5 posts tagged with "business"

Multi-cloud migration is the process of moving an organization's IT resources and workloads from one or more traditional on-premises environments to multiple cloud computing environments or you can understand it as Multi-cloud migration is the process of moving workloads and applications from a single cloud infrastructure to multiple cloud providers. This approach provides businesses with greater flexibility, scalability, and cost savings.

For traditional businesses, this typically involves moving applications, data, and other resources from their data centers to one or more public cloud providers, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

This can bring many benefits to traditional businesses, such as increased scalability, flexibility, and cost savings, as well as improved disaster recovery and data backup options.

Moving a business to the cloud involves several steps and considerations#

● Assessment:#

The first step in a multi-cloud migration is to assess the current state of the business's IT infrastructure. This includes identifying the current workloads and applications that need to be migrated, as well as any dependencies or constraints that may impact the migration.

● Planning:#

Once the assessment is complete, the next step is to develop a detailed migration plan. This includes identifying the target cloud environments.

● Prepare your environment:#

Before migrating your workloads to the cloud, ensure that your environment is ready by configuring network and security settings, creating accounts and permissions, and setting up monitoring and logging after this.

● Choose a cloud provider and a migration:#

Decide a cloud provider, such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform then, move your data to the cloud using a variety of methods, including data replication, backup and restore, or lift and shift.

Once your data is in the cloud, test and validate your applications and services to ensure they are working correctly.

● Deployment and Go-live:#

Once the migration has been successfully tested and validated, the final step is to deploy the applications to the target cloud environments and go live.

This includes configuring the cloud environments, setting up monitoring and management tools, and providing support for the users.

● Monitor and optimize:#

After the migration is complete, monitor the performance of your applications and services to ensure they are meeting the needs of your business. Optimize your cloud environment as needed to improve performance, reduce costs, and increase efficiency.

● Continuously improve:#

Cloud migration is not a one-time event. Continuously look for opportunities to improve, to adapt to changing business needs and new features offered by your cloud provider.

● Maintenance and Optimization:#

Once the applications are live, it's important to continuously monitor and optimize them to ensure they are running at peak performance. This includes monitoring for any issues, troubleshooting and resolving problems, and making adjustments as needed to optimize performance and cost efficiency.

By following these steps, businesses can ensure a smooth transition to a multi-cloud environment and take advantage of the benefits that it offers. However, it's important to note that each business is unique and the steps may vary depending on the specific requirements of the organization.

Traditional businesses that are looking to adopt a multi-cloud strategy have several options available to them. One approach is to use a cloud-agnostic platform, such as Kubernetes, to manage the deployment and scaling of workloads across multiple cloud providers. This allows businesses to easily move workloads between different cloud environments, without having to re-architect their applications.

Overall, while multi-cloud migration can be a complex and challenging process, it can also provide traditional businesses with significant benefits in terms of flexibility, scalability, and cost savings. By carefully planning and executing their migration strategy, businesses can ensure a smooth transition to a multi-cloud environment.

Let us have a look at an example - Netflix's Cloud Migration#

Netflix emerged as one of the best streaming services globally. It plays a leading role now in its field. But, before achieving this position, Netflix went through a lot of struggles and miseries.

In 2008, Netflix got a major change in the operations of its databases. It was then based on costly hardware and the Oracle database. But, the hardware failure resulted in a new strategy. The company realized that there is no need for expensive hardware. Instead, cost-efficient cloud infrastructure is more suitable.

A year later, after implementing this strategy, the company had huge growth. Very soon, it was in a need of more data storage. But, it could not predict the requirement and the future, as its past data was based on DVD shipping.

Netflix assumed a thousandfold increase in its streaming services. With quick growth, it encountered the need for more data centers. Now, it had two options. One: estimate data requirements and build a high-end data center. Two: use Amazon Web Services. It conducted several tests over the platform and signed a license agreement with AWS.

By moving to AWS, it became easy for Netflix to get on-demand data capacity. Later, they moved all of their time-critical operations to AWS. From simple API sequences to all of their web pages are based on the cloud.

Netflix we see and use today exists just because of cloud computing. Migration to cloud computing ensured the success of the company. Nowadays, any company could simply and easily migrate to the cloud.

Some other examples include:#

● Walmart:#

The retail giant has migrated its e-commerce platform to a multi-cloud environment to improve scalability and reduce costs.

