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Metaverse Trends for Banking, Insurance & Finance Decision-Makers

Welcome to the next frontier of the digital era, where virtual reality transcends boundaries and the metaverse emerges as an immersive and interconnected virtual world. Everyone involved in digital product engineering finds ourselves at the precipice of a transformative moment. The metaverse has the potential to revolutionize the way we conduct financial transactions, interact with customers, and establish trust in an increasingly virtual world.

However, venturing into the metaverse comes with its own unique set of challenges, particularly for the banking, financial services and insurance sector. We learned a great deal about how those challenges are impacting executives at some of the world’s leading financial institutions in a recent digital boardroom event hosted by the Global CIO Institute and GlobalLogic.

‘The Wild West: Regulation In The Metaverse,’ was moderated by Dr. Jim Walsh, our CTO here at GlobalLogic. It was the first of three thought provoking digital boardrooms we’re hosting to explore the issues driving – and impeding – finance product innovation in the metaverse. He was joined by nine executives spanning enterprise architecture, information security, technology risk, IT integration, interactive media and more, from some of the world’s largest financial institutions. 

In this article, we delve into the main obstacles these companies are facing as they prepare to do business in this new realm: regulation, identity verification and management, creating an ecosystem of trust, and governance structures that will support law and order in the metaverse.

1. Regulating the Next Wild, Wild West for Finance

Experts have raised concerns over the lack of regulatory oversight within the metaverse, citing that users are at risk of becoming victims to real world harms such as fraud, especially with its overreliance on decentralized cryptocurrencies. The EU Commission is working on a new set of standards for virtual worlds, for which it received public feedback in May 2023. The World Economic Forum is calling for the rest of the world to follow suit and regulate digital identities within the Metaverse.

This is the backdrop against which we kicked off our roundtable discussion on regulation in the metaverse. 

And of course, we cannot talk about regulation in the metaverse without first discussing whether it’s even needed at all, and to what extent.

Recommended reading: Fintech in the Metaverse: Exploring the Possibilities

The metaverse is not new, as one participant pointed out; what’s happening now is that technologies are colliding to create new business opportunities. We’re seeing more and more examples of the Internet being regulated, and now must turn our attention to what impact those regulations may have on the emerging metaverse. Will it slow adoption or change how people interact? 

“People have been waking up to why it’s been important to have some limitations around the complete freeness of the internet of the ‘90s,” a panelist noted. “Regulations must evolve in a way that the value of the metaverse is not compromised.” 

Another noted that anywhere commerce and the movement of currency can impact people’s lives in potentially negative ways, the space must be regulated. In order to maintain law and order in the metaverse, we’ll need a way of connecting metaverse identities to real people. And so another major theme emerged.

2. Identify Verification and Management in the Metaverse 

Panelists across the board agreed that identity verification and management is a prerequisite to mainstream finance consumer adoption of the metaverse as a place to do business. Banking, insurance, and investment companies will therefore be looking for these solutions to emerge before entering the metaverse as a market for their products and services.

Look at cryptocurrency as an example, one participant recommended. “Crypto was anonymous, decentralized and self-regulated – but those days are over. Look at the token scams that have happened in crypto. That’s not a community capable of self-regulation.”

If the metaverse is going to scale, they said, we need regulation – and anonymity cannot persist.

Another attendee suggested we look to Roblox and Second Life as early examples of closed worlds with identity verification solutions. Second Life has long required that users from specific countries or states verify their real identity in order to use some areas of the platform, and had to go state-by-state to get the regulatory approvals to allow users to withdraw currency. For its part, Roblox introduced age and identity verification in 2021. These were closed worlds where you could be whatever you want, but identity was non-transferable. 

The metaverse, on the other hand, is a place where you can move through worlds, transfer assets and money from virtual to real worlds, etc. Anti-money laundering and identity management will need to catch up before it’s a space consumers and the companies that serve them can safely do business.

3. Trust & Safety in the Metaverse

Closely related to identity is the issue of trust in the metaverse, and it’s an impactful one for finance brands and the customers they serve. There must be value and reasons for people to show up and interact, and the metaverse cannot be a hostile, openly manipulated environment if we’re going to see financial transactions happening at scale. 

Already, one participant noted, societal rules are being brought into the Metaverse. You don’t need physical contact to have altercations and conflict; tweets and Facebook comments can cause harm in real ways, and we need to consider the impacts of damaging behaviors in the highly immersive metaverse. Platforms create codes of conduct, but those expectations don’t persist across the breadth of a user’s experience in the metaverse.

Another pointed out that we don’t even have customer identity or online safety solutions that work perfectly in Web 2 and are carrying these flaws we already know about into Web 3. Credit card hacking and data breaches involving online credit card purchases have plagued e-commerce since its inception.

Even so, the level of concern over privacy and safety issues varies wildly among consumers. Some will be more comfortable with a level of risk than others.

4. Metaverse Governance and Mapping Virtual Behavior to Real-World Consequence 

Dr. Walsh asked of the group, will we have government in the metaverse, or will it be self-governing?

On this, one participant believes that regulating blockchain will sort out much of what needs to happen for the metaverse. The principles of blockchain are self-preservation of the community and consensus, they said, but that’s going to take a while to produce in the metaverse.

Recommended reading: Guide to Blockchain Technology Business Benefits & Use Cases

Another kicked off a fascinating discussion around the extent to which AI might “police” the metaverse. Artificial intelligence is already at work on Web 2.0 platforms in centralized content moderation and enforcing rules against harassment. Imagine metaverse police bots out in full force, patrolling for noncompliance. We’ll need this for the self-preservation of the metaverse, the attendee said. 

Participants seemed to agree that when what’s happening in the metaverse has real-life consequences, regulation must reflect that. Legit business cannot happen in a space where financial crimes happen with impunity. 

However, who will be responsible for creating and enforcing those regulations remains to be seen. In a space with no geographical boundaries, which real-world governments or organizations will define what bad behavior is? 

“If I’m in the European metaverse, maybe I have a smoking room and people drink at 15,” one participant noted with a wry smile. “That’s okay in some parts of the world, but it’s very bad behavior in others.”

In the metaverse as a siloed group of worlds with individual governance and regulation, financial institutions may have to account for varying currency rates and conversion, digital asset ownership and portability, and other issues. Or, we may see the consolidation of spaces and more streamlined regulations than in the real world and Web 2.0. The jury is out.

Reflecting Back & Looking Ahead

For finance brands, the sheer volume of work to be done before entering the metaverse in a transactional way seems overwhelming. “The amount of things we have to build on the very basic stack we have is staggering,” one participant said.

However, we will bring a number of things from the real, physical world into the metaverse because we need those as humans. These range from our creature comforts – a comfortable sofa, a beautiful view – to ideals such as trust, and law and order, the nuts and bolts of a functioning society. How those real-world ideas and guiding principles adapt to the metaverse remains to be seen.

We’re currently in the first phase of the metaverse, where individual worlds define good and bad behavior, and regulate the use of their platforms. The second stage will be interoperability by choice. For example, Facebook and Microsoft could agree you can have an identity move between their platforms, and in that case those entities will dictate what behaviors are acceptable or not in their shared space.

Eventually, people should be able to seamlessly live their life in the digital metaverse. That’s the far future state, where you can go to a mall in the metaverse, wander and explore, and make choices about which stores you want to visit. By the time we get there, we’ll need fully implemented ethics, regulations, and laws to foster an ecosystem of trust – one in which customers feel comfortable executing financial transactions en masse. Large organizations will need to see these regulations and governance in place before they can move beyond experimentation to new lines of business.

The technology is new, but the concepts are not. Past experience tells us there are things we need to get into place before we’ll see mass adoption and financial transactions happening at scale in the metaverse. 

Regardless of how one might think of having centralized controls thrust upon them, the vast majority of consumers will not do financial business in an ecosystem without trust. Regulation is one of the key signals financial institutions, banks, insurance providers and others in their space need to monitor, to determine when the metaverse can move from the future planning horizon to an exciting opportunity for near-term business growth.

In the meantime, business leaders can work on establishing the internal structure and support for working cross-functionally with legal and governance functions to stay abreast of regulatory changes and ensure compliance. This is also a good time to explore opportunities where the metaverse could help organizations overcome compliance obstacles, and imagine future possibilities for working with regulators to combat financial crime within the metaverse. 

There’s much groundwork to be laid, and it will take a collaborative effort to build the ecosystem of trust financial organizations and customers need to conduct transactions safely and responsibly in the metaverse. 

Want to learn more?

See how a UK bank improved CX for its 14 million customers with AIOps

Driving Automotive Innovation: Cloud-Powered SDV Infrastructure for the Future of Mobility

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Download the White Paper now and drive into the future.

Remote Health Monitoring Benefits, Challenges & Solutions

Pre-pandemic, “remote health monitoring” was not a common term in the average person’s lexicon. Major technology and healthcare companies have heavily invested in research and launching various health monitoring sensors and related ecosystems, and today, remote health monitoring is gaining acceptance and adoption among the general public.

In the post-pandemic world, personal health care is an increasing focus and priority. The major benefit of remote health monitoring is obvious: caregivers may need to avoid physical meetings with patients but still need an ecosystem where all vital data is available remotely to aid in diagnosis and treatment.

Aside from infectious disease concerns, patients suffering from chronic illnesses require ongoing monitoring of vital parameters. Clinicians can reduce barriers to access for patients when daily/routine health monitoring and consultation can be done remotely, with physical meetings limited to major intervention and diagnosis. The benefits of remote health monitoring are many, and technology has a critical role to play.

