Cream of the Crop: Top Software Development Insights of Expiring 2024

The new year is just around the corner, and it’s time to look back and recollect all the vibrant topics we’ve touched upon on our blog. From thought-provoking discussions about emerging technologies to in-depth analyses of industry trends and best practices, we’ve covered a wide range of content designed to inform and inspire.

That’s what we’d like to share with you, dear readers. In this final blog post of the year, we’ve shortlisted the most captivating articles our team worked on in the expiring 2024 and invite you to read our top 10 below.

Artificial intelligence is now anywhere and everywhere, so it’s no surprise that this topic takes the first position. This piece of content explores AI and, specifically, its generative models. These models have the remarkable ability to create diverse types of data, including texts, audio, video, and images. However, they don’t produce content out of thin air. Instead, generative models leverage existing datasets as references and are trained on new inputs to learn patterns, styles, and context.

But what are GenAI model-building principles and application areas? In this blog post, we cover the following points:

• Steps of GenAI model creation
• What is a foundation model within the GenAI model
• Main types of GenAI models, including auto-regressive models, deep belief networks, generative adversarial networks, and more
• The comparison of GenAI models and Large Language Models
• How GenAI is used across industries and which problems it’s able to solve

Read the full article to know Everything about GenAI Models

Here we continue exploring the GenAI topic, but this time with an industry-specific focus. The next article highlights how Generative AI is transforming the insurance sector, enabling businesses to automate operations, streamline claims processing, enhance risk management, generate and handle policies, and more.

In addition to the common applications of GenAI in the insurance industry, this article covers other aspects, such as:

• Tasks GenAI can fulfill in the context of insurance
• Key limitations of the technology that must be kept in mind
• Why you should implement GenAI into your insurance workflows with caution
• Real-life use cases

Read the full blog post to know more about GenAI Implementation in Insurance

Undoubtedly, AI technology seems to be really promising in almost all spheres, including education. It helps to alleviate teachers’ workloads, assess students’ performance more comprehensively and fairly, and approach teaching in a more personalized way.

However, the use of technology is fraught with a set of risks and dangers. In this article, we discuss why educating young minds can’t be fully and entirely shifted to technology, and here is a brief summary of these reasons:

• AI does not shine with multitasking
• Security issues are one of the biggest concerns of AI usage
• Implementation costs are exorbitant for the majority of educational institutions
• Overtrust to GenAI models hinders its development since they are unable to draw knowledge from nowhere Students’ motivation risks to come to naught
• Lack of variability and insufficient soft skills development

Impressive list, isn’t it? That’s why it’s better to consider AI only as a valuable assistant and not to shift such important missions as education to the technology unconditionally and uncontrollably.

Read the full list of Risks of AI in Education and How to Steer the Technology Wisely

Each century witnesses technological breakthroughs that reshape industries and society. The advent of Industry 4.0 in the early 21st century marks a new era of innovation, where cutting-edge technologies are seamlessly integrated into traditional industrial operations.

Internet of Things, cloud computing, artificial intelligence and machine learning, virtual and augmented reality — all these technologies are no longer futuristic concepts. Currently, they are widely used in multiple industries and the most representative use cases are:

• Usage of Big Data and Business Intelligence in manufacturing to maintain high product quality
• Artificial intelligence, data analytics, and IoT for logistics process optimization and cost reduction
• Machine learning algorithms for personalized user experience in e-commerce
• Smart IIoT systems to ensure ultimate accuracy in construction
• IoT and IIoT in agriculture and fieldwork for sustainable resource management
• AR/VR in product design to accelerate the product development cycle
• Advanced data analytics for preventive treatment in healthcare

Read more Examples of Industry 4.0

Traditional patient care is gradually sinking into oblivion. With HealthTech evolving, patients now expect unparalleled accessibility to healthcare services, greater flexibility from medical personnel, and a more personalized approach to treatments. And they don’t agree for less.

That’s precisely where a digital front door comes in. It serves as a unified entry point, streamlining access to healthcare resources and improving patient experience. From booking appointments online and virtual consultations to personalized health insights and seamless communication with providers, this approach allows patients to take better control over their health journeys.

In the article dedicated to the digital front door in healthcare, we discuss:

• Main goals and elements of DFD solutions
• Characteristics and functionality common DFD features
• DFD benefits for patients and caregivers
• Some strategic tips on DFD implementation

Explore more about Digital Front Door and Its Implementation in Healthcare

Simply owning data is not enough — it’s crucial to manage it effectively. This principle applies across industries, but it holds particular significance in manufacturing. A data-driven approach to decision-making is pivotal in enhancing production quality, optimizing supply chain management, ensuring rational energy consumption, improving equipment maintenance, and more.

