AI will continue to be used by businesses. AI is a hugely promising technology, but it also poses development and implementation problems.

Artificial Intelligence (AI) has become a part of the business world, affecting all industries from healthcare to banking, and is aimed at improving efficiency and profitability.

Fifty percent (50%) of respondents to The State of AI in 2020Opens new window McKinsey’s global survey reported that their companies had adopted AI for at least one function.

AI will continue to be used by businesses. AI is a hugely promising technology, but it also poses development and implementation problems.

Ten challenges (and solutions) for AI implementation and development

You’ll probably encounter some obstacles if you decide to develop an artificial intelligence app for your business. Understanding these obstacles beforehand can make your job easier. Here are some of the most common issues you may encounter with AI implementation and development, and how you can handle them.

1. What data sets should you use?

AI requires data quality and availability. To ensure that a company has the best and most timely AI capabilities, it should have the right data and a source of clean, relevant, accessible, well-governed, and secure data. It is not possible to configure AI algorithms in a way that controls the flow of inaccurate and low-quality data. However, businesses can work with AI experts to overcome the challenges associated with implementing AI.

2. The bias problem

Data quality is a key factor in the success of AI systems. The quality of artificial intelligence services is largely determined by the data that they use. In the absence of good data, AI implementation can be difficult due to biases. These include anomalies in the output from the ML algorithm Opens a New Window or prejudices within the training data. Low-quality data is often associated with racial and gender biases, as well as communal and ethnic prejudices.

These biases need to be eliminated. The real change can come from either training AI systems with unbiased datasets or developing algorithms that are easily understood. Many companies that develop artificial intelligence spend a lot of money on developing control frameworks, techniques, and tools to improve trust and transparency as well as identify biases in AI algorithms.

3. Data Security and Storage

The majority of artificial intelligence services are dependent on large data sets to train algorithms. While generating large amounts of data can lead to better business opportunities on the one hand, they also create data storage and security problems on the other. Data leakage is more likely to occur when more data is created and more users can access it. Data security and storage issues are now global issues since millions of users generate this data. Businesses must ensure they are using the best data management system for sensitive data, and for training algorithms to be used in AI applications.

4. Infrastructure

Artificial intelligence solutions are changing our lives, and they provide us with daily utility via high internet speeds. AI systems can only achieve this speed if a company is equipped with a high-performance infrastructure. Most organizations still use outdated infrastructures, devices, and applications to run their IT operation. Management is often afraid of the costs required to upgrade the systems. They choose to ignore AI altogether. While companies that develop artificial intelligence, or adopt it, should be prepared to take their IT services up to a higher level of quality and efficiency than ever before. However, replacing outdated infrastructures with traditional legacy systems is still a major challenge for many IT firms.

5. AI Integration

The first challenge in implementing AI for business is the need to integrate AI into existing systems. This requires AI solution providers who have extensive experience and expertise. It is not enough to simply add new plugins on your website to make the transition to AI. It is important to protect the data input, storage, and infrastructure from any negative effects. It is important to ensure that the systems are compatible with AI and can run smoothly. Once the transition is complete, employees will need to be properly trained on the new system.

6. Computation

Information technology faces many challenges and must be updated constantly. No other industry has grown as quickly. The biggest challenge the industry has faced is achieving enough computing power to handle the massive volumes of data required for AI systems. It can be difficult to reach and finance that level of computing, especially for small and startup companies.

7. Niche Skillset

Searching for and training people with the proper skillset and expertise for artificial intelligence implementation and deployment is one of the most frequently-referenced challenges. Lack of knowledge hinders the adoption of AI technologies and hinders companies on their AI journey. This is a major challenge for the IT industry. Companies should consider spending extra budgets on AI app development training or hiring AI developers. They can also buy and license capabilities from larger IT companies.

8. Rare and expensive

As we have already mentioned, AI integration and implementation opens a New Window and implementation requires a specialist with certain skills and expertise, such as a data scientist. The fact that AI experts are costly and rare in the IT market is one of the biggest challenges in implementing AI. Small-budget companies will find it difficult to hire the specialists needed for the project. Once you decide to develop or implement an AI-based system, you will need to train them constantly, which could require high-end experts.

9. Legal Issues

Companies need to pay attention to several legal issues that surround the development and implementation of artificial intelligence apps. The algorithms’ data collection from users is very sensitive. Incorrect algorithms and data governance systems will lead to incorrect predictions and ultimately loss of profit for the company. It can also violate laws and regulations, which could lead to legal challenges.

10. Explainability

Human nature is to only trust things that are easy to understand. Uncertainty about how deep learning models can be used to predict outputs and form solutions for problems is a critical AI implementation challenge. AI must be transparent in its decisions and algorithms. Organizations must develop policies to examine the impact of AI on decision-making, conduct frequent audits, and provide regular training.

The AI Implementation Roadmap

Artificial intelligence application development is now a common practice in the IT industry. Businesses must learn about AI and how to implement it and overcome AI development and implementation challenges. The AI roadmap is a tricky one, there’s no doubt about that. But if you prepare and follow a step-by-step AI implementation plan, it can make the process easier.