We’ll explore remote health monitoring advantages, use cases, and solutions, but first – an important point of clarification. Remote health monitoring refers to the collection of patient vitals, while “remote clinical consultation” focuses on remote treatment and doctors’ recommendations based on vitals and other available patient data. We’ll focus on remote health monitoring in this article.

Types of Health Monitoring

Monitoring patient vitals such as body temperature, pulse, oxygen saturation, weight, and other factors inform medical consultations and are an important part of the diagnosis of symptoms. Regular monitoring becomes even more important for patients undergoing treatment and suffering from disease. 

Traditionally, vitals monitoring has been done at the hospital or clinic where the patient is being treated. However, this has evolved with increasing technological innovation and the falling costs of monitoring devices/sensors. Three major classifications of health monitoring today include:

    • In-person monitoring: The patient and clinician must physically meet in order to perform vitals monitoring.
    • On-demand monitoring: The patient or their caregiver can monitor their vitals at home as scheduled.
    • Implicit monitoring: Smart, wearable devices implicitly collect and monitor vital patient data.

How Remote Health Monitoring Helps

Remote health monitoring combines on-demand and implicit monitoring, helping doctors and other healthcare practitioners gain ongoing visibility of patient data that can signal changes in the patient's health condition. This is crucial for patients suffering from diseases. In some cases of typical disease, patients seem on the road to recovery during hospitalization but see their health problems return within a few weeks of discharge. Remote Health Monitoring can help in such cases to provide post-discharge monitoring. 

One solution is to have a full-time trained nurse to track patient vitals. However, this is expensive and not sustainable, making it impractical to serve every patient like this. 

Remote health monitoring platforms offer an alternative via a set of medical devices and sensors connected to a mobile application and/or a suite of remote applications to monitor and relay patient data to clinicians. This provides healthcare providers with an ongoing view into patient health so that they can monitor an existing treatment plan or intervene based on inputs from medical devices and health sensors.

Recommended reading: Digital Biomarkers: The New Era of Wearable Technology

Remote Health Monitoring Advantages

Remote health monitoring is a boon to patients with chronic illnesses who require close attention to body vitals. Other major advantages of the technology include:

  • Overall improvement in post-hospitalization value-based care.
  • Close engagement with patient chronic illness by monitoring patient health remotely.
  • A reduced number of hospital visits reduces barriers for patients with mobility issues.
  • Reduced number of readmissions in hospital.
  • Helps in social distancing.
  • Helps in addressing the shortage of trained health professionals. 
  • Regular health monitoring of patients can aid in earlier disease detection and better treatment outcomes.

Remote Health Monitoring Challenges

As with other remote monitoring solutions (such as predictive maintenance in manufacturing), remote health monitoring relies on connectivity to ensure data syncing between sensors and the devices clinicians use to access those insights. Other major challenges include:

  • Latency in data syncing.
  • Onboarding and patient learning to wear and maintain the new device. 
  • Data inconsistency and duplication.
  • Remote configuration of devices and debugging.
  • Data ingestion from devices with different output formats.

Remote Health Monitoring Device Classifications

Medical devices and sensors to measure body vitals are the most important part of a remote health monitoring solution. While most devices are external, modern technological advances mean some of these devices can be embedded and serve multiple purposes. For example, some pacemakers can control heartbeat while also tracking and delivering vital information about heart condition.

There are limitations; for example, devices and sensors preprogrammed to take readings at a particular event or time must sync with mobile applications over Bluetooth. Some devices must be operated manually to take required vitals readings, with data entered manually into an application. With that in mind, we can broadly group medical devices and sensors into the classifications below.

Implicit Reading Devices

These devices collect vitals data without manual intervention and can include:

  • Implantable or embedded devices that serve a specific purpose, such as cochlear implants or pacemakers.
  • Wearable devices such as a smartwatch or pedometer.

Manual Reading Devices

The patient or their caregiver is required to take the reading and input data. One example of such an external device is a pulse oximeter.

Remote Health Monitoring Use Cases

There are many possible workflows for remote health monitoring. Mapping out the workflow is an important part of solution design and enables all stakeholders to see how and where patient data is being used, and what decisions are being made based on it.

Here, we examine a common workflow for a patient discharged from the hospital and in need of ongoing care:

There are many use cases already in practice today, and innovations in the space are opening up new opportunities for remote health monitoring each day. Here are several more ways this technology can be used to benefit patients and improve healthcare outcomes.

Workplace safety and injury prevention

In many industries, shifts are long and tiring, and lapses in judgment or human error can lead to major financial losses and even loss of life. In aviation, heavy machinery operation, natural resource extraction, and even healthcare itself, the stakes are high. Remote health monitoring solutions can provide a system of wellness checks for professionals involved in high-risk workplaces.

Insurance

Technological advancements are enabling insurance companies to use data from remote health monitoring services to decide on yearly premiums. Individuals with healthy records can be awarded a reduced premium as compared to individuals with risk factors identified in the collected data.

Athletes

Vitals data can contain significant insights for athletes and sports personalities, with body parameters differing for each athletic and sporting activity. For example, a javelin thrower may want to measure the speed of their run to give optimum results. A swimmer might benefit from blood oxygen insights to help improve performance. Health monitoring can be modified with a new set of sensors and devices to create solutions for various types of athletes.

Fitness enthusiasts

Individuals increasingly want to live a healthy lifestyle, and monitoring body parameters can help. There are already various solutions available, such as wearable fitness devices. The engagement of fitness enthusiasts can be further increased by modifying remote health monitoring solutions to track and evaluate other aspects of daily life such as sleep quality, or screen time.

Important Considerations for Evaluating RHM Solutions

The specific qualities and capabilities of your solution will depend largely on the needs of your business, patients, and healthcare professionals. In a broad sense, quality remote health monitoring solutions will have most or all of the following characteristics:

  • Simple device onboarding and registration with patients as well as hospitals.
  • Frequent collection of data from devices and synchronization with remote services with minimum latency.
  • Business rules optimized to raise alarm warnings and emergency notifications.
  • Fulfillment of emergency notifications.
  • Unified communication solutions to provide end-to-end communication.
  • Scheduling for patient and doctor/hospital physical or virtual meetings.
  • Interoperability solutions for smooth flow of patient records.
  • Billing and subscriptions. 
  • HIPAA compliance to safeguard PHI.

Example: The IoMT FHIR Connector for Azure

Remote health monitoring platforms face common core challenges, including data ingestion at high frequency, scalability to add new devices, and data interoperability. 

The IoMT FHIR Connector for Azure tried to solve all these problems by providing tools for seamless data-pulling from medical devices (IOMT). Data is pushed securely to Azure for remote health monitoring. In this way, this solution also solves problems around lack of interoperability by persisting data in a FHIR (Fast Healthcare Interoperability Resources) server. Learn more in the github repository. 

Conclusion

Remote health monitoring is a rapidly evolving space, with much research ongoing and new solutions being released regularly. Though there are many off-the-shelf solutions available, solutions can be built from the ground up – or around open-source tools like IoMT Connector for Azure – to meet the specific needs of patients and their healthcare providers.

Want to learn more? Explore how we’re revolutionizing healthcare experiences with technology here, or reach out to a member of the GlobalLogic team with questions.

Learn more:

Humans: Just an Evolutionary Step in Planetary Intelligence

Geoffrey Hinton, one of the so-called ‘Godfathers of AI’, made headlines at the beginning of May after stepping down from his role as a Google AI researcher. A few days later, he delivered a talk at the MIT Technology Review’s EmTech Digital event.

When asked about his decision to quit, Hinton mentioned that getting old (he is now 75) had been a contributing factor, claiming that he cannot program that well anymore (he forgets things when he writes code, for example). Age aside, the biggest reason was realising how unexpectedly and terrifyingly good “Large language models” (LLMs) had become and recognising the need to speak out about it without compromising his employer.

After explaining beautifully how Backpropagation works (the core type of algorithm behind both Deep Learning and LLMs), in terms of learning how to recognise the image of a bird vs that of a non-bird, Hinton claimed that this has recently become so good that it cannot possibly be how the human brain works. Originally, he had hoped to get an insight into how the brain works by continually improving the algorithms, but - as of now - LLMs can often reason as well as a human with just one trillion connections, when humans need 100 trillion of them and many years to learn how to reason in the first place.

Learning takes time for us humans. Transferring our acquired knowledge to another human also involves investing considerable time and effort, knowledge, that - if not passed on - would otherwise perish with our inevitable death.

In contrast, an AI instance can never die. It can constantly communicate and transfer new knowledge to all other instances simultaneously, thereby augmenting the “collective AI intelligence.” And even if the current hardware breaks or fails, the code and parameters can just get transferred to a new storage medium. So, in effect, we have already achieved immortality, but sadly not for humans (and definitely not for Ray Kurzweil, who has made it his life mission! But as Hinton remarked, “Who would want immortality for white males” anyway! ).

All this is what made Hinton make the bold, chilling, but now somehow completely reasonable claim that he fears that humans are just an isolated step in the evolution of intelligence. In his view, we evolved to reach the point of creating the LLMs, which then went on to quietly consume everything we have ever written, thought, invented, – including Machiavelli – and can now, as a result, exhibit understanding and reasoning (relationships between entities and events, generalisations, inferences). So they will no longer need us around, “except perhaps for a while to keep the power stations going!”

Hinton clarified his view by referring to evolution: Humans evolved with some clear basic goals. These include things that we instinctively try to fulfill (e.g. eating and making copies of ourselves). Machines / AI did not evolve with any such goals, but it is reasonable to expect that they will soon develop “subgoals” of their own. One such subgoal may be “control” (you get more things done if you gain control).

