Today, how you and I use technology for work and life has dramatically changed. Each year brings with it innovations and trends that have the potential to revolutionize industries and reshape our daily lives. As Leaders, IT professionals and companies, we are focused on technologies that will help us reduce further investments and acquire more returns.  

In 2023, several technology trends have emerged that hold significant promise for transforming various sectors. This year began with renewed vigour and enthusiasm for adopting technologies that will be worthwhile for accelerating business performance.  

In this blog, we will delve into eight technology trends you should consider this year.  

Recently, one new trend that has been making waves in the tech world is Generative AI. It promises to drive sustainable growth, solve global challenges, and transform business operations significantly. Generative AI is the upgraded version of AI, which adds to the innovative capabilities of existing technologies like applied AI and industrial machine learning.  

A June 2023 report by McKinsey titled The Economic Potential of Generative AI: The Next Productivity Frontier states generative AI and related models have transformed how businesses will use AI in the future.  

For a better perspective, you can read our blog on privacy and security concerns on Generative AI.   

Generative AI upgrades components within assistive technology by accelerating application development and making powerful capabilities available to the non-technical user base.  

By looking at the latest predictions, we can deduce Generative AI is likely to add $4.4 trillion in economic value by harnessing specific use cases and other diverse uses like drafting email templates to increase overall productivity.  

Although we have not seen significant tech investments over the past year, we can remain optimistic about future growth investment prospects. The year 2022 witnessed almost $1 trillion in tech investments, cementing our faith in the value potential of trending technologies.  

Despite witnessing a decline in the adoption and uptake of applied AI, advanced connectivity, cloud, and edge computing, we can conclude that it is partially due to their maturity levels. Mature technologies are often exposed to short-term budget dynamics more than recent ones, such as climate and mobility technologies.  

One example would be Open AI, which is predicted to go bankrupt by the end of 2024. The operational expenses for Open AI amount to $700,000 per day. Despite experiencing robust success initially, these financial struggles are hindering the company from achieving a revenue level that covers crucial expenditures.  

Despite much noise about trending technologies, we must primarily focus on the value and potential of relevant technologies for our business progress. With a careful assessment of emerging landscapes and a balanced approach towards old and innovative technologies, we can harness innovation for sustainable growth.  

Let us look at the current and upcoming trends around relevant technologies that tech companies should watch out for.  

1. Applied AI   

2. Automating Machine Learning   

3. Generative AI 

4. Next-Gen Software Development 

For a detailed insight into Inovar’s Low Code/No Code practices and approaches, download our exclusive whitepaper resource by clicking here.  

5. Digital Trust Architecture and Identity 

6. Cloud Computing Services    

If you are considering migrating to the cloud, download our Cloud Migration Guide.   

7. Cybersecurity     

Download our Security Case Study that we implemented for a large non-profit education organization.  

8. Internet Of Things    

Download our IoT whitepaper to check on scenarios you can implement for your business.  

Trending technologies of 2023 are the result of the rapid pace at which technology and innovation are evolving. From AI-driven upgrades to finding suitable solutions and upcoming technology patterns, these trends will change how we work, interact, and succeed in this digital landscape.  

Technology experts believe 2023 will probably be an exhilarating year for witnessing rapid technological advancements in terms of new tools, their diverse capabilities and how businesses will leverage them to tackle bottlenecks and changes.  

The selection and adoption of relevant technologies will pave the direction for the success of several industries in the future.  

For more understanding of 2023 Cloud Native Trends, please refer to our informational video on Top Cloud Native Trends to Watch in 2023.


  1. McKinsey Technology Trends Outlook 2023. (McKinsey Report) 
  1. Introducing GitHub Copilot: your AI pair programmer. (GitHub) 
  1. OpenAI might go bankrupt by end of 2024, ChatGPT costing over Rs 5.80 crore per day. (Business India Report) 
  2. Gmail is bringing in AI security for where humans fail. (CNET)

Generative AI is the new kid on the AI block that is scoring high marks in reducing the time taken in application development with improved productivity.  

Generative AI is estimated to boost economic growth and value by $4.4 trillion, delivering powerful capabilities to non-technical users.  

Despite the robustness of Generative AI models, they still present ethical challenges towards cybersecurity, balancing precariously between privacy and production.  

The technology has both fascinated and alarmed security experts due to its potential to create realistic and sophisticated content. 

