manufacturing to precision surgery, energy automation to smart RFID tags, building automation to smart farming, predictive maintenance systems to chatbots, climate control to intelligent shipment tracking – the things that big data, IoT and AI are helping achieve is incredible! Our customers are also impacted by these technology movements. We started seeing more opportunity to marry these technologies into the solutions we were already providing. It seemed clear, to continue to serve the market we just had to add these three disruptive technologies to our development and testing portfolio to enable our customers to leverage the stunning benefits and experience growth like never before.

  1. Big Data:
    As technology makes inroads into the business world, the problem of information overload has become rampant. Organizations grappling with massive amounts of data are embracing new strategies such as big data to analyze data and uncover critical insights. According to a report, revenue from big data is expected to reach $210 billion by 2020. We believe that big data has the immense capability in discovering hidden patterns, unknown correlations, customer preferences, and other vital information, enabling organizations to make informed decisions. Our big data services include predictive analytics, data mining, text mining, data optimization, data management, & forecasting that can enable organizations to uncover hidden business opportunities and accelerate business growth. By making smart, data-driven decisions, organizations can identify risks ahead of time and improve operations and risk management.
  2. bid data services

    Background vector created by Rawpixel.com – Freepik.com

  3. IoT:
    The explosion of IoT has completely transformed the technology world and is bringing the physical and digital aspects of life closer than ever. The total economic value-add for IoT is expected to reach $1.9 trillion by 2020. IoT is enabling businesses to boost operational efficiency and transform their business models. We at ThinkSys are quite certain IoT has the capability to create a world of opportunities; with a more direct integration of the physical world with the digital, IoT will improve business efficiency and accuracy through more intelligent data capture from the edges and more seamless automation. As IoT makes its way into every sector, we aim to cater to the distinct demands of every commercial enterprise and industry. Our end-to-end customized IoT consulting services and implementation solutions can enable organizations to optimize operations, reduce costs, and achieve revenue goals.
  4. IoT consulting services

    Background vector created by Rawpixel.com – Freepik.com

  5. AI:
    A fundamental shift in business operations is being brought about by AI; according to reports, global spending on AI is expected to reach a whopping $57.6 billion by 2021. Although AI finds great application across industries such as banking, finance, e-commerce, healthcare, and telecommunication, it is reinventing the way goods are manufactured and delivered. The recent proliferation of AI has brought with it a multitude of associated technologies that are enabling organizations to automate processes, improve efficiency and transform businesses. Our foray into AI marks the beginning of our digital journey into advanced AI technologies such as cognitive computing, machine learning, natural language processing, among others. We are already working on solutions that will bring in the required intelligence to improve the speed of processes, reduce errors, and increase accuracy, and precision – thus enabling our clients to be agile, smart and innovative.
  6. AI Services

    Background vector created by Rawpixel.com – Freepik.com

    Drive Business Value:

    At ThinkSys, we believe technology has the power to fuel business transformation. Leveraging our capabilities and knowledge of the latest tools and applications, we offer time-tested and reliable technology services across a comprehensive portfolio of advanced technologies. Our team of experienced and knowledgeable experts make use of the latest strategies and deliver solutions to solve complex business problems. By expanding our technology portfolio and including big data, IoT, and AI into our service offering, we aim to assist businesses in understanding the information contained within large data sets, to automate critical business processes, and enable them to drive substantial business value in all that they do.

5 Technologies that are the Building Blocks of Digital Transformation

Organizations are in the quest to accelerate business activities and offer an exceptional product and customer experiences by driving digital transformation. With 57% of organizations believing that digital transformation is a competitive opportunity, it is without the doubt that technology is enabling organizations to become more agile, responsive, innovative, and efficient in addressing their needs.

The Building Blocks of Digital Transformation

In a highly dynamic and competitive world, having an amorphous digital transformation goal is not enough; what is required is an understanding of the tools and technologies that can enable you to get there. Digital transformation spending is expected to reach $1.7 trillion by the end of 2019. Clearly, leveraging modern technology to significantly drive transformation has become a mandate for organizations around the world. However, only 10 percent of companies around the world describe themselves as fully digital – a significant gap. One of the challenges is the vastness of the scope. Where to start? What technologies will play a role? The questions are many.

