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Decoding the Consultant Life: Real stories from real consultants – Miska & Saka

Decoding the Consultant Life: Real stories from real consultants – Miska & Saka

Miska, a Data Scientist, discusses working with our customer, Saka, to optimize their inventory logistics.

 

I am Perttu Pakkanen, and my interest as Codento’s talent acquisition lead is to better articulate why consulting at Codento could be a great career choice.

When potential customers ponder whether they should use our services, they usually like to see some reference cases. Why wouldn’t our potential employees think the same?

So, I had a chat with our Data Scientist, Miska. He has been working with our customer Saka for optimizing their inventory logistics. Saka is a used car retail chain with over 30 locations across Finland, with thousands of cars in their inventory at any given time. So, optimizing this puzzle had a clear business case.

I asked Miska to sit down with me in our office’s meeting room on a November Tuesday morning. The weather outside was just as you’d expect November weather to be in Finland, but it didn’t slow us down.

Here we go:

 

What kind of solution have you built?

“We developed an end-to-end data science solution in Google Cloud to improve car logistics.

As part of the project, we also had to define what constitutes good car placement and logistics across the entire network of dealerships in Finland, and what metrics we would start optimizing.

The work involved modeling predictive factors and features based on multiple data sources the client has been collecting in their data warehouse in Google Cloud.

I liked that the work was structured according to the data science process: I got to delve into the database and discuss with the customer stakeholders, and only then start building and putting the solution to production. Enough time was allocated for us to carefully consider the background data.

Now, the ongoing work involves continuously developing the solution and implementing improvements in good collaboration.”

 

What kind of tasks have you done in the project?

”The project was a classic data science case, and the model we followed adhered quite closely to the standard data science project steps. I got to execute the process meticulously in the correct order, just as a data scientist should.

In addition to the technical work, I was heavily involved in project management and client communication.

I also played a strong role in the project’s sales phase, so I got to shape how the project is planned and executed from the beginning.

Currently, we are engaged in iterative model improvement.

Overall, I was involved in the project from end to end.”

 

What has been the most interesting thing you have done working for the customer?

”One of the most rewarding aspects was the extensive data exploration phase. I had access to a large data warehouse, which allowed me to build various features. 

This gave me the opportunity to work with a truly massive amount of data and focus on feature engineering, leading to the development of a highly tailored solution. 

It was not always straightforward to capture the most meaningful signals from the data, which was an interesting challenge.”

 

What has been the most difficult part?

“One of the challenges we encountered was the intrinsically random nature of the problem. Aggregating a holistic view from the intrinsically random process of selling an individual car.

I had the opportunity to manage the entire project broadly, which allowed me to learn and be flexible in my approach. This was a challenge and a learning experience.

This was also one of the client’s first larger AI application cases, meaning the technology and operating environment were still taking shape, particularly in establishing a smooth data flow.

Everything went well with good collaboration, though!”

 

What have you learned?

“This was a fully Google Cloud project, which allowed me to effectively utilize the skills I learned from the certifications in a production environment.

I gained practical experience with Vertex AI, including model registries, pipelines and other related components.

So, lots of Google Cloud learnings!”

 

That’s a lot of learning! Any last words to wrap things up?

“From a Data Scientist’s perspective, this project was executed correctly right from the start, and in a modern cloud native approach on Google Cloud.”

Thanks, Miska!

 

—

Being part of pioneering projects like this allows for both personal and professional development. I strongly feel that at Codento, you can engage in work that is not only challenging but also highly impactful in many industries.

Read more about us from our career site and see if there are any suitable opportunities for you, and connect with us on our recruitment system!

 

Data Scientist - Miska

About the interviewee:

Miska is a data scientist who takes ownership of the full data science lifecycle, bridging the gap between high-level business strategy and complex technical execution. He ensures that challenging projects transform from initial client concepts into robust, production-ready solutions.

 

Perttu Pakkanen | Codento

About the interviewer:

Perttu Pakkanen is responsible for talent acquisition at Codento. Perttu wants to make sure that the employees enjoy themselves at Codento because it makes his job much easier.

Copilot or Gemini Enterprise? – A Guide for Do-It-Yourself Comparison

Copilot or Gemini Enterprise? – A Guide for Do-It-Yourself Comparison

 

Business decision-makers are currently weighing two major ecosystems: Copilots (powered by OpenAI’s models) and Gemini Enterprise (powered by Google Gemini models). However, marketing hype does not tell the full story.

This guide provides a framework developed by Codento to simulate genuine business challenges and evaluate the strategic capabilities of these models yourself.

This test (instructions in the end) uses consumer versions of the models (e.g., ChatGPT GPT-5, Gemini 3) and publicly available data. It measures the model’s “general intelligence” and reasoning capabilities, not its ability to connect, seacr and analyse internal data.

 

Defining the Right Approach

Instead of using generic evaluation metrics, this method asks the language model itself to act as an expert consultant. It must first analyze the specific role (e.g., CFO) and determine what constitutes a “perfect” answer for that specific context before evaluating the responses.

This ensures the evaluation weights are dynamic and relevant—for example, a CFO role might prioritize risk analysis and strategic foresight, while a Customer Service role might prioritize empathy and tone.

 

Interpreting the Strategic Implications

The results of your test have consequences that go far beyond a simple score. In an enterprise environment, the difference between a “good” and a “bad” model directly impacts risk and revenue.

Benefits of the High-Performing Model: If a model consistently scores higher in your blind tests, it demonstrates a capability for “reasoning” rather than just “retrieving.”

  • Strategic Advantage: A model that can identify nuanced business risks (e.g., suggesting a “deductible campaign” instead of a blunt “price hike”) acts as a junior consultant, augmenting the strategic capacity of your leadership.
  • Operational Efficiency: Employees spend less time correcting the AI. High accuracy reduces “operational drag,” where employees must verify every single output before using it.
  • Trust and Adoption: Reliability builds trust. When employees trust the tool, adoption scales faster, leading to higher ROI.

Risks of the Lower-Performing Model: A model that performs poorly in reasoning tasks poses significant dangers if deployed in high-stakes roles.

  • The Cost of Hallucinations: In an enterprise context, “hallucinations” (confident falsehoods) are not just glitches; they are business risks. They can lead to regulatory violations, financial missteps, and reputational damage if a customer-facing agent invents policy or facts.
  • Brand Damage: An AI agent that fails to adopt the correct empathetic tone (e.g., sounding robotic during a customer complaint) can erode customer loyalty instantly.
  • Feature-Listing vs. Problem-Solving: A common failure mode is a model that lists what it can do (generic features) rather than solving the user’s problem. This provides zero business value and frustrates users.

 

Beyond the Model – Evaluating the Ecosystem

While the reasoning capability of the language model (LLM) is the “engine,” the success of your AI initiative depends on the “car”—the platform and ecosystem around it. When making your final decision, assess these critical parameters:

  1. Security, Governance, and Data Residency: The most intelligent model is useless if it leaks data.
  • Data Sovereignty: Ensure the platform guarantees data residency (e.g., keeping data within the EU/GDPR zones).
  • No Training on Your Data: Verify that the enterprise license explicitly states that your inputs are not used to train the public model—this should be a non-negotiable default.
  • Access Control (RBAC): The platform must respect your existing Role-Based Access Controls (e.g., a junior employee asking “What are the CEO’s bonuses?” should be denied based on their AD/Entra ID role).
  1. Agent Lifecycle Management (LLMOps): Building an agent is easy; keeping it alive is hard.
  • Drift Detection: Models change, and data changes. Does the platform have tools to monitor “drift” (when the model’s accuracy degrades over time)?.
  • Versioning and Retirement: You need a clear process for version control and “retiring” agents that are no longer accurate or useful.
  • Observability: Can you see why the agent made a decision? You need audit logs that show the prompt, the intermediate reasoning steps, and the tool outputs for compliance and debugging.
  1. Grounding and RAG (Retrieval-Augmented Generation)
  • Connecting to Truth: A generic model knows the internet; an enterprise agent must know your intranet. Assess how easily the platform connects to your specific data sources (SharePoint, Salesforce, proprietary databases).
  • Relevance Scoring: Does the system verify that the retrieved document is actually relevant to the user’s question before generating an answer? This is the primary defense against hallucinations.
  1. Ease of Use and Marketplace
  • Low-Code vs. Pro-Code: Can business analysts build simple agents using natural language (e.g., Copilot Studio, Vertex AI Agent Builder), or does every change require a developer?
  • Marketplace Availability: Does the ecosystem offer pre-built agents or “skills” (e.g., a pre-made “IT Helpdesk” agent) that you can deploy immediately, or must you build everything from scratch?

 

Summary and Next Steps

This test is an eye-opening experience regarding how AI models operate behind the scenes.

  • Remember: A blind test is the only way to verify quality impartially.
  • Share your experience: Did you get surprising results? Was one model clearly more strategic? Share your findings in the discussion on Codento’s LinkedIn page.

If you want to conduct this test securely using your own data or need help interpreting the results, contact the experts.

Download  a step-by-step testing guid by filling your contact information:

Did you drive a 30-year-old Opel to work today? Your AI strategy might be doing just that.

Did you drive a 30-year-old Opel to work today? Your AI strategy might be doing just that.

Author: Anthony Gyursanszky, CEO, Codento

Do you remember 1995? Finland joined the European Union, recovered from a recession, and most importantly, won the Ice Hockey World Championship. At the same time, the world of technology saw the birth of three reigning categories that still define almost everything we do at work: the WWW, CRM, and ERP.

These innovations created the foundation of the digital world, a rule-based paradigm that we know and experience every day. Its basic building blocks are menus, forms, folders, reports, and search fields. On top of this, we built our processes, our organizations, and our professions.

For the last 30 years, we have effectively been driving this digital Opel, the most popular car of 1995 in Finland. We have installed air conditioning, fitted better tires, and added a reversing camera. But fundamentally, it is still the same car.

AI strategy – a new engine or just better windshield wipers?

We are now living in the age of AI, and every major technology vendor is rushing to bring their own AI assistants to the market. Microsoft, Salesforce, and SAP are all adding artificial intelligence to their existing systems. This is understandable, but restrictive and short-sighted.

These AI assistants are designed primarily for one reason: to make life easier within the confines of a 30-year-old paradigm. They help us fill out old forms faster and generate reports from old data structures more efficiently. They are like better windshield wipers on an old Opel – useful, but they don’t change what you drive or how you travel.

The incumbent vendors understandably have a vested interest. Their entire business model is built to protect this old world, not to create a new one.

Finland’s unique “AI-native” opportunity

What if we went back to 1995 for a moment and, instead of the World Championship title, we received today’s artificial intelligence, cloud, and data capabilities? Would we have built form-based CRM systems? Unlikely.

We would have created proactive agents that converse with salespeople, anticipate customer needs, and handle routines independently. We would have built a business that is not based on navigable applications, but on intelligent, autonomous services.

This is what I call an AI-native approach to AI strategy; it does not seek to fix the old, but to build the future from a clean slate. Herein lies a huge opportunity for Finnish companies. We do not have to carry the heavy legacy of incumbent vendors. We can leapfrog directly to the forefront of development.

What kind of platform enables the future, instead of locking you into the past?