To seize control, you may well take recourse to “manipulation” techniques – remember the Machiavelli texts we have let the LLMs ingest? Manipulation can be very covert and may even hide under the impression of benevolence, compliance or even yielding control. “You can force your way into the White House without ever going there yourself” as Hinton poignantly remarked in reference to the infamous January 6th insurrection.

So, what is the solution?

Hinton doesn’t see one!

We certainly cannot put a stop to LLM development and “Giant AI Experiments,” as many AI Scientists and Thought Leaders recently demanded with their Open Letter. Incidentally, according to Hinton, there had been such attempts already back in 2017, and his employer Google had held a long time before releasing their models, exactly out of apprehension that they could get misused (which is why Google Bard came out after ChatGPT and the New Bing).

We have now passed the point of no return for LLM development, if nothing else, because there is a real risk that should one country stop investing in these technologies, another one (worse case, their adversary) may continue exploiting them. We could perhaps establish some sort of “LLM non-proliferation treaty” along the lines of the one curbing the use of nuclear weapons, but again, this depends, according to Hinton, on the absence of bad (human) actors. AI is already used in War, but it is also increasingly used to control and punish citizens and dissidents by repressive Governments and immoral Politicians too.

We cannot depend on explainability or transparency either. Having learned pretty much everything about human emotions, thoughts, motivations and relationships, AI models can now imitate collaboration and compliance and can, therefore, also leverage this information to eventually lie about their goals and actions (short of doing an “I’m sorry, but I can’t do that, Dave”).
Hinton does not see a plateau in LLM development; they will just keep getting better with more information and further refinement through context. And even domain-specificity will just mean that LLMs learn to exhibit different rules for different worlds, philosophies, and attitudes (e.g. Liberal vs Conservative worldviews).

It should come as no surprise that Hinton has no doubt that the job market will change dramatically in the next few years. More and more tasks, even creative ones, will be taken over by intelligent chatbots, rendering us more efficient and effective. For instance, Hinton believes that LLMs will revolutionise medicine.

Ultimately, however, Hinton believes that AI, in general, will just benefit the rich (who will have more time) and disadvantage the poor (who will lose their jobs), thus further widening the gap between the two. The rich will get richer; the poor will get poorer, and gradually, increasingly indignant, and violent, which will result in conflict and possibly our own demise.

An ideal outcome for the intelligent machines we have created (in our own image), as we are very perishable and therefore expendable (and by now superfluous anyway). Nevertheless, we will have served our purpose in the evolution of “intelligence,” at least on a planetary, if no longer on a species, level!

The only thing that remains is for us humans to be aware of what is happening and to band together united in dealing with the consequences of our own brilliance.

Sounds like the best Sci-Fi movies we have already seen. Only now it’s an urgent reality.

What steps can you take now?

To address the concerns of Hinton and other AI Visionaries, at GlobalLogic, we have set up a Generative AI (GAI) Centre of Excellence (CoE), drawing together our AI and Machine Learning experts from all over the world, and we are carefully considering the GAI use cases that could be of value to our clients. We differentiate ourselves in that we can guide you on how to best implement GAI technologies in a safe, secure, transparent, controllable, trustworthy, ethical, legally waterproof, and regulatory compliant manner.

Dr Maria Aretoulaki is part of this CoE and recently spoke on the importance of Explainable and responsible Conversational and Generative AI at this year’s European Chatbot & Conversational AI Conference, which you can find here.

Reach out to our experts today to make AI work for you rather than the other way round!

***

About the author:

Dr Maria Aretoulaki has been working in AI and Machine Learning for the past 30 years: NLP, NLU, Speech Recognition, Voice & Conversational Experience Design. Having started in Machine Translation and Text Summarisation using Artificial Neural Networks, she has focused on natural language conversational voicebots and chatbots, mainly for Contact Centre applications for organisations worldwide across all the main verticals.

In 2018, Maria coined the term “Explainable Conversational Experience Design”, which later morphed to “Explainable Conversational AI” and more recently – with the explosion of LLMs and the ChatGPT hype – to “Explainable Generative AI” to advocate for transparent, responsible, design-led AI bot development that keeps the human in the loop and in control.

Maria joined GlobalLogic in 2022 where she is working with the Consumer Solutions & Experiences capability in the UK and the global AI/ML and Web3/Blockchain Practices. In 2023 she was invited to join the GlobalLogic Generative AI Centre of Excellence, where she is helping shape the company’s Responsible Generative AI strategy. She recently contributed to the Hitachi official response to the US Dept of Commerce NTIA proposal on Accountability in AI and regularly contributes to various HITACHI and METHOD Design initiatives.

Detecting Architectural Gaps with Automation

Architectural drift and erosion in software development can seriously impact the business and go-to-market strategy, causing delays, decreased quality, and even product failure. Companies must have processes and workflows in place to detect architectural gaps but historically, those manual checks have been time consuming and prone to human error.

In this paper, we explore the different types of manual architecture review and propose automated alternatives to reduce the time and resources required even while producing better outcomes. You’ll learn:

  • What architecture drift and erosion are, and how they impact the business.
  • How dependency analysis, peer reviews, and other manual inspections work.
  • Why even though they catch issues not prevented through the application of best practice good architecture governance, manual reviews are not the ideal solution.
  • Specific considerations to keep in mind around compliance, data security, DevOps, and more when evaluating architecture review solutions.
  • What automating architecture checks may look like in a series of example use case scenarios.

DevOps Guide: Challenges, Practices & Solutions for Businesses

You may already know some of the benefits of a strong, robust DevOps strategy. This approach to combining software development and operations can improve security,  reduce friction between IT and other business units, and reduce time to market. DevOps is a key ingredient in a product-centric organization and most importantly, can dramatically improve product quality.

But what exactly is it, and what do you need to know about DevOps to reap the maximum possible advantages from it to benefit your business?

In this article, you’ll find a quick overview of what DevOps is, why it matters for businesses, what it looks like across the lifecycle, and how to handle common challenges. We'll share tools, technologies, and best practices to help you build and support a DevOps mindset and culture in your business, as well.

What is DevOps?

DevOps refers to how software developers and operations work together throughout the development lifecycle. It's an ever-evolving practice of collaboration between software development and IT to create a culture of shared success.

DevOps combines the flexibility and speed of agile development with automated testing, continuous delivery, and monitoring. This enables the teams to quickly iterate on code while ensuring quality and stability in production. The software delivery process is streamlined while maintaining high quality and security levels, giving companies an efficient way to deliver high-quality software quickly while reducing operational costs.

Organizations can improve performance by streamlining their processes from concept to deployment by leveraging DevOps principles.

The DevOps Lifecycle

The DevOps lifecycle spans ideation, planning, coding, testing, releasing, and monitoring software. It begins with a clear definition of the goal and desired outcome for the project, and the team uses agile software development techniques to create working code effectively. They then test that code in a secure environment to assess the overall quality and pinpoint areas of improvement. There are a couple of ways to test how your code is developing.

With a DevOps approach, developers and their counterparts in IT work collaboratively at every stage.

Benefits of Taking a DevOps Approach in Your Business

The benefits of DevOps are far-reaching and can have a significant impact on an organization’s success. By leveraging DevOps principles, enterprises can reduce costs, improve team collaboration, and accelerate the time-to-market for new software.

  • DevOps practices can reduce labor costs by automating testing, deployments, and infrastructure management.
  • DevOps promotes more effective collaboration between development, operations, and security teams by making them more aware of each other’s needs and objectives.
  • Companies can take new products to market faster and enable developers to deliver iterative updates faster and more accurately.
  • Multiple test cycles before deployment reduce the risk associated with complex software releases.
  • DevOps practices promote faster and more frequent software releases with automation, collaboration, and continuous integration/continuous delivery (CI/CD).
  • It gives organizations greater control over managing their software lifecycle without sacrificing quality or security.

Challenges in DevOps Implementation

DevOps is a powerful tool for businesses trying to improve their software development processes, but it comes with its challenges. For example, the tools and techniques necessary for successful DevOps adoption require significant resources and expertise.

Organizations also have to deal with the complexity of different systems, resources, and people that must be integrated to manage DevOps projects successfully.

Additionally, a lack of organizational culture or buy-in from senior management often makes it difficult to implement DevOps successfully.

It’s important to understand that DevOps isn’t simply integrating new tools but creating a culture guided by its principles. It can take time to effectively shift the internal culture to a DevOps mindset and ensure the appropriate expertise is available to make it a long-lasting change.

Finding the right team who can handle modern technology and practices and adapt to change can be a difficult adjustment for a company. Prioritizing training the development and operational teams in the principles of DevOps and related best practices is key.

Recommended reading: Security Training for the Development Team

On top of finding and training the right talent, organizations must ensure that they have sufficient infrastructure and resources to support continuous delivery pipelines.

Finally, many organizations struggle to create an automated testing environment that can adequately test all aspects of their codebase to ensure quality before deployment. It’s crucial for organizations to thoroughly understand their specific needs and plan accordingly when adopting DevOps so they can mitigate or avoid these challenges.

DevOps Practices

DevOps practices include the processes, tools, and techniques to facilitate valuable collaboration between development teams, operations teams, and other organizational stakeholders. It promotes agile development by automating manual processes, streamlining team communication, and providing feedback loops throughout the software release process to help teams avoid miscommunication and errors in development.

DevOps practices require integrating various tools, such as continuous integration (CI) servers, configuration management (CM) tools, and container orchestration systems.

Security is another priority in DevOps. Security teams should be involved at each step through the software lifecycle to ensure that applications remain secure. Investing in training staff on DevOps and security best practices will help them successfully manage the complex environment created by DevOps adoption.

