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AI makes our daily life easier - although we mostly use it to increase our performance and efficiency at work, we still take advantage of it at home too. Writing and updating our grocery list or asking Alexa to play our favourite music or call our loved ones, AI is becoming more integrated into our lives. As it is still a new feature, we are not completely aware of its downfalls and challenges. While we might envision and experience how easy life can be with the support of AI, we might also become its victims. So, a new question has arisen: are we getting smarter or dumber due to AI?

What do we use AI for?

As we already explored in a previous article, Are you an AI power user?, AI is crucial in our work. The chatbot we interact with online when we have questions regarding our subscriptions, when we try to pay an electricity bill, or when we are contacted on LinkedIn by a “recruiter” are all examples of AI in action.

More and more industries benefit from using AI, whether it’s healthcare, finance, or transportation, but even education can harness its advantages. Diagnostics, appointment scheduling, answering the most searched medical questions, analysing market data, creating budgets, or weather forecasting all rely heavily on predictive modelling used by AI and machine learning.

Software developers use ChatGPT to fix bugs in the code they wrote, marketers create images, brainstorming teams ask for ideas and solutions from an AI, and newsletters are scheduled and sent out to target audiences. All of this is making some professions extinct and getting the job done - faster, more efficiently, but not always better than humans would do.

People quickly learned to notice when an article or post was written by AI, and when videos were created by AII. Whether it’s missing fingers from people’s hands in a video or too many emojis used in a text. We are becoming more and more avoidant toward anything that has something to do with AI, and yet still drawn to human-created content. Personalisation has never been this easy, but still, uncanny valley - the eerie sensation when we encounter a robot with human-like characteristics - is on the rise.

AI is similar to calculators – we cannot live without them anymore when we try to divide 24,569 by 45, but in return, we forget how to use basic math equations.

AI and critical thinking – enhancing or diminishing?

A team of researchers from Carnegie Mellon University and Microsoft decided to look into the effect of AI on critical thinking. Their recent paper conducted a survey with 319 knowledge workers to explore when and how people perceive their own critical experience. According to the results, when they primarily use GenAI to ensure the quality of their work - for example, meeting specific criteria - they engage in critical thinking, and it can improve work efficiency.

However, it can lead to overreliance on GenAI tools, resulting in fewer critical thinking efforts. The efforts shift to information verification and AI response integration instead of problem-solving, and to task stewardship instead of execution. This means we rely on the information we are provided by GenAI without fact-checking or even questioning the content we read. GenAI, however, doesn’t work like that - you can ask it to argue for or against the same topic, and it will be able to convince you of either, depending on your preconceptions.

AI tool usage and cognitive offloading

According to another recently published study, there is a significant negative correlation between the frequency of using AI tools and critical thinking. Even though AI tools have their astonishing benefits, they also decrease our engagement in deep and reflective critical thinking through cognitive offloading. Cognitive offloading means relying on the external environment to reduce our cognitive demand, such as taking notes during a meeting or writing a shopping list.

We are prone to use AI the same way, encouraging us to use our brain and memory less - and why wouldn’t we, if there is a tool to do our cognitively challenging tasks? Younger participants are also more at risk of AI dependence and scored lower in critical thinking than older participants in the study. Higher educational attainment led to better critical thinking, so this might be a good way to avoid AI dependence and support the development of the correct way of using GenAI.

What’s next for AI and our critical thinking abilities?

The more we use AI in our work life or private life, the more we trust its output. This means we forget to verify its accuracy, and we can fall prey to compromising on the standards of excellence. There should be a balance, as we should treat AI as a tool to support us and our work, not to replace human interaction or critical thinking.

Higher education, regulations, and AI training need to be involved to ensure that professionals don’t rely heavily on GenAI and that they understand AI’s limitations and flaws in verification. Without proper training and regulation, we will become dumber and might lose one of our biggest assets: critical thinking.

2020 completely changed how we see jobs and how we work, and we met the advantages of remote work. White-collar workers switched from one day to the next by staying in the safety of their homes. Meetings and business calls were moved up into the clouds. Zoom and Microsoft Teams skyrocketed as the demand for these online meeting software increased so much that the companies were not able to fulfill the needs.