Building an AI-native future requires a foundation designed for it. This is why independent, open, and scalable AI platforms are a strategically compelling choice.

With such a platform, no one is trying to sell you a better version of an old ERP. What if you adopted world-class tools – the best language models, data capabilities, and infrastructure – on top of which you can build your own unique competitive advantage? It gives you the freedom to create, not force you into an old mold.

We are facing a fundamental choice. Do we remain loyal customers of the old Opel Group, continuously buying new accessories and hoping for the best? Or do we decide to build our own factory for self-driving cars, one that will redefine the rules of the entire industry?

Which car is your company building?

 

Ask more about Codento’s AI Agent Launchpad service, which is specifically designed for leveraging AI-native agent platforms.

 

 

About the author:

Anthony Gyursanszky, CEO, joined Codento in late 2019 with more than 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. Hehas also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. His experience covers business management, product management, product development, software business, SaaS business, process management, and software development outsourcing. Anthony is also a certified Cloud Digital Leader.

The New Front Door for AI in the Workplace Is Here: Codento Accelerates Google’s New Gemini Enterprise Platform Adoption

The New Front Door for AI in the Workplace Is Here

Codento Accelerates Google’s New Gemini Enterprise Platform Adoption

 

Authors: Janne Flinck, Matthew Wooller, Sami-Ahma-aho

For the past year, enterprises have been adopting  agentic AI in the midst of constant development of technology and tools. The dream has been a transformational set of capabilities, on a secure platform, not just designed to tackle simple tasks.

Yesterday evening Google announced Gemini Enterprise, the single front door for AI in the workplace. Gemini Enterprise not only offers significant new features but consolidates existing ones by incorporating established tools like Agentspace and the Agent Devlopment Kit into a platform that offers open industry standards (ADK,A2A and A2P)  to connect up your organisation

We will dive into what this announcement really signifies, why it is a significant evolution for businesses, and how you can get ready to use it to your advantage.

 

What is Gemini Enterprise?

Gemini Enterprise is a unified, agentic AI platform. In simple terms, it allows the organization to build, manage, and deploy specialized AI agents that can automate complex, multi-step business processes from start to finish. It also provides the tooling for anyone in an organization—not just developers— to create agents in a controlled and secure manner.

This is the long-awaited evolution from personal productivity AI categories, to a true, AI-powered workforce designed to autonomously achieve business goals, not just complete simple tasks.

 

Core Components

The platform is built on several core components:

  • The Brains: It’s powered by Google’s most advanced models, including the newly announced Nano Banana (Gemini 2.5 Flash Image), ensuring agents have state-of-the-art reasoning and creative capabilities.
  • The Workbench: A user-friendly, no-code interface where employees can define goals and orchestrate multiple agents to complete complex tasks across different systems.
  • Secure Data Integration: Gemini Enterprise securely connects with the help of Agents and Connectors to all of your company’s data, whether it’s in Google Workspace, Microsoft 365, Salesforce, SAP, or other core business applications.
  • Centralized Governance: providing a single dashboard to manage, secure, and audit all AI agents, their permissions, and their access to sensitive data.
  • A Rich Ecosystem: Get started immediately with a vast marketplace of pre-built agents from Google and its massive ecosystem of over 100,000 partners.

 

Why This is a Catalyst for Change for Businesses?

Translating features into value, Gemini Enterprise is poised to deliver a significant impact on three key fronts:

(1) Automating Complex Workflows, Not Just Tasks

This platform moves beyond simple, single-shot commands like “summarize this document.” Its power lies in orchestrating multi-step business processes.

Imagine a marketing team wanting to launch a campaign for a trending product. They can now deploy a fleet of agents from a single prompt: one agent analyzes social media to confirm the trend, another cross-references inventory levels in the company database, a third connects to ServiceNow to generate a purchase order, and a final agent drafts the entire multi-channel marketing campaign. The possible hindrance is the organization’s maturity to create these types of agents, rather than the technology.

This is not just assistance; it is the end-to-end automation of a core business function.

(2) Democratizing AI Development

Gemini Enterprise includes a no-code Workbench for agents which is a major development. Now every employee can become an agent builder. 

Your finance team can build an agent to automate quarterly reporting by pulling data from multiple spreadsheets and generating a summary draft. Your HR department can create an agent to streamline new-hire onboarding, from provisioning accounts to scheduling introductory meetings. 

This puts the power of AI creation directly into the hands of the subject-matter experts, drastically accelerating innovation without relying on overloaded IT departments. While tooling for agent deployment and controls for data access keep the company processes and data secure.

(3) Scaling Innovation Without Scaling Risk

True business impact comes from enabling your entire organization, but organisation scale capabilities need to be managed. Gemini Enterprise directly addresses this with its Centralized Governance framework. This is a concrete capability that allows organisations to set, manage, and audit the permissions of every AI agent. They can control which agents can access which data sources and what actions they are allowed to take. This means you can finally empower your teams to build and innovate at scale, confident that it is all happening within a secure and compliant environment.

 

The Codento Advantage: From Platform Potential to Business Value

A powerful platform provides a fantastic starting point. Codento is a trusted-partner to help you navigating and implementing AI agentst towards real-world ROI.

 With us we go throught the strategic questions before starting building.

  • Where do we start? The biggest challenge is not building agents, but identifying the workflows where they’ll create the most value. Our  AI Agent Launchpad programme continues to enable your organisation to identify the cases that will deliver the most impact and the most value
  • How do we use agents? Understanding how to build and deploy agents is crucial. Our approach focuses on human-centric design, ensuring the agents you build are of the right size for the task, intuitive, reliable, and seamlessly integrated into the daily flow of work, turning end-user skepticism into advocacy. 
  • How do we scale successfully? We help you establish a robust approach from day one, ensuring that as AI scales across your organization, it does so in a way that is secure, compliant, and consistently governed.

 

Beyond the Headlines: Understated Capabilities with Massive Impact

While the agentic framework rightly commands attention, Google also nonchalantly released capabilities that could quietly revolutionize specific areas of your business:

  • The Data Science Agent: For organizations grappling with complex datasets, the introduction of a specialized data science agent is a significant boost. This agent is designed to help with sophisticated data analysis, hypothesis testing, and model development, democratizing access to advanced analytics insights for a wider range of users, not just professional data scientists.
  • From Slides to Stories (with Video): Imagine turning static presentations into engaging video content with minimal effort. New capabilities allow for the transformation of presentation slides into dynamic video formats, complete with AI-generated voiceovers and transitions. This dramatically cuts down on the time and resources required for creating compelling visual communications.
  • Global Communication Barriers Crumbling: The platform introduces advanced speech translation capabilities that go beyond simple text-to-text. With real-time, context-aware translation of spoken language, international meetings and collaborations can become genuinely seamless, breaking down language barriers that have historically hampered global teams.

 

An Open Invate for You to Join the Gemini Enterprise Journey

This is the power of Gemini Enterprise: the new front door for AI in the workplace. We are bringing the best of Google AI to every employee, for every workflow. And we are excited to support you every step of the way.

Learn more  about  our AI Agent Launchpad, designed for Gemini Enterprise and start your journey today!

Part 2 – Gearing Up For The Self-Driving Business – Google Takes the Lead on Agentic AI

Agents – Part Two

Gearing Up For The Self-Driving Business

Google Takes the Lead on Agentic AI

Building upon my recent overview of Gemini Enterprise, which focuses on enterprise and desktop users, I will now turn the spotlight to some of the other AI agent technologies announced in Google Cloud’s Next ‘25 event back in April. These new tools are already reshaping business far beyond the scope of just the desktop user.

Google has developed powerful tools that go beyond Gemini Enterprise. They help you create and deploy autonomous agents across your entire technological landscape. One such tool is the Agent Development Kit (ADK). The ADK provides a toolkit for your technical teams to develop powerful agents that interact not just with the user, but with each other. This open-source framework simplifies building sophisticated multi-agent systems while maintaining precise control over agent behavior.

ADK in Action: Automating Complex User Experiences

Think of the ADK as a set of advanced, open-source tools that empower your developers to build incredibly flexible, custom agents that go far beyond off-the-shelf products. The ADK provides the framework to create these agents efficiently, test them effectively, and deploy them reliably.

Let’s look at a real-world example of multi-agent AI in action and examine how the ADK and other innovative Google tools empower you and your organisation. In this example, multi-agent AI solutions replicate human interaction in online shopping, addressing the impossibility of having live operators for every session.

Consider a customer needing to exchange an amplifier purchased last week because it lacks sufficient volume and reverb. Upon logging in, they chat with a Dialog Manager Agent—the system’s orchestrator. The customer explains their desire to return the item and exchange it for a new one with specific features.

The Dialog Manager consults a Product Specialist Agent, which has indexed all product manuals. This agent confirms that the “Neighbour Blaster 3000” meets the customer’s specifications. The Dialog Manager then queries the Stock Agent, verifying that one unit is available.

Concurrently, a Customer Record Agent updates the customer’s profile not just with codes, but in natural language, capturing the interaction’s details, request, and sentiment. This provides richer context for human review and enables agent learning.

Finally, the Dialog Manager Agent confirms the exchange transaction and passes the details to the Order Agent, which handles subsequent status updates from the warehouse to the doorstep.

This multi-agent system efficiently manages a complex return, exchange, feature check, and stock query, updating customer records meaningfully—all while mimicking a helpful human interaction. Taking this further, it is possible to integrate features like voice from Google’s AI Customer Engagement Suite, meaning you can achieve all of this through natural, spoken conversation.

Out of the ADK box come agent-to-agent protocols which enable multiple agents to work together to achieve customer satisfaction. It simplifies building robust, autonomous agents and ensures deployment readiness with options like Google Cloud Run and Vertex AI Agent Engine. The ADK also offers flexibility and freedom, and works with many varied AI models.

What’s In It For My Business?

So much for the technology and how it works – the real question is, what do these advancements do for your business?

  • Increase Efficiency and Speed Across Your Business:

ADK and the agents built with it will help you automate any multi-step process across your operations and leverage parallel processing where multiple agents work simultaneously.

  • Improve Decision Support at Every Level:

You will be able to equip your teams with powerful, yet flexible agents that can gather information, analyze data, and provide insights directly within their workflow.

  • Future-Proof Your Organisation:

ADK will form a solid foundation for leveraging advanced AI capabilities across your organisation, eventually creating an “agent workforce” tailored to automate and optimize various aspects of your business operations.

  • Dramatically Enhance Your Customer Engagement:

Automating the workflows for handling customer inquiries, providing faster responses, offering personalized interactions (using customer history), proactive issue resolution, and even AI-driven voice communication will help you achieve better customer outcomes and higher satisfaction.

  • Solve Complex Operational Problems:

Tackle challenges across different departments that change frequently or require specialized knowledge by assigning tasks to dedicated agents working in concert. Free your people to meaningful and more complex tasks requiring a human touch.

Debrief: The Agentic AI Summary

The Google Agent Development Kit is a powerful, flexible toolkit, and it is available now. It empowers teams to build sophisticated, custom AI agent systems that can automate complex tasks, enhance customer interaction, and solve challenging business problems. By enabling the creation of collaborative multi-agent systems, the ADK offers a strategic pathway to harness AI for tangible business benefits and increased operational efficiency.

