For many businesses, the impact of Covid-19 has brought the benefits and limitations of their IT landscape into focus. As companies look ahead to a post-pandemic era, many will need to consider how to meet different needs and priorities with potentially limited resources. The challenge is that many are overly reliant on legacy infrastructure, with these systems accounting for 74% of a company’s IT spend.

Making important changes such as migrating on-premises workloads to the cloud will eventually help reduce costs, increase agility, and pay ROI dividends. However, with the current business conditions, this is easier said than done and there are multiple factors that organisations need to consider. Enterprises need to be empowered with knowledge of the best tools and practices related to cloud-based technology in order to prioritise investments and optimise costs. Here are five key ways for companies to better their IT spending and operational efficiencies:

Take advantage of new tools for cloud agility and IT cost reduction
During unpredictable times, businesses need an architecture that provides the agility and flexibility needed to help adapt to change or even take advantage of it. New container-based “cloud-native” tools and services have emerged to serve this purpose and aim to simplify and accelerate app modernisation both on-premises in public clouds.

New tools like Google Cloud’s Anthos help companies develop, secure, and manage modern cloud apps at scale across multi clouds and leverage existing hosting investments. It can help cut licensing and training costs, increase development speed and the efficiency of operations teams.

Avoid costly hardware refreshes and reduce on-premises infrastructure costs by migrating VMs to the cloud
Migrating VMware workloads to the cloud is another method for businesses who are looking to harness the advantages of hybrid cloud, while streamlining their operations and reducing costs. However, many enterprises often find the process of migrating VMs daunting. As a solution, there are now fully managed VMware-as-a-service tools available from cloud providers which can migrate and run VMware workloads seamlessly.

This is a great option for businesses that understand the benefits of moving to the cloud but consider migration challenging. After all, it’s not just moving applications, it’s moving disaster recovery, backup, changes in security policies, monitoring, and more.

Using a VMware-as-a-service solution means that VMware-based workloads can benefit from the performance, scale, and security of the cloud without the need to refactor during the migration process.

Modernise your data warehouse for scalability and seamless access to advanced analytics
According to IDC, the amount of data will grow from 33 ZB in 2018 to 175 ZB by 2026. Legacy data warehouses were not designed to handle today’s explosive data growth and are not optimised to run advanced analytics or scale quickly and efficiently. They’re also extremely expensive to purchase and maintain, and easily run out of capacity, leaving them unable to keep up with increased business analytics demands. Similarly, alternative “big data” solutions are difficult to operate at scale, suffer from infrastructure complexity and require specialist skills to use.

In an age where consumer behaviour is rapidly shifting, data warehouse modernisation can support today’s growing analytics needs by increasing agility and efficiency with serveless analytics — all at a lower cost. Cloud data warehouses drive insights and actions much faster. so organisations can make smarter, informed decisions and improve business operations. It also provides easy access to advanced analytics through artificial intelligence and machine learning.

Companies that use data effectively are 2.3x more likely to succeed during industry disruptions. They are 6x more likely to retain customers and 19x more likely to be profitable.

Apply AI to rapidly respond to customers across contact centres and more
As entire industries have shifted to meet customer needs in new and unexpected ways, many organisations are experiencing dramatic spikes in customer questions and support requests. Applying AI speech and language understanding models can help businesses answer simple questions promptly, freeing up contact centre agents time to focus on more complex customer needs. Integrating AI into contact centre customer services can help to improve call deflection rates, achieve shorter handling times, and reduce agent training costs, making overall operations faster and more effective.

Organisations can set up new chatbot-based services within two weeks to help respond to their customers more quickly and efficiently, especially as it relates to critical information around COVID-19. These models are designed to provide a first line of response through 24/7 conversational self-service support via chat or over the phone.

Transform the way your teams work together
Migrating parts of your IT ecosystem to the cloud is only one part of operational efficiency. It’s important not to ignore how you can better serve your employees. Individuals and teams also need tools that allow them to collaborate seamlessly and work from anywhere. That said, keeping teams connected and productive outside of the office has never been more challenging.

