Software engineering tackles designing, testing, and maintaining software (programs). This branch involves many technologies and tools that assist in the process of creating programs for many different niches.

Here, we’ll provide an answer to the “What is software engineering?” question. We’ll also explain the key concepts related to it, the skills required to become a software engineer, and introduce you to career opportunities.

Basics of Software Engineering

History and Evolution of Software Engineering

Before digging into the nitty-gritty behind software engineering, let’s have a (very short) history lesson.

We can say that software engineering is relatively young compared to many other industries: it was “born” in 1963. Margaret Hamilton, an American computer scientist, was working on the software for the Apollo spacecraft. It was she who coined the term “software engineer” to describe her work at the time.

Two NATO software engineering conferences took place a few years later, confirming the industry’s significance and allowing it to find its place under the computer-science sun.

During the 1980s, software engineering was widely recognized in many countries and by various experts. Since then, the field has advanced immensely thanks to technological developments. It’s used in many spheres and offers a wide array of benefits.

Different Types of Software

What software does software engineering really tackle? You won’t be wrong if you say all software. But learning about the actual types can’t hurt:

  • System software – This software powers a computer system. It gives life to computer hardware and represents the “breeding ground” for applications. The most basic example of system software is an operating system like Windows or Linux.
  • Application software – This is what you use to listen to music, create a document, edit a photo, watch a movie, or perform any other action on your computer.
  • Embedded software – This is specialized software found in an embedded device that controls its specific functions.

Software Development Life Cycle (SDLC)

What does the life of software look like? Let’s analyze the key stages.

Planning and Analysis

During this stage, experts analyze the market, clients’ needs, customers’ input, and other factors. Then, they compile this information to plan the software’s development and measure its feasibility. This is also the time when experts identify potential risks and brainstorm solutions.

Design

Now it’s time to create a design plan, i.e., design specification. This plan will go to stakeholders, who will review it and offer feedback. Although it may seem trivial, this stage is crucial to ensure everyone’s on the same page. If that’s not the case, the whole project could collapse in the blink of an eye.

Implementation

After everyone gives the green light, software engineers start developing the software. This stage is called “implementation” and it’s the longest part of the life cycle. Engineers can make the process more efficient by dividing it into smaller, more “digestible” chunks.

Testing

Before the software reaches its customers, you need to ensure it’s working properly, hence the testing stage. Here, testers check the software for errors, bugs, and issues. This can also be a great learning stage for inexperienced testers, who can observe the process and pick up on the most common issues.

Deployment

The deployment stage involves launching the software on the market. Before doing that, engineers will once again check with stakeholders to see if everything’s good to go. They may make some last-minute changes depending on the provided feedback.

Maintenance

Just because software is on the market doesn’t mean it can be neglected. Every software requires some degree of care. If not maintained regularly, the software can malfunction and cause various issues. Besides maintenance, engineers ensure the software is updated. Since the market is evolving rapidly, it’s necessary to introduce new features to the software to ensure it fulfills the customers’ needs.

Key Concepts in Software Engineering

Those new to the software engineering world often feel overwhelmed by the number of concepts thrown at them. But this can also happen to seasoned engineers who are switching jobs and/or industries. Whatever your situation, here are the basic concepts you should acquire.

Requirements Engineering

Requirements engineering is the basis for developing software. It deals with listening and understanding the customers’ needs, putting them on paper, and defining them. These needs are turned into clearly organized requirements for efficient software development.

Software Design Principles

Modularity

Software engineers break down the software into sections (modules) to make the process easier, quicker, more detailed, and independent.

Abstraction

Most software users don’t want to see the boring details about the software they’re using. Being the computer wizards they are, software engineers wave their magic wand to hide the more “abstract” information about the software and highlight other aspects customers consider more relevant.

Encapsulation

Encapsulation refers to grouping certain data together into a single unit. It also represents the process when software engineers put specific parts of the software in a secure bubble so that they’re protected from external changes.

Coupling and Cohesion

These two concepts define a software’s functionality, maintainability, and reliability. They denote how much software modules depend on each other and how elements within one module work together.

Software Development Methodologies

Waterfall

The basic principle of the waterfall methodology is to have the entire software development process run smoothly using a sequential approach. Each stage of the life cycle we discussed above needs to be fully completed before the next one begins.

Agile Methodologies

With agile methodologies, the focus is on speed, collaboration, efficiency, and high customer satisfaction. Team members work together and aim for continual improvement by applying different agile strategies.

DevOps

DevOps (development + operations) asks the question, “What can be done to improve an organization’s capability to develop software faster?” It’s basically a set of tools and practices that automate different aspects of the software development process and make the work easier.

Quality Assurance and Testing

Software engineers don’t just put the software in use as soon as they wrap up the design stage. Before the software gets the green light, its quality needs to be tested. This process involves testing every aspect of the software to ensure it’s good to go.