● BMW:#

The automaker has adopted a multi-cloud strategy to improve the scalability and security of its manufacturing and supply chain operations.

● Adobe:#

The software company has adopted a multi-cloud strategy to improve the scalability and performance of its creative cloud services.

● FedEx:#

The courier delivery company has adopted a multi-cloud strategy to improve the scalability and performance of its logistics and transportation operations.

The specific date or year when these companies adopted multi-cloud migration, as it varies from company to company and it's not always publicly announced. Some companies have been gradually transitioning to multi-cloud environments for several years, while others may have made the switch more recently.

Additionally, companies may have adopted multi-cloud migration in different areas of their operations at different times.

Merits of Multi-Cloud Migration#

There are several benefits of adopting a multi-cloud strategy for businesses. Some of the key merits include:

● Flexibility:#

By using multiple cloud providers, businesses have greater flexibility in terms of the services they can access and the way they can deploy and scale their applications. This allows them to choose the best provider for each specific use case and to easily move workloads between providers as needed.

● Cost Savings:#

By using multiple cloud providers, businesses can take advantage of the different pricing models and services offered by each provider. This can help them to reduce costs and optimize their overall cloud spending.

● High availability:#

By distributing workloads across multiple cloud providers, businesses can achieve higher levels of availability and disaster recovery. In case of an outage or a problem with one cloud provider, the workloads can be easily shifted to another provider, minimizing the risk of service interruption.

● Reduced Vendor lock-in:#

A multi-cloud strategy reduces the dependency on a single cloud provider, minimizing the risk of vendor lock-in. This gives businesses more control over their IT infrastructure and the ability to easily move workloads to other providers if needed.

● Compliance:#

A multi-cloud strategy allows businesses to comply with data sovereignty laws and regulations by storing data in the cloud providers that operate in the same jurisdiction.

● Specialized Services:#

By using multiple cloud providers, businesses can take advantage of the specialized services offered by each provider. For example, some providers may have specialized services for artificial intelligence, machine learning, big data, or IoT.

De-merits of Multi-Cloud Migration#

● Complexity:#

Managing multiple cloud providers can be complex and requires additional resources, such as specialized staff and tools, to ensure a smooth transition and ongoing management.

● Security Risks:#

By using multiple cloud providers, businesses may introduce additional security risks, such as increased attack surface and difficulty in managing and monitoring security across multiple environments.

● Integration Challenges:#

Integrating different cloud providers and their services can be challenging, requiring significant time and resources.

● Lack of standardization:#

Each cloud provider has its own set of services and tools, which can make it difficult to standardize processes and procedures across the organization.

● Limited support:#

If the organization is not familiar with a cloud provider, it might face challenges in getting support and troubleshooting problems.

While multi-cloud migration can bring many benefits to a business, it also has its own set of de-merits. It's important for businesses to carefully consider these de-merits and weigh them against the benefits before embarking on a multi-cloud migration. Additionally, having a well-planned strategy and the right tools and resources in place can help to mitigate these de-merits and ensure a successful multi-cloud migration.

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Development and operations are critical aspects of every software company. Your company's success depends on effectively coordinating these roles to increase software delivery speed and quality. DevOps as a service is also crucial to delivering software more quickly and efficiently.

Development and operations teams often operate in isolation, but DevOps acts as a bridge to enhance cooperation and efficiency.

Why Does DevOps Matter in Modern Business?#

Implementing DevOps methods successfully in your firm can substantially influence efficiency, security, and corporate cooperation. According to the 2017 State of DevOps Report, firms that use DevOps principles spend 21% less time on unplanned work and rework and 44% more time on additional work, resulting in improved efficiency (Díaz et al., 2021).

8 Reasons Why DevOps is Essential for Modern Businesses#

1. Reduced Development Cycles#

Companies thrive by innovating more quickly than their competition. The primary goals of DevOps are automation, continuous delivery, and a short feedback loop, similar to Microsoft Azure DevOps. Immediate and constant feedback allows for speedier releases. In Cloud DevOps, merging development and operations activities results in the rapid creation and distribution of applications to the market [(Khan et al., 2022)]. The overall advantage is a shorter cycle time to fully realize an idea, with superior quality and precise alignment.

