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.