In the publication “Intelligent Agents in AI Really Can Work Alone. Here’s How.” by Gartner, the authors predict that by 2028, 33% of business applications will use agentic artificial intelligence, compared to less than 1% in 2024. This development will enable autonomous decision-making for up to 15% of daily operational tasks. Is your company prepared to take advantage of these changes?

Technological readiness is a broad concept that encompasses IT infrastructure, team capabilities, the maturity of business processes, organizational readiness for change, and compliance with legal and security policies. Companies planning to implement ERP systems, identity and access management (IAM) tools, or AI-based solutions need a strong technical and operational foundation to succeed.

How to Assess an Enterprise’s Technological Readiness?

To accurately determine an enterprise’s technological readiness, it’s worth conducting an assessment across several key areas. This process is similar to an audit, helping to answer a crucial question: is the company prepared to successfully implement and make use of a new technology?

IT Infrastructure

The assessment of IT infrastructure should begin with a clear definition of the requirements of the new technology. These requirements determine which resources will be necessary — in terms of performance, architecture, security, and availability.

Only once you have a clear technological specification can you reliably assess whether your current IT environment is capable of meeting those requirements. If you’re considering introducing AI-based tools into your organization and want to understand the exact costs associated with deploying and maintaining a large language model, be sure to check out our latest analysis.

LLM Implementation and Maintenance Costs for Businesses: A Detailed Breakdown

Managing and Accessing Data

The assessment of data management should begin by identifying what data will be used by the new technology, in what format, how frequently, and from which sources. It’s the system’s requirements that define what data is needed and in what form it must be available.

 

Only based on this can you determine whether the data within the organization is ready for use. It’s essential to verify whether there are technical means for retrieving data from sources (e.g., APIs, data exchange files), whether the data has a consistent structure, and whether it meets minimum quality standards. Inconsistencies, duplicates, incomplete records, or unstructured data may require an additional processing stage before the data can be effectively used.

Organizational accessibility is equally important — data must be available not only technically, but also in accordance with internal policies and regulations. For sensitive data, it’s crucial to ensure that access complies with security policies and legal frameworks such as the GDPR.

 

If the planned technology involves integration of multiple sources, real-time analysis, or processing of large volumes of data, it may be necessary to prepare a dedicated integration layer or reorganize the company’s data management approach.

Team Preparation and Training

Assessing team readiness should start with an analysis of the competencies required to operate the new technology. Depending on the solution, this may involve both technical skills (e.g., system configuration, data analysis) and operational knowledge (e.g., understanding business processes, interpreting system outputs).

Only once roles and responsibilities in the new environment are clearly defined can you assess whether the team has the necessary qualifications or if additional training is needed. This might involve upskilling through training sessions, involving external experts, or securing temporary support from the technology provider.

Special attention should be given to those responsible for maintaining and developing the system — they need early access to information about the architecture, data model, failure scenarios, and access controls. Without this, the new technology risks becoming a “black box,” increasing the likelihood of operational errors and making future improvements more difficult.

 

Team preparation should not be a one-time effort. It’s important to plan for post-implementation activities such as mentoring, internal documentation, and continuous development of skills in areas supported by the new technology.

Measuring Business Readiness

Assessing technological readiness should be treated as a process. The most effective approach is to create a roadmap of preparatory actions and then measure progress based on clearly defined stages and evaluation criteria.

The roadmap should include key areas such as IT infrastructure, data availability, team readiness, system integration, change management, and regulatory compliance. For each of these areas, it’s important to define the target requirements as well as assess the current state. This approach not only helps estimate the overall level of readiness but also identifies specific obstacles and weak points that may hinder the implementation process.

Step-by-step evaluation — based on the schedule and tasks assigned to specific teams — allows for ongoing verification of whether the company is moving toward operational readiness. This approach helps minimize the risk of unexpected delays and costs, as potential issues can be detected early, before entering the actual implementation phase.

Technological readiness is not a single end result. It’s the sum of many elements — technical, organizational, and competency-related — all of which should be assessed in the context of the specific implementation and its requirements.

How We Work

When working with clients on the implementation of new applications and systems, we always begin by discussing the business context and the organization’s actual needs. We don’t recommend off-the-shelf solutions without prior analysis — instead, we help identify which technologies have the potential to truly improve processes, and which may only lead to unnecessary costs and complications.