Agentic AI is a new way of putting technology to work when solving problems and innovating. The Agent Development Kit is the means of starting construction, but the bigger picture here is the revolution in systems thinking and how we deploy software to enhance and supplement the work we do. Agentic AI challenges us to rethink how technology can shape the organisations we are building and leading. In many ways, it is not too far-fetched to start envisioning the ‘self-driving business’.

Contact us here at Codento to discuss the potential applications of AI agents and ADK. We can show you where you could be heading with Agentic AI.

 

For more details on Agentic-AI or any of the other topics raised here, contact matthew.wooller@codento.com

 

Matthew Wooller, Data and AI Strategist, Codento

 

Contact us for more information on our services:

Part 1 – The Not So Secret Agents: What Every Decision Maker Should Know About Agentic AI

The Not So Secret Agents: What Every Decision Maker Should Know About Agentic AI

Back in April of this year, Google Cloud made over 228 announcements at their Next ‘25 event in Vegas. Three months on and it is still hard to fully absorb the scale of what Google is doing with cloud and AI, but without a shadow of a doubt one phrase has taken centre stage: Agentic AI.

A License to Build

One of the conceits of Ian Fleming’s novels was that James Bond would always announce himself, bold as brass, informing the villain exactly who he was and what he was up to. Back in April Google’s agents did the same thing on stage in Las Vegas. We were introduced to them and we got to learn exactly what they are and what their plans for our future are.

So let’s look at those agents and review their plans in a little more detail. Hopefully, we can help you understand why how you get things done has already started changing forever. But first of all, just exactly what is an AI agent?

According to Google, a good agent displays the following qualities;

  1. Reasoning: Understands context, analyzes data, makes smart decisions, and offers relevant advice (e.g., when sales are up, the agent reminds you to thank the new sales exec who brought new clients with him).
  2. Acting: Takes action and gets things done in the real world (e.g., it books the regular meetings with all those new clients).
  3. Observing: Gathers information through various senses and data (e.g., sees all the new customer data, deeply researches details, and provides insights).
  4. Planning: Strategizes based on the situation, anticipates problems, and suggests solutions (e.g., when your customer support gets stretched with the new customers, the agent prioritizes tasks and recommends training).
  5. Collaborating: Works with people and other agents to achieve goals, communicating and coordinating effectively (e.g., multiple agents track new sales’ impact across the entire business and share updates).
  6. Self-Refining: Learns from experience, adapts, and continuously improves. (e.g., next time a new sales exec arrives with new customers, prepare in advance the support she needs to shine!).

Agents are autonomous; they have a clearly defined role and persona, remember, and autonomously employ tools to work with. At the core of the agents, Large Language Models (LLMs) allow them to process and generate language, whilst other components provide reasoning and action.

Just like Bond against the villain in Casino Royale, Google went ‘all-in’ on Agentic AI with a slew of announcements in April. Here’s what has appeared in the days and weeks since then, and how you can benefit from it.

“Take a Letter, Miss Moneypenny”

So now we know what an agent is, let me introduce you to Gemini Enterprise. Gemini Enterprise is a platform for building and deploying AI agents that understand, reason, and act in the real world. Gemini Enterprise provides tools and infrastructure to create those intelligent assistants that can automate tasks, provide information, and collaborate with users in a dynamic and contextual way. Think of it as Google’s hub for unleashing the power of autonomous AI.

Gemini Enterprise agents act on your behalf, but they remain firmly under your control, and it is a truly liberating experience. What do I mean? Well, each week, I send out meeting requests for daily scrum gatherings, and organise weekly client updates and governance meetings. Before each meeting, I need to create an agenda; after the meeting, I should write up the minutes. All of that is a chore, and it takes time away from me being able to add value elsewhere. Gemini Enterprise allows me to offload the work to my digital double and get on with doing things elsewhere that create a greater impact for clients and customers.

The power of Gemini Enterprise lies not just in its ability to ‘do’, but also very much in its ability to ‘know’. Like every organisation on the planet, yours also keeps its knowledge in many disparate and almost certainly unconnected systems. You can hook Gemini Enterprise into all of them via ready-made connectors. And if a ready-made connector does not exist for your particular legacy system flavour, you (or a friendly coder) can build one. The agents in Gemini Enterprise can provide you with an in-depth overview of your organisation, one that you can interact with, one that you can ask questions of — and most importantly, one that is a unified, amalgamation of all your data, regardless of silos.

As well as apps, Google also offers NotebookLM as part of Gemini Enterprise. Google NotebookLM is an AI-powered research assistant that helps you understand and synthesize information from your documents. It acts like a smart, personalized notebook that can answer questions, summarize key points, extract insights, and even suggest related topics, making research faster and more efficient. Not only does NotebookLM offer you written summaries, but it can also come up with audio content for you, creating a bespoke ‘podcast’ dedicated to the topic you are interested in. NotebookLM is your AI research sidekick for making sense of your notes and documents.

Google has positioned Gemini Enterprise and its other agent tools as competitors to Microsoft’s Copilot tooling – and has already established a lead over the competition. Out of the gate, Gemini Enterprise has a degree of autonomy that is missing elsewhere. Gemini Enterprise doesn’t just react to questions, it pre-empts them. It doesn’t just do things when you ask, it does things when you need them done. The competitors are playing catch-up right now.

With an Gemini Enterprise application or NotebookLM working on your behalf, you are better informed, you are in control and your time is freed up for all the other important things you have to do – all with the peace of mind that comes from knowing that Gemini Enterprise is backed by enterprise-grade security, privacy, and compliance controls.

“Now, pay attention, 007”

You might expect that getting things up and running could be tricky, but that isn’t necessarily the case. Right now, I just need to do three things to create a straightforward agent that can tell me about my customers and their history with my company.

#1 Create a data source – in this case, my company’s ‘sales’ folder in Google Drive – or any other storage container or application, it doesn’t have to be Google!

#2 Create an application that utilises both search and AI, linked to the data source I created in step one.

#3 Publish the URL for the app to those who want to use it.

Simple. Once I have done that, anyone in my company can interrogate the sales material, asking questions like “Can you summarise all the proposals to Acme?” and “Who was the guy from Universal Exports who ordered all the cigars?”.

And on the roadmap, new features to make things even simpler. Soon you will be able to issue a spoken prompt like this:

“Create an Gemini Enterprise agent that connects to the sales folder in Drive and can tell me about all the material there.”

Yes, that’s right, you can use AI to create AI agents. With that sort of power, the sky’s the limit – and you’ll probably find yourself thinking, “The World is Not Enough”.

 

Next time, we will look at some more agent news from Google, this time taking your customer engagement to the next level.

If you want to know more about Gemini Enterprise, contact me, Matthew Wooller at Codento, and let’s talk about how a not-so-secret agent of your own can make your life easier and more productive.

 

Matthew Wooller, Data and AI Strategist, Codento

 

Read Part 2 – Gearing Up For The Self-Driving Business – Google Takes the Lead on Agentic AI

 

Contact us for more information on Agentic AI & our services:

From Vegas to Victory: Unleashing the AI Revolution with Codento!

From Vegas to Victory: Unleashing the AI Revolution with Codento!

Hey everyone, Toni Kaikkonen here, fresh off the whirlwind of Google Cloud Next ’25 in Las Vegas! The energy here has been electric. It wasn’t just the keynotes’ bright lights and buzzing crowds; it was the sense of a real, transformative shift in how businesses are going to leverage AI. We’re not just talking about buzzwords anymore, but real, tangible platforms and tools that are going to help engage your customers like never before, supercharge your operations, and unlock insights you could not have imagined.

Las Vegas Lights Up with the Business Benefits of AI

This year, the spotlight was firmly on agentic AI. Forget passive chatbots; we’re talking about intelligent agents that proactively solve complex problems, orchestrate workflows, and connect seamlessly across your entire enterprise ecosystem – on your behalf, but under your control. That’s the power we’re unlocking with announcements around Gemini Enterprise, the Agent Development Kit (ADK), and the Agent2Agent (A2A) protocol!

The data analytics announcements were equally mind-blowing, with Google Cloud’s BigQuery evolving into an autonomous data-to-AI platform with AI-assisted notebooks and an AI Query Engine that blends structured and unstructured data with real-world context, all securely accessible through natural-language interactions.

For Codento customers, this translates to:

  • Reinvent Customer Engagement: Gemini Enterprise’s custom AI agent tools help you build personalized experiences that enhance your every customer encounter, regardless of underlying systems like ServiceNow, Salesforce.com, or SAP. AI-assisted notebooks in BigQuery empower significantly faster analysis, delivering quicker insights for your business. The BigQuery AI Query Engine unlocks previously inaccessible insights, enabling, e.g., more accurate predictive models and nuanced market segmentation.
  • Redefine Efficiency: The announced technologies allow architecting complex, automated workflows that bridge previously siloed internal systems and processes, never mind what technology stack your organization is using. HR or IT helpdesk interactions can trigger AI agents to log a ticket, query the needed databases, and update the internal customer, all automatically. With Looker embracing conversational analytics, your business users can query data and generate insights using natural language prompts. With the announced advancements in Vertex AI’s generative media capabilities, you can create richer, more engaging interactions like natural-sounding voice agents, visual support guides, and AI-powered image analysis.

Build the Future on Powerful Infrastructure

All this AI magic needs a solid foundation, and Google Cloud is delivering. The unveiling of the Ironwood TPU and the AI Hypercomputer marks a major leap in performance, especially for demanding AI workloads. With advancements in GKE (Google Kubernetes Engine) like Inference Gateway and Pathways, you can deploy containerized AI applications with enhanced cost-efficiency, lower latency, and higher throughput.

For your business, this means:

  • Optimized AI Performance & Cost: With a capable partner, can design solutions leveraging Ironwood and the AI Hypercomputer to achieve substantial performance gains and significantly lower your total cost of ownership.
  • Scalable Deployments: GKE Inferencing and Pathways enable deploying containerized AI applications with enhanced cost-efficiency and lower latency, perfect for real-time applications.
  • Enhanced Connectivity & Hybrid Deployment: With Cloud WAN and Google Distributed Cloud (GDC), you can establish highly reliable, secure, and performant connectivity and deploy AI solutions directly within your own data center or edge locations.

Partner with Codento

Google leads in AI, and we lead in Google Cloud. We’re ready to help you navigate this exciting AI-powered landscape. Our comprehensive solution portfolio – spanning Customer Engagement, Redefined Efficiency, Technology Strategies, AI & Data Cloud, and App & Infra Cloud – is perfectly aligned with the key innovations now unveiled.

We’re not just implementers but also your strategic partners. We’ll help you understand these technologies, tailor them to your specific business challenges, and implement solutions that deliver tangible, measurable growth and innovation.

Contact our team today for a consultation, or if you want to hear first-hand experiences from our customers like Verkkokauppa, Saka, and City of Hamina, join our AI for Growth event in Helsinki 13.5.2025!

Best,

Toni Kaikkonen 

Service Director, Codento

 

Contact us for more information on our services:

Decoding the Consultant Life: Real stories from real consultants – Timo & Underground City

Decoding the Consultant Life: Real stories from real consultants

Timo Koola, Lead Cloud Architect, about working for our customer Underground City

 

I am Perttu Pakkanen and my interest as Codento’s talent acquisition lead is to better articulate why consulting could be a great career choice.

