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Overview

We’ve partnered with AILA, an AI-powered legal admin startup to develop an intelligent assistant PoC, capable of processing legal inboxes, drafting responses using legal documents, and raising Jira issues to streamline workflow. The solution needed to seamlessly integrate with existing email and internal tools, without disrupting the established legal workflows.

This was a Proof of Concept (PoC) aimed at quickly validating the product-market fit for the client’s AI-powered solution, while ensuring the system was lean, secure, and capable of supporting rapid iterations based on user feedback.

Problem

The client, an AI-powered legal admin startup, faced a significant challenge within its legal teams. Their experts were overwhelmed by routine tasks such as inbox management, follow-ups, and issue tracking, with no central agent to automate or streamline these processes. Specifically:

• Legal inbox overload: Legal teams spent substantial time managing and responding to routine emails, often losing focus on high-priority legal work.
• Disconnected systems: There was no central solution to connect emails, legal documents, and Jira-based work tracking, making it difficult to manage the volume of tasks efficiently.
• Need for a fast MVP: The client wanted to ship a working MVP in under two weeks to quickly test product-market fit and gain valuable feedback from users.
• Integration challenges: The solution needed seamless integration with existing systems (email, Jira, document management) to avoid disrupting established workflows and ensure that legal professionals could continue to use the tools they were familiar with.

The project needed to address these pain points with a simple yet scalable AI solution that could be deployed quickly while being secure and extensible.

Solution

To meet the client’s needs, we designed a modular architecture that utilized cutting-edge AI tools and cloud services to automate routine tasks and integrate smoothly with the client’s existing systems.

Implementation Process:

1. Integration with Google Inbox: We connected the system to the client’s Google inbox using the Gmail API, enabling the AI assistant to automatically fetch and process incoming emails.
2. Jira REST API: Integrated with Jira to allow the assistant to automatically create, update, and track issues from legal emails and responses.
3. Document Retrieval: Azure AI Search was used to retrieve relevant legal documents and templates, which were then used by the AI assistant to draft responses.
4. Serverless Architecture: To minimize overhead, the entire solution was deployed using serverless Azure resources. This allowed the system to scale rapidly as usage increased, without the need for complex infrastructure management.

The modular design of the system allowed for rapid iteration, with the flexibility to adjust and add new features based on feedback from legal professionals.

Impact

Delivery: The PoC was delivered in just 10 days - a rapid turnaround that exceeded expectations. The solution was fully integrated with email, Jira, and document management tools, and was tested thoroughly to ensure that it met the client’s needs.

Efficiency: The AI-powered assistant dramatically improved the efficiency of the client’s legal teams by automating routine tasks, reducing the time spent on inbox management and follow-ups. With a human-in-the-loop supervision model, legal professionals could quickly review and approve draft responses, enabling them to focus on more critical work.

User feedback and iteration: The client was able to test the MVP with internal users immediately, gaining valuable insights that would shape the future iterations of the AI assistant. The assistant’s ability to learn from early feedback ensured a rapid cycle of refinement and improvement, positioning the solution for future scaling.

Scalability: The architecture was designed with scalability in mind, using serverless Azure resources and a modular framework that allowed for easy addition of new features as needed. The PoC set a solid foundation for scaling the AI assistant to handle more complex legal workflows in the future.

Key takeaways

This AI-powered legal assistant PoC demonstrated the power of leveraging modern AI frameworks, cloud-based architecture, and seamless integration to solve a real-world problem in the legal field. By freeing up legal experts from non-substantive tasks, the solution significantly improved internal efficiency while supporting human oversight.

In just two weeks, we delivered a lean, effective, and secure solution that met the client’s goals of rapid MVP deployment, seamless integration, and scalable architecture. The project set the stage for future iterations based on user feedback and positioned the client for successful product-market fit in the competitive legal tech industry.

As mid-sized healthcare companies continue to embrace digital transformation, choosing the right cloud provider becomes a critical decision. Among the most prominent players, Microsoft Azure and Amazon Web Services (AWS) lead the pack. Both offer robust cloud services, but each has specific strengths that suit different business needs. For healthcare providers, where data privacy, regulatory compliance, cost, and scalability are crucial, understanding the nuances of Azure and AWS is vital. This article dives into the pros, cons, cost considerations, and migration tips tailored specifically for mid-sized healthcare organisations.

Why do cloud solutions matter for healthcare?