As the pandemic hit, everyone got comfortable - we would be sitting in our joggers all day long, with messy hair and our favorite coffee mug in one hand. In the meantime, we could do laundry, cook a quick lunch, or have a slow morning. Without the commute and getting ready in the morning, we could sleep in and maybe have more time to think through the day ahead.

Employees wouldn’t leave their homes to meet colleagues, but doing so we opened up the possibility of blurring the lines between work and private life. One second we would all be in a call with our boss at our desk, at the end of the bedroom or in the living room, and the next minute we were having dinner with our family or going to bed in the same space.

Slowly but surely, we started to have questions about whether we should stick with the newly found freedom or go back to the office. And if so, how many times and in what forms? The IT industry has been at the forefront of remote work adoption, but is full remote the future, or does hybrid work offer the best of both worlds?

Remote work or working from home?

Remote work and working from home are terms that are often misused or switched. With remote work, you can be anywhere in the globe, even if it means a serious time difference compared to other colleagues or clients. However, in 2020, what we experienced was working from home. As we could not leave the comfort of our home, we stayed at home, hopefully not too far from the office, in the same city. We were not allowed to travel, therefore we stayed indoors.

Remote work means that you can sit in the middle of the jungle and work while playing with monkeys – if you have Wi-Fi, of course. You can have global clients while exploring the world. Especially as an IT professional, where speaking English is already a must, you can choose jobs from a higher-paying country, either as an employee or a freelancer.

Why is it better to work from home or remotely?

Although many companies are trying to attract back their employees to the workplace - mostly in a form of hybrid work - WFH actually has its own benefits!

• Better productivity

Even though employers were afraid that a decrease in productivity would cause issues when working from home, the opposite is true. According to several studies and surveys, it looks like people work longer hours! Saving the time of commuting to your workplace, but the kids still need to get to school? Well, you have some time left, why not just start working at 8:30 instead of 9 a.m.?

Employees also report less work-related stress and an improved work-life balance. Having lunch with your friends or with your family instead of talking about work-related issues with your colleagues sounds relaxing.

Employees also take more breaks, even if it’s only standing up and going for a coffee, calling a friend, going for a run, meditating, or doing the dishes. These all can also boost productivity, as they distract our brain from the issues we are trying to solve in our job.

• Boost employee engagement

In most offices—and especially IT—professionals value remote work, and they report higher job satisfaction. The flexibility and the trust remote work offers make employees happy and engaged, so they are more likely to stay longer at the company.

• Remote workers are healthier

According to surveys, remote workers are able to fit their work schedules around other activities, making them engage in exercise, run, marathon, or just walk outdoors. Walking your dog frequently in the nearby park or picking up running in the morning instead of sitting in the car commuting makes you healthier. We also don’t have to go to the office to catch a cold or virus from our colleagues, so we may be sick less often.

Why IT professionals prefer remote work

So, why does remote work dominate the discussion in IT? Here are some key reasons that might be the biggest factors:

1. Global talent access Remote work removes geographical barriers, allowing companies to hire the best talent regardless of location. IT professionals also enjoy more job opportunities without the need to relocate. Nonetheless, for most companies, working in the same time zone (+/-1 hour) is still very important. This access to global talent also contributed to the prosperity and growth of IT in several Asian regions, such as India!
2. Better work-life balance For many, remote work means skipping long commutes and enjoying a more balanced lifestyle. Flexibility allows IT professionals to structure their day around their most productive hours.
3. Cost savings Both companies and employees benefit financially from remote setups. Businesses save on office expenses, while employees reduce commuting and living costs, especially in high-rent cities. However, most companies still have offices or rent co-working spaces to provide the employees with a quiet workplace for more focused work.

What’s next? The future of IT workplaces

With remote work being the preferred choice for most IT professionals, companies need to adapt by offering flexible work models. Hybrid setups may remain a strong alternative, but fully in-office IT jobs seem to be fading.

Data engineering and AI are two very similar constructs and it isn’t easy to distinguish between them, especially for decision makers. In this article, we will shed light on how to differentiate them and how they work together.

In the rapidly evolving landscape of technology, data engineering and artificial intelligence (AI) stand as two interconnected yet distinct fields that shape modern data-driven applications. While both are fundamental for business insights, decision-making, and automation, they serve different roles in the data ecosystem. Understanding their differences and similarities is crucial for organiSations aiming to ensure the full power of their data assets.