2. Reduced Failure Rates of Implementation#

DevOps automation encourages regular code versions, leading to easier and quicker identification of coding errors. Teams can use agile programming techniques to reduce the number of implementation failures (Maroukian and Gulliver, 2020). Recovery from mistakes is faster when development and operations teams collaborate, addressing issues collectively.

3. Continuous Improvement and Software Delivery#

Implementing DevOps principles enhances software quality while releasing new features and enables rapid changes. Continuous Integration and Continuous Deployment (CI/CD) involves making incremental changes and swiftly merging them into the source code, as seen in Azure DevOps. This approach allows software to reach the market faster while addressing consumer complaints promptly. [DevOps as a service] fosters higher quality and efficiency in continuous release and deployment.

4. Improved Inter-Team Communication#

DevOps automation enhances business agility by promoting a culture of cooperation, effective communication, and integration among all global teams within an IT company. The DevOps culture values performance over individual ambitions, making procedures more transparent and allowing employees to learn quickly and impact the business significantly.

5. Increased Value Delivery Scope#

Using DevOps as a service fosters a continuous delivery environment, focusing on innovations and better value generation through digital transformation (Wiedemann et al., 2019). This approach ensures that work is adequately integrated and managed in a conducive environment.

6. Reduced Deployment Time#

DevOps methods improve the effectiveness of building new systems by incorporating feedback from developers, stakeholders, and colleagues (Plant, van Hillegersberg, and Aldea, 2021). This approach results in consistent execution and faster deployment compared to competitors.

7. Faster Response#

One of the primary benefits of Cloud DevOps' continuous deployment cycle is the ability for firms to iterate rapidly based on consumer feedback and evaluations. This enhances the ability to manage uncertainty and speeds up procedures.

8. Reduces Waste#

Enterprises that adopt lean practices and iterate quickly use resources more effectively and minimize waste. DevOps as a service helps firms reduce operational inefficiencies by shifting various responsibilities to the development team.

A technology-first mindset change is happening. Digital leaders want to grow worldwide with flexibility, surge forward, and provide new world-class user experiences while doubling digital output. Making the transfer to the cloud is not only a technological or operational problem but also a huge culture shift that begins at the top, with the computers and systems accountable for assuring the success of the transformation.

Digital-First Culture#

Developing a digital-first culture entails more than just using cutting-edge technologies. Create an agile company where technologies and business culture collaborate to optimize processes, maximize efficiency, and provide an outstanding customer experience (Merkt, Lang, and Schmidt, 2021). To do this, corporate leaders must first work on instilling a digital-first attitude in their employees, ensuring that they are digitally literate and comfortable adjusting to new technology.

Need to Adopt a Digital-First Culture#

Business leaders cannot afford to overlook the importance of culture. It is critical to comprehend the magnitude of the digital transformation's core strategic paradigm change. Culture is the collection of attitudes and behaviors that define how things are done in a company (Tuukkanen, Wolgsjö, and Rusu, 2022).

A digitalization-friendly culture possesses the following characteristics:

1. Encourages an external rather than an internal orientation.
2. Delegation takes precedence over control.
3. Emphasizes daring rather than prudence.
4. Focuses on action rather than preparation.
5. Prefers teamwork over solo effort.

Benefits of a Digital-First Culture#

A digital-first culture can assist the leader in future-proofing the organization and emerging as a leader who establishes new industry norms and standards. At the very least, it will assist the company in being fluid and responsive to market and socioeconomic conditions (Ghosh et al., 2021). Among the more precise benefits of engaging in a digital-first workplace are:

• Reducing team silos and increasing openness.
• Increasing overall agility and adaptability.
• Enhancing data collection.

Strategies for Creating a Digital-First Culture#

1. Concentrate on the People: Since people are typically resistant to change, introducing new technology without adequate support will not produce the expected outcomes. Furthermore, some people are concerned that automation and technology will eliminate their jobs. To effectively develop a digital-first culture, address these concerns as soon as feasible.

2. Begin at the Top: Senior management has the key to developing a business culture. Leaders must advocate the strategy in everything they do while attempting to develop a digital-first culture. Set a good example.

3. Embrace Technology: Digitization reduces the possibility of data loss or missing crucial information. That is why it is critical to integrate your various technologies as much as possible so that diverse company operations can run smoothly.