When potential customers ponder whether they should use our services, they usually like to see some reference cases. Why wouldn’t our potential employees think the same?

So, I had a chat with our Lead Cloud Architect, Timo Koola. Timo has been working with our customer Underground City to help water utility companies in managing their water and sewer networks better.

I asked Timo to sit with me in our office meeting room during a busy Tuesday afternoon and share some of his thoughts on the project.

Here we go:

 

Can you tell me about the solution you’ve built?

The customer had an old React application hosted on a virtual machine with PostgreSQL. We migrated it to Google Cloud. Now we have a modern Node.js backend with Google Cloud services including e.g. Cloud Run and Cloud SQL.

The software itself has a very meaningful function: To protect the Baltic Sea. More specifically, sewer network asset management.

So, Underground City addresses the challenges faced by water utility companies by offering more cost-effective and sustainable solutions for locating and repairing faults.

 

That sounds interesting. What’s been your role in the project?

Architect, developer, a bit of everything. Also helped with data analysis. So, pretty much an all-around champion helping with many things. 

I provided the customer senior-level architecture and programming expertise. In the future, the tasks will most likely revolve more around architecture work.

 

Out of everything you’ve worked on, what’s been the most interesting?

Developing the analysis side – helping the customer do things that no one else does with data. With this, we can present the state of the sewer network on a map, both at a high level and by drilling down into details. A unique solution for presenting sewer data.

 

To continue from that, what keeps you excited about your work?

Interesting problems and improving water bodies. Therefore, making a real impact in the real world.

 

That is for sure an exceptional benefit in a project. What has been the most difficult part?

There’s a lot of data, and presenting it in a reasonably comprehensible way across multiple levels. It’s not only about the data analysis but it’s also essential to make the data understandable to different stakeholders.

 

What have you learned?

How data compiled from basic elements can be valuable to the customer. Even if the data could be simple from the beginning, having it in the right form can provide much value.

When aggregated and analyzed effectively, data reveal patterns, trends, and insights that were previously hidden, enabling customers to uncover opportunities or solve problems with greater precision.

 

Finally, any thoughts to wrap things up?

It’s incredibly interesting to bring software development solutions into a traditional industrial sector. Physical work coupled with software creates a lot of added value.

 

Thanks Timo for the interview!

 

—

 

Being part of pioneering projects like this allows for both personal and professional development. I strongly feel that at Codento you can engage in work that is not only challenging but also highly impactful in many industries.

Read more about us from our career site and see if there are any suitable opportunities for you and connect with us on our recruitment system!

 

About the interviewee:

Timo Koola is an experienced software engineer and cloud architect. He has been in the field for almost 30 years and has a diverse background in many different roles and technologies.

Codento | Perttu Pakkanen

About the interviewer:

Perttu Pakkanen is responsible for talent acquisition at Codento. Perttu wants to make sure that the employees enjoy themselves at Codento because it makes his job much easier.

Decoding the Consultant Life: Real stories from real consultants – Faiz & Plugit

Decoding the Consultant Life: Real stories from real consultants

Muhammad Faiz Wahjoe, Data Engineer, about working for our customer Plugit

 

I am Perttu Pakkanen and my interest as Codento’s talent acquisition lead is to better articulate why consulting could be a great career choice.

When potential customers ponder whether they should use our services, they usually like to see some reference cases. Why wouldn’t our potential employees think the same?

So, I did an email interview with our data engineer, Faiz. 

Faiz has been working with the Codento team to gather the different use cases of data and ultimately establish a robust data warehouse to centralize Plugit’s data management and enhance its organization.

In customer’s own words, it has been “the best-run consulting project we’ve experienced”.

I asked Faiz some questions about the project and to my delight, he gave very comprehensible answers.

Read more below:

 

What kind of solution have you built?

In brief, we did modernization of the client’s data platform. The client operates numerous charging points for electric vehicles and as such, they handle a lot of data. 

The motivation for this project was while the client’s previous data platform was already established and fulfilled the day-to-day requirements, Performance and scalability issues started to be detected. Hence, modernization was deemed necessary. 

The modernization was about creating a GCP-focused BigQuery-based data platform and its related tools, for example, Dataform for data transformation purposes. We then created a pipeline to connect customer’s data sources to BigQuery in which we use Dataflow and then we leverage Terraform as our IaC tool to set up and manage our system’s infrastructure on GCP.

 

What kind of tasks have you done in the project?

I spend most of my development time in Developing data pipeline using Dataflow and setting up transformation script using Dataform.

 

What has been the most interesting thing you have done working for the customer?

We held a workshop together with the client and we identified one particularly interesting insight that we are keen to know. This insight is about automatic problem detection of the client’s many charge points by looking at the pattern of one of the particular kinds of data they produce. 

This sounds quite simple but to realize that such an important insight could be derived from such simple data, with the right transformation, is intriguing – in my culture we have a saying “We could see ants across the sea but missed an elephant in our face”.

Even more interesting, while deriving this insight from a limited size of dataset could be said as simple, Deriving it from millions of data record, every day, across tens of thousands of data producer points bring another dimension of challenge and here, the strenghts of tools that we are leveraging shine. The powerful data processing capability provided by BQ and Dataform made this operation reliable both in performance and accuracy and easy to develop and manage.

 

What brings enthusiasm to your workdays?

I always like if my work produces real impact(s) in solving a problem for our clients and users and in this project those impacts are plentiful to see. I also put a positive and productive work atmosphere as one of the main factors and it is quite visible in this project as well, working with my fellow codento consultant and the periodic, routine discussion with the client has been quite a positive experience.

On top of that, I got to work on many modern innovative tools in solving the client’s problem, while I always think of a tool as a means to solve the Client’s problem e.g. we should not pick a tool based on “hype” or “that’s what everyone is doing” alone but rather the suitability.

 

What has been the most difficult part?

I think the most challenging part for us is understanding and navigating Dataflow traits, quirks, and limitations. 

Especially the Python SDK library that we are using. It’s a relatively new tool and its rather specific use case means community support is not as plentiful as we want it to be. This also has a consequence that LLM tools quite often fails to give consistent and accurate help. I also do not have extensive experience in production-grade Dataflow so the learning curve was quite steep.

But, we worked together, leveraging our experience so we could learn and understand the tools better faster, and in the end, we were able to deliver the project in a state that the client was really satisfied. So, the client’s satisfaction and my own skillset growth have been more than rewarding.

 

What have you learned?

I learned a lot. 

From the technical perspective, I gained important knowledge and experience and got to improve my skillset in the various tools that we are using: Dataflow, Dataform, BigQuery, and so on. 

From a non-technical perspective, I also gained experience in how a consultancy project is run and managed, how to identify important milestones and navigate challenges, and how to deliver a solution that solves the problem and produces a positive impact on the client’s case.

I also met and worked with new people, understanding not only their needs and requirements but also how to effectively communicate and cooperate in teamwork. As such, interacting and working together with them and fellow Codento consultants has been quite a pleasant and fruitful learning opportunity and experience.

 

Thank you Faiz for the awesome and comprehensive answers! 

 

—

 

Being part of pioneering projects like this allows for both personal and professional development. I strongly feel that at Codento you can engage in work that is not only challenging but also highly impactful in many industries.

Read more about us from our career site and see if there are any suitable opportunities for you and connect with us on our recruitment system!

 

About the interviewee:

Faiz is a talented data engineer with a computer science degree from Aalto University. 

Codento | Perttu Pakkanen

About the interviewer:

Perttu Pakkanen is responsible for talent acquisition at Codento. Perttu wants to make sure that the employees enjoy themselves at Codento because it makes his job much easier.

Decoding the Consultant Life: Real stories from real consultants – Olli & Smartvatten

Decoding the Consultant Life: Real stories from real consultants

Olli Alm, Senior Software Engineer, about working for our customer Smartvatten

 

I am Perttu Pakkanen and my interest as Codento’s talent acquisition lead is to better articulate why consulting could be a great career choice.

When potential customers ponder whether they should use our services, they usually like to see some reference cases. Why wouldn’t our potential employees think the same?

So, I had a chat with our Senior Software Engineer, Olli Alm. Olli has been working with our customer Smartvatten for their water efficiency technology. They are leading experts in the field in Northern Europe, helping their customers save hundreds of millions of liters of water every year.

I asked Olli to sit down with me in our office’s meeting room on a Tuesday afternoon. The weather outside was a bit rainy and dim, but it didn’t slow us down.

Here we go:

 

What kind of solution have you built?

I’ve worked on building a system for leakage monitoring and water consumption measurement, utilizing GCP infrastructure.

The client’s system is a water metering solution with two main functionalities: measuring water consumption and sending alerts, for example, in the case of leaks. What makes this particularly interesting is that it’s an IoT system, which means we’re constantly also dealing with hardware solutions and how they indirectly influence the larger system.

What I find fascinating is the challenge of keeping the whole system cohesive and building a unified architecture. In the IoT world, there’s an abundance of real-time data coming in from various countries and time zones, which raises unique challenges like efficiency, data accuracy, and synchronization.

 

The solution indeed seems like a unique one. What kind of tasks have you done in the project?

My work has been primarily focused on data-oriented back-end development, though I’ve also contributed to some front-end work.

On the social side, I interact daily with the client across multiple levels, including product owners, the CTO, and a smaller stakeholder team. The collaboration has been so close that I can confidently say that we have a good sense of trust with the client.

I’ve also been heavily involved in architectural work, which has been both challenging and rewarding.

Why I’ve enjoyed this project: The relationship with the client is very good and straightforward. There’s no unjustified pressure, communication is easy, and mutual trust makes it a pleasant working environment. I feel like an important part of the project and can see the impact of my work.

 

Those are good qualities in a working environment. What has been the most interesting thing you have done working for the customer?

One highlight has been working with time-series data and developing robust data pipelines. I also enjoyed contributing to the overall system architecture to make it more stable and efficient. Both aspects required creative problem-solving and provided a lot of professional growth.

 

What brings enthusiasm to your workdays?

There’s a constant but manageable level of pressure, which keeps things exciting without being overwhelming. Every day brings new and unexpected challenges – there’s always something fresh to tackle, which ensures I never get bored.

My days are usually quite busy, with a mix of maintenance tasks and new development work. The diversity of tasks keeps things engaging. Occasionally, there are urgent issues that require immediate attention, but that’s part of the fun.

Over time, I’ve learned so much that I can now work far more efficiently. It’s rewarding to see how much I’ve grown professionally through this experience.

 

What has been the most difficult part?

In general, learning to deal with uncertainty has been a big takeaway for me. It’s something I’ve improved at significantly during my time as a consultant.

A reflection on consulting: Consulting is like a marathon. Building a strong trust relationship with a client over the years allows the work to flow more smoothly. But occasionally, you’re faced with tough problems that take time to solve. The longer you work with the same client, the more efficient and effective you become.

 

What have you learned?

Working on a small team means the range of tasks I’ve handled has been extremely broad – from building infrastructure to fixing UI bugs. This has helped me develop a wide skill set.

Over the past three years, I’ve worked with Google Cloud Platform every single day, which has allowed me to deepen my expertise significantly. I’ve also refined my skills in routines, documentation, code quality, best practices, and multitasking, often juggling multiple responsibilities simultaneously.