What is data engineering?

Data engineering is the foundational pillar of data management. It focuses on designing, constructing, and maintaining systems that collect, store, process, and structure data for analytical or operational use. Data engineers work to ensure that raw data is transformed into clean, structured, and accessible formats, making it usable for AI, machine learning (ML), and business intelligence (BI) applications.

• Data collection & integration: Gathering data from various sources, including databases, APIs, IoT devices, and cloud platforms.
• Data cleaning & transformation: Ensuring data is accurate, consistent, and formatted correctly to facilitate downstream processing.
• Building data pipelines: Automating workflows to move data from source systems to storage and processing layers.
• Database & storage management: Designing and optimizing databases, data warehouses, and data lakes for efficient querying.
• Ensuring data quality & governance: Implementing policies to maintain data integrity, security, and compliance with regulations.

Therefore, data engineers focus on designing, building, and maintaining the infrastructure needed to collect, store, and process data efficiently. Their goal is to ensure that high-quality, well-structured data is available for analysis and AI applications, also making AI engineers work easier.

What do data engineers do?

Data engineers use specific data engineering tools and technologies, while they are also expert in programming languages. Some of the tools they master day by day are Python, SQL, Scala or Java, Azure (Data Factory, Synapse Analytics), Kafka, Apache Spark, PostgreSQL / MySQL / Oracle.

What is AI?

Artificial intelligence, in contrast, is the field that focuses on developing systems capable of mimicking human intelligence to perform complex tasks such as natural language understanding, image recognition, decision-making, and predictive analytics. AI systems rely on vast amounts of structured and unstructured data, which is often prepared by data engineers, to train and refine models. For learning more about the latest AI tools and our trend predictions for 2025, read our latest article, Are you an AI power user? Latest AI tools 2025.

What do AI engineers do?

AI engineers (based on data engineers’ prepared data) develop and deploy machine learning (ML) and deep learning models. Their work involves building AI applications that can analyze data, recognize patterns, and make intelligent decisions.

• Machine learning & deep learning: AI models, especially ML and deep learning algorithms, learn from historical data to make predictions and automate tasks.
• Natural Language Processing (NLP): Enables machines to understand and generate human language for applications like chatbots, sentiment analysis, and language translation.
• Computer vision: AI systems analyse images and videos for object detection, facial recognition, and autonomous navigation.
• Reinforcement learning: AI agents learn from interactions with their environment to optimize decision-making, commonly seen in robotics and gaming.
• AI-driven automation: AI enhances business processes by automating repetitive tasks, improving efficiency and decision-making capabilities.

There is also a difference about the tools they use. AI engineers are, similarly to software engineers, might be masters of programming languages such as C++, but also knows machine learning frameworks, such as TensorFlow or Keras, Pytorch and Azure ML, in addition to data engineering tools, such as Python and R.

What are the similarities between data engineering and AI?

Despite their differences, data engineering and AI share several commonalities that make them complementary fields:

1. Data dependency - Both rely heavily on high-quality, structured data. AI models require well-prepared datasets, which data engineers facilitate through pipelines and data cleaning processes.
2. Scalability - Both disciplines focus on handling large-scale data efficiently. Data engineering builds scalable infrastructure, while AI optimizes decision-making for large datasets.
3. Automation & optimization - AI-driven systems often enhance data engineering processes by automating workflows, anomaly detection, and data quality checks.
4. Cloud integration – Both fields leverage cloud computing platforms like AWS, Google Cloud, and Azure for scalable storage, processing, and AI model deployment.
5. Collaboration - Data engineers and AI specialists frequently collaborate. Engineers build and maintain data infrastructure, while AI teams analyze the data to derive insights and predictions. Data engineers prepare the data that AI engineers use to train and optimize models. Without clean, well-structured data, AI models wouldn’t perform effectively. Both roles are essential for a successful AI-driven system.

What are the differences between data engineering and AI?

While both fields are integral to modern technology, they differ in focus, skill sets, and methodologies. For example, data engineering collects, organizes and manages the data, using skills such as SQL, ETL pipelines, big data tools. The output is the clean, structured data.