4. Share a Common Vision: Managers, executives, and employees all need to push for the same goal: the success of the company. When writing job descriptions, be sure to include the technological tools, talents, and working style that the company anticipates (Kontić and Vidicki, 2018).

Developing a Cloud Mindset#

Hybrid cloud migration is about more than just technology; it is also a huge culture shift that necessitates careful consideration of the systems and technology involved in the journey. A transition to the cloud necessitates a much broader change in management style than other innovation initiatives due to the impact on skills and money, as well as on both commercial and technology goals (Marty, 2014).

Bringing the "cloud mindset" to use!

A transition to the cloud necessitates a much deeper change in management style than many other technology-driven initiatives due to the influence on skills and money, as well as on both business and technical goals. Rather than lifting VM instances and throwing them over the wall into somebody else's data center, organizations should shift to a "move and improve" mindset that allows them to accept the cloud's native functionality to deliver various business benefits (Solberg, Traavik, and Wong, 2020).

Thinking “Cloud-First” vs “Lift and Shift”#

The capacity to benefit from the cloud's flexibility, scalability, and safety does not come by just transferring VM instances to a cloud computing platform; leaders must think very differently regarding existing software and services and think cloud-first.

Leaders should look for a cloud partner that not only knows how to construct and maintain world-class data centers but will also work with them to establish the culture and processes required for the business to be successful in the cloud (Baumgartner, Hartl, and Hess, 2021).

Conclusion#

Certainly, digital transformation is all about a new attitude as much as it is about technology. As part of the overall organizational change plan, organizations should be able to create a cultural roadmap and a cultural change strategy, which will then be a component of the entire transformation program (Ghosh et al., 2021).

Following a meaningful digital transformation, a plan is more than just checking boxes. Cloud - Check. Mobile app - Check. A brand-new website - Check. If it were that simple, everyone would have done it by now.

Can an AI-based edge computing platform drive businesses or is that a myth? We explore this topic here._

Introduction#

For a long time, artificial intelligence has been a hot issue. We've all heard successful tales of forward-thinking corporations creating one brilliant technique or another to use Artificial Intelligence technology or organizations that promise to put AI-first or be truly "AI-driven." For a few years now, Artificial Intelligence (AI) has been impacting sectors all around the world. Businesses that surpass their rivals are certainly employing AI to assist in guiding their marketing decisions, even if it isn't always visible to the human eye (Davenport et al., 2019). Machine learning methods enable AI to be characterized as machines or processes with human-like intelligence. One of the most appealing features of AI is that it may be used in any sector. By evaluating and exploiting excellent data, AI can solve problems and boost business efficiency regardless of the size of a company (Eitel-Porter, 2020). Companies are no longer demanding to be at the forefront or even second in their sectors; instead, businesses are approaching this transition as if it were a natural progression.

Artificial Intelligence's (AI-driven) Business Benefits#

Businesses had to depend on analytics researchers in the past to evaluate their data and spot patterns. It was practically difficult for them to notice each pattern or useful bit of data due to the huge volume of data accessible and the brief period in their shift. Data may now be evaluated and processed in real-time thanks to artificial intelligence. As a result, businesses can speed up the optimization process when it comes to business decisions, resulting in better results in less time. These effects can range from little improvements in internal corporate procedures to major improvements in traffic efficiency in large cities (Abduljabbar et al., 2019). The list of AI's additional advantages is nearly endless. Let's have a look at how businesses can benefit:

• A More Positive Customer Experience: Among the most significant advantages of AI is the improved customer experience it provides. Artificial intelligence helps businesses to improve their current products by analyzing customer behavior systematically and continuously. AI can also help engage customers by providing more appropriate advertisements and product suggestions (Palaiogeorgou et al., 2021).

• Boost Your Company's Efficiency: The capacity to automate corporate procedures is another advantage of artificial intelligence. Instead of wasting labor hours by having a person execute repeated activities, you may utilize an AI-based solution to complete those duties instantly. Furthermore, by utilizing machine learning technologies, the program can instantly suggest enhancements for both on-premise and cloud-based business processes (Daugherty, 2018). This leads to time and financial savings due to increased productivity and, in many cases, more accurate work.

• Boost Data Security: The fraud and threat security capabilities that AI can provide to businesses are a major bonus. AI displays usage patterns that can help to recognize cyber security risks, both externally and internally. An AI-based security solution could analyze when specific employees log into a cloud solution, which device they used, and from where they accessed cloud data regularly.