We all know that privacy in healthcare is elementary. Healthcare companies deal with sensitive patient data, from blood test results to diagnosis, thus require secure data storage and sharing, and often face fluctuating data volumes. Cloud computing allows them to:

• Improve operational efficiency
• Enable telehealth and remote monitoring
• Comply with HIPAA and GDPR regulations
• Improved patient care with AI and data analytics

Azure vs AWS: Core strengths overview

The TOP 2 leader providers in cloud computing are Microsoft and Amazon: Microsoft Azure and Amazon Web Services. Both solutions are great but we think that Microsoft Azure has some advantages. 

Microsoft Azure

• Integration with Microsoft 365 and Dynamics 365: Ideal for organisations already using Microsoft tools.
• HIPAA-compliant services: Azure provides numerous compliance certifications, including HITRUST and HIPAA.
• Hybrid capabilities: Azure Arc and Stack offer flexible hybrid deployment models.
• AI and ML services: Azure Health Bot, Azure Machine Learning.

Amazon Web Services (AWS)

• Market leader: AWS holds the largest cloud market share with a mature ecosystem.
• Wide range of services: More than 200 services across storage, computing, analytics, etc.
• Robust compliance programs: Includes HIPAA eligibility, HITRUST CSF certification.
• Advanced analytics and ML tools: Amazon Comprehend Medical, SageMaker.

In healthcare companies, integrations with Microsoft 365 and HIPAA-compliant services must be priority, therefore Azure might be a better choice when it comes to cloud migration. 

Cost considerations: Azure vs AWS

Cloud pricing can be complex, and sometimes it’s hard to predict the final cost. However, it is vital to move to the cloud and leave servers behind. For choosing th best option for your cloud migration, healthcare companies must consider:

• Compute and storage costs: Azure and AWS both offer pay-as-you-go and reserved pricing models. Azure tends to have slightly more competitive pricing for Windows-based services.
• Data transfer costs: AWS charges more for outbound data transfers, which can be significant for telehealth solutions.
•  Licensing: If your organisation already has Microsoft enterprise agreements, Azure offers cost advantages through Azure Hybrid Benefit.
• Pricing calculators: Both providers offer calculators for accurate estimation:

o   Azure Pricing Calculator

o  * AWS Pricing Calculator*

Security and compliance

In healthcare, compliance isn’t optional, it’s critical. Both Azure and AWS offer:

• Encryption at rest and in transit
• Multi-factor authentication
• HIPAA-eligible services
• Audit logging and monitoring tools

Azure offers more out-of-the-box tools tailored for compliance (e.g., Azure Compliance Manager), while AWS provides more flexibility for customisation.

Performance and global reach

Both platforms have extensive global availability zones, with minimal latency and high uptime SLAs. However, Azure has a stronger presence in Europe and offers better integration with on-prem infrastructure, which is a plus for healthcare providers operating across multiple jurisdictions.

Ease of migration: Azure vs AWS

The migration itself can be a hassle – but not with these cloud providers! Both Azure and AWS provide migration tools, to smoothen your transition.

Azure migration tools:

• Azure Migrate: Assessments, discovery, and migration of servers, databases, and VMs
• Database Migration Service
• Azure Site Recovery for DR

AWS migration tools:

• AWS Migration Hub
• AWS Application Migration Service
• AWS Database Migration Service

In a previous article, we also shared an ultimate guide about migration, which you can read by clicking here.

Case Study: UK mid-sized healthcare provider

A UK-based mid-sized healthcare provider with 400 employees needed to modernise its legacy IT infrastructure to support telehealth, reduce server costs, and meet NHS Digital compliance standards.

Challenges:

1. Legacy on-prem systems with high maintenance cost
2. Growing telemedicine usage requiring scalability
3. GDPR and NHS DSP Toolkit compliance

Solution:

• Migrated to Azure due to existing Microsoft licensing and strong hybrid capabilities.
• Used Azure Migrate and Azure Site Recovery.
• Enabled Azure Monitor and Security Center for governance.

Outcome:

• Reduced IT operational costs by 32%
• Improved application uptime from 96% to 99.95%
• Full compliance with GDPR and NHS Digital requirements

Selection Criteria Checklist:

• HIPAA/GDPR compliance support
• Pricing model fit
• Integration with existing tools
• Data sovereignty and regional coverage
• Partner ecosystem and support
• Migration tools and consulting availability

Conclusion

For mid-sized healthcare companies, both Azure and AWS offer powerful capabilities for cloud migration, security, and scalability. Azure often wins when deep Microsoft integration, hybrid cloud, and cost-saving through existing licensing is critical. AWS offers broader service depth and flexibility but may be more costly for certain workloads.