This is where AI engineers join in: they use this data to analyze and make decisions with implementing machine learning, deep learning and algorithm development. In this way, they are able to predic, classify and create automation to processes.

AI would not be as powerful or effective without data engineering – it heavily relies on it. The success of any AI project depends on the quality and reliability of the data it processes. Data engineers play a primary role in ensuring AI systems have clean, well-structured data to learn from.

Example workflow: from raw data to AI insights

1. Data collection - Engineers aggregate data from multiple sources (e.g., web traffic, CRM, IoT devices).
2. Data cleaning & preprocessing - They remove inconsistencies, missing values, and duplicate records.
3. Data storage & pipelines - The cleaned data is stored in warehouses/lakes and accessed through automated pipelines.
4. Feature engineering - AI teams refine the data further by selecting and transforming features.
5. Model training & deployment - AI models process structured datasets to make predictions and generate insights.

The future of data engineering and AI

The lines between data engineering and AI are increasingly blurring, with advancements in AI-driven data engineering tools and automated machine learning (AutoML) frameworks. Some key trends shaping their future include:

• AI-powered data management - AI algorithms will play a bigger role in optimising data pipelines, automating schema detection, and predicting data quality issues.
• Real-time data processing - Faster AI inference will require real-time data engineering solutions to process and stream live data.
• Increased data security & ethics - As AI becomes more pervasive, data engineers will focus on enforcing ethical AI practices, ensuring data privacy, and mitigating biases.
• Low-code/No-code AI & data engineering - Tools like DataRobot and Google AutoML are making AI and data processing more accessible to non-technical users.

Conclusion

Data engineering and AI are two sides of the same coin. While data engineering focuses on preparing and managing data, AI leverages that data to build intelligent models and automate decision-making. The common thread between them is their reliance on high-quality, structured data, scalability, and automation. Organisations looking to gain a competitive edge in the digital age must embrace both disciplines, ensuring a seamless flow from raw data to actionable intelligence.

AI has taken over the world, and the more you know about using different AI tools, the greater advantage you have in the job market. As an AI power user, staying updated on the latest trends and mastering the hottest AI tools of 2025 is essential - let’s dive in!

The impact of AI Power Users

According to the Microsoft and LinkedIn Work Trend Index, four types of AI users have appeared recently, and the spectrum is wide -  from people who rarely use AI to power users who use it extensively.  Currently, three out of four employees use AI at work, as it saves time, increases performance and is able to run multiple tasks simultaneously. 

AI power users have fundamentally reshaped their workdays, incorporating AI into routine tasks and saving over 30 minutes per day. More than 90% of these users believe AI makes their workload more manageable and their jobs more enjoyable.

However, while many professionals see AI as a tool for productivity, others associate it with job displacement. Employers, on the other hand, now consider AI skills critical - two-thirds of hiring managers wouldn’t recruit candidates without them. Some employees view AI as an opportunity to delegate repetitive tasks, allowing them to focus on skill development and more complex responsibilities.

As AI advances rapidly, newer and more powerful tools are emerging. Compared to these innovations, ChatGPT is now seen as a relatively simple tool. Let’s explore some of the latest AI trends and cutting-edge solutions revolutionising industries.

The hottest AI trends in 2025

Let’s explore the hottest AI trends in 2025 which will be crucial for every real AI power user. Whether it’s an AI assistant to help managing your everyday tasks, or to stay ahead of the curve in your own profession, these AI tools can become very handy!

• AI as a Co-Pilot

AI is evolving into a collaborative assistant, helping professionals make decisions, automate repetitive tasks, and streamline workflows. Whether in healthcare (diagnosing conditions) or engineering (designing smarter systems), AI is increasingly becoming a crucial co-pilot across industries.

• Real-time language translation

Communication barriers are disappearing thanks to AI-powered real-time translation. This technology enables seamless multilingual interactions, fostering global collaboration like never before.

• Advanced personalisation

AI is transforming customer experiences with ultra-personalized recommendations, automated marketing, and predictive analytics. Businesses are using AI to understand user preferences at an unprecedented level, ensuring highly tailored interactions.

• Autonomous AI agents

Unlike traditional AI tools that handle individual tasks, these agents can manage complex workflows, adapt their behavior based on results, and even make independent decisions. This brings AI closer to general intelligence but also raises concerns about oversight and accountability.