Software Maintenance and Evolution

Humans are capable of adapting their behavior depending on the situation. Let’s suppose it’s really cold outside, even though it’s summer. Chances are, you won’t go out in a T-shirt and a pair of shorts. And if you catch a cold due to cold weather, you’ll take precautions (drink tea, visit a doctor, or take medicine).

While humans can interpret new situations and “update” their behavior, the software doesn’t work that way. They can’t fix themselves or change how they function. That’s why they need leaders, a.k.a. software engineers, who can keep them in tip-top shape and ensure they’re on top of the new trends.

Essential Skills for Software Engineers

What do you need to be a software engineer?

Programming Languages

If you can’t “speak” a programming language, you can’t develop software. Here are a few of the most popular languages:

  • Java – It runs on various platforms and uses C and C++.
  • Python – A general-purpose programming language that is a classic among software engineers.
  • C++ – An object-oriented language that almost all computers contain, so you can understand its importance.
  • JavaScript – A programming language that can handle complex tasks and is one of the web’s three key technologies.

Problem-Solving and Critical Skills

A software engineer needs to be able to look at the bigger picture, identify a problem, and see what it can be done to resolve it.

Communication and Collaboration

Developing software isn’t a one-man job. You need to communicate and collaborate with other team members if you want the best results.

Time Management and Organization

Software engineers often race against the clock to complete tasks. They need to have excellent organizational and time management skills to prevent being late.

Continuous Learning and Adaptability

Technology evolves rapidly, and you need to do that as well if you want to stay current.

Career Opportunities in Software Engineering

Job Roles and Titles

  • Software Developer – If you love to get all technical and offer the world practical solutions for their problems, this is the perfect job role.
  • Software Tester – Do you like checking other people’s work? Software testing may be the way to go.
  • Software Architect – The position involves planning, analyzing, and organizing, so if you find that interesting, check it out.
  • Project Manager – If you see yourself supervising every part of the process and ensuring it’s completed with flying colors, this is the ideal position.

Industries and Sectors

  • Technology – Many software engineers find their dream jobs in the technology industry. Whether developing software for their employer’s needs or working with a major client, software engineers leave a permanent mark on this industry.
  • Finance – From developing credit card software to building major financial education software, working as a software engineer in this industry can be rewarding (and very lucrative).
  • Healthcare – Software engineers may not be doctors, but they can save lives. They can create patient portals, cloud systems, or consumer health apps and improve the entire healthcare industry with their work.
  • Entertainment – The entertainment industry would collapse without software engineers who develop content streaming apps, video games, animations, and much more.

Education and Certifications

  • Bachelor’s degree in computer science or related field – Many on-campus and online universities and institutes offer bachelor’s degree programs that could set you up for success in the industry.
  • Professional certifications – These certifications can be a great starting point or a way to strengthen the skills you already have.
  • Online courses and boot camps – Various popular platforms (think Coursera and Udemy) offer excellent software engineering courses.

Hop on the Software Engineering Train

There’s something special and rewarding about knowing you’ve left your mark in this world. As a software engineer, you can improve the lives of millions of people and create simple solutions to seemingly complicated problems.

If you want to make your work even more meaningful and reap the many benefits this industry offers, you need to improve your skills constantly and follow the latest trends.

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Agenda Digitale: Regenerative Business – The Future of Business Is Net-Positive
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Dec 8, 2025 5 min read

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The net-positive model transcends traditional sustainability by aiming to generate more value than is consumed. Blockchain, AI, and IoT enable scalable circular models. Case studies demonstrate how profitability and positive impact combine to regenerate business and the environment.

By Francesco Derchi, Professor and Area Chair in Digital Business @ OPIT – Open Institute of Technology

In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.

This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.

Blockchain, AI, and IoT: The Technological Triad of Regeneration

Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.

Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.

Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .

Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.

These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.

New business models: from product-as-a-service to incentive tokens

Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.

At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.

To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.

Measuring Impact: Integrated Metrics for Net-Positiveness

One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.

In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.

Case studies: RePlanet Energy, RIFO, and Ogyre

Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.

These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.

From Net Zero to Net Positive: The Role of Massive Transformative Purpose

The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.

This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.

Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.

The Regenerative Future: Technology, Skills, and Shared Value

Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.

The combination of blockchainAI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.

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Raconteur: AI on your terms – meet the enterprise-ready AI operating model
OPIT - Open Institute of Technology
OPIT - Open Institute of Technology
Nov 18, 2025 5 min read

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  • Raconteur, published on November 06th, 2025

What is the AI technology operating model – and why does it matter? A well-designed AI operating model provides the structure, governance and cultural alignment needed to turn pilot projects into enterprise-wide transformation

By Duncan Jefferies

Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?

Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.

“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”

But while the importance of this framework is clear, how should enterprises establish and embed it?

“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.

Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.

“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”

An open approach

The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.

“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”

A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.

“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”

A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”

Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.

“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”

Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.

In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.

 

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