For many organisations, this has meant needing to quickly shift to cloud-based collaboration solutions to support effective remote work and collaboration. Tools like these have helped remote teams to work successfully together through video meetings, group chat, and document collaboration apps. By switching to collaboration tools such as G Suite can result in businesses seeing an increase in revenue and efficiency, risk reduction and a reduction in costs.

While there are a growing number of benefits for businesses migrating to the cloud, many still find the process overwhelming. Every company will have its own unique needs and considerations. This is why it’s important to understand the advantages and disadvantages of the options that are available from cloud providers and how these can help to solve operational efficiency now and in the future.

The data generated in DevOps runs very well into exabytes. Not only it becomes difficult for the DevOps team to effectively absorb the data but also makes it challenging for them to apply solutions from this massive amount of data. The data generated by continuous integration and tools deployment is humongous. In fact, simple issues such as finding critical events usually take hundreds of hours. The number of integrations, the success rate, and defects per integration is only useful when they are timely evaluated and correlated.

Given such a situation, there have been increased efforts to integrate AI into DevOps, which helps save time and increase efficiency. It is expected that in the near future AI will be able to emerge as a tool to compute, analyse and transform how teams develop and manage applications. Apart from its conventional use in the DevOps environment, AI can also prove to be beneficial in addressing security issues and data leaks, for organising memory management, and in garbage collection.

Here are five ways in which AI can transform DevOps:
Software Testing
DevOps includes different types of testing, such as regression testing, functional testing, and user-acceptance testing. These tests generate a large amount of data. AI, in such a situation, can prove to smoothen the system to a great extent. It identifies patterns of such collected data and then evaluates the coding practice that led to an error. Such systems eliminate test coverage overlaps, optimise current testing mechanisms and accelerate from just defect detection to defect prevention. It can also identify module interdependencies, thereby improving the overall product quality.

Managing Project Requirements
Several organisations have introduced AI-enabled digital assistants that can analyse documents, identify inconsistencies, and recommend solutions. Powered by natural language processing, such systems are trained on referenced guidelines to learn high-quality writing requirements. If done manually, the whole process of requirement management, which includes gathering, validating, tracking user requirements, would cause grave delays and in worse cases, complete failure of the final project. With automation in place, the requirements review is more streamlined. In fact, as per a report, such tools helped in reducing the requirement review time by over 50%.

Security
Security is one of the most critical aspects of the DevOps system. Businesses need to protect themselves from security breaches and distributed denial of service (DDoS) attacks. AI and machine learning can be used to identify the abnormal condition and take corrective actions accordingly. Further, AI can be used to augment DevSecOps and improve security by recording threats and deploying an ML-based central logging architecture for anomaly detection.

In Application Development & Enhancement
AI can also be used to help developers during the application process. AI-systems can examine build-compile success, testing completions, and operational performance of the past applications to make recommendations to the developers on the code they are writing. Further, monitoring tools using machine learning can gather information about log files, performance metric, and datasheets to identify issues in advance and make appropriate alterations to the application.
Managing Alerts
DevOps encourages ‘fail, but fail fast’; considering this, the system needs to be quick in identifying the flaw and sound alert. However, with conventional systems, all incoming alerts are marked with the same severity, making it difficult for the team to react. AI learns several factors such as the magnitude of the alert, past behaviour, and the source of the alert. This way, the team is assisted in prioritising their responses to the alerts without overwhelming the system.

Wrapping Up
AI in DevOps helps in directing human intelligence and creativity towards more intricate projects and tasks, while it helps with managing the efficiency, speed and productivity of the task, which in turn enhances the customer experience. AI is delivered value to DevOps by auto-suggesting code segments, improving quality assurance techniques, and streamlining requirement management.

The downside of deploying AI-based DevOps is with open-source projects, where the developers may unintentionally propagate security risks into their codes. It is important that AI is introduced in a controlled manner to ensure that they become the backbone of the DevOps system and not act as rogue elements that need constant remediation.