Ultimately, the right choice depends on your current environment, compliance needs, and digital transformation goals. Working with a Microsoft-certified consultancy like Peruzzi Solutions can help simplify the decision and execute your migration with confidence.

The low-code/no-code trend has been up and coming for the past few years, with the promise of building applications, websites or software with minimum or no coding experience. This allows faster development and for a lower price – instead of financing software engineers and software architects, almost anyone (after a few weeks or couple of months of training) can build whatever the organisation needs.

These low-code methods can be found in data engineering solutions too. The rise of ETL (extract, transform, load) tools aim to simplify data management and to build data pipelines without extensive coding experience. This is where data engineering solutions come into play, with the help of low-code/no-code ETLs. In this comprehensive guide, we’ll explore:

• What data engineering solutions are • Why they’re critical for businesses • The rise of low-code/no-code platforms • Real-world use cases • Key benefits • Choosing the right solution for your needs

What are data engineering solutions?

Let’s dive deep, but start from the beginning. From driving decision-making to enabling predictive analytics, modern businesses rely heavily on data to maintain a competitive edge. But raw data alone isn’t enough - it needs to be organised, accessible, and actionable.

Data engineering refers to the process of designing, building, and maintaining systems that collect, store, and analyze large volumes of data. The goal is to create robust data pipelines and architectures that deliver clean, reliable, and timely data to users and applications.

Data engineering solutions are the tools (such as the ETL), platforms, and services that help businesses build these pipelines efficiently. These can range from open-source frameworks like Apache Spark and Kafka to fully managed cloud services such as Microsoft Azure Data Factory, AWS Glue, and Google Cloud Dataflow.

In recent years, low-code and no-code data engineering solutions have emerged, enabling non-technical users to participate in building and managing data pipelines without needing advanced coding skills. This means, that instead writing codes, these ETL tools use a visual, user-friendly interface to design, build or manage data. Drag-and-drop functionality (just like when you move folders or upload photos), reusable templates (such as WordPress) and pre-built connectors help you to work fast and efficiently.

Types of data engineering solutions: traditional, cloud-native and low-code

Before we dive deep into the low-code/no-code data engineering solutions and their benefits, we need to mention the other solutions.

1. Traditional code-first solutions

This is the “ancient” data engineering solution, which we all think about when it comes to data engineering. For organisations with in-house engineering expertise, traditional solutions like:

• Apache Airflow (workflow orchestration)
• Apache Spark (large-scale data processing)
• SQL scripts & stored procedures are still widely used.

These solutions provide maximum flexibility and customisation but require technical skills to maintain, expert software engineers and data engineers.

2. Cloud-Native Managed Services

Cloud providers have developed fully managed data engineering platforms, including:

• Azure Data Factory (ideal for Azure cloud ecosystems)
• AWS Glue (serverless ETL for AWS)
• Google Cloud Dataflow (for real-time and batch data processing)

These reduce infrastructure overhead and provide scalability on demand. If you want to know more about how cloud native applications work, we recently wrote a guide about cloud native applications.

3. Low-code/no-code platforms

For businesses wanting to accelerate development with fewer technical barriers, low-code/no-code platforms include:

• Alteryx
• Knime
• Microsoft Power BI Dataflows
• Google Cloud DataPrep
• Apache NiFi

These platforms offer drag-and-drop interfaces, pre-built connectors, and templates, allowing data analysts and business users to build pipelines without coding.

The rise of low-code/no-code in data engineering

Low-code/no-code data engineering solutions democratise access to data workflows. With a visual interface and minimal code, even teams without dedicated data engineers can:

• Extract data from multiple sources
• Clean and transform datasets
• Feed data into BI tools or ML models

Example:

A marketing team could use a low-code tool to automatically pull data from Google Analytics, clean it, and push it into a dashboard without writing any code. While low-code tools accelerate delivery and reduce IT bottlenecks, they’re typically best suited for small to medium-sized projects or for prototyping before full-scale engineering implementation.