Since markets, customer demands, and technology is changing rapidly, leading digital change requires you to embrace modern technologies. This will allow you to evolve with the rapid pace of digital change. Here are 5 technologies that are the building blocks of digital transformation:

  1. Cloud:
    For digital transformation to have a profound impact on business activities, the cloud must play an important role. The cloud offers digital organizations the flexibility to do business from anywhere, freeing them from the hassle of investing in and managing physical IT resources. It offers businesses the scale and speed needed to become agile and focus on continuous transformation. The cloud enables organizations to fuel better collaboration and constantly develop, deploy, deliver, innovate, and implement modern solutions. By offering flexible, on-demand access to resources, the cloud enables organizations to execute plans faster and address the changing needs of the market. With the cloud computing market projected to reach $162 billion in 2020, it is past the time for businesses everywhere to embrace cloud solutions to drive digital transformation.
  2. Mobility:
    A key pillar of digital transformation in today’s fast-paced world is mobility. Digital transformation involves radical reconsideration of how organizations use technology to build new revenue streams or business models. Mobility enables them to do that and achieve a host of benefits: anytime anywhere access to information, improved productivity, better process efficiency, lower operational cost, and an enhanced customer experience. Since mobile technology bridges the gap between the physical and digital world, it helps organizations make use of the right data in the right context at the right time and at the right place. It also fuels better communication and collaboration within the organization – helping organizations make more informed decisions, be more proactive, and engage with their customers and employees better. With 82% of organizations believing mobile is the face of digital transformation, the role that it plays is now self-evident.
  3. Big Data and Analytics:
    In today’s digital economy, organizations need to embrace technology not just to support existing business processes, but also to drive new avenues of competitive differentiation. Big data and analytics are driving organizations to analyze humongous amounts of data and unearth critical insights. They are examining business processes, customer behavior, market trends, and competition data and creating value. Since today’s digital customers are a major catalyst for digital transformation, harnessing the right data helps organizations to understand customer needs, make data-driven decisions, build products and processes to meet those needs, and shape the right experiences for them. As big data and analytics enable organizations to get answers to critical questions in near real-time, it allows businesses to react quickly to change, improve performance, and build competitive advantage.
  4. IoT:
    The Internet of Things is driving substantial transformation across industries by linking critical assets in a connected ecosystem. The data generated from these systems can be used to drive sufficient business value, potentially transforming operations and improving business efficiency. Gartner predicts that 1 million new IoT devices will be sold every hour and that IoT spending will reach $2.5 million per minute by 2021. Using IoT, organizations can extend their enterprise and make the most of the exciting business opportunities for transformative business growth. IoT can help drive industrial automation, derive insights into equipment data, enable predictive maintenance, and improve the safety of the workforce. What’s more, IoT data can also be used to boost efficiency, improve customer experiences, and increase overall business agility.
  5. AI:
    In a bid to drive transformation, organizations across the world are looking at ways of applying Artificial Intelligence to boost their business outcomes. AI is already a key driver of digital transformation across a wide range of sectors. Nearly 9 in 10 businesses believe that AI will serve as a key competitive advantage, and help them explore new opportunities and revenue streams. AI can drive significant automation in the enterprise. It enables organizations to apply a more agile framework for digital transformation and create repeatable, reliable functions that can be used widely. By understanding customer journeys and the outcome of future interactions with customers, AI can also be used to drive personalization in customer engagement – a key expectation of the modern digital customer.

Technology at the Core

The pace at which the world is moving is compelling organizations to embrace modern and innovative technologies. The aim is to become extremely agile, to quickly respond to market changes, and to address customer needs. Digital transformation requires you to leverage the available technology to enhance your business process efficiency and become more competitive. Modern technology advancements such as cloud, mobility, big data and analytics, IoT, and AI, offer a variety of potential business benefits. Picking an area of impact to your business and applying the right technology mix will help you take the first successful step towards digital transformation.

Categories
Follow us on Twitter