Avainsana: Nextgen foundation

People, stop misusing Kubernetes!

Unless you have a viable use case like edge computing

 

Author: Jari Timonen, Lead Cloud Architect

 

No matter what color of gift paper you wrap it in, Kubernetes is complex and costly.

Initially, Google developed the predecessor of Kubernetes and named it Borg. Since then, Google has open-sourced the technology to benefit the broader community and to advance the state-of-the-art in container cluster management. And just like Google envisioned, Kubernetes has become a crucial part of modern container orchestration. Virtually everything in Google’s own environments, for example, runs as a container, managed with Kubernetes. To me, this seems like solid proof of Kubernetes’ reliability and scalability for huge corporations like Google.

But seriously, how many companies in the Nordics are Google, or even come close?

The fact, namely, remains that moving from virtual machines to containers and Kubernetes is a big investment. Therefore, this step just isn’t for most companies and organizations. I am concerned that there are countless companies whose business comes closer to “man and dog” than that of the global cloud giants, which are spending their time playing with Kubernetes.

How many companies in the Nordics are Google?

However, one viable use case for Kubernetes is emerging: edge computing. As our CTO Markku Tuomala wrote in his recent blog, edge computing – processing data closer to its source – offers big benefits in terms of latency, bandwidth, and efficiency for large industrial companies, telecom operators, and electricity providers.

Justifying Kubernetes: Edge computing

In the otherwise fast-changing world of industrial technology, edge computing has been annoyingly “just around the corner” for years. Things are about to change, however, since a number of very handy technologies from Google are making the orchestration of Kubernetes clusters more achievable. In this blog, I will share my experiences on how GKE, GKE Enterprise, and Anthos can revolutionize edge computing for industries that need very low-latency online services.

Google Kubernetes Engine (GKE) is Google’s managed Kubernetes service. It’s a robust solution for building and managing the capabilities needed for edge computing. Anthos, in turn, extends GKE to manage Kubernetes clusters across multi-cloud and hybrid environments. GKE Enterprise, the newest addition to the mix of solutions, allows Kubernetes clusters to be managed in a multitenant architecture, across clouds and on-premises environments, eliminating the need for extra servers. Google Distributed Cloud, finally, combines software and hardware to provide a fully integrated system. Such an integrated system supports edge computing scenarios, among others.

A standout feature of GKE is its team management capabilities. GKE allows the distribution of clusters and assigning specific teams to manage them. For example, team members in different locations—Pertti in Seinäjoki and Petra in Stockholm—can be given access and control from the cloud, eliminating manual interventions. This centralized control ensures all necessary tools and permissions are included in the package, simplifying operations significantly.

In edge computing scenarios, GKE offers unmatched ease of management. For example, updating a cluster can be as simple as making one change and deploying it across the network. This ease of operation is crucial for environments where Kubernetes management and updates are usually difficult. For instance, the North American Major League Baseball, uses Anthos to host applications like real-time game analytics, which need to run locally in the park for performance reasons.

Telia and Codento lead the way to Edge as a Service

Over the past two years, Codento’s team has pioneered using GKE and GKE Enterprise for edge computing. I am proud to say that we have achieved something no one else in the world has yet.

Our journey with Nordic telecom giant Telia began 2.5 years ago. Telia wanted to maximize the return on their 5G network investments beyond only speed. They also wanted to test Anthos’s capabilities in multi-cluster management.

Significant improvements in multi-cluster management.

Our joint efforts have been successful. Significant improvements in multi-cluster management reduced the time needed to run system upgrades from weeks to the minute it requires to change one configuration number. The first pilot customer is already using Telia’s platform.

Eye on the ball – Kubernetes can add or dilute value

Despite its advantages, Kubernetes is still complex and costly, often rightfully seen as a last resort. Managing multiple Kubernetes clusters is labor-intensive and expensive, so it is usually for organizations with strong technology know-how and advanced cloud environments.