Speaking with AI Pioneers and Newcomers#

Surprisingly, by reaching out on a larger scale, researchers were able to identify a variety of firms at various stages of AI maturity. Researchers split everyone into three groups: AI leaders, AI followers, and AI beginners (Brock and von Wangenheim, 2019). The AI leaders have completely adopted AI and data analysis tools in their company, whilst the AI beginners are just getting started. The road to becoming AI-powered is paved with obstacles that might impede any development. In sum, 99% of the survey respondents have encountered difficulties with AI implementation. And it appears that the more we work at it, the more difficult it becomes. 75% or more of individuals who launched their projects 4-5 years ago faced troubles. Even the AI leaders, who had more effort than the other two groups and began 4-5 years earlier, had over 60% of their projects encounter difficulties. When it comes to AI and advanced analytics, it appears that many companies are having trouble getting their employees on board. The staff was resistant to embracing new methods of working or were afraid of losing their employment. Considering this, it should be unsurprising that the most important tactics for overcoming obstacles include culture and traditions (Campbell et al., 2019). Overall, it's evident that the transition to AI-driven operations is a cultural one!

The Long-Term Strategic Incentive to Invest#

Most firms that begin on an organizational improvement foresee moving from one stable condition to a new stable one after a period of controlled turbulence (ideally). When developers look at how these AI-adopting companies envision the future, however, this does not appear to be the case. Developers should concentrate their efforts on the AI leaders to better grasp what it will be like to be entirely AI-driven since these are the individuals who've already progressed the most and may have a better understanding of where they're headed. It's reasonable to anticipate AI leaders to continue to outpace rival firms in the future (Daugherty, 2018). Maybe it's because they have a different perspective on the current, solid reality that is forming. The vision that AI leaders envisage is not one of consistency and "doneness" in terms of process. Consider a forthcoming business wherein new programs are always being developed, with the ability to increase efficiency, modify job processing tasks, impact judgment, and offer novel issue resolution. It appears that the steady state developers are looking for will be one of constant evolution. An organization in which AI implementation will never be finished. And it is for this reason that we must start preparing for AI Edge Computing Platform to pave the way for the future.

References#

• Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S.A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), p.189. Available at: link.
• Brock, J.K.-U., & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61(4), pp.110–134.
• Campbell, C., Sands, S., Ferraro, C., Tsao, H.-Y. (Jody), & Mavrommatis, A. (2019). From Data to Action: How Marketers Can Leverage AI. Business Horizons.
• Daugherty, P.R. (2018). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press.
• Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2019). How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48(1), pp.24–42. Available at: link.
• Eitel-Porter, R. (2020). Beyond the Promise: Implementing Ethical AI. AI and Ethics.
• Palaiogeorgou, P., Gizelis, C.A., Misargopoulos, A., Nikolopoulos-Gkamatsis, F., Kefalogiannis, M., & Christonasis, A.M. (2021). AI: Opportunities and Challenges - The Optimal Exploitation of (Telecom) Corporate Data. Responsible AI and Analytics for an Ethical and Inclusive Digitized Society, pp.47–59.

Learn more about Artificial Intelligence - AI in the workforce in this article.

Introduction#

An increase in data usage demands a network effectiveness strategy, with a primary focus on lowering overall costs. The sophistication of networks is expanding all the time. The arrival of 5G on top of existing 2G, 3G, and 4G networks, along with customers' growing demands for a user platform comparable to fibre internet, places immense strain on telecommunication operators handling day-to-day activities (Mishra, 2018). Network operators are also facing significant financial issues as a result of declining revenue per gigabyte and market share, making maximizing the impact on network investment strategies vital for existence.

How can businesses use AI to change the way businesses make network financial decisions?#

From sluggish and labor-intensive to quick, scalable, and adaptable decisions - The traditional manual planning method necessitates a significant investment of both money and time. Months of labor-intensive operations such as data gathering, aggregation of data, prediction, prompting, proportioning, and prioritizing are required for a typical medium-sized system of 10,000 nodes. Each cell is simulated separately using machine learning, depending on its special properties. Several Key performance indicators are used in multivariable modeling approaches to estimate the efficiency per unit separately. By combining diverse planning inputs into the application, operators may examine alternative possibilities due to the significant reduction in turnaround time (Raei, 2017).

