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5 Tech Trends Shaping Businesses in 2024



The potential for technology as a societal and enterprise progress catalyst left many adopters enthusiastic about investing in innovation in the first half of 2023. However, making a successful impact isn’t based on adopting the freshest and allegedly most ground-breaking innovation available—it’s about making an investment that will deliver continuous and cumulative improvements to the organizations. 

Therefore, before implementing new trending technologies, tech executives need to enter a deliberate and realistic mindset, ensuring that each effort is intentional and no time is wasted on experiments. 

To facilitate this task, our article offers a detailed and insightful look at top enterprise technology trends and breaks down their impact on business operations in 2024.



1) Tipping the enterprise process efficiency scales with generative AI


Albeit the opinions about generative AI have become polarized due to hype and over exaggerations regarding the technology's opportunities, it still remains one of the enterprise software trends that will make it to 2024 and evolve. It is expected that in the following years Gen AI will move beyond providing non-technical employees with easy access to previously complicated knowledge—the technology leaders will discover new capabilities for augmenting every process in their enterprise.

  • Enhanced asset management provided by generative AI making accurate budgeting calculations and financial forecasting allows enterprises to plan their revenue and expenses with greater certainty. By using GenAI to comb through historical data and locate promising trends, organizations manage to peer through multiple layers and identify new opportunities for profit.

  • Automated data entry performed by GenAI tools relieves employees from tedious hand-managed tasks and assists with processing and systemizing large documents within seconds. Such an improvement to data processing and organization makes around 72% of executives optimistic about increasing productivity across their organizations.

  • Intelligent fraud and suspicious activity detection offered by generative AI helps with enhancing already existing systems with more than satisfying. According to JPMorgan Chase, leveraging GenAI tools for fraud identification, led to an increase in customer satisfaction, more accurate results, and allowed to reduce costs.

  • Smart contextual search across the company systemsfacilitates data management and information retrieval, enabling employees to complete tasks and generate reports at a faster rate. Meanwhile, customers receive fast and detailed responses to their individual queries instead of having to refer to static FAQ pages.

  • Personalized content generation enabled by combining enterprise databases with generative AI creates new opportunities for customer operations and unlocks hyper personalization options.  Within such combinations, employees only need to enter relevant prompts for the models to produce exclusive and specialized texts, visuals, or messaging curated to the specific partner needs deduced from detailed data analysis.

It’s important to remember that the value of the GenAI technology remains directly connected to the quality of data and the skills of engineers working on it. Current off-the-shelf solutions often lack the enterprise-specific context necessary for generating high-value prompts and enabling integration with other tools.

Accordingly, all the enterprise technology trends related to GenAI emphasize finding and onboarding teams of AI strategists and data scientists capable of developing solutions tailored to an individual organization's tech and security requirements and its in-house development teams. 

2) Expanding machine learning operations

Also known as MLOps or industrialized machine learning, machine learning operations is an umbrella term for processes and practices of accelerating and scaling the development of ML models within an enterprise, complete with support, performance monitoring, and continuous improvement. 

MLOps are realized within an ecosystem of software and hardware solutions with a great focus on interoperability, which delivers new levels of value and functionality to enterprises. With the help of industrialized ML, businesses can accelerate analytics, timely identify production issues, and turn PoC projects into fully polished products within a shorter time frame.

While initially industrialized machine learning was explored by a limited number of leaders like Hewlett Packard and Databricks, the commitment to adopting this innovation has soared, with more companies dedicatedly investing and including MLOps into their budget. According to IDC, industrialized ML will be implemented by around 60% of businesses by 2024. 

The spike in commitment signifies the transition of ML technology from its pilot stage to an entirely new phase of proactivity and decisive adoption steps. The enterprises are setting course towards finding and leveraging competitive advantage with the help of industrialized machine learning—and they are confident about succeeding.

3) Injecting intelligence into applications

AI-enhanced software applications have also joined the leading enterprise software trends as Gartner predicts that by 2026 around 30% of newly developed apps will be using artificial intelligence for improved decision-making and personalization options.

The growth of intelligent app popularity stems directly from the increase in the use of mobile payment options, wearable apps, and apps enabling VR and AR features. Driving a more pleasant user experience, ensuring faster and more accurate user command execution, and enriching users with more detailed analytics requires more technological flexibility—as a result,  modern apps are often equipped with AI-algorithms and machine learning. 

However, what makes intelligent apps a valuable addition to the future of enterprise?

Augmented analytics

AI-powered apps can process large volumes of data from various data sources (user interactions, files, transactions) and deliver them straight to users as visualized real-time insights.

Efficient decision-making

With intelligent applications taking care of omitting irrelevant and obsolete data, leaders get more informative and comprehensive reports on enterprise processes, which allows them to make more impactful decisions.