Drawing on our experience from IT projects across various industries, we provide step-by-step guidance — from the planning stage, through readiness assessment, to proper implementation and stabilization. We make sure the technology fits the organization’s capabilities and genuinely supports its operational development, rather than becoming an additional burden.

A well-planned implementation doesn’t end with launching an application — it ends with achieving the intended business outcomes.

Artykuł Is Your Company Ready for New Technology? How to Evaluate Technological Readiness pochodzi z serwisu Inero Software - Software Consulting.

Artykuł What are AI Agents and how can they help your company pochodzi z serwisu Inero Software - Software Consulting.

The interconnected nature of today’s economy continuously brings disruptions to traditional business models. For this reason it’s wise to keep an eye on tech development. After all, it’s technology that was named the main determinant of humanity’s future welfare.

In the previous entry we looked at the issue of digital transformation and its defining aspect – the use of computer modeling to enhance our business operations. The more and better data available, the more precisely we can model something, which in turn allows us to predict the way things will change or behave in future.

Over the past decade machine learning and artificial intelligence developments as well as real time access to IoT sensors have raised computer modeling to the next level, allowing us to model not only theoretically, but iteratively. By using the talents of data science on information flowing from the sensors, we can create what’s known as digital twins – real-time replicas of what exist in reality. 

A digital twin is a dynamic software model of physical assets, processes, people, places, systems, or devices, which uses physics data on how the components of the entity operate, respond to and interact with the environment. Data, bridging the real and digital world, is transferred flawlessly, allowing the virtual model to exist simultaneously with the physical entity. Thanks to the fast expanding body of data, such models can be continuously updated, allowing for more extended analyses.

Next Generation Computer Modeling

Digital twins’ usage varies from analyses and simulation of real world conditions, responding to changes, efficiency assessment, design, to maintenance, and many other applications, and there’s potential for literally billions of things to be represented by their digital twins. As a result of this, the use of digital twins is increasingly becoming a business imperative in many industries, as in case of the automotive industry, where they allow to design a product, simulate it and be able to test and validate its capabilities before the physical prototype of the product is built. All this to enable efficiency increases in manufacturing process and daily operations. At this point, the technology behind digital twins has expanded to include buildings, aircraft engines, container ships, and cities. One of the more ambitious plans with the use of this technology is The UK government’s plan to create a digital twin of its entire national infrastructure to prepare for future challenges.

Deliver-M8

One of the projects where we have implemented the digital twin concept is a delivery optimization system, designed to determine the most cost-effective routes, named Deliver-M8. It’s a software intended for fleet operations optimization in two structurally analogous sub-areas of the TSL sector: first and last mile delivery (FMD & LMD).

A demo version showing how the system works is available as a public site: deliverM8.com, where you can register and enter information on your organization’s logistics assets, such as: vehicle fleet, drivers and their working times, type of cargo, number of warehouses and their capacity, as well as customers’ availability, maximum time delay, and other variables, key for your operations. At your request the system will generate the most optimal delivery routes along with time schedules for your drivers within the time limits you set for it. Thanks to our API this information can be easily integrated with your other systems.

Say you are a dispatcher at a parcel warehouse with packages of different sizes and weights addressed to various locations in the city. You have a number of drivers working different shifts, and a number of vehicles at your disposal, plus a lot of places to visit. This and other input data is processed by the system, which offers the possibility of defining such constraints as the time window each recipient is available for delivery, the maximum delay time, and many other. Deliver-M8 assigns appropriate trucks to the drivers and plans the loading of packages in each car in the correct order. By clicking “Calculate plan” you can generate an optimal route for each driver.

Dynamic Balancing

Routes for individual trucks are divided on the map by color, you also see the drivers’ schedules broken down by hours. You can send the plan to each driver via email or a text message with a link. There’s a smartphone application that the driver can install as well, which asks them for their location, and shows them on the real-time web dashboard for dispatchers as a truck icon. The location is refreshed while driving, allowing you, the dispatcher, to see where the cars are at all times.

Our proprietary version of optimization algorithms implemented in Deliver-M8 allowed us to offer a very unique feature, i.e. dynamic balancing, which enables decisions to be made in real time in response to a logistics crisis. Upon clicking on the link they received from the dispatcher, the driver sees all their delivery addresses in a chronological order. At any point during the workday they are able to send feedback to the system using the application, noting visited addresses and unavailable customers, or postponement in the delivery. 

