Top 6 Technologies To Transform Your Business Development in 2024

  • By Yurii Dobrianskyi
  • 23-05-2024
  • Technology
Business Development

The year 2024 marks a turning point in the evolution of business. The rapid adoption and integration of advanced technologies provide numerous opportunities for monetization and your business model transformation. Reach out to our comprehensive overview of the most innovative business trends and bring high ROI this year.

Artificial Intelligence (AI)

Surprising fact: PwC’s 2023 Emerging Technology Survey shows that most US companies (73%) have implemented AI in their business. Those who have not used it are just left behind.


Of course, AI is not an answer to all your business problems. However, it is a perfect tool to tackle them more effectively. Gartner says utilizing AI will lead to a 70?crease in operational costs by 2027.

One of the subdomains gaining popularity recently is Generative AI. This type of Artificial Intelligence is designed to create new data, like text, images, or videos, based on existing data. You certainly heard about ChatGPT, which is a shiny example of what this technology is capable of. Experts say this is just the beginning of the tech revolution.

The right approach toward this technology is capable of:

  • Optimizing your workflows

Example: Using AI, your company can streamline the processes and reduce operational costs.

  • Enhancing employee productivity by automating repetitive tasks

Example: In the healthcare industry, AI capabilities help to automate patient scheduling or claims processing.

  • Speeding up Time-to-Market

Example: AI-powered data analytics help to identify market trends and make predictions.

  • Expanding your audience and increasing your clients' loyalty

Example: With the help of AI-driven targeted marketing strategies, you reach wider demographics strategically (simultaneously employing a personalized approach).

Machine Learning (ML)

Machine Learning (ML), a critical component of AI, shapes the future with its predictive capabilities. Most companies find this business technology crucial in forecasting market demands, personalizing customer experiences, and managing supply chains.

ML is different from AI. While Artificial Intelligence calculates and makes decisions based on a large dataset, Machine Learning refers to engineering aspects. It is interesting that Google was one of the pioneers in using this technique. A search history needed for advertisement suggestions is one of the applications of Machine Learning. In fact, the company has claimed to be the first machine learning company, and Bloomberg said that Google Knows You Better Than Your Doctor Ever Could.

ML algorithms are now adept at detecting subtle patterns in vast datasets, offering valuable insights into consumer behavior. The ability to predict demand and satisfy it before it emerges allows companies to remain proactive and provide relevant services without risks of failure.

Here are key applications of this innovative technology:

Predictive analytics

Machine Learning could help forecast future trends, allowing businesses to make informed decisions.

How does it work: ML gathers relevant data. Then, smart algorithms сlean data to ensure its consistency. This includes handling missing values, changing the data format, or removing outliers. After that, there is a stage of selecting relevant features that are likely to influence the outcome. The model is trained in order to learn underlying patterns and evaluated to ensure that there is no unseen data.

Fraud Detection

Fraud detection is one of the most popular applications of Machine Learning, especially when it comes to banking and financial services. With the help of fraud detection, companies can minimize losses and maintain security.

How does it work: The fraud detection mechanism is similar to the one with predictive analytics. The difference lies in the fact that models are trained to identify unusual patterns. This means that fraud detection and predictive analytics differ in their goals.

Recommendation Systems

Personalized recommendation engines suggest products or services based on users’ preferences, interactions, or browsing habits.

How does it work: Personalized Systems aim to identify similarities between users and items. This also implies using different techniques, specifically collaborative or content-based filtering, matrix factorization, neural collaborative filtering, etc.

So, now, we know particular areas in which Machine Learning can be handy. But which companies have already benefited from its use?

  • Uber

With the help of Machine Learning algorithms, the company is able to provide an established arrival time and destination. The company also matches riders with drivers based on a vast number of factors, such as availability and proximity, to improve the overall user experience.

  • Spotify

This company's personalized music recommendations are based on various behavioral patterns and listening histories.

  • PayPal

With the help of Machine Learning, PayPal enhances fraud detection and streamlines transaction processing.

Of course, that’s not the full list. However, by seeing these examples, it is evident that Machine Learning has become integral to the operations of many leading companies across various industries.

Big Data

Big Data remains one of the most significant wonders for businesses and the biggest concern simultaneously. Thanks to IoT and a broad range of smart devices worldwide, companies can process terabytes of customer data.

Just think: a single airplane produces about 20 terabytes of information per hour. What to say about more complex issues?

When considering Big Data as a core, business leaders can change their models and commercial offerings with accurate information in mind. Moreover, the plenty of insights received make it possible to reduce time for decision-making.

Such giants as Amazon, Netflix, and UPS have already leveraged data analytics to gain insights and a competitive advantage in the marketplace. One of the most significant advantages of Big Data is the ability to process different data formats from various sources. This includes structured, semi-structured, unstructured data, text, images, social media interactions, and videos.

What are the pitfalls?

First, it's still tricky for companies to find valuable data in vast amounts of information. We don't have a versatile recipe to approach this process in the right way. What’s more, implementing Big Data can be costly, and this is an obstacle for small businesses wishing to benefit from this tool.

The second danger is compliance. Cyberattacks have become more sophisticated. People share their sensitive data more actively. Companies try to resell or reuse the information collected. In the meantime, governments act and issue more data protection laws, increasing the burden on companies. Businesses should choose between 100% compliance or multi-million dollar penalties.