This blog dives deep into the cybersecurity risks of Generative AI, what industry experts think of this emerging technology and some plausible solutions to prevent AI-driven cyber threats.  

Cybersecurity Curve Balls 

Generative AI has shown impressive capabilities in generating realistic results, synthesizing creative artworks, and even generating conversational responses indistinguishable from human speech.  

However, these same abilities can be exploited for malicious purposes, such as creating sophisticated AI generated phishing attacks, spreading disinformation, or even fabricating information for altering public opinion. 

Let us take a look at some curve balls Generative AI presents us with-

1. Data Privacy and Misuse 

Generative AI systems use extensive data to determine and deliver accurate outputs. This substantial data collection and storage raises concerns about user privacy and potential misuse of confidential information.  

2. Malicious Use Cases 

Generative AI is vulnerable to AI-generated phishing attacks, providing scope for impersonation, or AI generated deepfakes, making it challenging for users to distinguish between real and fake information. 

3. Bias And Discrimination 

If the training data used for Generative AI models are biased, the generated content may reflect and amplify those biases, leading to discrimination or doctored outcomes. 

4. Intellectual Property Concerns 

With Generative AI capable of creating original content, there are concerns about intellectual property rights and copyright infringement. 

5. AI-Augmented Cyberattacks 

As AI evolves, AI-driven cyber threats could become more sophisticated, with attackers employing Generative AI to create ever-changing attack patterns that evade traditional security measures. 

When asked about privacy concerns with Generative AI, Prashant Choudhary, Ey India’s Cybersecurity Partner, expounded, “Generative AI poses several privacy challenges. While some challenges have been discovered and many more are still coming out as more and more use cases pop up. And it is pervasive across all Generative AIs –Chat GPT, BERT, DALL-E, Midjourney, and so on.  

The whole model is that you use training data, and then the AI comes out with whatever output it is supposed to give. It will give (output) based on the data that was used to train the model. 

In this business, the data source is the internet and, there is a lot of web scraping involved, which brings the data to train these base models or the Large Language Models (LLMs).”( 1) 

Plausible Preventive Measures 

Although, we are unsure whether we can find foolproof solutions to mitigate Generative AI threats in cybersecurity, here are some plausible solutions we can adopt to address them. 

1. Responsible Data Usage 

2. Robust AI Verification 

Developing AI-powered malware detection and prevention solutions for detecting and verifying the authenticity of the content delivered by Generative AI; can help user identify potential risks effectively.  

3. Explainable AI 

Be sure to implement techniques that make AI models transparent and open to interpretation. This allows users to understand the decision-making process and identify potential biases. 

4. Collaborative Efforts 

Encourage collaboration between AI and researchers, cybersecurity experts, regulatory authorities, and ethical governance bodies to determine and address ethical implications of Generative AI in cybersecurity.

5. Adaptive Cybersecurity Measures 

Consistently update AI and cybersecurity policy to counter AI-driven cyber threats effectively. Also, use of AI technologies to develop proactive defence mechanisms against evolving threats can be beneficial.  

6. Informed Consent 

The use of Generative AI in various applications, such as virtual assistants, chatbots, or customer service interactions, raises questions about whether users should be explicitly informed when they are interacting with an AI system instead of a human. 

In addition to these preventive measures, EY Cybersecurity partner, Prashant Choudhary believes “Synthetic data is a very interesting conversation to address all the copyright, legal, and other concerns when it comes to training LLMs. There are multiple interpretations of synthetic data, but for this conversation, I am assuming that synthetic data is basically when you generate a data using a computer and then you use that to train the LLM.”  

He further explained that using computer-generated or synthetic data may appear to be a reasonable solution due to lower data costs, scalability, and the ability to generate multiple variants. However, this approach presents a challenge as the data will always reflect the algorithm used to generate it. 

Industry experts are participating in discussions about using anonymized or tokenized versions of Personally Identifiable Information (PII) and other sensitive data.  

With this approach, data is still extracted, but PII and other sensitive information are identified and replaced with anonymous labels to protect individual identities. This method can address privacy and other related issues and can be used to train LLM. 

There are several regulatory authorities around the world who are giving their inputs around this issue. The NIST (National Institute of Standards and Technology) has developed the AI Risk Management Framework; the European Parliament is insisting on the EU Artificial Intelligence Act; the European Union Agency for Cybersecurity under discussion about cybersecurity for AI; the US Securities and Exchange Commission (SEC) are having conversations around AI, cybersecurity, and risk management. 