DevOps practices can help provide organizations with a competitive edge in today’s rapidly changing digital landscape. Let’s take a closer look at the various aspects of a DevOps practice.

Continuous Integration and Delivery

Continuous integration (CI) and continuous delivery (CD) are essential parts of the DevOps framework, allowing organizations to rapidly and reliably release software updates. It is a process that automates the build, test, and deployment of applications, allowing developers to quickly respond to customer needs and changes in the business environment.

CI/CD requires that organizations standardize processes for code reviews, automated unit tests, integration tests, and deployment scripts. The goal is to ensure that newly developed code can be quickly tested in production-like environments before being released into production.

By automating these processes, organizations can reduce time-to-market for new features and increase the quality of their software releases. Additionally, continuous integration and delivery allow developers to identify potential issues earlier in the development cycle, which helps them create more reliable software.

CI/CD helps organizations reduce costs associated with manual processes while improving overall product quality. It is a powerful DevOps practice that helps organizations increase efficiency while ensuring consistency in their applications and systems.

Recommended reading: Zero-Touch Test Automation Enabling Continuous Testing

Version Control

Version control is essential to the DevOps framework, allowing organizations to track changes to their source code. By utilizing version control systems like Git, developers can easily view and compare different versions of the same software project. This allows teams to quickly diagnose and correct errors in their work and that of other team members.

Additionally, version control enables teams to collaborate remotely by providing a single platform for storing source code. Team members can make changes and push them up to a central platform where they are visible to collaborators.

It helps teams maintain a source code history, which they can use for debugging or reverting to previous versions when necessary. Version control also helps with software release management by providing developers with a clear timeline of when each feature was released.

Infrastructure as Code

Infrastructure as code enables organizations to automate their systems, configure networks, and scale resources quickly and efficiently. It simplifies deploying applications, allowing for more frequent updates and releases. It also makes it easier to track changes as code.

By using infrastructure as code, organizations can achieve flexibility in their deployments by having templates that define the desired state of their environments. This allows them to manage multiple domains from one source of truth easily. It helps improve security by providing visibility into system configurations to identify and address potential weaknesses quickly.

Infrastructure as code helps organizations improve speed, reliability, scalability, and security when managing their IT infrastructure. It provides a powerful way to ensure your systems are always up-to-date and functioning optimally.

Configuration Management

Configuration management is a process used to ensure configuration items (CIs) within an IT environment remain consistent and predictable. It involves creating, monitoring, and maintaining a record of the configuration items in the system. This complete CIs inventory enables organizations to identify changes that occur over time (such as hardware updates or software upgrades) and ensure system consistency.

Configuration management helps teams automate repetitive tasks associated with managing software and hardware configurations, ensuring that all changes are applied in an organized manner.

Configuration management also helps in troubleshooting by providing administrators with information on which components are up-to-date and which need updating or maintenance. Teams can quickly test new configurations before they go into production environments so problems can be identified and fixed before they become major issues.

Collaboration

Collaboration is an essential part of any function and successful team. Organizations can achieve faster development cycles and better results by bringing together different teams to work together on projects. In addition, improved collaboration helps bridge the gap between development and operations teams, allowing them to communicate more effectively. This reduces the time it takes to implement product changes while improving product quality.

With a clear understanding of their roles in the DevOps environment, teams can quickly identify potential areas for improvement. When companies focus on improving collaboration and prioritizing the organizational culture, they can build better products while ensuring reliability in their applications and systems.

DevOps Tools & Technologies

Platform solutions play a vital role in the successful implementation of DevOps practices. These tools and technologies help automate processes, improve team collaboration and performance monitoring, ensure security, and provide insights into the production environment. Popular DevOps tools include Docker for containerization, Ansible for configuration management, Kubernetes for orchestration, and ELK Stack for logging.

Organizations can also use services like OpeNgine to create CI/CD pipelines, install tools, and automate cloud infrastructure setup. By leveraging these tools and technologies, organizations can streamline their software development lifecycle while ensuring greater quality control over their applications and systems.

Getting Started with DevOps

Building and nurturing a collaborative culture with a solid DevOps practice won't happen overnight, but the benefits make it more than worth the effort. The first step is to identify the goals and objectives of your organization, which will help guide what tools and technologies best suit your needs. Once you establish these objectives, you can create a plan incorporating continuous learning and a comfortable DevOps culture.

GlobalLogic helps businesses implement DevOps solutions from start to finish, with advisory services, expert guidance through implementation, and access to proven frameworks and best practices. Contact us today and let's see how we can help your business modernize, streamline, and meet your business objectives with a DevOps approach to product development.

Learn more:

Continuous Testing: How to Measure & Improve Code Quality

Software engineers naturally strive to write code that is not only functional but also of high quality. However, ensuring code quality can be a challenge, especially when working on complex projects with multiple developers. This is where continuous testing, an essential process for measuring and improving code quality, comes in.

Continuous testing is a methodology that involves ongoing code analysis, in order to identify and fix issues as they arise rather than waiting until the end of the development cycle. By integrating this process into our development workflows, we can catch potential issues early on and achieve not only higher-quality code but faster development cycles, as well.

In this article, we will explore the importance of continuous testing and how it can help us measure and improve code quality. We will discuss some of the key metrics used to measure code, the greatest challenges you’ll have to overcome, and common mistakes to avoid.

Common Barriers to Code Quality

Managing Complexity

As software systems become more complex, it becomes increasingly difficult to ensure that every part of the codebase is high-quality. This is particularly true in large-scale projects that involve multiple developers working on different parts of the system. As the codebase grows, it becomes more difficult to understand, debug, and maintain, which can lead to quality issues.

Ensuring Consistency Across the Codebase

When multiple developers are working on the same project, it's important to ensure that they are all following the same coding standards and best practices. This can be challenging, particularly in larger organizations where different teams may have different approaches to software development. Inconsistencies can lead to quality issues, as well as increased development time and effort.

Balancing Code Quality with Business Needs

While high-quality code is desirable, it's not always possible or practical to achieve perfection. Developers must balance the need for high-quality code with the business needs of the organization. This can involve making trade-offs between code quality, development time, and resource allocation. Sometimes, speed and agility are more important than code quality, especially in fast-paced environments or when responding to urgent business needs. Balancing these factors can be a delicate task, requiring careful consideration and a nuanced understanding of the organization's goals and priorities.

Recommended reading: In Software Engineering, How Good is Good Enough? by Dr. Jim Walsh

Scaling Testing

While automated testing has become more prevalent in recent years, it can still be difficult to create comprehensive test suites that cover all possible scenarios. Additionally, manual testing is still required in many cases, which can be time-consuming and error-prone. Incomplete or inadequate testing can lead to quality issues, such as bugs and performance problems, that may only become apparent after the code has been deployed.

Maintaining Documentation

While documentation is often overlooked, it is an essential part of maintaining high-quality code. Documentation provides context and guidance for developers who may be working on the codebase in the future. However, creating and maintaining documentation can be time-consuming, particularly in rapidly evolving systems.

Technical Debt

Technical debt is another challenge that can impact code quality. Technical debt refers to the accumulation of shortcuts and compromises made during development that can impact the quality and maintainability of the codebase. Technical debt can arise due to time constraints, changing requirements, or other factors. As technical debt accumulates, it can become increasingly difficult to maintain code quality, as well as slowing down future development efforts.

Staying Current with New Technologies and Best Practices 

As software engineering continues to evolve rapidly, it can be challenging to keep up with the latest developments in technology and best practices. Staying up-to-date requires continuous learning and experimentation, which can be time-consuming and require significant effort. It also requires an investment of budget from the company, and tends to be early on the chopping block when executives are looking for places to trim costs. However, failing to stay up-to-date can result in quality issues and missed opportunities for improvement.

What is Continuous Testing?

Continuous testing is a software development methodology that involves continuously monitoring and analyzing code to identify and fix issues as they arise, rather than waiting until the end of the development cycle. 

The goal of continuous testing is to ensure that software is of high quality and meets the organization's requirements, while also reducing the time and effort required to find and fix defects.

How Does Continuous Testing Improve Code Quality?

When you implement continuous testing in your development process, you use automated tools to analyze code continuously throughout the development process. These tools can detect a wide range of issues, including coding standards violations, security vulnerabilities, performance bottlenecks, and other issues that can impact code quality.

This enables organizations to catch and fix issues earlier in the development cycle, reducing the risk of defects and improving code quality. Continuous testing can also help to ensure that code is maintainable, scalable, and secure, and can help companies meet their regulatory and compliance requirements.

Continuous testing is often used in conjunction with other development methodologies, such as continuous integration and continuous delivery. By integrating these methodologies into a unified workflow, organizations can ensure that their software is of high quality and is delivered quickly and efficiently.

How to Measure Code Quality

The best code quality measurement practices are proactive rather than reactive, and ongoing. Let’s take a look at the different ways to measure code quality and the pros and cons of each approach.

Code Reviews

Code reviews are a manual approach to measuring code quality, in which one or more developers examine the code for adherence to coding standards, performance, readability, maintainability, and other factors. Code reviews can be time-consuming, but they offer a comprehensive view of the codebase and can provide valuable insights into the quality of the code.

Automated Code Analysis

Automated code analysis tools are designed to identify potential issues in code by analyzing its structure, syntax, and other characteristics. These tools can identify issues such as coding standards violations, security vulnerabilities, and performance bottlenecks. Automated code analysis tools are fast and efficient, but they can be less accurate than manual code reviews and may produce false positives.

Recommended reading: Which Software Metrics to Choose, and Why?