It’s hard to point to one specific thing I’ve learned because the experience has been so comprehensive. However, I’d highlight Clojure as an interesting element – it’s something I’ve enjoyed exploring and using in this project.

 

That’s a lot of learning! Any last words to wrap things up?

In this project, my tasks have been very self-guided, which is a typical aspect of working on a small team. I appreciate the responsibility that comes with the role and enjoy the freedom to make my own decisions. This autonomy enhances my sense of self-efficacy and allows me to express my capabilities fully.

Codento fosters a professional environment with a strong emphasis on trust and responsibility, allowing me to thrive and deliver quality results in my daily work.

 

(We then continued to talk about different stuff not related to the subject per se and ended up using almost an hour for this. This was a great talk and I also learned a lot.)

 

—

 

Being part of pioneering projects like this allows for both personal and professional development. I strongly feel that at Codento you can engage in work that is not only challenging but also highly impactful in many industries.

Read more about us from our career site and see if there are any suitable opportunities for you and connect with us on our recruitment system!

 

 

About the interviewee:

Olli Alm is a Senior Software Engineer at Codento. He has 20 years of experience in different software development and architecture positions as well as teaching and research.

About the interviewer:

Perttu Pakkanen is responsible for talent acquisition at Codento. Perttu wants to make sure that the employees enjoy themselves at Codento because it makes his job much easier.

Boosting Contact Center Effectiveness with AI

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

 

Codento | Janne Flinck

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.

Living on the Edge – Google Kubernetes Engine makes edge computing finally real

Living on the Edge

Google Kubernetes Engine makes edge computing finally real

 

Author: Markku Tuomala, CTO 

Edge computing has been an unkept promise of 5G networks for years. Industrial companies, energy and utilities, and transportation and logistics businesses have been longing for low-latency services that would allow them to monitor and react in real-time to happenings on the field. Telecom operators, in turn, have dreamt of a genuinely novel business case for their 5G network investments, in which they would offer a scalable, cost-effective edge computing solution as a service to their customers.

Google Cloud’s packaged tools enable Edge as a Service

Edge computing is the practice of processing data closer to the source rather than relying solely on centralized cloud data centers. It offers a range of practical benefits, such as reducing latency, enhancing real-time data processing, and improving system performance. The most mentioned use cases of edge computing are real-time monitoring and control of manufacturing processes, automation of production lines, fleet management, and employee safety.

Two concepts are essential to understanding the hurdles that have been blocking the widespread use of edge computing: containerization and Kubernetes. Containerization involves packaging an application and all its dependencies into a lightweight, portable unit called a container. This allows the application to run consistently across different devices, making it ideal for deployment on edge devices with limited computing capacity. Kubernetes, in turn, acts as a management system for these containers, orchestrating their deployment, scaling, and operation to ensure they run smoothly and efficiently. Jointly, containerization and Kubernetes enable efficient, scalable, and reliable edge computing by ensuring applications can be easily deployed and managed across numerous edge locations.

Managing containerized applications with Kubernetes is a complex technological endeavor that has been a showstopper for many interesting edge computing use cases until recently. In late 2023, however, Google launched a managed service called Google Kubernetes Engine (GKE) Enterprise that will revolutionize the opportunities to offer and deploy edge computing.

Google Kubernetes Engine Enterprise for multitenant edge computing

GKE Enterprise is a tool for managing multitenant edge environments where you can cost-effectively and safely offer computing capacity from the edge for several users. These users can be the manufacturing sites of a single corporation in the same geographical area or a group of clients of a telecom operator or water or electricity company. By using GKE Enterprise, companies can efficiently manage workloads across cloud and edge environments, ensuring seamless operation and high safety availability of applications that require extremely short latency.

Chicken and egg: are use cases awaiting the technology or vice versa?

Some have claimed that edge computing is a fad, as the network connections with 30 – 60 ms latencies in the Nordics, especially, are supposedly enough for 90% of the use cases. The ambitious goal of edge computing to diminish the latency to less than ten milliseconds. This will enable some examples described above, which cannot be realized over the current networks. From my experience, I am convinced that when the appropriately priced chicken is available, the application eggs will follow in numbers. In other words, when the cost of the mature platform technology is on the right level, the game-changing use cases and applications will follow.

Aiming at <10 millisecond latencies

We at Codento have talked with more than a hundred organizations about their plans and aspirations for using artificial intelligence. Customers have delightfully novel ideas for using video surveillance connected to AI, e.g., for identifying the crossing paths of an autonomous forklift and a maintenance worker. With real-time video and a predicting AI solution, a system could reach the upcoming incident faster than a human can, potentially saving the worker’s life.

Last week, we were thrilled to introduce our first customer case in this area to the world. Telecom operator Telia and Codento have collaborated to make edge computing available to Nordic organizations through Telia’s Sirius innovation platform, with ferry operator Finferries being the first customer to pilot the service.

Edge computing transforms industries by enabling secure, low-latency, real-time data processing. For Nordic telecom operators and industrial companies, Google Kubernetes Engine Enterprise offers a powerful platform to harness its benefits.

Codento’s expert team has extensive experience with industrial customers’ businesses and processes, in-depth understanding of the AI-related use cases that Nordic companies are investigating, and awarded capabilities in Google Cloud technologies. We are eager and prepared to help your organization fully utilize edge computing and its applications. Be it a solution you want to build for your use or a platform you want to offer as a service to your customers, we are here to help.

Key takeaways:

  1. Edge computing will enable novel use cases like video monitoring and real-time reactions to events in, e.g., industrial processes
  2. Google Kubernetes Engine Enterprise is a solution enabling multitenant edge computing environments, adding scalability, cost, and security to “Edge as a Service”
  3. Codento can help industrial corporations or telecom, water or electricity companies to build use cases and services based on edge computing

 

About the author:

Markku Tuomala, CTO,  joined Codento in 2021. Markku has 25 years of experience in software development and cloud from Elisa, the leading telecom operator in Finland. Markku was responsible for Telco and IT services cloudification strategy and was a member of Elisa’s production management team. Key tasks included Elisa software strategy and operational services setup for business critical IT outsourcing. Markku drove customer oriented development and was instrumental in business growth to Elisa Viihde, Kirja, Lompakko, Self Services and Network automation. Markku also led Elisa data center operations transformation to DevOps.  

 

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.

Breathe New Life into Cornerstone Systems

Breathe New Life into Cornerstone Systems

Take your Salesforce, SAP, Power BI, Oracle, AWS, and VMware solutions to the next level with Google Cloud Integration

 

Author: Anthony Gyursanszky, CEO

We all want AI and analytics to boost our business and enable growth, but few of us have the deep pockets needed to redo our entire IT environment.

Most Nordic organizations have invested significantly in leading technologies like Salesforce, SAP, Microsoft Power BI, Oracle, AWS, and VMware. However, the jungle of AI capabilities is scattered and a coherent AI roadmap is difficult to envision.

Integrating Google Cloud with the technologies mentioned above, allows you to unlock new synergies and use advanced AI capabilities without extensive reconfiguration or additional capital expenditure.

 

Turbo boost your current system environment without overlapping investments

Adding Google Cloud to your IT strategy does not necessarily mean replacing existing systems. Instead, you can compliment them, enabling them to work together more effectively and deliver greater value with minimal disruption. Google Cloud Integration ensures seamless interoperability and helps your existing tools like Salesforce, SAP, Oracle, and VMware deliver superior results.

For example, Google Kubernetes Engine (GKE) Enterprise enables seamless deployment and management of your existing applications across hybrid and multi-cloud environments. Your Salesforce, SAP, Oracle, and VMware systems can work together more efficiently, with Google Cloud as the glue between them. The result is a more streamlined, agile IT environment that enhances the capabilities of your current investments.

Google Cloud VMware Engine, in turn, allows you to extend your existing VMware environments to Google Cloud without costly migrations or re-architecting. This enables your business to tap into Google Cloud’s vast computing and storage resources, advanced AI tools like Vertex AI machine learning platform, and robust analytics platforms like BigQuery—without a revolution in your current infrastructure.

 

Harness all your data and deploy the market-leading AI tools

Data-driven decision-making is crucial today for maintaining a competitive edge in any field of business. Integrating Google Cloud with, e.g., your existing Microsoft Power BI deployment will significantly enhance your analytics capabilities. Google Cloud’s BigQuery offers a robust, serverless data warehouse that can process vast amounts of data in real-time, providing deeper and faster insights than traditional analytics tools. By connecting BigQuery to Power BI, you can easily analyze data from various sources like SAP, Oracle, or Salesforce and visualize it in dashboards familiar to your end users. Such integration enables your teams to quickly draw informed conclusions based on comprehensive, up-to-date data without significant additional investment.

Furthermore, Google Cloud’s Vertex AI can integrate into your existing data workflows. This way, you can take advantage of Google’s advanced machine learning and predictive analytics tools, and the analysis results can be visualized and acted upon within Power BI.

You can also activate your SAP data with Google Cloud AI for advanced analytics and for building cutting-edge AI/ML and generative AI applications. This enhances the value of your data and positions your business to respond more swiftly to market changes.

For businesses using Oracle, Google Cloud’s Cross-Cloud Interconnect provides secure, high-performance connectivity between Google Cloud and Oracle Cloud Infrastructure (OCI). This allows you to continue leveraging Oracle’s strengths while benefiting from Google Cloud’s advanced AI, analytics, and compute capabilities—without being tied to a single vendor.

 

Start small, and grow compliantly as you go

One key advantage of Google Cloud is that you can start benefiting from the advanced capabilities almost immediately, driving innovation and competitive advantage with only minor incremental investments. Google Cloud Integration allows businesses to scale while leveraging existing IT investments efficiently, maintaining security and compliance in the process. Google Cloud’s pay-as-you-go model and flexible pricing allow you to start small, scaling up only as needed and as you gain tangible proof of the business value. This approach minimizes upfront costs while providing access to cutting-edge technologies that can accelerate your business growth.

As your business’s cloud capabilities expand, maintaining data security and compliance remains a top priority especially in the Nordic region, where regulations like GDPR are stringent. Google Cloud’s Hamina data center in Finland provides secure, EU-based infrastructure where your data stays within the region, meeting all local compliance requirements.

Google Cloud also offers advanced security features, such as Identity and Access Management (IAM), that integrate seamlessly with your existing systems like Microsoft Power BI and VMware. This ensures your data is protected across all platforms, allowing you to grow your cloud footprint securely and confidently.

 

Don’t put all your digital eggs in the same basket

Google Cloud’s open standards and commitment to interoperability ensure that you’re not locked into any single vendor, preserving your ability to adapt and evolve your IT strategy as needed. This strategic flexibility is crucial for businesses that want to maintain control over their IT destiny, avoiding the limitations and costs associated with vendor lock-in.

Google Cloud complements your existing IT investments and helps you gain a competitive edge from technology choices you have already made. At Codento, we specialize in helping Nordic businesses integrate Google Cloud into their IT strategies. We ensure that you can maximize the value of your current investments while positioning your business for future growth.

