All challenges and limitations AI is currently facing may be divided into three groups:
- Data quantity and quality: that is a common fact – the AI system you build depends on data amount and its quality.
AI learns from data, and it needs much more data to identify patterns than human needs. In addition, if data has any imprecision, it will reflect on the outcomes.
Data scarcity is the issue occurring when the question of its unethical use arises, and developers use only available local data to create AI systems for worldwide implementation.
2. Data labeling: most AI systems using machine learning or deep learning require data to be labeled since a large share of data today is comprised of visual elements like images or video.
Businesses lack people to label those amounts of data. There are a few ways to solve this issue, like outsource labeling, for instance, or use scripts to automatically label data.
3. Task-specific narrow focus: modern AI systems can perform some clearly defined tasks. AI designed to drive a car cannot help with a disease diagnostic or give you financial advice. AI created to detect financial fraud cannot detect fraud in health care, and so on.
Artificial Intelligence is not able to transfer its experience from one set of circumstances to another.
4. Bias factor: let’s face it – AI today cannot be biased. It is not conscious, and cannot have its own opinion. AI makes decisions basing on the available data only.
Still, we got the case when Amazon AI recruiter decided not to hire an applicant over a gender. It happened because data for training AI come from people. People tend to have stereotypes, and Amazon technical departments consist mostly of men employees. This way, the AI recruiter learned that male applicants are preferable, and eliminated all resumes that included the word ‘women’.
5. Computational capacities and computing power: AI needs a lot of data to learn. And they need even more afterward. The most common bottleneck comes to the front when machine learning and deep learning algorithms demand more and more cores, GPUs, and other hardware to work as expected.
That’s the AI scaling challenge most businesses struggle globally.
6. Scarcity of AI experts: the contradiction of having only technical knowledge or business understanding occurs in almost every business.
C-level cares about the real business models, while AI specialists dive into a technical part of the project and don’t go into details of how their algorithm will work or which problems solve.
Top AI talents able to intersect a tech solution with a real business task are nearly unicorns.
7. Lack of general understanding: AI adoption is usually a part of a company’s business strategy. Every strategy involves team members from both tech and non-tech teams. Non-technical employees may spread stereotypes regarding AI, ranging from mundane things, like hiring a team of in-house Data Scientists, previously worked in Google or Facebook, to some sci-fi myths.
The lack of AI understanding slows down AI adoption and encourages working towards impossible goals. The solution is simple: education. Getting some knowledge will help to align AI possibilities with business expectations.
8. Failure of business alignment: AI strategy is always part of a company’s business strategy. That’s an assumption.
Some companies dive into AI without even setting any objectives, identifying KPIs, or intention of tracking ROI. Usually, this is the result of the lack of a general understanding of AI. And the solution is the same – educate yourself and get some more knowledge about AI first.
9. Integration issues: most companies have been successfully functioning for a long time before they decided to implement an AI solution. And after making a decision, they need to change a lot since AI is not a CMS plugin.
It’s crucial to make sure that there is a clear business strategy, and everyone understands what’s going on to overcome integration concerns. After integrating AI into an everyday workflow, it’s vital to train all team members on how to use the new model.
10. Legal issues: before implementing any AI solution, there are a few law questions. For example, GDPR compliance. Data privacy, security, and safety became crucial with GDPR. Since AI algorithms employ big data, it’s necessary to have the answers for questions like ‘How to handle data in a GDPR compliant way?’, ‘What we collect, and how we store this data?’, etc.
And there’s still no answer to questions of who is responsible and what to do if AI causes damage.
- AI technology has its limitations. These challenges are divided into three groups: Data, People, and Business dependent.
- Data challenges mostly relate to data volume and quality. People challenges touch on general understanding AI, and the lack of experts on the market, while business issues are mostly on business alignment and legal planes.