In this article, we discuss how Enterprise Manufacturing Intelligence (EMI) makes the production process smarter and increases its efficiency manifold. Key points you’ll learn from this blog post:

• The essence of Enterprise Manufacturing Intelligence and its focus
• Key components of EMI that help turn raw data into valuable insights
• Why EMI systems are must-haves for any manufacturing business
• Why employees of manufacturing companies might approach such systems with hesitation, and other challenges related to EMI implementation
• EMI systems’ development vector and which trends we’ll observe in the future

Read more details on Enterprise Manufacturing Intelligence

We continue the data topic, but this time in the context of the real estate industry. As in the example with manufacturing, real estate needs better visibility and consistency to proceed with precise property evaluation, track patterns, and trends, propose more personalized offerings to clients, and monitor agents’ performance. How can this be done without comprehensive data analytics? Sure thing, you can rely on your gut feelings and luck, but the probability that the result will satisfy you is negligible.

This article explains why real estate businesses are lucky to operate piles of data, why the precision of your analytics depends on data volumes, and many other aspects, such as:

• Types of data real estate companies use for comprehensive analytics
• Why data is a precious asset that can hardly be overestimated, and how real estate companies can benefit from analytics
• Real-life successful analytics implementation case, based on our project
• Why analytics is not as simple as it may seem
• Why real estate needs predictive analytics, and what affects forecast precision

Read more about the Importance of Data Analytics for Real Estate

The number of businesses that abandon on-premises servers and migrate their infrastructures to the cloud is constantly growing, and healthcare service providers are no exception. There is a variety of reasons to decide in favor of transition: from exorbitant initial costs (especially painful if you are a small narrow-purpose healthcare facility) to downtime risks if something happens with an on-prem data center (you can’t provide medical services until problems are fixed).

Deploying your infrastructure on cloud servers, you gain flexibility, an exceptional level of data security, and, of course, cost-efficiency. In this article, we cover such points as:

• Why does the cloud outperform on-prem in terms of costs and agility
• Cloud computing models, how they differ, and which one is the optimal choice for your healthcare facility
• Why it’s not all roses, and what should be kept in mind when selecting cloud to handle your health data
• Cloud-based AI tools and how they facilitate healthcare data analytics and alleviate diagnosis determination

Read more How Cloud Can Contribute to Healthcare

Security is a major pain point to any system, and many say that completely secure systems don’t even exist, there are only ones that haven’t been hacked. However, this doesn’t mean that you should leave it to chance and not bother with security assurance at all, assuming that your effort is vain.

Cloud platforms claim to be highly secure, and it’s much easier to fine-tune security configurations within the environment in comparison with on-premises. In this article, we speak about:

• Main reasons to take care of your cloud infrastructure and data security
• Common threats and risks to be aware of
• Why ensuring security to cloud-based applications is easier than in the case of on-prem
• Must-dos to ensure the ultimate security level of your cloud infrastructure

Discover more information on the Security Assurance of Your Cloud-Based System

Low-code/no-code platforms accelerate time-to-market and help to save on development — that’s what businesses love them for. No doubt, LC/NC systems are a kind of blessing if you have tight timelines and lack seasoned engineers able to create a comprehensive system from the ground up. However, although such platforms claim to be fully flexible, there are cases when you can’t do without traditional development to add the functionality you need.

Our 10th and final article is dedicated to benchmarking low-code/no-code and traditional development and discovers:

• Why low-code/no-code development is not a new concept and how it was called before the emergence of the term
• Comparison of LC/NC and traditional development in such aspects as security, customization level, flexibility, and more
• Cases when you can’t do without traditional development with examples from our projects
• Flexibility boundaries of low-code/no-code systems to be aware of before you purchase the subscription

Read the full article Low-Code/No-code vs. Traditional Development to Find Out If They Are Rivals or a Powerful Duo

Further — More. Looking Forward to 2025 to Provide You with More Tech Insights

In this blog post, we’ve gathered the most insightful articles created with the support of our in-house experts. AI, cloud computing, business intelligence, and low-code/no-code are the technologies we explored this year, sharing knowledge and expertise to help businesses grow and evolve.

We have grand plans for 2025 and look forward to new challenges and innovations to share them with you on our blog. Stay tuned for more engaging content and expert insights to help your business thrive in the upcoming year!

About the author

Shelly Parker

With a background in business analysis and a broad interest in the software development field, Shelly covers the most meaningful IT news, digital trends, and events.