 

About the author:

Anthony Gyursanszky, CEO, joined Codento in late 2019 with more than 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. Hehas also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. His experience covers business management, product management, product development, software business, SaaS business, process management, and software development outsourcing. Anthony is also a certified Cloud Digital Leader.

 

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.

Unique AI-powered Employee Experience: Employee Help Desk with Google Cloud HR Agent Technology

Unique AI-powered Employee Experience: Employee Help Desk with Google Cloud HR Agent Technology

 

Overview

Based on feedback from HR peers, we have created a unique AI solution that allows employees to easily find answers to HR-maintained guidelines, practices, and policies through a chat window embedded in the intranet. No more digging through files or web pages. This streamlines employee onboarding, saves time for staff, supervisors, and the HR department, and boosts employee satisfaction.

 

Challenges

Challenges facing organizations:

  • Increasing cost pressures and resource challenges for HR
  • Delayed productivity of new employees due to slow onboarding and difficulty finding information
  • Loss of tacit knowledge due to employee turnover
  • Increased workload for supervisors
  • Competition for skilled employees
  • Growth of information and challenges in finding the right information
  • Increased time pressures on employees
  • Remote work and reduced face-to-face interactions
  • Impact of employee motivation on performance
  • Less time and opportunity for employee training

 

Our Solution

Google Cloud and Codento offer a solution: AI-powered Employee Help Desk. This provides quick, accurate answers to employees seeking information on complex HR processes or documents (compensation, benefits, etc.) through:

  • Chatbot/Q&A and Search engine capabilities for HR documents without requiring engineering expertise, tuning, or configuration.
  • Agent Builder allows users to simply describe the configuration of the chat agent instead of defining it manually.

 

Implementation

Based on unique Google Cloud HR AI Agent technology and its turnkey lightweight implementation.

    • A Generative AI HR Agent is an application that aims to achieve a goal by observing the world and acting upon it using the tools at its disposal
    • User interface is the current HR intranet or equivalent HR portal, into which the HR agent’s chatbot is seamlessly integrated
    • The agent has access to all necessary HR guidelines and documentation
    • Learns over time to provide better and more relevant answers
    • Adapts to updated materials
    • Supports multiple languages

 

  • Codento, a Google Cloud Partner of the Year, configures and deploys the solution
    • The client needs to create 25 test questions and 25 corresponding sample answers based on HR documentation. Codento handles the rest
    • The solution is operational within few weeks of decision
    • Can be implemented in the client’s existing Google Cloud environment, a new environment (additional setup cost), or Codento’s provided Google Cloud platform

 

Benefits

  • Speed: operational in just few weeks
  • Low cost: ask for an offer
  • Low risk: Codento has extensive experience with similar deployments using Google Cloud technology
  • Solution quality: Codento’s NPS is consistently over 70

 

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The Executive’s Guide to Generative AI: Kickstart Your Generative AI Journey with a 10-Step Plan 

The Executive’s Guide to Generative AI: Kickstart Your Generative AI Journey with a 10-Step Plan 

 

 

Not sure where to start with generative AI?See what your industry peers are doing and use Google Cloud’s 10-step, 30-day plan to hit the ground running with your first use case

AI’s impact will be huge. Yet right now, only 15% of businesses and IT decision makers feel they have the expert knowledge needed in this fast-moving area.This comprehensive guide will not only bring you up to speed, but help you chart a clear path forward for adopting generative AI in your business. In it, you’ll find:

  • A quick primer on generative AI.
  • A 30-day step-by-step guide to getting started.
  • KPIs to measure generative AI’s impact.
  • Industry-specific use cases and customer stories from Deutsche Bank, TIME, and more.

Dive in today to discover how generative AI can help deliver new value in your business.

 

Submit your contact information to get the report:

Get Your Copy of Google Cloud 2024 Data and AI Trends Report

Get Your Copy of Google Cloud 2024 Data and AI Trends Report

 

 

Your company is ready for generative AI. But is your data? In the AI-powered era, many organizations are scrambling to keep pace with the changes rippling across the entire data stack.

This new report from Google Cloud shares the findings from a recent survey of business and IT leaders about their goals and strategies for harnessing gen AI — and what it means for their data.

Get your copy to explore these five trends emerging from the survey:

  • Gen AI will speed the delivery of insights across organizations
  • The roles of data and AI will blur
  • Data governance weaknesses will be exposed
  • Operational data will unlock gen AI potential for enterprise apps
  • 2024 will be the year of rapid data platform modernization

 

 

 

Submit your contact information below to get the report:

Google Cloud Next’24 Top 10 Highlights of the First Day

Google Cloud Next’24 Top 10 Highlights of the First Day

 

Authors: Codento Consulting Team

 

Google Cloud Momentum Continues

The Google Cloud Next event is taking place this week in Las Vegas showcases a strong momentum with AI and Google Cloud innovations with more than 30 000 participants.

Codento is actively participating to the event in Las Vegas with Ulf Sandlund and Markku Pulkkinen and remotely via the entire Codento team. Earlier on Tuesday Codento was awarded as the Google Cloud Service Partner of the Year in Finland.

As the battle is becoming more fierce among the hyperscalers we can fairly observe that Google Cloud has taken a great position going forward:

  • Rapid growth of Google Cloud with a $36 Billion run rate outpacing its hyperscaler peers on a percentage basis
  • Continuous deep investments in AI and Gen AI progress with over a million models trained 
  • 90% of unicorns use Google Cloud showcasing a strong position with startups
  • A lot of reference stories were shared. A broad range of various industries are now using Google Cloud and its AI stack
  • And strong ecosystem momentum globally in all geographies and locally

 

Top 10 Announcements for Google Cloud Customers

Codento consultants followed every second of the first day and picked our favorite top 10 announcements based on the value to Google Cloud customers:

1. Gemini 1.5 Pro available in public preview on Vertex AI. It can now process from 128,000 tokens up to 1 million tokens. Google truly emphasizes its multi-modal capabilities. The battle against other hyperscalers in AI is becoming more fierce.

2. Gemini is being embedded across a broad range of Google Cloud services addressing a variety of use cases and becoming a true differentiator, for example:

  • New BigQuery integrations with Gemini models in Vertex AI support multimodal analytics, vector embeddings, and fine-tuning of LLMs from within BigQuery, applied to your enterprise data.
  • Gemini in Looker enables business users to chat with their enterprise data and generate visualizations and reports

3. Gemini Code Assist is a direct competitor to GitHub’s Copilot Enterprise. Code Assist can also be fine-tuned based on a company’s internal code base which is essential to match Copilot.

4. Imagen 2. Google came out with the enhanced image-generating tool embedded in Vertex AI developer platform with more of a focus on enterprise. Imagen 2 is now generally available.

5. Vertex AI Agent Builder to help companies build AI agents. This makes it possible for customers to very easily and quickly build conversational agents and instruct and guide them the same way that you do humans. To improve the quality and correctness of answers from models,  a process called grounding is used based on Google Search.

6. Gemini in Databases is a collection of AI-powered, developer-focused tools to create, monitor and migrate app databases.

7. Generative AI-powered security: number of new products and features aimed at large companies. These include Threat Intelligence, Chronicle to assist with cybersecurity investigations) and  Security Command Center.

8. Hardware announcements: Nvidia’s next-generation Blackwell platform coming to Google Cloud in early 2025 and Google Cloud joins AWS and Azure in announcing its first custom-built Arm processor, dubbed Axion

9. Run AI anywhere, generative AI search packaged solution powered by Gemma designed to help customers easily retrieve and analyze data at the edge or on-premises with GDC, this solution will be available in preview in Q2 2024.

10. Data sovereignty. Google is renewing its focus on data sovereignty with emphasis on partnerships, less to building its own sovereign clouds.

There were also a lot of new announcements in the domains of employee productivity and Chrome, but we shall leave those areas for later discussion.

Conclusions

So far the list of announcements has been truly remarkable. As we anticipate the coming days of the Next event we are eager to get deeper into the details and understand what all this means in practice.

What is already known convinces us that Google Cloud and its AI approach continues to be completely enterprise-ready providing capabilities to support deployments from pilot to production. 

To make all this real capable partners, like Codento, are needed to assist the entire journey: AI and data strategy, prioritized use cases, building the data foundation, implementing AI projects with strong grounding and integration, consider security and governance, and eventually build MLOps practices to scale the adoption.

For us partners, much anticipated news came in the form of a new specialization: Generative AI specialization will be available in June 2024. Codento is ready for this challenge with the practice and experience already in place.

To follow the Google Cloud Next 2024 event and announcements the best place is Google Cloud blog.

 

Contact us for more information on our services:

 

Smart Operations: Embracing AI for Efficiency and Growth

Smart Operations: Embracing AI for Efficiency and Growth

 

Author: Antti Pohjolainen, Codento

As mentioned in the previous blog post, AI is not just a technological leap; it’s a strategic asset, revolutionizing how businesses function, make decisions, and serve their customers. This also holds true for the domain of operations, where  AI is poised to revolutionize traditional processes, driving efficiency, enhancing productivity, and paving the way for sustainable growth.

 

Unlocking the Potential of AI for Operations

AI’s impact on operations extends across various facets of business, including:

  • Predictive Maintenance: AI algorithms can analyze vast amounts of data, including sensor readings and historical performance records, to predict equipment failures before they occur. This proactive approach minimizes downtime, reduces maintenance costs, and enhances overall asset utilization.
  • Smart Scheduling: AI-powered scheduling solutions can optimize resource allocation and task assignment, ensuring that employees are matched with the right tasks at the right time. This leads to improved productivity, reduced overtime costs, and improved employee satisfaction.
  • Supply Chain Optimization: AI can analyze demand patterns, identify disruptions, and optimize inventory levels, resulting in a more efficient and responsive supply chain. This translates into reduced costs, improved delivery times, and enhanced customer satisfaction.
  • Risk Mitigation: AI can monitor operational data and identify anomalies or patterns that could indicate potential risks. This allows businesses to take preemptive action, avert costly incidents, and protect their assets and reputation.

Codento has been working together with some of the Finnish forefront companies in manufacturing to implement AI in their operations. Take Fastems for example where Codento implemented AI-powered Smart Scheduling and predictive maintenance capabilities. For more information, please see our reference case stories here and here.

 

The Journey Towards Smart Operations

Implementing AI in operations requires a strategic approach that considers the specific needs and challenges of each organization. Key steps include:

  • Identifying Pain Points: The first step is to identify areas where AI can bring the most significant benefits, such as reducing costs, improving efficiency, or enhancing decision-making.
  • Data Preparation: High-quality data is essential for AI to function effectively. This involves cleaning, organizing, and standardizing data to ensure its accuracy and reliability.
  • Model Development and Deployment: AI models are developed using machine learning algorithms that train on the prepared data. These models are then deployed to production environments to automate tasks and provide insights.
  • Continuous Monitoring and Improvement: AI models are not static; they need to be continuously monitored and updated as data and business conditions evolve. This ensures that they remain accurate, relevant, and effective.

 

About the author: Antti  “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2020. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Central & Eastern Europe. Apo has been working in different sales roles longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. Apo received his MBA from the University of Northampton. His final business research study dealt with Multi-Cloud. Apo has frequently lectured about AI in Business at the Haaga-Helia University of Applied Sciences.  