Today, however, the burden of management and monitoring is much lower, allowing teams to focus on innovation and growth. GKE Enterprise, with its robust features and ease of multitenant environment management, will in my opinion be a game-changer for large industrial companies and service providers looking to harness the power of edge computing. By simplifying cluster operations and offering centralized control, GKE Enterprise enables businesses – specifically the businesses that have the needed maturity to lead modern cloud teams – to deploy and manage edge computing capabilities efficiently.

When all these prerequisites are fulfilled, Kubernetes will stop being a value destroyer that sucks time and energy and become a driver of innovation and operational excellence.

Key takeaways:

1. Google Kubernetes Engine (GKE), GKE Enterprise, Anthos, and Google Distributed Cloud offer a comprehensive solution for managing Kubernetes clusters across different environments.
2. Kubernetes has traditionally been seen as costly and complex , but these technologies make it more accessible, enabling advanced solutions like edge computing.
3. With GKE Enterprise, telecom players like Telia already offer their customers multitenant edge computing services based on Kubernetes clusters.

 

About the author:

Jari Timonen, is an experienced software professional with more than 20 years of experience in the IT field. Jari’s passion is to build bridges between the business and the technical teams, where he has worked in his previous position at Cargotec, for example. At Codento, he is at his element in piloting customers towards future-compatible cloud and hybrid cloud environments.

 

Stay tuned for more detailed information and examples of the use cases! If you need more information about specific scenarios or want to schedule a free workshop to explore the opportunities in your organization, feel free to reach out to us.

Boosting Contact Center Effectiveness with AI

Conversational AI happens at competitors’ CCs while you’re busy making other plans

 

Author: Janne Flinck, Data & AI Lead

Working for Nordic organizations in various industries, I have gladly noted that front-runners are already deploying modern Artificial Intelligence tools to increase their contact center efficiency and customer satisfaction. In contrast, the majority are still looking for marginal improvements via tweaks in ticket handling or streamlining the edges of their onboarding processes.

“Life is what happens to you while you’re busy making other plans.” This familiar motto applies to many Nordic customer service and contact center decision-makers regarding conversational AI: It’s happening at competitors’ contact centers while you’re reading this blog.

 

Exceeding customer expectations while managing costs

Whether you’re a Nordic public sector entity or a private company running your business here, exceptional customer service is crucial. According to Salesforce, nearly 90% of customers today perceive the experience delivered as important as the actual products or services. Customer service leaders and marketing and sales officers face a common challenge: providing consistent, high-quality service while managing costs and resources effectively.

Nearly 90% of customers perceive the experience delivered as important as the products or services.

You want to ensure prompt, accurate responses to customers within acceptable wait times, regardless of the time of day. Simultaneously, you must balance the cost of contact center teams and onboarding new agents. You want to stay agile and be able to scale to meet the needs of growing organizations or seasonal peaks. Moreover, you want to gain insights into customer behavior and service performance to steer strategic decisions for optimizing operations and improving service quality. This is where Google’s Customer Engagement Suite comes into play.

 

Agents for agents

Generative and conversational AI agents are revolutionizing customer service, particularly in contact centers. Customer Engagement Suite is a collection of Google Cloud products designed to enhance contact center agent productivity, boost customer satisfaction, and reduce operational costs.

When your agent starts a call with a customer, Customer Engagement Suite provides live transcription, real-time answers to the customers’ questions, and a discussion summary. This helps the agent focus on customer interactions without worrying about taking notes. Customer Engagement Suite’s omnichannel support covers chat, SMS, VoIP, and video, ensuring seamless customer experiences across all channels.

Generative AI agents produce automated answers to customers’ questions by integrating to enterprise knowledge bases and other internal and external data sources. Customer Engagement Suite can also automate tasks like checking order status or updating payment details, ensuring customers always receive up-to-date information and services tailored to their needs. All this increases the efficiency of operations, and we have seen customers reduce call durations by up to 10%, yielding a significant payback to the system investment.