Pros and cons of low-code and no-code in data engineering

Without the constraints of actual coding, these low-code no-code methods increase productivity in data engineering. These automated features also decrease the mistakes and errors caused by humans, which increases data quality. These also allow data engineers to focus on higher-level problem solving – instead of taking so much time to code, they can use their time to planning or strategic initiatives. It is also cost-effective for small and mid sized businesses, as it reduces the dependence on specialised developers.

However, one of the biggest downside of the ETLs is that these tools are less flexible when it comes to complex or large-scale systems. As with every automatisation and visually appealing user-interface, you cannot do anything in these tools you want to. These cannot be customised for specific use cases therefore they might not provide you exactly what you need for your organisation to thrive.

Real-world use cases of data engineering solutions

1. Retail A large e-commerce platform uses AWS Glue to collect and transform purchase data across regions, delivering personalized product recommendations in real-time. 2. Finance A fintech startup leverages Azure Data Factory for ETL pipelines that consolidate transactional data for real-time fraud detection. 3. Healthcare A hospital network uses Knime, a no-code tool, to combine patient health records from various systems to improve diagnostics and operational efficiency.

The future of data engineering solutions

As businesses continue their journey toward digital transformation, the role of data engineering will only grow. The trend is clear:

• More automation
• Greater accessibility through low-code/no-code
• Deeper integration with AI and ML systems Companies that invest in robust data engineering solutions today will set themselves up for competitive advantages tomorrow.

Key takeaways

Data engineering solutions are no longer optional - they are a business necessity. Whether you’re a small startup or a global enterprise, choosing the right mix of traditional, cloud-native, and low-code/no-code tools can accelerate your data-driven success.

Cloud native application is the base for cloud computing architecture. Organisations are under the pressure to deliver software faster, operate more efficiently, and scale without compromising reliability, and this is what cloud native development can provide.

Enter cloud native development: a transformative approach that lets businesses build and run scalable applications in dynamic environments like public, private, and hybrid clouds. In this comprehensive guide, we’ll explore what cloud native means, how it integrates with DevOps, why Kubernetes is a game-changer, and what it takes to build truly modern, resilient applications.

What is cloud native?

Before getting into the key characteristics of cloud native applications, let’s dig into the term “native app”. A native app is a software designed to be used on a specific platform or device. Cloud native applications are built in the cloud to take full advantage of cloud computing. Moreover, as cloud technology ensures modern, fast and agile solutions, these applications are flexible, resilient and predictable.

If your applications are not in the cloud yet, in a previous article, we explored 7 proven cloud migration strategies.

Cloud native is an approach to building and running applications that fully exploit the advantages of the cloud computing model. Cloud native systems are designed to be resilient, manageable, and observable, using technologies like containers, microservices, service meshes, and declarative APIs.

Let’s see and example!

We all love to watch series and movies on everyone’s favourite streaming service: Netflix. Netflix is one of the most prominent examples of a cloud-native application. Netflix is a cloud-native pioneer, showing how a company can scale to serve a global audience through a combination of microservices, DevOps, automation, and cloud infrastructure. Here’s how:

1. Microservices architecture Netflix transitioned from a monolithic architecture to microservices, where each component (user recommendations, streaming, billing, etc.) is independently developed, deployed, and scaled. This allows the platform to handle millions of users simultaneously without downtime.
2. DevOps + continuous deployment Netflix uses DevOps practices and an internal platform called Spinnaker for continuous integration and continuous delivery (CI/CD). This enables thousands of deployments per day, reducing risk and increasing innovation speed.
3. Cloud native infrastructure Netflix runs on Amazon Web Services (AWS) and takes full advantage of cloud scalability, elasticity, and global distribution. They can automatically scale up during peak hours (e.g., evenings or new show releases) and scale down when traffic is low.
4. Resilience & observability Netflix built tools like Chaos Monkey to deliberately cause failures in their systems to test resilience. This demonstrates cloud-native reliability practices like observability, self-healing, and automated recovery.
5. Containerization & Kubernetes (in some components) While Netflix doesn’t run fully on Kubernetes, some services are containerized for consistency, scalability, and faster deployment - a hallmark of cloud-native DevOps with Kubernetes.

This way, Netflix is fast, reacts to changes quickly and it doesn’t really bug and lag - which is why it’s a pioneer!

Just to summarise, these are the key characteristics of cloud native applications:

• Containerised: Each component is packaged in its own container for consistency across environments.
• Dynamically orchestrated: Containers are managed and scheduled by orchestration platforms like Kubernetes.
• Microservices-based: Applications are broken down into loosely coupled services that can be independently deployed and scaled.
• Designed for continuous delivery: Supports fast, frequent changes without downtime.