 

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Introduction to AI in Business Blog Series: Unveiling the Future

Introduction to AI in Business Blog Series: Unveiling the Future

Author: Antti Pohjolainen, Codento

 

Foreword

In today’s dynamic business landscape, the integration of Artificial Intelligence (AI) has emerged as a transformative force, reshaping the way industries operate and paving the way for innovation. Companies of all sizes are implementing AI-based solutions.

AI is not just a technological leap; it’s a strategic asset, revolutionizing how businesses function, make decisions, and serve their customers.

In discussions and workshops with our customers, we have identified close to 250 different use cases for a wide range of industries. 

 

Our AI in Business Blog Series

In addition to publishing our AI.cast on-demand video production, we summarize our key learnings and insights in the “AI in Business” blog series.

This blog series will delve into the multifaceted role AI plays in reshaping business operations, customer relations, and overall software intelligence. In the following blog posts, each post has a specific viewpoint concentrating on a business need. Each perspective contains examples and customer references of innovative ways to implement AI.

In the next part – Customer Foresight – we’ll discuss how AI will provide businesses with better customer understanding based on their buying behavior, better use of various customer data, and analyzing customer feedback.

In part three – Smart Operations – we’ll look at examples of benefits customers have gained by implementing AI into their operations, including smart scheduling and supply chain optimization.

In part four – Software Intelligence – we’ll concentrate on using AI in software development.

Implementing AI to solve your business needs could provide better decision-making capabilities, increase operational efficiency, improve customer experiences, and help mitigate risks.

The potential of AI in business is vast, and these blog posts aim to illuminate the path toward leveraging AI for enhanced business growth, efficiency, and customer satisfaction. Join us in unlocking the true potential of AI in the business world.

Stay tuned for our next installment: “Customer Foresight” – Unveiling the Power of Predictive Analytics in Understanding Customer Behavior.!

 

 

About the author: Antti  “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2020. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Central & Eastern Europe. Apo has been working in different sales roles longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. Apo received his MBA from the University of Northampton. His final business research study dealt with Multi-Cloud. Apo has frequently lectured about AI in Business at the Haaga-Helia University of Applied Sciences.  

 

 

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100 Customer Conversations Shaped Our New AI and Apps Service Offering 

100 Customer Conversations Shaped Our New AI and Apps Service Offering 

 

Author: Anthony Gyursanszky, CEO, Codento

 

Foreword

A few months back, in a manufacturing industry event: Codento  just finished our keynote together with Google and our people started mingling among the audience. Our target was to agree on a follow-up discussions about how to utilize Artificial Intelligence (AI) and modern applications for their business.

The outcome of that mingling session was staggering. 50% of the people we talked with wanted to continue the dialogue with us after the event. The hit rate was not 10%, not 15%, but 50%. 

We knew before already that AI will change everything, but with this, our  confidence climbed to another level . Not because we believed in this, but because we realized that so many others did, too.

AI will change the way we serve customers and manufacture things, the way we diagnose and treat illnesses, the way we travel and commute, and the way we learn. AI is everywhere, and not surprisingly, it is also the most common topic that gets executives excited and interested in talking. 

AI does not solve the use cases without application innovations. Applications  integrate the algorithms to an existing operating environment, they provide required user interfaces, and  they handle the orchestration in a more complex setup.

 

We address your industry- and role-specific needs with AI and application innovations 

We at Codento have been working with AI and Apps for several years now. Some years back, we also sharpened our strategy to be the partner of choice in Finland for Google Cloud Platform-based solutions in the AI and applications innovation space. 

During the past six months, we have been on a mission to workshop with as many organizations as possible about their needs and aspirations for AI and Apps. This mission has led us to more than a hundred discussions with dozens and dozens of people from the manufacturing industry to retail and healthcare to public services.

Based on these dialogues, we concluded that it is time for Codento to move from generic technology talks to more specific messages that speak the language of our customers. 

Thus, we are thrilled to introduce our new service portfolio, shaped by those extensive conversations with various organizations’ business, operations, development, and technology experts.

Tailored precisely to address your industry and role-specific requirements, we now promise you more transparent customer foresight, smarter operations, and increased software intelligence – all built on a future-proof, next-generation foundation on Google Cloud. 

These four solution areas will form the pillars of Codento’s future business. Here we go.

 

AI and Apps for Customer Foresight

As we engaged with sales, marketing and customer services officers we learned that the majority is stuck with limited visibility of customer understanding and of the impact their decisions and actions have on their bottom line. AI and Apps can change all this.

For example, with almost three out of four online shoppers expecting brands to understand their unique needs, the time of flying blind on marketing, sales, and customer service is over.

Codento’s Customer Foresight offering is your key to thriving in tomorrow’s markets.  

  • Use data and Google’s innovative tech, trained on the world’s most enormous public datasets, to find the right opportunities, spot customers’ needs, discover new markets, and boost sales with more intelligent marketing. 
  • Exceed your customers’ expectations by elevating your retention game with great experiences based on new technology. Keep customers returning by foreseeing their desires and giving them what they want when and how they want it – even before they realize their needs themselves. 
  • Optimize Your Profits with precise data-driven decisions based on discovering your customers’ value with Google’s ready templates for calculating Customer Lifetime Value. With that, you can focus on the best customers, make products that sell, and set prices that work. 

 

AI and Apps for Smart Operations 

BCG has stated that 89% of industrial companies plan to implement AI in their production networks. As we have been discussing with the operations, logistics and supply chain directors, we have seen this to be true – the appetite is there.

Our renewed Smart Operations offering is your path to operational excellence and increased resilience. You should not leave this potential untapped in your organization. 

  • By smart scheduling your operations, we will help streamline your factory, logistics, projects, and supply chain operations. With the help of Google’s extensive AI tools for manufacturing and logistics operations, you can deliver on time, within budget, and with superior efficiency. 
  • Minimize risks related to disruptions, protect your reputation, and save resources, thereby boosting employee and customer satisfaction while cutting costs.  
  • Stay one step ahead with the power of AI, transparent data, and analytics. Smart Operations keeps you in the know, enabling you to foresee and tackle disruptions before they even happen. 

 

AI and Apps for Software Intelligence 

For the product development executives of software companies, Codento offers tools and resources for unleashing innovation. The time to start benefiting from AI in software development is now. 

Gartner predicts that 15% of new applications will be automatically generated by AI in the year 2027 – that is, without any interaction with a human. As a whopping 70% of the world’s generative AI startups already rely on Google Cloud’s AI capabilities, we want to help your development organization do the same. 

  • Codento’s support for building an AI-driven software strategy will help you confidently chart your journey. You can rely on Google’s strong product vision and our expertise in harnessing the platform’s AI potential. 
  • Supercharge your software development and accelerate your market entry with cutting-edge AI-powered development tools. With Codento’s experts, your teams can embrace state-of-the-art DevOps capabilities and Google’s cloud-native application architecture. 
  • When your resources fall short, you can scale efficiently by complementing your development capacity with our AI and app experts. Whether it’s Minimum Viable Products, rapid scaling, or continuous operations, we’ve got your back. 

 

Nextgen Foundation to enable AI and Apps

While the business teams are moving ahead with AI and App  initiatives related to Customer Foresight, Smart Operations, and Software Intelligence   IT functions are often bound to legacy IT and data  architectures and application portfolios. This creates pressure for the IT departments to keep up with the pace.

All the above-mentioned comes down to having the proper foundation to build on, i.e., preparing your business for the innovations that AI and application technologies can bring. Moving to a modern cloud platform will allow you to harness the potential of AI and modern applications, but it is also a cost-cutting endeavor.BCG has studied companies that are forerunners in digital and concluded that they can save up to 30% on their IT costs when moving applications and infrastructure to the cloud. 

  • Future-proof your architecture and operations with Google’s secure, compliant, and cost-efficient cloud platform that will scale to whatever comes next. Whether you choose a single cloud strategy or embrace multi-cloud environments, Codento has got you covered. 
  • You can unleash the power and amplify the value of your data through real-time availability, sustainable management, and AI readiness. With Machine Learning Ops (MLOps), we streamline your organization’s scaling of AI usage. 
  • We can also help modernize your dated application portfolio with cloud-native applications designed for scale, elasticity, resiliency, and flexibility. 

 

Sharpened messages wing Codento’s entry to the Nordic market 

With these four solution areas, we aim to discover the solutions to your business challenges quickly and efficiently. We break the barriers between business and technology with our offerings that speak the language of the target person. We are dedicated to consistently delivering solutions that meet your needs and learn and become even more efficient over time.  

Simultaneously, we eagerly plan to launch Codento’s services and solutions to the Nordic market. Our goal is to guarantee that our customers across the Nordics can seize the endless benefits of Google’s cutting-edge AI and application technologies without missing a beat.

About the author:

Anthony Gyursanszky, CEO, joined Codento in late 2019 with more than 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. Hehas also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. His experience covers business management, product management, product development, software business, SaaS business, process management, and software development outsourcing. Anthony is also a certified Cloud Digital Leader.

 

Contact us for more information on our services:

 

AI in Manufacturing: AI Visual Quality Control

AI in Manufacturing: AI Visual Quality Control

 

Author: Janne Flinck

 

Introduction

Inspired by the Smart Industry event, we decided to start a series of blog posts that tackle some of the issues in manufacturing with AI. In this first section, we will talk about automating quality control with vision AI.

Manufacturing companies, as well as companies in other industries like logistics, prioritize the effectiveness and efficiency of their quality control processes. In recent years, computer vision-based automation has emerged as a highly efficient solution for reducing quality costs and defect rates. 

The American Society of Quality estimates that most manufacturers spend the equivalent of 15% to 20% of revenues on “true quality-related costs.” Some organizations go as high as 40% cost-of-quality in their operations. Cost centers that affect quality in manufacturing come in three different areas:

  • Appraisal costs: Verification of material and processes, quality audits of the entire system, supplier ratings
  • Internal failure costs: Waste of resources or errors from poor planning or organization, correction of errors on finished products, failure of analysis regarding internal procedures
  • External failure costs: Repairs and servicing of delivered products, warranty claims, complaints, returns

Artificial intelligence is helping manufacturers improve in all these areas, which is why leading enterprises have been embracing it. According to a 2021 survey of more than 1,000 manufacturing executives across seven countries interviewed by Google Cloud, 39% of manufacturers are using AI for quality inspection, while 35% are using it for quality checks on the production line itself.

Top 5 areas where AI is currently deployed in day-to-day operations:

  • Quality inspection 39%
  • Supply chain management 36%
  • Risk management 36%
  • Product and/or production line quality checks 35%
  • Inventory management 34%

Source: Google Cloud Manufacturing Report

With the assistance of vision AI, production line workers are able to reduce the amount of time spent on repetitive product inspections, allowing them to shift their attention towards more intricate tasks, such as conducting root cause analysis. 

Modern computer vision models and frameworks offer versatility and cost-effectiveness, with specialized cloud-native services for model training and edge deployment further reducing implementation complexities.

 

Solution overview

In this blog post, we focus on the challenge of defect detection on assembly and sorting lines. The real-time visual quality control solution, implemented using Google Clouds Vertex AI and AutoML services, can track multiple objects and evaluate the probability of defects or damages.

The first stage involves preparing the video stream by splitting the stream into frames for analysis. The next stage utilizes a model to identify bounding boxes around objects.