Streamlined sales operations

Intelligent apps assist sales reps with successful lead generation and nurturing by scoring leads and identifying ones with the highest probability of conversion based on behavior data analysis.

Management shortcut discovery

When used as a part of enterprise IT technology and tapping into their advanced analytics, intelligent apps gather data on business processes and employee performance, revealing options to cut repetitiveness and improve employee engagement.

Another reason why intelligent applications made it to enterprise software trends is their ability for continuous improvement. Based on ML models and AI algorithms, such apps can be adapted to new requirements and prompts, updating their output and maintaining high-quality performance.


4) Building new business models with web3 development

The further enterprises proceed into the future and opportunities presented by digital transformation, the more applications web3 finds across different sectors. Beyond cryptocurrency trading, web3 technology is now among the enterprise technology trends dedicated to creating solutions for various purposes.

Web3 is gaining traction across such sectors as BFSI, media and entertainment, logistics, and retail because it allows harnessing the most important element of modern business interactions—trust.


  • Reduced authority intervention The trust in centralized authority is gradually decreasing as more and more business relationship participants see them less as regulators and more as intruders who use their personal data and assets to their advantage. The purpose of a web3-based framework is to give more power to businesses and protect their ownership of their assets. No data is used without other parties knowing. No participants benefit more than others. Everyone shares equal responsibility.

  • New business model development The greater focus on transparency and trust makes web3 development one of the top enterprise software trends for discovering new business models. After all, the lack of centralized authority doesn't necessarily imply chaos or a complete lack of monitoring. Instead, it prompts the creation of new governance approaches known as Decentralized Authority Organizations (DAOs). These entities inject democracy into business management, overseeing fair and transparent business operations through automation, smart contracts, and sharing a single source of truth between all investors and stakeholders. Accordingly, DAOs enable new opportunities for building and managing businesses for creative talents who wish to reshape their industry.

  • Shaping web3 regulations While web3 technology is meant to be anti-system, it must still be integrated into the business world to drive value. The lack of firm regulations and experience with web3 utilities have slowed the implementation of web3 for enterprises. However, the only way for the leading tech minds of the enterprise to accelerate the adoption and create a reliable system of regulations is to actively participate in normalizing and advocating web3 technology. It's always the early adopters who lay down the groundwork for rules and standards everyone will follow. Given the growing investments and need for web3 talents, business leaders are well aware of such opportunities.

Aside from fueling processes with trust, implementing and forwarding web3 technology allows businesses to reach much-needed sustainability. The increased investment in decentralized technology prompts the development of more energy-efficient solutions. Such practices are crucial for organizations wanting to achieve their maximum potential in industry 4.0 before proceeding to industry 5.0.

5) Reimagining enterprise infrastructure with cloud computing

While some technologies mentioned in the top enterprise technology trends list are merely going from being experimental to rooting themselves in a new business reality, cloud computing is the exception. With most enterprises having already moved from traditional platforms to the cloud, this technology continues to make a long-lasting impact on the enterprise processes' present and future. Simultaneously, technology leaders keep making  plans to optimize and modernize their cloud infrastructure, which is why cloud computing is expanding into the following innovative directions:


  • Combined cloud and edge computing Although edge computing (based on the principles of processing data closer to its source) is already part of trending technologies, the way it complements the cloud tech allows for unlocking new meanings of flexibility regarding a company's infrastructure and data storage. In synergy, these two types of computing let organizations lower data transfer costs and achieve better latency without compromising data autonomy and reinforcing cybersecurity. 

  • Hybrid models as the middle ground The rate for migration has considerably slowed down, but not because businesses don't consider cloud one of the top enterprise technology trends worth exploring. In 2022, around 82% of business owners invested in hybrid cloud adoption to achieve a middle ground between on-premise and cloud-based apps. Such a strategy especially benefits developers regarding workload distribution and cross-department collaboration. Additionally, practicing hybrid cloud models enables more flexibility regarding costs and investments. After all, migrating some solutions entirely to the cloud can be a very complicated and resource-intensive process without a predictable ROI. 

  • Getting smart with AI/ML As expected, the versatility of artificial intelligence and machine learning made its way to the cloud following the intent to optimize the workload. The introduction of AI-powered tools facilitates cloud computing service and security monitoring and increases the flow of real-time insights. The latter is particularly important for decision-making and data analytics since important information is shared between all relevant departments operating in a collaborative cloud space, reducing communication issues. The combination of cloud and AI also resulted in Artificial Intelligence as a Service (AIaas), providing users with cloud-based AI platforms for simple and low-risk AI testing.

In the foreseeable future, cloud computing will be playing a detrimental role in enterprise IT technology. This forecast is backed up by IDC estimates, according to which the spending on software and hardware cloud components will surpass $1.0 trillion by 2024. Therefore, businesses are increasingly investing in expanding their internal development teams with professionals equipped with necessary cloud computing skills and exploring the potential of the latest cloud-powered directions.