Deliver-M8 allows you to compare the feedback data with the original plan and detect if the registered disruptions threaten to change its outcome in real time. You can modify the plan at any point, including while the drivers are on the road. If you decide to change the original plan, the system generates a new one, taking into account real-time traffic information for which it queries external websites, as well as sends a new schedule to the drivers.

The above are examples of a broad range of Deliver-M8’s features, which go beyond the basics of route planning. Deliver-M8 is available for the FMD/LMD sector in a Software as a Service (SaaS) model, allowing for tests at an inexpensive cost and implementation either as a SaaS software with minor modifications, or as an in-house solution. To find out more about Deliver-M8 you can contact us at hi@inero-software.com

Advanced Developments Possible – Digital Twins

The digital twin concept is one of many new technologies changing the current business landscape. Applied to route optimization and paired with high-end machine learning algorithms it transfers into a powerful logistical tool, having potential to save your business time and money. As mentioned before, it is received with open arms far and wide – from the medical and insurance to security industries. As the concept is inseparably linked with IoT, endless influx of data will ensure its further deployment, and continuous advance of artificial intelligence and machine learning will safeguard more and more diverse appliances of digital twins in future.

Inero Software provides knowledge and expertise on how to successfully use cutting edge technologies and data to shape corporate digital products of the future.

In the blog post section you will find other articles about IT systems and more!

keywords: #digitaltransformation #IoT #data #datadrivendecisions #ProcessThinking #BusinessProcessModeling #BusinessProcessesOptimization #machinelearning #artificialintelligence #digitaltwins

Artykuł Digital Twins – Dynamic Models of Reality pochodzi z serwisu Inero Software - Software Consulting.

Novelty keeps knocking on our door

We’ve talked a lot about the changing environment in which we operate nowadays – either as individuals, enterprises or society as a whole. Certain voguish buzzwords, such as the Fourth Industrial Revolution, Industry 4.0 (its subset), or digital transformation can serve as useful labels to name some of the processes powering the onset of a new era.

And while it’s less clear than when we switched from steam to electricity or from analogue electronic technology to digital electronics, the irreversible shift from simple digitization to innovation, based on combination of technologies (IoT, AI, robotics, augmented reality, 3-D printing, etc.), is forcing companies to reevaluate the way they do business.

Behind the lingo

‘Fourth Industrial Revolution’ is a term first introduced in 2015, which encompasses the age of technologies that combine hardware, software, and biology (cyber-physical systems), highly advanced in the spheres of communication and connectivity. The current time is being marked by breakthroughs in emerging technologies in fields such as robotics, artificial intelligence, nanotechnology, decentralized consensus, quantum computing, biotechnology, the Internet of Things (IoT), fifth-generation wireless technologies (5G), 3D printing and fully autonomous vehicles.

‘Digital transformation’ on the other hand covers the fast-paced and profound metamorphosis of business and organizational processes, capabilities and models, designed to enable growth with a mix of digital technologies. Although chiefly used in a business context, digital transformation also impacts governments, public sector agencies and various organizations which are involved in tackling societal challenges.

Who thrives in the current era?

Among the new desired competencies to attain with the use of the new tools are the capacities to be more agile, efficient, people-oriented, well-organized and able to change the status quo for the purpose of gaining new information and service-driven revenues. Some of the new digital solutions enable also new types of innovation and creativity, and these companies which strive towards continuous optimization across processes can truly have the chance to become leaders in the digital solutions realm.

Although already underway, digital transformation is not proceeding at the same pace everywhere. European consumers are heavily invested in digital technologies, however, the EU businesses have a long way to go to fully explore the potential of digitization. As European Commission’s 2019 Digital Economy and Society Index (DESI) shows, Finland, Sweden, the Netherlands and Denmark have the most advanced digital economies in the EU, followed by the UK, Luxembourg, Ireland and Estonia. On the opposite pole are Bulgaria, Romania, Greece and Poland, who score the lowest on DESI.

In a ‘Business digitization’ category – encompassing electronic information sharing, use of social media, big data analysis and cloud solutions, as well as e-Commerce – the highest level was reached by Ireland, followed by the Netherlands, Belgium and Denmark. When it comes to e-business technologies, the leading countries are again the Netherlands, Finland (forerunner in the use of cloud solutions) and Belgium (the most advanced in electronic information sharing).