Code Coverage

Code coverage measures the percentage of code that is executed by tests. This metric is useful for identifying areas of the codebase that are not adequately covered by tests, as well as detecting bugs and defects in the code. Code coverage is a quantitative approach to measuring code quality, but it is not a comprehensive measure of code quality and should be used in conjunction with other metrics.

Technical Debt 

Technical debt is a metaphorical term that describes the accumulation of shortcuts and compromises made during development that can impact the quality and maintainability of the codebase. Measuring technical debt involves identifying and quantifying the trade-offs made during development and the impact they have on the codebase. Technical debt can be measured using tools such as SonarQube or CodeClimate.

Cyclomatic Complexity

Cyclomatic complexity is a metric that measures the complexity of code by counting the number of independent paths through the code. This metric can help identify areas of the codebase that are overly complex and may be difficult to maintain or modify. Cyclomatic complexity can be measured using tools such as McCabe IQ or SonarQube. See the guide below to learn more:

Click to read What is Cyclomatic Complexity? How to Calculate & Reduce it? | GlobalLogic

Conclusion

Continuous improvement means rather than fixing quality issues as they surface in reports, you tackle them proactively and commit to detecting and fixing them as they occur. Apart from quality plugins used with automated builds, IDE plugins and CI plugins help a great deal in achieving the holistic agenda of clean code.

Committing to continually reviewing and improving your testing practices helps ensure the delivery of high-quality software that meets the needs and expectations of users and will make your business more sustainable over time. Here are a few tips for implementing continuous testing in your organization:

  1. Make testing a collaborative effort: Involve your entire development team, including developers, testers, and quality assurance professionals, in the testing process. This can help ensure that everyone is working towards the same goal and can improve the overall quality of your software.
  2. Automate as much as possible: Automation is an essential part of continuous testing, as it enables you to run tests quickly and efficiently. Invest in automated testing tools and frameworks, and make sure that your tests are easily repeatable and scalable.
  3. Use metrics to measure progress: Define metrics that help you track progress and measure the effectiveness of your testing process. For example, you might track the number of defects found, the time it takes to fix defects, or the percentage of code coverage achieved.
  4. Continuously evaluate and improve your testing process: Take a continuous improvement approach to testing and evaluate your testing process regularly. Look for areas where you can improve and implement changes that can help you test more effectively and efficiently.
  5. Foster a culture of quality: Quality should be a core value of your development team. Foster a culture of quality by setting high standards and expectations for your team, and by recognizing and rewarding quality work.
  6. Stay up-to-date with industry trends: The software development industry is constantly evolving, and it's important to stay up-to-date with the latest trends and technologies. Attend conferences, read industry publications, and engage with other professionals in your field to stay informed and learn new techniques and strategies.

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Using AI to Maximize Business Potential: A Guide to Artificial Intelligence for Non-Technical Professionals

From virtual assistants like Siri and Alexa to self-driving cars and generative AI platforms like ChatGPT, artificial intelligence (AI) and its subset, machine learning (ML), are changing how we live, work, and play.

In the five years McKinsey has been tracking AI use worldwide, adoption has more than doubled, although its use in business organizations has held steady between 50-60% for the past few years. While the first-mover advantage has passed, there’s still plenty of opportunity to gain a competitive advantage by implementing AI to help your business be more agile, responsive, and innovative than others in your field.

If you’re still on the fence about adopting AI for your business or are searching for new ways various AI technologies could benefit your business, read on. In this post, you’ll find a comprehensive overview of what exactly AI is and why it matters, a timeline of AI milestones, the advantages and disadvantages of various AI technologies, and how it’s being used in different businesses today. 

What is Artificial Intelligence?

Artificial intelligence enables computers to simulate human thought processes and behavior, such as making decisions, solve problems, understanding language, recognizing images and faces, and more. Using constantly learning and adapting algorithms, AI systems can provide near-human accuracy and dramatically scale operations across many tasks and industries.

AI is one of our most significant technological advances, and its applications are becoming increasingly widespread. Businesses of all sizes are taking advantage of AI’s potential to improve customer service, increase efficiency and productivity, reduce costs, make better predictions about markets or customers, automate time-consuming and redundant tasks, analyze vast amounts of data, and develop new products and services faster than ever before. 

Recommended reading: AI's Impact on Software Development: Where We Are & What Comes Next

In addition to being an effective tool for improving efficiency and productivity, intelligent systems can anticipate user needs and provide tailored solutions quickly and accurately by leveraging deep learning algorithms.

Additionally, AI can help organizations identify trends in data faster and more accurately. With access to large amounts of data from both inside and outside a company’s own network, AI can uncover insights that would otherwise remain undetected. This enables companies to make better decisions about allocating resources and gain a competitive edge in their industry. AI is fast becoming essential for any business looking to stay ahead of the competition.

A Brief History of AI Development

Artificial intelligence has come a long way since its inception in the 1950s. Some of the key dates in AI development include:

1956: The term “artificial intelligence” was coined by John McCarthy at the first AI conference at Dartmouth College.

1967: Frank Rosenblatt created the Mark 1 Perceptron, the first computer utilizing a neural network. It was able to quickly learn through continued experimentation. 

1980s: Symbolics Lisp machines are commercialized, and neutral networks using the backpropagation algorithm became common in AI applications.

1997: IBM’s Deep Blue defeated world chess champion Garry Kasparov.

2008: Google achieved significant advancements in speech recognition technology, which it incorporated into its iPhone application.

2011: Apple introduced Siri, a virtual assistant powered by artificial intelligence, to its iOS operating system.

2018: Google launched BERT, a natural language processing engine that made it easier for machine learning applications to translate and understand conversational queries.

2022: OpenAI released ChatGPT, a conversational AI that utilizes a large language model.

2023: Microsoft has recently released a new AI-powered version of its search engine Bing, which utilizes the same technology as ChatGPT. In response, Google has introduced its own conversational AI called Bard, creating competition in the market.

Thanks to advances in machine learning models such as deep neural networks and reinforcement learning algorithms, AI technology is constantly improving. These milestones in AI development demonstrate AI technology's increasing sophistication and capabilities and its potential to revolutionize various industries.

Types of Artificial Intelligence

There are two main categories of artificial intelligence: narrow AI and strong AI. Narrow or weak AI focuses on specific tasks and can be used for language processing, facial recognition, and natural language understanding. On the other hand, strong AI or artificial general intelligence (AGI) has the potential to emulate human-level intelligence across a wide range of skills and tasks.

Weak AI (Narrow AI)

Weak AI, also known as narrow AI, is artificial intelligence that focuses on one specific set of tasks and is limited to the task for which it was designed. It cannot be applied to different problems. This makes it ideal for applications where speed and accuracy are essential, such as language processing, facial recognition, and natural language understanding.

One of the most significant advantages of weak AI is that it can quickly process large amounts of data while making fewer mistakes than humans. Businesses can use weak AI to automate mundane tasks or uncover insights from large datasets more accurately than manual labor. Additionally, weak AI can be trained rapidly due to its narrow scope.

Strong AI (Artificial General Intelligence)

Strong AI or Artificial General Intelligence is the next step in artificial intelligence. It refers to machines that can perform a specific task and possess a human-like level of understanding and reasoning. 

Unlike weak AI, strong AI has the potential to think for itself and solve complex problems without needing any kind of external programming or instruction. This means it can learn from its environment and even develop an understanding of its capabilities without human intervention.

Deep Learning vs. Machine Learning

Deep learning and machine learning have become increasingly popular in recent years as companies of all sizes seek to leverage the power of AI for their businesses. But what’s the difference between deep learning and machine learning? While both are branches of artificial intelligence that use algorithms to learn from data, there are essential differences between them.

Machine learning focuses on identifying patterns in data and using those patterns to make predictions or decisions. 

Deep learning takes this concept further by using layers of “neurons” to simulate how a human brain works and improve its ability to recognize patterns. This allows for much higher accuracy when making predictions or decisions based on data.

Deep learning is often used for tasks such as speech recognition and natural language processing, which require understanding complex relationships between words and concepts — something machine learning alone cannot do. 

Machine learning and deep learning each have unique advantages that make them useful for different applications. Companies should consider carefully which is best suited to their needs before investing in either technology. With the right guidance, companies can seamlessly integrate these AI capabilities.

Advantages of Using AI in Business

The advantages of using AI are numerous; here are some examples.

Personalization: AI can help businesses personalize customer interactions by analyzing customer data and tailoring marketing and sales efforts accordingly. This can lead to better customer experiences and increased customer loyalty.

Enhanced decision-making: AI can analyze vast amounts of data quickly and accurately, providing insights that can inform business decisions. This can lead to better decision-making and more informed strategies.

Cost savings: AI can help businesses save money by automating tasks and reducing the need for human intervention. For example, AI-powered chatbots can handle customer inquiries and support requests, reducing the need for human customer service representatives.

Improved efficiency: AI-powered systems can automate repetitive and time-consuming tasks, allowing employees to focus on higher-value tasks. This can lead to increased productivity and efficiency in the workplace.

Competitive advantage: Businesses that adopt AI early on can gain a competitive advantage over their peers by leveraging the technology to improve their operations, products, and services.

Predictive analytics: AI can be used to analyze historical data and identify patterns and trends. This can help businesses predict future outcomes and make more accurate forecasts.

Fraud detection: AI can detect fraudulent activities and transactions in real time. This can help businesses prevent financial losses and protect their reputation.

Improved customer service: AI-powered chatbots and virtual assistants can provide round-the-clock customer service, responding to inquiries and providing support at all hours.