• AI-Powered Voice Assistants

AI-driven voice assistants are becoming more human-like.

• OpenAI’s ChatGPT now features advanced voice capabilities for natural conversations.
• Google’s Gemini integrates AI voice assistants into mobile devices, enabling more intuitive interactions.
• Quantum AI

Though still in its early stages, quantum computing is revolutionizing AI, enabling ultra-fast computations for breakthroughs in areas like vaccine development and materials science.

• AI in disinformation control

Governments worldwide - including the EU and China - are cracking down on AI-generated fake content and deepfakes, developing tools to detect and regulate misinformation.

• Sustainable AI

AI models require massive computing power, leading to environmental concerns. In 2025, there’s a growing emphasis on sustainable AI solutions, including energy-efficient data centers and AI-powered climate solutions. Read more about Sustainable AI in our previous article: Sustainable AI

The best AI tools of 2025

There are hundreds if not thousands of AI tools emerging the market, from lead generation to content writing or advanced personalisation. However, we selected a few which we think are the most advanced and useful tools for 2025.

1. Gemini 2.0 by Google as your newest agent

One of the most advanced AI models, Gemini 2.0 is designed for both creative and professional applications. It generates and edits visuals, making it useful for designers & marketers, summarises, drafts, and enhances research and writing, whether it’s for social media or research. Its seamless integration with Google Search, Gmail and all other Google services makes it easy to execute code, and visualise data.

2. Tidio - the hottest chatbot out there

An AI-powered customer support and chatbot platform that automates responses, boosts customer engagement, and integrates with Shopify & WordPress. Used for live chat & AI-powered chatbots to reach out and engage with potential customers and for automated email marketing to reach and compel your audience. This tool is one of the most used chatbots you have definitely encountered with!

3. Textio - for recruitment and HR

However, most people are afraid that AI would replace them, this tool is actually useful to source and find talents. Textio is an AI-driven writing enhancement tool focused on recruitment & HR. It helps companies create inclusive job descriptions and performance reviews by eliminating biased language. However, its pricing starts at $15,000 per year, making it a premium tool for larger organisations.

4. Perplexity AI - the antagonist to ChatGPT

We were all shocked but ChatGPT’s capabilities back in 2022. The chatbot quickly gained global attention due to its ability to generate human-like responses, answer complex questions, and assist with a wide range of tasks, from coding, bugging, to daily questions. Although it went through many serious updates and it’s still the most well-known chatbot, Perplexity AI is a more advanced version of it. This AI-powered search engine  provides real-time, conversational answers by pulling information from multiple sources. Compared to ChatGPT, Perplexity offers stronger live search capabilities, making it a powerful research tool.

Are You Ready for AI?

AI has reshaped industries, from automation and research to image & video creation, chatbots, and decision-making tools. AI power users hold a significant advantage in the workplace. The question is: are you ready for AI?

In today’s fast-paced digital world, businesses must adapt to evolving technology to stay competitive. One of the most significant shifts in recent years has been the move to cloud computing. Cloud migration enables companies to streamline operations, reduce costs, and enhance security while increasing efficiency and scalability. But why should businesses migrate to the cloud, and how can they ensure a seamless transition? This step-by-step guide explores the key benefits of cloud migration and provides insights into how businesses can make the move effectively.

What is cloud migration?

Cloud migration is the process of moving digital assets, applications, data, and IT infrastructure from on-premises servers to cloud-based environments. We all remember those huge server rooms in every office building where it was too cold to stay for long - to ensure the servers don’t overheat. This transition can involve different cloud models, including:

1. Public Cloud

AWS, Microsoft Azure, Google Cloud) - Hosted by third-party providers and shared across multiple businesses.

2. Private Cloud

A dedicated cloud infrastructure exclusive to one organisation.

3. Hybrid Cloud

A combination of both public and private cloud solutions.

Top Reasons to migrate to the cloud

1. Cost efficiency

One of the most compelling reasons for cloud migration is cost savings. Traditional on-premises infrastructure requires substantial investments in hardware, maintenance, and IT staff. The cloud eliminates these expenses by offering pay-as-you-go pricing models, ensuring businesses only pay for what they use.

