Explanation:
Limitations of artificial intelligence. One of the main barriers to implementing AI is the availability of data. Data is often siloed or inconsistent and of poor quality, all of which presents challenges for businesses looking to create value from AI at scale.
Answer:
Risks and limitations of artificial intelligence in business
Businesses are increasingly looking for ways to put artificial intelligence (AI) technologies to work to improve their productivity, profitability and business results.
However, while there are many business benefits of artificial intelligence, there are also certain barriers and disadvantages to keep in mind.
Limitations of artificial intelligence
One of the main barriers to implementing AI is the availability of data. Data is often siloed or inconsistent and of poor quality, all of which presents challenges for businesses looking to create value from AI at scale. To overcome this, you should have a clear strategy from the outset for sourcing the data that your AI will require.
Another key roadblock to AI adoption is the skills shortage and the availability of technical staff with the experience and training necessary to effectively deploy and operate AI solutions. Research suggests experienced data scientists are in short supply as are other specialised data professionals skilled in machine learning, training good models, etc.
Cost is another key consideration with procuring AI technologies. Businesses that lack in-house skills or are unfamiliar with AI often have to outsource, which is where challenges of cost and maintenance come in. Due to their complex nature, smart technologies can be expensive and you can incur further costs for repair and ongoing maintenance. The computational cost for training data models etc can also be an additional expense.
Software programs need regular upgrading to adapt to the changing business environment and, in case of breakdown, present a risk of losing code or important data. Restoring this is often time-consuming and costly. However, this risk is no greater with AI than with other software development. Provided that the system is designed well and that those procuring AI understand their requirements and options, these risks can be mitigated.
See also Industry 4.0 challenges and risks.
Other AI limitations relate to:
implementation times, which may be lengthy depending on what you are trying to implement
integration challenges and lack of understanding of the state-of-the-art systems
usability and interoperability with other systems and platforms
If you're deciding whether to take on AI-driven technology, you should also consider:
customer privacy
potential lack of transparency
technological complexity
If you're considering writing a tender document to procure AI, you can seek help from the Northern Ireland Artificial Intelligence Collaborative Network(link is external).
AI and ethical concerns
With the rapid development of AI, a number of ethical issues have cropped up. These include:
the potential of automation technology to give rise to job losses
the need to redeploy or retrain employees to keep them in jobs
fair distribution of wealth created by machines
the effect of machine interaction on human behaviour and attention
the need to address algorithmic bias originating from human bias in the data
the security of AI systems (eg autonomous weapons) that can potentially cause damage
the need to mitigate against unintended consequences, as smart machines are thought to learn and develop independently
While you can't ignore these risks, it is worth keeping in mind that advances in AI can - for the most part - create better business and better lives for everyone. If implemented responsibly, artificial intelligence has immense and beneficial potential.