Moving from a network-centric to a user-centric approach - Basic guidelines are commonly used to compare usage to bandwidth. Customer bandwidth is influenced by some parameters, including resource consumption, such as DLPRB utilization. Individual unit KPI analysis utilizing machine learning solves this inefficacy, with the major two processes involved being traffic prediction and KPI predictions. The Key performance indicator model is a useful part of cognitive planning that is specific to each cell and is trained every day using the most up-to-date data. The per-cell model's gradient and angles are governed by its unique properties, which are impacted by bandwidth, workload, broadcast strength, as well as other factors (Kibria et al., 2018). This strategy provides more granularity and precision in predicting each cell's KPI and effectiveness.

From one-dimensional to two-dimensional to three-dimensional - Availability and efficiency are frequently studied in a one-dimensional manner, with one-to-one mappings of assets such as PRB to quality and productivity. Nevertheless, additional crucial elements such as broadcast frequency or workload have a significant impact on cell quality and productivity. Optimal TCO necessitates a new method of capacity evaluation that guarantees the correct solution is implemented for each challenge (Pahlavan, 2021).

Candidate selection for improvement - Units with poor wireless reliability and effectiveness are highlighted as candidates for improvement rather than growth using additional parameters such as radio quality (CQI) and spectrum efficiency in cognitive planning. As a first resort, optimization operations can be used to solve low radio-quality cells to increase network capacity and performance. Instead of investing CAPEX in hardware expansion, cognitive planning finds low radio-quality cells where capacity may be enhanced through optimization (Athanasiadou et al., 2019).

Candidate selection for load-balancing#

Before advocating capacity increase, cognitive planning tools will always model load-balancing among co-sector operators. This is done to eliminate any potential for load-balancing-related benefits before investing. The load-balancing impact is modeled using the machine-learning-trained KPI model by assuming traffic shifts from one operator to another and then forecasting the efficiency of all operators even in the same section (He et al., 2016). If the expected performance after the test does not satisfy the defined experience requirements, an extension is suggested; alternatively, the program generates a list of suggested units for load-balancing.

Prioritization's worth for AI in the workforce#

When network operators are hesitant to spend CAPEX, a strong prioritizing technique is vital to maximizing the return on investment (ROI) while guaranteeing that even the most relevant aspects are handled. This goal is jeopardized by outdated approaches, which struggle to determine the appropriate response and have the versatility to gather all important indicators. In the case of network modeling, load corresponds to the number of consumers, utilization (DLPRB utilization) to the space occupancy levels, and quality (CQI) to the size (Maksymyuk, Brych and Masyuk, 2015). The amount of RRC users, which is near to demand as a priority measure, is put into the prioritizing procedure, taking into account the leftover areas. Further priority levels are adjusted based on cell bandwidth, resulting in a more realistic order.

Developers give ideal suggestions and growth flow (e.g. efficiency and load rebalancing ahead of growth) and generate actual value by combining all of these elements, as opposed to the conventional way, which involves a full examination of months of field data:

• Optimization activities are used as a first option wherever possible, resulting in a 25% reduction in carrier and site expansions.

• When compared to crowded cells detected by operators, congested cells found by cognitive planning had a greater user and traffic density, with an average of 21% more RRC users per cell and 19% more data volume per cell. As a result, the return on investment from the capacity increase is maximized (Pahlavan, 2021).

• Three months before the experience objective was missed, >75 percent of the field-verified accuracy in determining which cells to grow when was achieved.

• Reduce churn

Conclusion for AI in the workforce#

The radio access network (RAN) is a major component of a customer service provider's (CSP) entire mobile phone network infrastructural development, contributing to around 20% of a cellular manufacturer's capital expenditures (CapEx). According to the findings, carriers with superior connection speeds have greater average revenue per user (+31%) and lower overall turnover (-27 percent) (Mishra, 2018). As highlighted in this blog, using Machine learning and artificial intelligence for capacity management is critical for making intelligent network financial decisions that optimize total cost of ownership (TCO) while offering the highest return in terms of service quality: a critical pillar for customer service provider's (CSP) commercial viability.

Learn more about Nife to be informed about Edge Computing and its usage in different fields https://docs.nife.io/blog