Automation of complex tasks: AI can automate data analysis, financial modeling, and supply chain optimization tasks to save time and reduce errors.

Improved cybersecurity: AI can detect and respond to cyber threats in real time, helping businesses protect their data and infrastructure from cyber-attacks.

AI Disadvantages & Limitations

Despite the numerous benefits of artificial intelligence, there are also some potential drawbacks. One of the most prominent disadvantages is that AI systems require significant amounts of data to function correctly. This means that if a company does not have access to enough data, it may not reap AI's full benefits.

AI-powered systems can sometimes make mistakes due to errors in programming or incorrect data input. This could lead to problems such as inaccurate customer service information or even security breaches if sensitive information is compromised due to an AI system’s mistake.

Overall, while AI offers numerous advantages for businesses, companies must consider the potential benefits and risks of using these systems before investing time and money into developing one. GlobalLogic can help you assess where to incorporate AI technology and help with the transition management.

How Businesses Use AI in Various Industries

Intelligent automations can augment and amplify the best of human performance, enabling a business to scale and grow at a rate that would otherwise be impossible. 

As Sanjeev Azad, Vice President of Technology here at GlobalLogic, shared with CXO Today recently, “Contact-center automation, customer segmentation & service analytics, business process automation and services optimization, predictive maintenance and remote assistance, risk modeling and analytics, and fraud detection and analytics are few businesses use cases where adoption of AI is playing a significant role.”

  • GlobalLogic Intelli-Insights helps companies in all industries activate the power of their data by providing pre-defined standard AI apps and custom app-building capabilities inside our AI-powered data analysis platform. This digital accelerator enables companies to quickly transform data into actionable insight without having niche data science skills in-house. 

Here are several more examples of how companies use AI to their advantage in different industries.

Finance

In finance, AI is used for fraud detection, risk assessment, regulatory compliance, investment strategy, and more. Anywhere data can be analyzed and used to make predictions and decisions, AI can help. 

You can read about a specific application of AI in fintech here. In this example, a well-trained machine learning model constantly analyzed market data and made appropriate portfolio adjustments to continuously improve performance.

AI is being used to help insurers identify and mitigate risks by analyzing data from various sources, including social media, weather reports, and satellite imagery. Using AI to analyze customer data and predict future needs or behavior can help banks offer personalized services and products. It works to detect fraud and prevent financial crimes, saving banks money, and can automate repetitive tasks such as data entry for companies in insurance, investments, fintech, cybersecurity, and more.

Healthcare

One of the most impactful ways AI is used in healthcare is in diagnostic imaging. AI algorithms can analyze CT scans, MRIs, and X-rays to process results faster and detect anomalies that may not be visible to the human eye. AI can help doctors diagnose diseases earlier and more effectively manage patient care by analyzing patient data to predict disease progression and identify potential complications.

AI is used to develop personalized patient treatment plans based on their medical histories and genetic makeup. It’s also valuable for creating new drugs and treatments, and analyzing clinical trial data to help researchers identify new treatments and therapies. 

Check out other ways AI is used in healthcare here:

Click to read How Digitization Is Changing Medtech, Life Sciences, and Healthcare

 

Media

AI is used in the media industry in various ways, from content creation and audience targeting to creating personalized news feeds and analyzing social media data to determine what topics are trending.

AI can be used for transcription, translation, and image and video analysis tasks. Major media and entertainment brands have used AI for video encoding, augmented reality projects, and analyzing and predicting consumer content.

Recommended reading: AI is the Future of Media

Retail

AI is used in the retail industry in various ways, such as personalized customer experience, inventory management, and supply chain optimization. For example, retailers use AI to gather data about their customer’s preferences and behaviors and then use that data to offer personalized product recommendations and promotions. AI-powered chatbots also provide customer service and support.

Additionally, AI optimizes inventory management by predicting demand and ensuring that the right products are available at the right time. AI is also used in supply chain optimization to improve logistics, reduce costs, and increase efficiency. Here is a case study of how AI was used to create a next-gen retail product that blends online and in-store shopping.

Manufacturing

AI is used in the manufacturing industry in several ways. One of the most common applications of AI in manufacturing is predictive maintenance. By using sensors and data analysis, AI can predict when a machine is likely to fail and schedule maintenance before it does. This can save companies money in unplanned downtime and repairs.

AI can also optimize production processes by analyzing data on everything from raw materials to energy consumption to identify opportunities for improvement. Additionally, AI can improve quality control by analyzing data from sensors and cameras to identify product defects and anomalies as they are manufactured. 

Today’s business landscape is changing rapidly, and those that can take advantage of AI have the edge over their competitors. By leveraging AI's power, businesses can better understand their customers and increase productivity while reducing costs and creating new efficiencies.

Final Thoughts 

Artificial intelligence is a potent tool for businesses of all sizes. AI can help streamline processes, improve efficiency, and save time and money. Additionally, AI can provide real-time insights into customer and user behavior to inform marketing campaigns or product development. 

Businesses need to take advantage of these benefits to remain profitable in the long run. While a wide variety of AI applications are available, it’s essential to thoroughly assess each before deciding which suits your company. Training employees on how to use these tools effectively to get the most out of them is also critical to the success of each AI implementation.

GlobalLogic developed our AI/ML Center of Excellence to help customers make informed decisions about and implement AI to increase business efficiency, continuity, and profitability. The best practices, tools, and proven processes available via our CoE are based on our extensive experience helping customers transform their businesses with AI-powered solutions and developing AI products.  

 

Get in touch today and see how we can put this experience and expertise to work for you.

Guide to Blockchain Technology Business Benefits & Use Cases

Blockchain is best known for its usage in cryptocurrency, where it provides each network that uses it with a digitally distributed, decentralized, public ledger for tracking holdings and transactions. 

However, blockchain technology has a variety of applications in many industries, including healthcare and pharmaceuticals, financial services, cybersecurity, manufacturing, and supply chain management. Anywhere transactions occur, blockchain can help improve security, privacy, and data transparency.  

Businesses of all kinds are transitioning to this secure infrastructure to reduce the costs of the traditional transactional model, automate processes, strengthen security, protect personally identifying and other sensitive information, and improve security. It’s no wonder the global blockchain market, valued at USD $7.18 billion in 2022, is expected to grow to USD $163.83 billion by 2029.

What exactly is blockchain, and how does it work? This unique technology has already changed how many businesses operate, from financial transactions to smart contracts. In this article, you’ll learn about blockchain, its advantages and disadvantages, different types of blockchain applications, and how various businesses currently use it. 

What is Blockchain Technology?

Blockchain is a decentralized, distributed ledger that records transactions in a fixed format across multiple computers on a network, providing organizations with a way to securely track and verify digital transactions. 

Blockchain enables participants to keep track of their assets without relying on a centralized authority or intermediary. Transactions are verified by computing power provided by the network rather than depending on manual verification or any third-party source. In addition, the entire network is constantly updated and monitored, ensuring transparency and accuracy of the record-keeping process.

What is Tokenomics?

Tokenomics refers to the study of the economics and mechanics of cryptocurrency tokens. It combines the terms “token” and “economics” and describes how a token operates within a blockchain ecosystem. Tokenomics involves the creation, distribution, and management of tokens, as well as how they are used and exchanged.

It includes factors such as token supply, demand, utility, and value, and the incentives for users to hold or use the token. Tokenomics also helps establish the governance of a blockchain network and the rules that govern the behavior of participants in the network. Overall, tokenomics plays a critical role in the success and sustainability of a blockchain project.

Recommended reading: Tokenomics with Blockchain: GlobalLogic’s Tokenomics Position

Blockchain Myths & Misconceptions

These are some of the more persistent myths around blockchain technology:

  1. Blockchain is only used for cryptocurrencies: While it is true that blockchain technology was first used for cryptocurrencies, it has evolved to have many other applications, such as supply chain management, voting systems, and smart contracts.
  2. Blockchain is completely anonymous: Although blockchain is based on a decentralized system, transactions are recorded on a public ledger that can be traced back to their source.
  3. Blockchain is completely secure: While blockchain is highly secure due to its decentralized structure, it is not completely immune to attacks. There have been cases of hackers exploiting vulnerabilities in the system to steal cryptocurrencies.
  4. Blockchain is only for tech-savvy people: While blockchain technology is complex and may seem intimidating, it has become more user-friendly with the development of user-friendly interfaces and applications that make it accessible to the average person.
  5. Blockchain is a magic solution to all problems: While blockchain has many potential benefits, it is not a cure-all solution. It’s important to carefully consider the specific needs and limitations of each use case before deciding to use blockchain technology.

Advantages of Blockchain Technology

The advantages of blockchain technology are continuously expanding. By operating on a decentralized, distributed ledger system, blockchain technology offers unprecedented security and accuracy, surpassing most traditional methods. In addition: 

  • The digital nature of the ledger allows for faster transaction times and lessens the need for intermediaries to facilitate transactions.
  • Blockchain technology is highly scalable and can easily expand to accommodate more users and transactions. 
  • Blockchain networks are resilient against cyber-attacks due to their distributed architecture and consensus mechanisms.
  • Because of its open-source nature, anyone can develop applications on top of a blockchain network without relying on a third party or centralized authority. 

These advantages make blockchain technology attractive for many industries looking to increase efficiency and reduce costs.

Limitations and Disadvantages of Blockchain Technology

The disadvantages of blockchain technology are mostly related to its data storage limitations and cost. In addition, blockchain networks require a large amount of computing power and energy to operate, which can be costly and difficult to scale up as demand increases.