One of the examples was in 2017, when Equifax suffered a massive data breach. The incident has led to the loss of information of 143 million customers and huge financial setbacks for the company. Another example is the Cambridge Analytica scandal. The personal data of millions of Facebook users was compromised and exploited for political purposes.

Such cases do not mean that businesses should not use Big Data. One of the ways to mitigate negative effects is investing in data security and ensuring ethical considerations of Big Data collection. What’s more, stringent data government policies ensure transparent data handling throughout its lifecycle.

Smart Manufacturing

The concept of a smart factory has been discussed by experts worldwide for several years now. Smart Manufacturing means digitalization of all manufacturing aspects. It is important that the goal is not just to update business processes technologically but rather to automate operations and use data analysis. In a perfect world, the manufacturing domain can build completely autonomous factories by applying robotics, AI/ML, Big Data, and other Industry 4.0 technologies.

Increased use of smart manufacturing can lead to a 20% reduction in manufacturing costs. The product quality is also supposed to improve by 50%. According to McKinsey & Company, those businesses with smart manufacturing have seen an increase of 15% in productivity.

In today’s world, growing in terms of volume is not enough. Agility and quick adaptability is the key to keeping up with the current challenges. With Smart Manufacturing, traditional manufacturing processes are a thing of the past. Real-time monitoring and control, predictive maintenance, and enhanced product quality instead. Smart factories are considered a way to create an intelligent production ecosystem that is adaptive, efficient, and sustainable.

Talking more specifically, smart manufacturing offers:

Increased flexibility

By enabling adaptive and responsive business processes, it is possible to adapt production schedules and quickly respond to shifting conditions and rapidly changing market demands. This helps to create more efficient resource allocation, minimize downtime, and maximize productivity. Smart algorithms detect potential equipment failures before they occur, allowing for timely repairs and maintenance to keep operations running smoothly.

Example: Imagine an automotive company that faces unexpected fluctuations in customer demand. In this case, smart manufacturing helps to adjust production schedules swiftly. As a result, the company managed to adapt and maintain high levels of operational efficiency quickly.

Reduced costs

Now, it’s much easier to identify inefficiencies and lower operational costs. This data-driven approach enables manufacturers to make informed decisions, optimize processes, and identify opportunities for improvement.

Example: While continuing with the automotive company example, implementing of Smart Manufacturing helped reduce energy consumption costs. Real-time monitoring of energy usage has led to predicting the pattern and finding equipment with inefficient usage.

Improved efficiency

With the help of Smart Manufacturing, there is greater access to real-time data in the enterprise. Do not forget about the real-time detection of errors, which is a great way for immediate corrective action.

Example: Improved efficiency is a natural continuation of increased flexibility and reduced costs. When there are no sudden bottlenecks in every step of company development, there is no need to worry about extensive downtime or costly delays.

Smart Supply Chain

The last couple of years have made the supply chain a core pain point for most businesses: high transportation costs, a last-mile issue, the complexity of reliable supplier selection, etc. Anyway, companies still have an opportunity to enhance and optimize the process.

IoT and ML allow companies to get accurate predictive analytics and build supply chain digital twins to simulate changes and observe how they can affect the business without any risks. This way, organizations can accelerate the proof of concept stage, validate their ideas in a few days, and make informed decisions regarding efficient, innovative supply chain development.

Future Trends

The future of businesses is closely related to these technological advancements.

Apart from the trends we’ve discussed, other important fields are gaining momentum in 2024.


Transparent, secure, decentralized solutions are important for supply chain management, financial transactions, and digital identity verification. We could expect even more increased adoption of this field in healthcare, finance, and real estate industries. The same applies to DeFi and NFTs.

Why it’s important? Blockchain solutions are important because of their inherent transparency. For example, in supply chain management, stakeholders can follow the origin of goods and their movement, reducing the risk of fraud.

Edge Computing

Edge Computing helps users not rely on cloud platforms or centralized data sources. This philosophy is based on bringing applications as close to the user as possible to enable processing at greater speeds and volumes.

Why it’s important? It is important because most data is expected to come from decentralized sources by 2025. Edge Computing minimizes the time needed to reach a centralized cloud server. This is especially essential for apps with real-time responsiveness (f.e. industrial automation). Another benefit is enhancing data privacy, which comes from the ability to process sensitive data within the local network.

5G Technology

5G Technology features higher data speeds, lower latency, greater capacity, and enhanced connectivity. It is just a natural extension of 1G, 2G, 3G, and 4G/LTE. This technology is expected to interact in virtual environments. As an example, Microsoft and ThirdEye allow AR telepresence with the help of 3D holograms, meaning workers should not be on-site.

Why it’s important? The main goal of 5G Technology is to provide high data speed to facilitate faster and more reliable wireless communication. 5G Technology is also important in supporting advanced applications and services that require high data speed.


Since the tech revolution surprises us with innovations almost daily, you need to surf to gain a competitive advantage. Business development and scaling are always a result of digital transformation, so if you've already started your journey and want to take another step toward ultimate business goals, hire developers from Innovation Feel to turn your ideas into reality faster and cost-effectively.

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Yurii Dobrianskyi

Yurii Dobrianskyi, the author of this piece, is a co-founder and CEO of Innovation Feel and founder of Yurii's extensive experience as a Ruby On Rails software engineer aided him in forming strong teams that serve as a digital bridge between clients and engineers, allowing both sides to effortlessly interact.

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