Once the object is identified, the defect detection system processes the frame by cutting out the object using the bounding box, resizing it, and sending it to a defect detection model for classification. The output is a frame where the object is detected with bounding boxes and classified as either a defect or not a defect. The quick processing time enables real-time monitoring using the model’s output, automating the defect detection process and enhancing overall efficiency.

The core solution architecture on Google Cloud is as follows:

Implementation details

In this section I will touch upon some of the parts of the system, mainly what it takes to get started and what things to consider. The dataset is self created from objects I found at home, but this very same approach and algorithm can be used on any objects as long as the video quality is good.

Here is an example frame from the video, where we can see one defective object and three non-defective objects: 

We can also see that one of the objects is leaving the frame on the right side and another one is entering the frame from the left. 

The video can be found here.

 

Datasets and models overview

In our experiment, we used a video that simulates a conveyor belt scenario. The video showed objects moving from the left side of the screen to the right, some of which were defective or damaged. Our training dataset consists of approximately 20 different objects, with four of them being defective.

For visual quality control, we need to utilize an object detection model and an image classification model. There are three options to build the object detection model:

  1. Train a model powered by Google Vertex AI AutoML
  2. Use the prebuilt Google Cloud Vision API
  3. Train a custom model

For this prototype we decided to opt for both options 1 and 2. To train a Vertex AI AutoML model, we need an annotated dataset with bounding box coordinates. Due to the relatively small size of our dataset, we chose to use Google Clouds data annotation tool. However, for larger datasets, we recommend using Vertex AI data labeling jobs.

For this task, we manually drew bounding boxes for each object in the frames and annotated the objects. In total, we used 50 frames for training our object detection model, which is a very modest amount.

Machine learning models usually require a larger number of samples for training. However, for the purpose of this blog post, the quantity of samples was sufficient to evaluate the suitability of the cloud service for defect detection. In general, the more labeled data you can bring to the training process, the better your model will be. Another obvious critical requirement for the dataset is to have representative examples of both defects and regular instances.

The subsequent stages in creating the AutoML object detection and AutoML defect detection datasets involved partitioning the data into training, validation, and test subsets. By default, Vertex AI automatically distributes 80% of the images for training, 10% for validation, and 10% for testing. We used manual splitting to avoid data leakage. Specifically, we avoid having sets of sequential frames.

The process for creating the AutoML dataset and model is as follows:

As for using the out-of-the-box Google Cloud Vision API for object detection, there is no dataset annotation requirement. One just uses the client libraries to call the API and process the response, which consists of normalized bounding boxes and object names. From these object names we then filter for the ones that we are looking for. The process for Vision API is as follows:

Why would one train a custom model if using Google Cloud Vision API is this simple? For starters, the Vision API will detect generic objects, so if there is something very specific, it might not be in the labels list. Unfortunately, it looks like the complete list of labels detected by Google Cloud Vision API is not publicly available. One should try the Google Cloud Vision API and see if it is able to detect the objects of interest.

According to Vertex AI’s documentation, AutoML models perform optimally when the label with the lowest number of examples has at least 10% of the examples as the label with the highest number of examples. In a production case, it is important to capture roughly similar amounts of training examples for each category.

Even if you have an abundance of data for one label, it is best to have an equal distribution for each label. As our primary aim was to construct a prototype using a limited dataset, rather than enhancing model accuracy, we did not tackle the problem of imbalanced classes. 

 

Object tracking

We developed an object tracking algorithm, based on the OpenCV library, to address the specific challenges of our video scenario. The specific trackers we tested were CSRT, KCF and MOSSE. The following rules of thumb apply in our scenario as well:

  • Use CSRT when you need higher object tracking accuracy and can tolerate slower FPS throughput
  • Use KCF when you need faster FPS throughput but can handle slightly lower object tracking accuracy
  • Use MOSSE when you need pure speed

For object tracking we need to take into account the following characteristics of the video:

  • Each frame may contain one or multiple objects, or none at all
  • New objects may appear during the video and old objects disappear
  • Objects may only be partially visible when they enter or exit the frame
  • There may be overlapping bounding boxes for the same object
  • The same object will be in the video for multiple successive frames

To speed up the entire process, we only send each fully visible object to the defect detection model twice. We then average the probability output of the model and assign the label to that object permanently. This way we can save both computation time and money by not calling the model endpoint needlessly for the same object multiple times throughout the video.

 

Conclusion

Here is the result output video stream and an extracted frame from the quality control process. Blue means that the object has been detected but has not yet been classified because the object is not fully visible in the frame. Green means no defect detected and red is a defect:

The video can be found here.

These findings demonstrate that it is possible to develop an automated visual quality control pipeline with a minimal number of samples. In a real-world scenario, we would have access to much longer video streams and the ability to iteratively expand the dataset to enhance the model until it meets the desired quality standards.

Despite these limitations, thanks to Vertex AI, we were able to achieve reasonable quality in just the first training run, which took only a few hours, even with a small dataset. This highlights the efficiency and effectiveness of our approach of utilizing pretrained models and AutoML solutions, as we were able to achieve promising results in a very short time frame.

 

 

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.

 

 

Please contact us for more information on how to utilize artificial intelligence in industrial solutions.

 

Leading through Digital Turmoil

Leading through Digital Turmoil

Author: Anthony Gyursanszky, CEO, Codento

 

Foreword

Few decades back during my early university years I bacame familiar with Pascal coding and Michael Porter’s competitive strategy. “Select telecommunication courses next – it is the future”,  I was told. So I did, and the telecommunications disruption indeed accelerated my first career years.

The telecom disruption layed up the foundation for an even greater change we are now facing enabled by cloud capabilities, data technologoes, artificial intelligence and modern software. We see companies not only selecting between Porter’s lowest cost, differentation, or focus strategies, but with the help of digital disruption, the leaders utilize them all simultaneously.

Here at Codento we are in a mission to help various organization to succeed through digital turmoil, understand their current capabilities, envision their future business and technical environment, craft the most rational steps of transformation towards digital leadership, and support them throughout this process with advise and capability acceleration. In this process, we work closely with leading cloud technology enablers, like Google Cloud.

In this article, I will open up the journey towards digital leadership based on our experiences and available global studies.

 

What we mean by digital transformation now?

Blair Franklin, Contributing Writer, Google Cloud recently published a blogpost

Why the meaning of “digital transformation” is evolving. Google interviewed more than 2,100 global tech and business leaders around the question: “What does digital transformation mean to you?”

Five years ago the dominant view was “lift-and-shift” your IT infrastructure to the public cloud. Most organizations have now proceedded with this, mostly to seek for cost saving, but very little transformative business value has been visible to their own customers.

Today, the meaning of “digital transformation “has expanded according to Google Cloud survey. 72% consider it as much more than “lift-and-shift”. The survey claims that there are now two new attributes:

  1. Optimizing processes and becoming more operationally agile (47%). This in my opinion,  provides a foundation for both cost and differentiation strategy.
  2. Improving customer experience through technology (40%). This, in my opinion, boosts both focus and differentiation strategy.

In conclusion, we have now moved from “lift-and-shift” era to a “digital leader” era.

 

Why would one consider becoming a digital leader?

Boston Consulting Group and Google Cloud explored the benefits of putting effort on becoming “a digital leader” in Keys of Scaling Digital Value 2022 study. According to the study, about 30% of organizations were categorized as digital leaders. 

And what is truly interesting, digital leaders tend to outperform their peers: They bring 2x more solutions to scale and with scaling they deliver significantly better financial results (3x higher returns on investments, 15-20% faster revenue growth and simlar size of cost savings)

The study points out several characteristics of a digital leader, but one with the highest correlation is related how they utilize software in the cloud:  digital leaders deploy cloud-native solutions (64% vs. 3% of laggers) with modern modular architecture (94% vs. 21% laggers).

Cloud native means a concept of building and running applications to take advantage of the distributed computing offered by the cloud. Cloud native applications, on the other hand, are designed to utilize the scale, elasticity, resiliency, and flexibility of the cloud.

The opposite to this are legacy applications which have been designed to on-premises environments, bound to certain technologies, integrations, and even specific operating system and database versions.

 

How to to become a digital leader?

First, It is obvious that the journey towards digital leadership requires strong vision, determination, and investments as there are two essential reasons why the progress might be stalled:

  • According to a Mckinsey survey a lack of strategic clarity cause transformations to lose momentum or stall at the pilot stage.
  • Boston Consulting Group research found that only 40% of all companies manage to create an integrated transformation strategy. 

Second, Boston Consulting Group and Google Cloud “Keys of Scaling Digital Value 2022” study further pinpoints a more novel approach for digital leadership as a prerequisite for success. The study shows that the digital leaders:

  • Are organized around product-led platform teams (83% leaders vs. 25% laggers)
  • Staff cross-functional lighthouse teams (88% leaders vs. 23% laggers)
  • Establish a digital “control tower” (59% leaders vs. 4% laggers)

Third, as observed by us also here at Codento, most companies have structured their organizations and defined roles and process during the initial IT era into silos as they initially started to automate their manual processes with IT technologies  and applications. They added IT organizations next to their existing functions while keeping business and R&D functions separate.

All these three key functions have had their own mostly independent views of data, applications and cloud adoption, but while cloud enables and also requires seemless utilization of these capabilities ”as one”, companies need to rethink the way they organize themselves in a cloud-native way.

Without legacy investments this would obviously be a much easier process as “digital native” organizations, like Spotify, have showcased. Digital natives tend to design their operations ”free of silos” around cloud native application development and utilizing advanced cloud capabilities like unified data storage, processing and artificial intelligence.

Digital native organizations are flatter, nimbler, and roles are more flexible with broader accountability ss suggested by DevOps and Site Reliability Engineering models. Quite remarkable results follow successful adoption. DORA’s, 2021 Accelerate: State of DevOps Report reveals that peak performers in this area are 1.8 times more likely to report better business outcomes.

 

Yes, I want to jump to a digital leadr train. How to get started?

In summary, digital leaders are more successful than their peers and it is difficult to argument not to join that movement.

Digital leaders do not only consider digital transformation as an infrastructure cloudification initiative, but seek competitive egde by optimizing processes and improving customer experience. To become a digital leader requires a clear vision, support by top management and new structures enabled by cloud native applications accelerated by integrated data and artificial intelligence. 

We here at Codento are specialized in enabling our customers to become digital leaders with a three-phase-value discovery approach to crystallize your:

  1. Why? Assess where you are ar the moment and what is needed to flourish in the future business environment.
  2. What? Choose your strategic elements and target capabilities in order to succeed.
  3. How? Build and implement your transformation and execution journeys based on previous phases.

We help our clients not only throughout the entire thinking and implementation process, but also with specific improvement initiatives as needed.

To get more practical perspective on this you may want to visit our live digital leader showcase library:

You can also subscribe to our newsletters, join upcoming online-events and watch our event recordings. 

 

About the author: Anthony Gyursanszky, CEO, joined Codento in late 2019 with more than 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. Gyursanszky has also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. Anthony’s experience covers business management, product management, product development, software business, SaaS business, process management and software development outsourcing. Anthony is also a certified Cloud Digital Leader.

 

Contact us for more information on our  Value Discovery services.

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