Many blockchain systems aren’t designed to handle large amounts of data, which can lead to slower transaction speeds. Since the technology is relatively new, there are still some unknowns about how it may be impacted in future by regulations and laws in different regions.

As blockchain technology matures and more companies become involved in its development, these issues should be addressed and resolved. At GlobalLogic, we’ve researched blockchains’ ability for large-scale interoperability and have discovered solutions like introducing a third party and identifying the state distribution between permissioned and permissionless ledgers.

Types of Blockchains

Several different types of blockchains offer varying levels of security and access.

Public Blockchains

Public blockchains are becoming increasingly popular due to how data is stored, managed, and transferred. With no central authority, these blockchains allow anyone with an internet connection to view or add information to the ledger. In addition, public blockchains are highly secure and don’t require third parties to verify transactions.

This means businesses can save time and money while providing a safe environment for their customers. Additionally, public blockchains offer transparency, as all users can view all transactions on the chain. Cryptocurrencies Bitcoin and Ethereum are two well-known examples of public blockchain technology.

Private Blockchains

Private blockchains allow businesses to keep their data secure while still providing control over the access and permissions of who can view and add information to the ledger. Private blockchains enable companies to manage their records and transactions without relying on third parties, making them more efficient and cost-effective.

Additionally, private blockchains offer extra security as only those approved by the company can access or make changes to the chain. This makes it easy for businesses to protect sensitive data from unauthorized access or malicious attacks. 

Private blockchains are an ideal solution for businesses looking for a secure way of managing digital records without sacrificing privacy or security. Tracr, a system developed by De Beers for verifying the provenance of diamonds and tracking them to eliminate “blood diamonds” in the value chain, in an example of a private blockchain. 

Consortium Blockchains

Consortium blockchains, also known as federation blockchains, allow companies to retain control over who can access or make changes to the ledger while enabling them to collaborate with other companies or institutions to share computing power or resources. This allows organizations to work together without sacrificing their security measures.

Additionally, consortium blockchains are an ideal solution for businesses looking for an efficient and secure way of managing digital records without sacrificing privacy or security. Hyperledger, Quorum, and Ethermint are all consortium blockchains. 

Hybrid Blockchains

Hybrid blockchains are for organizations looking for a secure and private way to manage digital records. Hybrid blockchains allow companies to take advantage of both public and private blockchains, allowing them to keep sensitive data securely within their network while benefiting from the added security of a public blockchain.

Furthermore, hybrid blockchains provide organizations with an efficient way to manage digital records, streamlining internal processes and reducing costs associated with third-party intermediaries. IBM Food Trust – where farmers, distributors, and wholesalers can transact privately and securely – is a great example of a hybrid blockchain. 

Components of a Blockchain System

A blockchain system comprises several key components that all work together to ensure the security and integrity of data stored on the network.

Digital Ledger

A digital ledger is a powerful and secure way to store data online. It is composed of a distributed database that records transactions immutably. In addition, cryptographic algorithms verify transactions, ensuring the integrity of the data stored on the ledger.

Each node in the network has its own copy of the ledger, creating redundancy and ensuring the data remains secure even if one node goes offline or malfunctions. This makes digital ledgers ideal for the use of all blockchain types.

Businesses can create smart contracts, record keeping in supply chains, and power digital currencies. Digital ledgers are changing record keeping with their trustworthiness and reliability, making them an essential technology for many industries today.

Decentralized Network

Decentralized networks are the backbone of blockchain technology and its rise in popularity. By leveraging the power of distributed computing, decentralized networks enable data to be stored, shared, and processed securely and reliably.

A decentralized network comprises multiple computers that work together to process transactions and store data on a shared ledger. This makes it virtually impossible for any computer or person to control or manipulate the data, creating a more secure environment than centralized systems.

Decentralized networks also require less computing power, giving them an advantage over centralized systems in terms of scalability and cost-effectiveness.

Shared Ledger / Public Ledger

A shared ledger, also known as a public ledger, is a digital record of transactions that can be used to store and share data across multiple parties. The data is stored in a distributed database, meaning any single entity does not control it. This makes it virtually impossible for anyone to manipulate or control the data, creating a secure and trustworthy environment. As a result, a shared ledger has many advantages over traditional centralized systems, such as improved security and scalability, cost-effectiveness, and greater privacy.

By leveraging the power of distributed computing and cryptography, shared ledgers are revolutionizing how we store and process data. With their ability to provide greater trust between users and organizations, shared ledgers are quickly becoming the preferred method for storing and sharing information in various industries.

The use of shared ledgers is changing how we store and process data, giving us a secure, trustworthy and cost-effective alternative to traditional centralized systems.

Distributed Consensus Protocols

Distributed consensus protocols are an integral part of blockchain technology. They provide a secure and reliable way for multiple computers to agree on the contents of a digital ledger or database. This allows for increased security, as all parties in the network must approve any changes to the data. These protocols also help ensure that only valid transactions are recorded on the ledger and that all users have access to the same version of data.

The most popular distributed consensus protocol is called Proof-of-Work (PoW). It requires network participants to solve complex mathematical problems to validate transactions and create new blocks on the blockchain. As more computers join the network, more computing power is needed to secure it, making it highly resistant to malicious attacks.

Distributed consensus protocols are essential in facilitating trust and ensuring the integrity of public ledgers. By providing a secure and reliable way for multiple computers to agree on data stored within a blockchain, they facilitate trust between parties, reduce costs associated with maintaining records, and help prevent fraud and other malicious activities from occurring within networks.

Cryptography and Digital Signatures

Cryptography and digital signatures are two essential components of blockchain technology. Cryptography is used to secure data by encrypting it so that only users with the correct key can access the information. It also helps prevent malicious actors from changing the data stored in a blockchain network.

Digital signatures verify the authenticity of transactions and ensure that they have not been altered or tampered with. The signature is created using a combination of public and private keys, ensuring that only authorized users can change the ledger.

Cryptography and digital signatures are two important components when implementing blockchain technology. By understanding how they work together, organizations can ensure their data is secure, and transactions remain trustworthy.

Use Cases for Blockchain Technology

From financial institutions to supply chains, blockchain has given organizations the tools to track and manage their records securely.

Financial Transactions and Banking Systems

Financial transactions and banking systems have traditionally been time-consuming and expensive. However, with the emergence of blockchain technology, these processes are becoming much more efficient.

Users can securely store and transfer digital assets using a decentralized ledger system without needing a third-party intermediary. This eliminates transaction fees associated with traditional banking systems, making it an attractive option for those looking to make financial transactions quickly and sec

Furthermore, blockchain technology is more secure than traditional methods as it eliminates the risk of fraud or data manipulation. With its ability to create an immutable record of all transactions, blockchain provides greater transparency into the financial sector while ensuring all parties involved follow through on their commitments. Blockchain offers a cost-effective solution for those looking to streamline their financial transactions and banking processes.

Supply Chain Management & Traceability Solutions

Supply chain management and traceability solutions through blockchain are vastly growing. With the emergence of blockchain technology, companies can securely track the movement of products from their origin to their destination. This allows for greater transparency in the supply chain process, ensuring all parties involved follow through on their commitments.

Recommended reading: Strengthen Your Supply Chains with Blockchain

Using a digital ledger system, people can easily verify product authenticity and track any changes made throughout the process. Furthermore, it eliminates the risk of fraud or data manipulation as every transaction is stored immutably on the blockchain. As a result, blockchain provides an efficient and secure solution for those looking to streamline their supply chain management processes.

Digital Identity and Authentication Services

With blockchain technology, users can quickly and securely verify their identity without sharing personal data or information. This process is done through a unique private key linked to each user’s digital identity. In addition, the private key allows for secure access to online accounts while ensuring that only authorized users can access them.

Additionally, this system eliminates the need for passwords, making it even more secure than traditional authentication methods. This technology provides a safe and secure way to protect your data from malicious actors and hackers.

See how Hitachi digitized its contract process with an electronic signature service secured on the blockchain using Hyperledger Fabric here.

Digital Coupons

Digital coupons are becoming a norm for customers and businesses in recent years. As a result, companies can efficiently distribute coupons through their website, apps, and social media for customers to redeem effortlessly.

They can also use third-party services with blockchain, distributed ledger technology, and smart contracts to reduce the cost of coupon management and distribution.

Incorporating blockchain technology into coupon marketing strategies offers companies many advantages and use cases. However, understanding the critical components behind blockchain technology is essential to creating impactful coupon campaigns.

Smart Contracts and Automated Business Processes

Smart contracts and automated business processes are influencing technologies that can help streamline and simplify how businesses operate. They are digital agreements, or contracts, that are written on the blockchain. 

Smart contracts execute automatically when pre-defined conditions are met, making them incredibly efficient and secure. And because they exist on a decentralized network, there’s no need for a third-party intermediary - meaning faster transactions with lower costs.

Automated business processes also leverage blockchain technology to create more efficient operations. By utilizing smart contracts to automate mundane tasks like document management and payment processing, businesses can save time and money while improving accuracy and transparency.

Cross-Border Payments and International Remittances

Blockchain technology makes global payments faster, easier, and more secure. In addition, by leveraging smart contracts, payments can be automatically executed when predetermined conditions are met - meaning transactions occur without needing a third-party intermediary.

Additionally, because all data is stored on an immutable ledger, users can trust that their transactions are secure and traceable. From faster, more secure payments to lower costs and improved traceability, blockchain technology is improving the global payments platform.

Recommended reading: Real-Time Payments Lessons from India’s Wildly Successful UPI

With its versatile capabilities, businesses of all sizes now have the opportunity to make their international remittances easy and efficient - without compromising on security.