We have seen up to 10% reductions in call duration.

Quick access to relevant agent data will also shorten the time needed for new employee onboarding. When newcomers have speedier access to appropriate knowledge, the onboarding period can be up to 25% shorter, leading to a stark improvement in efficiency.

Many customer service calls involve tedious information-seeking, often for questions that repeat over time. Customer Engagement Suite’s virtual agent chatbots can relieve your agents of the repetitive burden by automatically finding answers to common questions using existing information sources and handling text, voice, and images in customer encounters. By reducing the need for human intervention in routine cases, the chatbots free human service agents to offer a more personal and richer interaction that increases customer and employee satisfaction.

Customer Engagement Suite offers powerful analytics tools that provide insights into customer interactions. These tools help your organization identify trends, improve processes, and make data-driven decisions.

As the Customer Engagement Suite is fully developed and managed by Google, it allows you to concentrate on extracting value for your operations. The deployments are efficient due to its seamless integration with telephony and contact center applications and tools for building custom features that adapt to your processes.

 

The Quantified Impact of AI in Contact Centers

In the bigger picture, AI will affect both new hires and existing employees in the coming years—in both negative and positive ways, depending on your position. In Metrigy’s AI for Business Success 2024-25 global research study of 697 companies, the following was discovered:

• New hires – More than half of companies were able to reduce the number of new agents they needed to hire. The numbers are substantial: Those who did not use AI in their contact center, had to hire almost twice the number of agents during the year 2023 compared to those who used AI.
• Existing employees – When contact centers were augmented with AI, nearly 40% of companies were able to reduce their headcount, with the average reduction being about one in every four employees.

For business leaders looking for technology to drive cost efficiencies, AI is doing its job. For example, with the addition of AI agent assist, the average handle time dropped by an average of 30%. At the same time, each supervisor saves nearly two hours per week when AI helps with scheduling and capacity planning. In addition to making agents and supervisors more efficient, AI-enabled self-service also helps automate customer interactions so that fewer of them even require live agent attention.

 

Real-world success stories in the making

I am honored to help several of our leading customers in the Nordics embrace the benefits of generative AI and conversational AI in their contact center operations. The most value can be extracted in organizations where the number of daily contacts is high, and the onboarding cost is noticeable due to complex product structures. Such fields include retail, travel and leisure, banking, and insurance. Similarly, organizations with high peak demand, such as nonprofits with surging inquiries during a fundraising campaign or public offices with specific deadlines for citizens’ input, could benefit from Customer Engagement Suite. It helps diminish the burden of agents on duty, channels routine questions directly to virtual agents, and makes onboarding seasonal employees more straightforward.

As an experienced and awarded Google Cloud Solutions integrator, Codento offers comprehensive support to ensure a smooth transition to your contact center’s era of AI. The fact that Customer Engagement Suite is a complete solution developed and managed by Google will ensure a robust platform integrated with all your relevant data sources and a foreseeable future roadmap on which to build your contact center success.

Key takeaways:

1. The experience delivered, e.g., by your contact center agents is as important for your business as the product or service you actually sell
2. Google has packaged Artificial Intelligence tools for excellent customer service into a managed solution called Customer Engagement Suite
3. The efficiency effect of AI in Contact Centers has already been quantified and, e.g., handling times have been seen to drop by 30%
4. Codento is already working with Nordic organizations to harness AI for better customer experience and more efficient Contact Center operations

 

About the author:

About the author: Janne Flinck is an AI & Data Lead at Codento. Janne joined Codento from Accenture 2022 with extensive experience in Google Cloud Platform, Data Science, and Data Engineering. His interests are in creating and architecting data-intensive applications and tooling. Janne has three professional certifications in Google Cloud and a Master’s Degree in Economics.

 

Stay tuned for more detailed information and examples of the use cases! If you need more information about specific scenarios or want to schedule a free workshop to explore the opportunities in your organization, feel free to reach out to us.