It is impossible to overestimate the importance of artificial intelligence in the corporate world and in modern human lives. Serhii Pospielov, AI practice lead at Exadel, examines the top ten challenges enterprises face in AI development and implementation and shares ten ways to overcome them.
Artificial intelligence (AI) is permeating the business world across different industries, from banking and finance to healthcare and media, with goals to improve efficiency and increase profitability, among others.
Fifty percent of respondents in The state of AI in 2020Opens a new window global survey by McKinsey reported that “their companies have adopted AI in at least one business function”; the numbers are forecasted to keep growing in the coming years, doubling the revenues generated by AI.
Companies are going to continue implementing AI in business. However, despite its huge potential, AI also creates development and implementation challenges.
Ten Challenges (and Solutions) for AI Implementation and Development
If you’re thinking of artificial intelligence app development for your company, you’ll likely come across some obstacles. Understanding them beforehand may ease your work. These are the most common problems with AI development and implementation you might encounter and ways in which you can manage them:
1. Determining the right data set
Data quality and availability are necessities for AI capabilities. For a company to ensure the most efficient and timely AI capabilities, it should use the right data sets and have a trusted source of relevant data that are clean, accessible, well-governed, and secured. Unfortunately, it is impossible to configure AI algorithms to control the flow of low-quality and inaccurate data; but businesses can get in touch with AI experts and work with the owners of different data sources to overcome the challenges of implementing AI.
2. The bias problem
The quality of AI systems depends on the data they are trained on. Good data are key to good artificial intelligence development services. If good data are lacking, companies face numerous AI implementation challenges stemming from biases — anomalies in the output of ML algorithmsOpens a new window when producing results based on discriminatory assumptions made during the machine learning process or prejudices in the training data. Low-quality data often go along with racial, gender, communal, and ethnic biases.
Such biases must be eliminated. Real change may come either from training the AI systems with unbiased data or from the development of easily-explained algorithms that can be easily read. Furthermore, many companies that develop artificial intelligence invest heavily in developing control frameworks and techniques to drive better trust and transparency and to identify bias in AI algorithms.
3. Data security and storage
Most artificial intelligence development services rely on the availability of large amounts of data to train the algorithms. Although generating large volumes of data provides better business opportunities, on the one hand, it simultaneously creates data storage and security issues on the other. The more data is generated and the more users have access, the higher the chances of data leakage into the hands of someone on the dark web. Data security and data storage issues have reached a global scale, as this data is generated from millions of users around the globe. This is why businesses need to ensure that the best data management environment for sensitive data and training algorithms for AI applications are being used.
See More: AI Implementation: What Does It Take to Adopt Artificial Intelligence in Business?
4. Infrastructure
Artificial intelligence-based solutions change our lives and provide daily utility through high internet speeds. AI systems achieve these speeds under the condition that a company has suitable infrastructure and premium processing capabilities. However, most organizations still rely on outdated infrastructures, applications, and devices to run their IT operations, as management often gets scared of the expenses needed to update the systems, choosing instead to reject implementing AI at all. Although companies that develop artificial intelligence or adopt it should be ready to bring their IT services to a new level, replacing outdated infrastructure with traditional legacy systems remains one of the biggest challenges for many IT companies.
5. AI integration
Challenges with implementing AI in business first arise from the necessity of integrating AI into existing systems. It requires the support of AI solutions providers with extensive experience and expertise. Transitioning to AI is more complicated than just adding new plugins to the current website. Infrastructure, data storage, and data input should be considered and secured from negative effects. Compatibility with all AI requirements, as well as smooth operation of the current systems, must be ensured. Additionally, once the transition is over, the employees must be given proper training on working with the new system.
6. Computation
The information technology industry encounters many challenges and constantly needs to keep updating. No other industry has developed as fast. But achieving the computing power to process the vast volumes of data necessary for building AI systems is the biggest challenge that the industry has ever faced. Reaching and financing that level of computation can be challenging, especially for startups and small-budget 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. A lack of knowledge prevents organizations from adopting AI technologies smoothly and hinders organizations on their AI journey. Because this is a significant challenge in the IT industry, companies should think about spending additional budget on artificial intelligence app development training, hiring AI development talents, or buying and licensing capabilities from bigger IT companies.
8. Expensive and rare
As mentioned above, AI integration, deploymentOpens a new window , and implementation require a specialist like a data scientist or a data engineer with a certain level of skills and expertise. One of the major challenges with implementing AI in business is that these experts are expensive and currently quite rare in the IT market. Companies with a small budget, then, face a challenge to bring in the suitable specialists that the project requires. Moreover, once you decide to implement or develop an AI-based system, you’ll have to provide constant training, which may require rare high-end specialists.
9. Legal issues
There are a lot of legal concerns around artificial intelligence app development and implementation that companies need to be concerned about. The data the algorithms collect from users are very sensitive. Erroneous algorithms and data governance systems installed in AI applications will always make incorrect predictions and bring losses to the company’s profit. Moreover, it can violate laws or regulations, putting the organization in the trap of legal challenges.
10. Explainability
It’s human nature to trust only things that are easily understood. One of the critical AI implementation challenges is the unknown nature of how deep learning models and a set of inputs can predict the output and formulate a solution for a problem. Explainability in AI is required to provide transparency in AI decisions, as well as the algorithms that lead to them. This means that organizations must work on the policies that inspect the impact of artificial intelligence on decision making, provide frequent audits of their systems, and have regular training.
The AI Implementation Roadmap
Artificial intelligence app development has become a part of IT industry life. Nevertheless, businesses have to learn how AI works and how to overcome AI implementation challenges and AI development challenges with minimal risks and losses. There’s no doubt that the AI implementation roadmap can be tricky, but getting familiar with the challenges beforehand and adopting a step-by-step AI implementation strategy can ease the process.
What are the challenges that you’ve faced in AI development and implementation? Tell us about your journey on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to know!
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