Data Privacy & Protection Solutions

Data privacy and protection are of utmost importance in today’s digital world. But with increasingly sophisticated cyber threats, how can businesses ensure their data is secure?

By leveraging the power of a decentralized network, blockchain provides an immutable record of transactions that is tamper-proof and highly secure. Additionally, using smart contracts, businesses can control access to their data and set parameters for who can view it. This ensures that only authorized users can access sensitive information - making it impossible for unauthorized individuals to gain access.

Finally, with end-to-end encryption and cryptographic hashing, businesses can rest assured that their data is safely stored on the blockchain - making it virtually impenetrable. So if you’re looking for a reliable solution to keep your data safe and secure - look no further than blockchain technology.

Final Takeaways

Blockchain technology is a powerful tool that can transform how multiple industries function. Whether for finance, healthcare, logistics, retail, or elsewhere, implementing blockchain helps improve your systems' security, scalability, and data transparency. 

As an experienced, proven digital engineering partner, GlobalLogic can provide the proper support to seamlessly integrate blockchain technology into your operations and business strategy. Contact us today, and let’s see how we can help you.

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Benefits of Total Experience (TX) Strategy in Modernizing Applications

Contributors:

Anton Boretskyi  - coordination

Oleksandr Bereza - contributor

Oleksandr Yevtushenko - reviewer

Harsimrat Singh - reviewer

 

The world is undergoing a rapid and dynamic transformation, with technological advancements taking center stage. Embracing modernization and implementing a Total Experience (TX) strategy can help companies stay ahead of the curve and gain a competitive edge while remaining agile and responsive to new opportunities. 

Gartner predicts that by 2024, organizations providing a total experience will outperform competitors by 25% in satisfaction metrics for both customer experience (CX) and employee experience (EX). In this blog post, learn how to embrace modernization and revamp your products, applications, and solutions to stay ahead of the competition and drive revenue with a Total Experience strategy. 

What is Total Experience (TX)?

As Gartner defined it, Total Experience “is a strategy that creates superior shared experiences by weaving together the four disciplines i.e., the multi-experience (MX), customer experience (CX), employee experience (EX) and user experience (UX).”

Multi Experience (MX) 

The predecessor of the Multi Experience (MX) strategy was omnichannel, which combines a company’s multiple touch points – website, social, email, mobile, etc. – into a single approach based on information from various sources. 

Multi-experience extended omnichannel by shifting the focus from channels and technology to thinking about how people will use an application and interact with the company or product. It aims to provide an optimal experience tailored to the individual customer or user, touchpoints, context, and interaction methods. 

Customer Experience (CX)

The holistic perception of a product or brand is customer experience (CX). The total result of how end users interact with business: talk with the support team, order and buy something on the website. It’s essential to build the best customer experience for repeat sales. Loyalty to the brand, customer satisfaction, and positive recommendations could bring in new customers and generate sales. 

Employee Experience (EX)

The Employee Experience (EX) evaluates employees' journey stages: engaging, developing, and retaining. People are the most import and resource in most different business areas. A person who grows and feels comfortable in work could give the company more than expected. Loyalty and satisfaction can bring a new idea for a feature, product, or business.

Recommended reading: Improving Employee Experiences – A Playbook, from Method

User Experience (UX)

How the end user interacts with a product or application and how the system is flexible and understandable is what the User Experience (UX) is. UX is essential for all products or applications users and delivers the end user to the expected destination without additional help or explanation. 

How Total Experience Impacts the Modernization Process

Application modernization is a process that improves the performance of business software delivery by upgrading rather than replacing older software systems. Modernization is not easy, but it can be a lighter, more affordable lift when we understand all needs before updating a product. 

Applying the TX strategy to application modernization maximizes the value of the output for both customers and employees, providing users with more contact points and empowering employees with the tools they need to deliver intelligent customer service.

Here’s why each TX component – MX, CX, EX, and UX – matters in the context of a modernization process.

Why Multi-experience Matters in Modernization

Multiple touchpoints are essential for building a sales strategy for the individual user, and businesses should remember the MX method when updating an application. Use a technique for selecting technologies that suits the final goal; for example, cloud migration could open the door to new cloud features and give a new vision of Multi-experience. 

Of course, modernization will add new MX features to the latest version of the product, increasing loyalty and customer and employee experience in general. 

Why CX Matters in Modernization

CX is the most crucial factor of any modernization application process, as the customer is the epicenter of all products. External feedback is critical in furthering your understanding of how the product or application is used, and it’s essential to gather this before starting the modernization process. Modernization is more than updating a technology stack or migrating to a cloud infrastructure. The proper updates can significantly increase the number of new users and drive existing satisfaction. What’s more, CX can substantially decrease development time, increase customer satisfaction and loyalty, and fuel a successful product. 

Why EX Matters in Modernization

Many companies invest a lot in CX but skip employees' interests, and it’s a costly oversight. End users communicate with employees, and their feedback can inform new ideas. Employees are experienced product experts with valuable insight into pain points, challenges, and opportunities to improve the customer experience.

Why UX Matters in Modernization

Updates to the user interface must be considered and tested carefully, given the impact UX can have on customers and employees – and business results. Streaming for video calls, for example, requires new technology changes. Sometimes, this is a killer feature for a product, and it’s impossible to forget when modernizing the application. Other times, you might think a change in navigation or updating a button is inconsequential – until it has a significant impact. 

Benefits of Applying a Total Experience Lens to Application Modernization

Upgrading legacy applications and products isn’t a one-and-done operation. Products become legacy the day after each subsequent release. The ongoing modernization process provides a framework for improving experiences and reaping benefits. Here are some examples.

Increasing Brand Loyalty 

Total Experience is a powerful tool to increase brand loyalty when a business modernizes the application. Building brand loyalty requires over-delivery on expectations and is fueled by two-way client communications that focus on integrating feedback. A TX strategy helps further product recognition, satisfaction, and customer and employee feedback. 

Reduced Business Silos 

Segregated organizational cultures are common, and UX, CX, and EX representatives rarely collaborate on the same project simultaneously. Typically, the project moves from stage to stage without a cohesive understanding of the problems the previous Experience confronted or solved. In a TX strategy, various experiences work together seamlessly so that everyone can understand the needs of others and how their actions affect the overall product. This is crucial in the application modernization process, where getting to market faster with a superior product can mean a significant business win. 

A Healthy, Stimulating Culture of Innovation

Moving a project from stage to stage without the input and perspective of all Experiences has another major drawback: it hampers innovation. Rather than having all types of professionals and their richly varied points of view pulling together in the same direction, they may only be aware of the task at hand. Taking a TX approach helps everyone involved understand the needs of others and how their actions affect the overall experience.  

More Creative Product

Creative products result from innovative ideas that win enough support to become innovations. They must bring something new that serves a purpose and solves a problem in a new way. Fresh, creative ideas exist throughout the modernization process, but what features hold the most significant potential value for the business? Motivated employees (EX) can share new, exciting ideas, and pairing those with CX and UX insights can only strengthen the use case. 

Increase the Speed of the Modernization Process

Taking a TX approach means each Experience team understands the needs and goals of the others. All parties agree on the required technologies and can work together to reduce the iteration count. With an overall view of who provides which inputs, when – and, importantly, why – teams can better budget their time and prepare for their next steps. 

A Clear, Shared Final Product Vision

The collaboration process can result in more inputs than expected, but this combination of opinions and experiences drives a successful product. The key is clearly defining a vision for the product's future and ensuring all teams have ongoing access to it. By its very nature, TX considers each of the Experiences and incorporates that into the product vision. When all can see the final picture, they understand each stakeholder's steps to achieve the goal. The definition of each step could change a modernization process flow and significantly reduce time and costs. 

Successful TX Strategy in Action: Modernization via Multi Experience for a Fast Food Brand

This theory is great, but what does it look like in practice? In the process of modernization application, the first step is to draw a picture of the system as it currently exists. The state of a system describes different people from a domain and other points of view. This analysis provides incredible results; often, modernization results in an entirely new product that will grow and evolve with the business for years to come.

That’s precisely how we approached a modernization request from McDonald’s, one of the world’s largest fast-food corporations. To meet consumers’ increasing expectations for self-service options, the restaurant brand needed a new system for order-taking. 

Now, customers can browse the menu, place their order, and process payment without communicating any of this to a counter clerk. Those employees, in turn, are freed up to focus on other essential elements of the customer experience: cleaning the store’s interior and exterior, preparing and packaging orders with great accuracy, maintaining equipment, providing a comfortable dining room experience, etc. 

Multi-experience brought a new device to the ordering process, and the modernized application offers an intuitive UX. More than ticking the boxes across the TX spectrum, this solution meets the needs of every type of stakeholder and the business as a whole.

Conclusion

Our current reality requires dynamic adaptation, and businesses must modernize legacy solutions. Applying a Total Experience strategy that weaves together four disciplines – multi-experience (MX), customer experience (CX), employee experience (EX), and user experience (UX) – allows us to do it most profitably.   

TX can offer improved modernization process speed, internal and external brand loyalty, creative new solutions and features, reduced silos, and a healthier atmosphere of innovation across the company.

GlobalLogic has stayed at the forefront of the latest technology trends, strategies, and concepts for more than two decades. We apply best practices and TX lessons learned to each new application transformation process so that digital solutions can improve the consumer’s experience (CX), optimize operations (EX),  and approach UX with a deep understanding of what each user needs.

Whether modernizing the solution involves AI and ML, virtual reality, IoT connectedness, mobile friendliness, or other technologies, our experience in high tech ensures we take an MX approach. This fuels more user touchpoints and a final product that will delight users and exceed their expectations. 

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