Cloud Native and DevOps: A perfect match

Cloud native and DevOps are not just compatible, they’re symbiotic. DevOps focuses on streamlining software delivery and infrastructure changes through automation and monitoring. Cloud native provides the tools and architecture to make this possible at scale.

Benefits of cloud native DevOps:

• Faster time to market: Continuous integration/continuous delivery (CI/CD) pipelines ensure rapid feature releases.
• Improved reliability: Automation reduces human error and increases uptime.
• Greater agility: Developers can test and deploy independently, making teams more responsive.
• Efficient resource usage: Autoscaling and containerization ensure optimal use of compute resources.

Cloud native DevOps with Kubernetes

Kubernetes has become the backbone of cloud native DevOps. It orchestrates containerized applications, handles scaling, self-healing, and service discovery - all crucial for modern application delivery.

Key Kubernetes features for DevOps:

• Automated rollouts and rollbacks
• Horizontal scaling
• Self-healing capabilities
• Load balancing and service discovery
• Secrets and configuration management

Kubernetes enables DevOps cloud native teams to create robust environments that are scalable, resilient, and secure.

Why does cloud native matter?

Cloud native technology gives businesses a huge advantage with its scalability, cost efficiency, speed and agility. It can be scaled down or up based on demand, businesses can pay only for the resource they use, deploy new features quickly to react market changes. It also gives freedom the developers! The advantages for developers is that they can use best-of-breed tools across the software lifecycle, and microservices and CI/CD pipelines allow for rapid iteration.

However, as everything, cloud native comes with its own set of challenges. Its complexity, meaning more and more tools and components to manage, security gaps and without proper cost management, it can be an expensive technology without a skilled team to manage it! It’s highly important to be prepared when you switch to cloud native architecture. Let’s see what you need to build a secure and agile cloud native architecture!

How to build a cloud native architecture?

If you are planning to transition to a cloud native architecture for your business, it involves more than just containers. It requires a shift in mindset and tooling. Here are the core building blocks:

1. Microservices: Split monolithic applications into smaller, independently deployable services.
2. Containers (e.g., Docker): Package applications and dependencies into a consistent runtime environment.
3. Orchestration (e.g., Kubernetes): Manage container deployment, scaling, and networking.
4. CI/CD pipelines: Automate the building, testing, and deployment of applications.
5. Service Mesh (e.g., Istio): Manage communication between services with traffic control, security, and observability.
6. Infrastructure as code (e.g., Terraform, Helm): Define and manage infrastructure through code to ensure repeatability and automation.
7. Observability and monitoring: Use tools like Prometheus, Grafana, or Datadog to monitor application health and performance. Best practices for cloud native success
8. Start small: Begin with a pilot project to understand tooling and workflows.
9. Invest in culture: Foster a DevOps culture that values collaboration and automation.
10. Standardize tooling: Choose tools that integrate well and support your stack.
11. Focus on observability: Make monitoring a first-class citizen from the start.
12. Automate everything: From testing to deployments, automation is key to efficiency.

With keeping these best practices, you will get exactly what you were promised with the cloud native technology: flexibility and agility.

Summary

Cloud native and DevOps are the pillars of modern software development. By embracing a cloud native architecture- backed by robust cloud native DevOps with Kubernetes, organisations can innovate faster, scale efficiently, and stay competitive.

As you begin your journey, start small, build the right foundations, and scale confidently with the right strategy and partners.

*Cloud migration *has become a strategic priority for businesses seeking agility, scalability, and cost-efficiency. However, executing a smooth transition to the cloud without disrupting business operations is not easy – it requires careful planning and the right cloud migration strategy.

At Peruzzi Solutions, we specialise in crafting tailored cloud migration strategies from full-scale transformation projects to incremental migrations using Microsoft Azure. In a previous article, we discussed a guideline about cloud migration. In this article, we’ll share the 7 proven types of cloud migration strategies and share practical insights to help you choose the best approach for your organization.

Why do cloud migration strategies matter?

Moving to the cloud is not a one-size-fits-all process. As each organisation has unique workloads, legacy systems, compliance requirements, and risk tolerance levels, adopting the wrong migration strategy can result in business downtime, data loss, or cost overruns. We all experienced the mini heartattack when hours of work did not get saved because the compuer froze or it needed an immediate restart. Now imagine this data loss with years of hard work just because of moving to the cloud! Fortunately, there are cloud migration strategies to avoid these very frustrating scenarios. 