As regards governments, which can make a very productive use of digitization by lifting the administrative burden off of citizens and firms – non-other state than Estonia has one of the most advanced e-governments in the world, with 99% of public services available online and 96% of people filing their tax returns electronically.

Digital business transformation areas

Among the domains predisposed to be subjected to digital transformation are business ecosystems, models, and processes, business asset management, organizational culture, partnership models, customer as well as worker and partner approaches.

Let us take a closer look at the issue of business processes and the question of how we can model them. Each business process is a collection of related, structured activities that happen within an organization or company, aiming to bring about a specific, desired effect. A business process is, for example, issuing of invoices. Namely, all the activities that run in a company from the moment a need to issue an invoice arises. This supposedly trivial example is in reality a quite lengthy business process that goes through several departments in the company until the entire process of a sale of a service or a product is finished, so a proof of purchase can be issued.

Of course, there is a plentitude of processes like this. They take place not only in companies but also in institutions, foundations and sole proprietorships. As a result of their complexity and distinctiveness, a management branch encompassing business process management and systems engineering was created, called: Business Process Modeling (BPM). BPM is the action of representing processes of an enterprise in order to analyze, improve, and automate them. The simulation is commonly performed by experts comprising business analysts, who provide expertise in the modeling discipline and subject matter professionals, who have specialized knowledge of the processes being modelled.

Approvals – flowchart of a business process; source: Wikipedia

Valuation of the benefits to digital transformation

The act of describing all stages of a particular business operation in the form of a model allows us to simulate how a business process progresses and capture the space for automation. It may expose some previously unidentified steps as suboptimal or unnecessary, in case unassociated people from different departments, responsible for only slices of the process, have not realized they were duplicating the work of their colleagues.

The traditional modeling tools were introduced to illustrate time and cost, while modern tools focus on improving multidimensional, cross-functional activities. New methodologies, aimed at streamlining commercial operations, include business process redesign, business process innovation, business process management, or integrated business planning, to name a few.

Business process modeling is primarily intended to enable attaining higher quality with lowered operational costs and less downtime, as well as to elevate customer satisfaction, but it also serves as documentation and formalization of the workflow.

BPMN Process that has two decisions in the process flow; source: Wikipedia

Description and simulation of business processes

As more and more IT systems were being developed and implemented in ever larger companies, every so often understanding of business processes would become difficult. The reason behind it is a continual specialization of various industry segments, which makes it difficult for the programmers who automate business processes to understand them in their entirety. One of Inero Software’s cases was the task of automating the process of settling water and rain fees in the port of Gdynia. In order to program and improve this business process it required us to understand the specificity of the business that runs outside of our area.

An activity that aims to present business processes in such a way that they are understandable for both IT specialists and those who carry out these processes (non-IT specialists), is called Business Process Model and Notation (BPMN) – a standardized way of recording business processes. The motor behind its creation was the aforementioned intercommunication challenge. BPMN allows you to not only describe particular business processes, but also model them after they have already undergone a digital transformation. It is supported by over 70 various tools, among others iGrafx, ADONIS, Borland, DYSANT and IBM tools.

Even though describing business processes with BPMN is more tricky to execute at large companies, it occurs there more often, as the gains are much more tangible. In a large company, divided on various departments, where daily interactions with people who often do not know each other or are located in different buildings or cities are limited, deploying BPMN to business processes not only grants us a view of all their stages, but also enables us to pick up substandard actions, making a huge difference in running smooth operations.

There’s no end to innovation

Even though BPMN represents just a fraction of the methodologies used over the years to document business processes, it’s certainly a good step into the digital age. More than just a buzzword, it’s a crucial element of ongoing technological evolution and innovation with a clear purpose, namely to allow us to enrich the overall culture of the professional activities we execute.

Inero Software provides knowledge and expertise on how to successfully use cutting edge technologies and data to shape corporate digital products of the future.

In the blog post section you will find other articles about IT systems and more!

keywords:

#DigitalTransformation, #FourthIndustrialRevolution, #Industry4.0 #BusinessProcess #BusinessProcessModeling #BusinessProcessesOptimization, #BPM, #BPMN, #ProcessThinking

Artykuł A Holistic Digital Transformation – the Key to Business Optimization pochodzi z serwisu Inero Software - Software Consulting.