2. Scalability & flexibility

Cloud computing enables businesses to scale resources up or down based on demand. Whether handling seasonal spikes in website traffic or expanding to new markets, the cloud offers unmatched flexibility compared to traditional IT infrastructure.

3. Enhanced security

Having on-premises servers raises many security issues, because if one server is down, the whole company can shut down for hours. Leading cloud providers invest heavily in cybersecurity, offering encryption, multi-factor authentication, and compliance certifications to protect sensitive data. Disaster recovery and backup solutions also ensure that data remains safe in case of an emergency.

4. Improved collaboration & remote work

With cloud-based applications, employees can access files and applications from anywhere, ensuring seamless remote work. Cloud collaboration tools like Microsoft 365, Google Workspace, and Slack empower teams to work efficiently, no matter their location.

5. Automatic updates & maintenance

Unlike on-premises infrastructure, which requires regular manual updates, cloud services automatically update software, security patches, and system upgrades, reducing IT workload and improving security.

6. Business continuity & disaster recovery

Cloud platforms offer redundancy and backup options, ensuring that data is never lost. In case of a cyberattack, natural disaster, or system failure, businesses can recover operations quickly without significant downtime.

7. Increased performance & speed

Cloud-based systems leverage high-speed global networks and optimized server performance, reducing latency and improving application speed. This results in better customer experiences and improved productivity.

How to migrate to the cloud successfully

Migrating to the cloud requires careful planning to minimize risks and ensure a smooth transition. Here’s a step-by-step guide:

Step 1: Assess your business needs

Evaluate your current IT infrastructure, applications, and workloads. Identify what needs to be migrated and choose the right cloud model (public, private, or hybrid).

Step 2: Choose a cloud provider

Select a cloud service provider that meets your business requirements. The top providers include:

• Microsoft Azure

  Best for businesses using Microsoft products. Azure provides flexible pricing, reserved instances, and tools like Azure Cost Management to control expenses and optimise resources.

• Amazon Web Services (AWS)

  Amazon’s cloud on-demand cloud computing platform with extensive features.

• Google Cloud Platform (GCP)

  Great for AI and analytics-driven businesses.

Step 3: Develop a migration strategy

Decide on a migration approach:

• Rehosting (lift and shift)

  Moving applications without modification.

• Refactoring

  Making minor changes to optimize for the cloud.

• Rebuilding

  Completely redesigning applications for the cloud.

Step 4: Ensure security & compliance

Review security policies and ensure compliance with industry regulations such as GDPR, HIPAA, and ISO certifications. Azure offers built-in zero-trust security, identity management (Azure AD), and compliance certifications like GDPR, HIPAA, and ISO 27001.

Step 5: Execute & monitor the migration

Perform a pilot migration to test performance before full deployment. Use cloud monitoring tools to track performance, security, and cost-efficiency post-migration.

Challenges of cloud migration: How to overcome them?

1. Downtime & disruptions

Plan migrations during off-peak hours and conduct extensive testing.

2. Security risks

Implement strong authentication measures and encryption.

3. Cost management

Monitor usage and optimise cloud costs using automation tools.

Conclusion

Migrating to the cloud is no longer a question of “if” but a “when”. Businesses that shift to cloud computing benefit from cost savings, scalability, security, and increased efficiency. However, a well-planned migration strategy is crucial for a seamless transition. By choosing the right cloud provider and approach, businesses can future-proof their operations and drive innovation in the digital era.

We are excited to share a special collaboration with our long-time partner, LA NET! Together, we’ve crafted a video where Stephen and Mitesh discuss how businesses can optimise their costs on Azure Cloud while maximising efficiency.

Key insights from the discussion:

• Azure Cost Management:

  Learn practical strategies to monitor and control cloud expenses without compromising performance.

• Scalable Solutions:

  Discover how Azure’s pay-as-you-go model ensures businesses only pay for the resources they need.

• Performance Optimization:

  Enhance your infrastructure with advanced tools and features for better ROI.

At Peruzzi Solutions, we specialize in helping businesses make the most of their cloud investments. Whether you’re looking to migrate, optimize, or scale, our Cloud Engineering Services offer tailored solutions to meet your unique needs.

🎥 Check out the full video now and discover how you can transform your cloud operations! Watch the Video