At Trustify Technology, we have been helping our clients in different business sectors explore the benefits of using artificial intelligence (AI) and prove them with relevant use cases.
In 2025, new developments in artificial intelligence (AI) seem to be happening all the time, everyday. And the business world is becoming more aware of the many possibilities provided by this cutting-edge technology. There are many visible benefits of using artificial intelligence in companies that care about streamlining business operations. Simplifying research and development, enabling advanced data analytics at scale, and automating repetitive tasks, etc. are just some of those.
However, as with any new technology, there are potential barriers to implementing those innovative solutions. This blog will explore the five biggest challenges in the AI engineering industry and how they can be overcome.
And if you are interested in powering your business with artificial intelligence, check out Trustify Technology’s AI services offerings.
Read on to see what are the biggest challenges in AI development today
Data Challenges in AI
The output of an AI solution is only as good as the data input: that is, the quality and quantity of data fed to train AI models must be as high as possible to maximize for the best possible results. To do this, it is important to develop data augmentation methods and optimized pipelines so that datasets can produce valuable, accurate results.
These solutions can be applied to other areas such as transfer learning (where machine learning is trained on one task and refined to perform another task), as well as synthetic data generation (in its simplest senses, this is hand-crafted data that simulate real-life situations and can be used in real-world applications and AI algorithms).
Modernizing Legacy Systems and Navigating AI Ethics
Many businesses today still rely on outdated, legacy software to run their operations. If you are still using outdated technology, it can be harder for your businesses to take full advantage of AI. This is especially important when it comes to handling large volumes of data, making different systems work together, and scaling up operations.
One common solution to this problem is using middleware. Middleware acts as a connector between old software and modern applications. It allows outdated systems to work with AI tools, enabling your businesses to use AI across operations without the high costs or disruptions that come with replacing or modernizing large, complex legacy systems.
This approach helps your businesses unlock the benefits of AI while keeping the existing systems in place.
Additionally, with the proliferation of artificial intelligence (AI) adoptions, companies are being required to operate it ethically. As business leaders are betting in the use of AI, ethical practices and transparency standards must be built in from the start.
Making AI Systems Scalable and Efficient
One of the biggest challenges in software engineering and AI development is making systems scalable. It can be hard to expand your AI systems without affecting their performance and quality.
Training AI models often needs large amounts of data, which can slow things down and put extra pressure on your systems. The good news is you can leverage cloud-based solutions to fix this, making sure resources are used efficiently. Cloud-based solutions let businesses use flexible computing power and storage to build systems that grow easily while keeping costs under control.
With cloud-based systems, you can combine different computing resources in virtual machines and link them to cloud storage. This makes it possible to scale systems efficiently and affordably. Ultimately, using the cloud technologies allows businesses to quickly adjust their resources to meet changing needs as they grow.
Keeping Pace with Innovations in AI Engineering
According to Bloomberg, the generative AI market is expected to reach the value of $1.3 Trillion by 2032. Furthermore, data from a report by market.us showing that the Global Artificial Intelligence Market is projected to reach a valuation of USD 3,527.8 billion by 2033. Thus, keeping pace with continuous innovation is a challenge for modern companies, even for the most resource-rich organizations.
The best way to stay at the forefront of AI is to have a system of continuous learning, collaborating with AI research groups around the world. Ideally, all AI stakeholders should be actively engaged in learning, studying the latest developments in AI and sharing what they learn with their colleagues. This can help build up AI interest in your organizations as well as sustainability in engineering AI solutions.
Bridging the AI Talent Gap: Solutions for a Skills Shortage
One of the biggest barriers to AI engineering effectiveness is the role of human developers in developing AI. For example, as per Salesforce, 60% of public-sector professionals say that a shortage of skilled AI engineers available is the biggest barrier to AI implementations. It’s true that there is a shortage of professionals in advanced technologies, but with the emergence and rapid development of artificial intelligence, the shortage of professionals in this particular area is higher than ever.
As finding the right talent has become increasingly difficult, many organizations are turning to training and development programs that integrate AI and machine learning into the daily works of employees. This can help instill an AI culture among employees and ensure that employees are aware of issues such as risk, regulatory compliance, and ethical use of AI.
A Roadmap for Solving Key AI Development Challenges
While some of these challenges may seem daunting, the good news is that none of them are too great to be overcome. What is needed is a carefully planned approach to AI implementation that considers these challenges and addresses them along the way.
From our extensive experience working with AI technologies and supporting businesses through their AI deployments, we recommend the following to overcome AI challenges:
- Set clear goals early on: Define what you want to achieve and what your business needs from AI right from the start.
- Choose the right data sources: Evaluate where your data is coming from to make sure it’s accurate and useful for training AI systems.
- Build effective algorithms: Develop systems that can process the data and turn it into meaningful insights for your business.
- Plan how AI fits into your business: Look at different ways to connect AI tools with your current systems and processes to ensure smooth integration.
- Prioritize ethics and responsibility: Make sure AI is used in a fair and responsible way throughout your organization.
- Engage with users: Work closely with employees and customers to encourage them to embrace and use the new AI solutions effectively.
Final-words
It can be difficult to get AI engineering right on your own, especially when tailoring it to your business. Furthermore, if you are not in the IT business with adequate resources and expertise, doing AI engineering in-house is even more challenging. That’s why teaming up with a skilled partner is the best choice for a smooth and successful AI implementation.
At Trustify Technology, a top software development company in Vietnam, we work with businesses around the world to use AI for digital transformation and intelligent decision-making. Check out our AI services and reach out to discuss how we can help you.