Despite these factors, none of these solutions can create a solid defensive layer of protection against AI-driven cyber threats. These are not sure shot solutions but only preventive measures.  

Generative AI presents an intriguing frontier in cybersecurity, offering both innovative solutions and ethical implications.  

It is imperative to address ethical implications of Generative AI in cybersecurity, as the technology continues to evolve to guarantee a secure digital environment for all. 

By encouraging a comprehensive discussion between stakeholders, implementing responsible AI and cybersecurity policies, and deploying innovative verification techniques, we can harness the power of Generative AI while mitigating its potential risks and fostering a safer digital environment for everyone. 

For a deep learning of AI powered analytical tool development and implementation, please refer to our exclusive whitepaper resource on AI-based Gross To Net (GTN) Tool.  


  1. Cybersecurity in the age of Generative AI: solving the ethical dilemma. (

Robotic Process Automation (RPA) has transformed how businesses automate repetitive manual tasks, enhance productivity, and optimize various operations. These compelling factors have driven many company leaders to hire more RPA developers in recent years.

A study by Precedence Research predicts Robotics Process Automation Investments are expected to reach USD 23.9 billion by 2030. (1)

With a plethora of RPA tools available in the market, the onus of choosing the right tool lies with organizations. So, how do you choose the right one to streamline your process automation services? Let us take a look at Gartner’s Magic Quadrant landscape for Robotic Process Automation to help you make more informed decisions around RPA vendors.

Gartner magic quadrant


Gartner’s Magic Quadrant provides a qualitative analysis of RPA vendors based on their ability to execute and completeness of vision. The four quadrants of this landscape are-



Niche Players

Despite these factors, we would advise you to consider the unique needs and requirements of your organization when using the Magic Quadrant to choose RPA vendors. To help you in this quest, we will take you through a list of pros and cons of these RPA tools to make your selection process simple.

Let us delve into UiPath first:


1. User-Friendly Interface– UiPath provides an intuitive user interface, making it accessible to technical and non-technical users. Its user-friendly interface speeds up the automation implementation process in cloud and on-premises architecture. 

2. Product Strategy– UiPath is renowned among RPA clients. It gives way to an integrated low-code platform with enhanced capabilities like IDO, process mining, cloud delivery and PaaS. It caters to a broad range of roles within the process automation lifecycle including IT staff, business techs and integrated teams.

4. Process Mining– UiPath is a powerful tool worth its price. It independently operates to identify the best automation candidates and provides extensive discovery results. 


1. Price Point– Its powerful capabilities come with a higher price tag, making it less accessible for smaller businesses with limited budgets. However, UiPath provides a simplified starter package for new customers, which may reduce the cost of entry for customers.

2. Automation Capabilities– Although UiPath has numerous capabilities, many competitors are entering the current market that exceeds or matches these features, especially in complex orchestration, decision automation and case management.

Now let us take a look at Power Automate- 


1. Native Integration: If your organization relies heavily on Microsoft tools and applications, Power Automate can be an ideal choice due to its seamless integration with the Microsoft ecosystem.

2. Pricing Flexibility: Power Automate offers various pricing plans, including a free tier , which makes it more attractive for smaller businesses and those looking to start their automation journey without a significant financial commitment.

3. AI Capabilities: Power Automate leverages Microsoft’s AI capabilities, such as AI Builder, to enable more advanced automation scenarios with natural language processing, image recognition, and more.

Microsoft Power Automate Process Mining with integrated generative AI aims to boost productivity with greater process insights, minimized complexity of processes and consistent process improvement with automation and low code applications.


1. UX Factor– Businesses and IT users may find it tedious and perplexing to constantly navigate between Teams, PAD and Power Automate. 

2. Learning Curve for Non-Microsoft Users: Users unfamiliar with the Microsoft ecosystem might have difficulty adapting to its interface and functionalities.

What To Choose?

Lastly, UiPath and Power Automate are powerful RPA tools with a unique set of strengths. It is essential to assess the organizational needs, conduct a thorough evaluation, and even consider running a proof-of-concept to determine which one of these tools aligns best with the business automation goals. 

For more insights on Power Platform functionality, please refer to our detailed white paper on Power Platform Automation.