Understanding the core migration strategies in cloud computing allows businesses to make informed decisions while minimising disruption and maximising return on investment.

The 7 R’s of cloud migration strategies

1. Rehosting (lift-and-shift)

Best for: Fast migration with minimal code changes

Use case: When speed is critical or you’re dealing with legacy systems

Rehosting involves moving existing applications to the cloud with little or no modification. This is often the starting point for many companies initiating their Azure cloud migration strategies or AWS cloud migration strategies.

At Peruzzi Solutions, we’ve helped retail clients rehost their entire e-commerce platforms to Microsoft Azure within weeks - maintaining business continuity throughout the process.

2. Replatforming (lift-tinker-and-shift)

Best for: Improving performance without changing the app’s core architecture

Use case: When small optimisations (e.g., using managed services) can reduce operational burden

This strategy involves slight adjustments to applications to take better advantage of cloud-native features. For example, moving from self-hosted databases to Azure SQL Database or Amazon RDS.

Our healthcare client, based in London, adopted this approach to move clinical data systems to Azure while enhancing uptime and security compliance.

3. Repurchasing (drop-and-shop)

Best for: Replacing legacy software with SaaS

Use case: When the existing solution no longer meets business needs

Repurchasing involves moving to a new, cloud-based product (often SaaS, Software as a Service). Think Salesforce replacing a legacy CRM. It’s a clean break and can significantly reduce costs.

One of our financial services clients replaced their outdated project management suite with Microsoft 365 and Power Platform, significantly improving collaboration and cost-efficiency.

4. Refactoring or re-architecting

Best for: Building cloud-native apps for scalability and performance

Use case: When legacy applications block innovation or don’t scale

This is the most complex but also the most rewarding of the cloud migration strategies. It involves reimagining how an application is architected and developed using cloud-native tools and frameworks.

We guided a fintech startup through complete refactoring on Azure, enabling microservices-based deployment, auto-scaling, and real-time analytics- leading to a 40% reduction in infrastructure costs.

5. Retire (eliminate)

Best for: Cutting unnecessary costs

Use case: When certain apps or services are no longer needed

Just like we humans, some applications also need to retire after they have done their job. Identifing redundant or unused applications during the migration assessment phase can save time, money, and complexity during your move.

During one cloud migration consulting engagement, we helped a client retire 15% of their applications - saving over £20,000 annually in licensing and maintenance costs.

6. Retain (revisit)

Best for: Apps not ready for cloud or with compliance constraints

Use case: When specific workloads must remain on-premises

Sometimes, the best decision is to retain certain apps temporarily. This is a strategic delay rather than resistance to change. The key is to document why they’re staying and when to reassess.

A government organisation we work with retained some systems due to GDPR and sovereignty concerns - while migrating less sensitive workloads to Azure Government Cloud.

7. Hybrid approach

Best for: Organisations with mixed cloud-readiness

Use case: Gradual migration, minimal risk

Many enterprises benefit from combining multiple strategies. A hybrid cloud migration strategy blends on-premises, public, and private cloud environments for flexibility and control.

At Peruzzi Solutions, we often recommend a hybrid approach for large-scale projects. For instance, in retail, POS systems might stay on-premise while backend inventory management and analytics move to the cloud.

Choosing the right strategy with our consultancy services

Whether you’re evaluating  Azure cloud migration strategies, or multi-cloud options, success starts with a tailored roadmap. Here’s what we consider:

• Workload analysis
• Compliance and security requirements
• Cost-efficiency and ROI
• Business continuity and risk tolerance
• Team readiness and training needs

Our cloud migration consulting services start with a readiness assessment and architecture review, followed by strategic recommendations that align with your goals.

As a trusted Microsoft Solutions Partner based in London, we provide end-to-end cloud migration managed services, from planning to post-migration support. Our clients span industries including healthcare, retail, financial services, and logistics.

We offer:

• Expert Azure migration specialists
• UK-based support teams
• Security-first migration methodologies
• Transparent pricing models
• Long-term cloud optimisation strategies

Summary

Choosing the right cloud migration strategy isn’t just a technical decision, it’s a business one. Whether you’re looking to lift-and-shift, replatform, or fully re-architect your applications, Peruzzi Solutions ensures a smooth journey to the cloud with minimal disruption.

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.