The essence of intelligence is learning. Machine learning (ML) is a subset of AI that focuses on computer programs capable of parsing data using specific algorithms. Such programs modify themselves without human intervention, generating the desired output based on the analyzed data. In essence, using ML techniques, the machine is trained to analyze large amounts of data and then learn to perform certain tasks.
Machine learning, an important subset of artificial intelligence, allows computers to learn from data analysis. While ML technology is expected to play a significant role in the future of many business types, it is poised to have a more significant impact on some industries than others.
Below are the IT sector industries that are most affected by machine learning and look forward to further integration of these industries and AI and ML in the coming years.
Almost all machine learning algorithms depend on huge amounts of data. The one industry that collects data from almost all of our interactions with the internet is advertising. Additionally, steep competition in the area creates demand for better and better algorithms. Some companies are already able to predict what we’d like to buy before we know it ourselves.-Robert Krajewski,Ideamative
2. Housing Construction
The housing construction industry is prime for reaping the benefits of mainstream AI/ML. Consider the housing construction process—the selection process of interiors, the renovation process, choices of selections and smart technologies. There is a wealth of data that are generated through these that can be harnessed to make smarter decisions and benefit the industry as we apply advanced analytics using AI. – Sudip Shekhawat, Interior Logic Group.
3. Credit Card Security
The discovery of fraudulent patterns in credit card transactions is one area where ML can have a huge impact as the main problem is going through so much data. Areas like medicine are not like that. The disadvantage of a faulty diagnosis in any direction is serious. It is important to associate ML with problems where it is appropriate. – Mike Lloyd, Redseal
Manufacturing will be most affected by machine learning, especially as it relates to strengthening the global supply chain. Mass shortages of PPE equipment, disinfectant products and toilet paper showed the need for accelerated digital transformation, with the widespread use of ML helping data-driven businesses improve manufacturing operations from the first concept to final delivery. – Ram Chakravarti, BMC Software
5. Banking And Finance
Global cloud migration of apps and content repositories continues and will change how data and content are indexed by enterprise search engines. This will enable big data and AI, thanks to on-demand processing and optimized storage costs. AI’s appetite for content and user behavior data will grow and drive a virtuous circle of user experience improvements, particularly in banking and finance. – Alexandre Bilger, Sinequa
As patient electronic medical records become universal, ML predictive analytics can be used to detect and prevent potential health issues before those issues actually present themselves. The better IT hardware products on the market already do that to eliminate downtime, and we should be doing it more broadly to help humans avoid health problems. – Ken Steinhardt, Infinidat
7. Consumer Goods
Consumer goods continue to be subject to real-time demand, so any investments in ML-assisted improvements in quality or distribution will be quickly recouped. Flawed products will be recognized during manufacturing, and predictive distribution will result in shorter delivery wait times and better-stocked retail, leading to higher customer satisfaction. – Luke Wallace, Bottle Rocket
9. Humanitarian Aid
Humanitarian nonprofit organizations stand to gain incredible insights into their missions through machine learning. Whether through the identification of at-risk refugees or solutions for time-sensitive problems, machine learning is poised to revolutionize the way nonprofit organizations operate. – Tal Frankfurt, Cloud for Good
Insurance companies will take full advantage of machine learning to fine-tune their risk calculations so they always come out ahead—they’re already doing this. I can’t say whether this will work out in favor of the consumer until every insurance company adopts machine learning and they must compete again on price. – Vaclav Vincalek, Future Infinitive
E-commerce has tremendous potential for growth with the help of machine learning technology. When e-commerce meets the street, plans go awry. Machine learning applications can prevent delays and ensure packages ordered online reach their destinations on time by sifting through big data and combinations of zip codes, package sizes, times of day, congestion and even the weather. – Adi Ekshtain, Amaryllis Payment Solutions.
12. Contact Centers
Contact centers are currently leading the charge when it comes to implementing ML. Adapting to Covid-19 has been particularly important for customer-facing organizations that need to find automated ways of interacting with customers. AI can help human agents deal with increased traffic while providing data insights to enable continually increasing personalization of the customer experience. – Martin Taylor, Content Guru
Sectors that require extensive copywriting—such as financial services, pharmaceuticals and e-commerce—will be most affected by ML. With all the advancements in natural language understanding, natural language generation and natural language processing, it’s logical to go for the next big thing, in which augmentation of human creativity by software is a must. – Robert Weissgraeber, AX Semantics.