Before I answer what will be the enterprise projects around AI, lets first look at the consumer-facing projects that use AI.
- Shopping recommendation on Amazon.com
- Song recommendation on Spotify
- Movies/Video recommendation on Netflix
- Face recognition on Facebook for tagging
- Image editing using FaceApp or similar
- Talking to Chat applications on company websites
- Google Assistant/Alexa/Siri conversational system
- Sentiment Analysis for social media customer service
- Self-driving cars
- Route planning on maps
- Facial recognition
- Object recognition
-Video surveillance (intrusion detection and object tracker)
-Audience voice mix in IPL matches in Sept 2020
(Covid season with no physical audience https://www.khaleejtimes.com/sports/ipl-2020/ipl-2020-can-do-without-the-plastic-noise)
In a similar manner, I am expecting that enterprise systems and applications will also start to implement similar projects in specifical industrial vertical as follows:
- Fraud detection of financial transactions (eg: Money Laundering, credit card frauds)
- Identifying threatening and ransom calls from Telecom call data
- Real-time Retail offers for customers
- Online Exam proctoring
- Utility (Gas/Power/Water) demand-based generation
- Improved Customer support systems (much better than today's dumb chatbots)
- Medical proactive detection from health data trackers
- Context-sensitive and time-sensitive advertisements
- Autonomous server elasticity optimizing billing and performance
- Genetic Algorithms in Drug discovery
- Genome analysis, early detection of diseases, warning and curing it is developing stages (like Cancer)
Now let's look into generic platforms that might come up in the next few years:
- AutoML Platform: An AutoML platform is that it can take any given data, prepare the data, and run multiple ML algorithms along with additional tuning to yield the best result. This would be an AI beginner's platform and will see a lot of adoption between 2020-2023.
- RPA: RPA is more of a solution than a problem. It’s the automation of tasks with AI. Most of the existing manual systems will be replaced by this.
- NLP platform: Voice to words (like writing meeting minutes, emails, etc) and words to voice. This would be for specialized enterprise applications including call center platform automation or meeting scheduler etc ( say YES instead of pressing 1).
- Semantic modeling: The majority of data is textual and needs an accurate correlation to make a lot of useful decisions, and this is where semantic modeling comes to help.
- Prognostics Platform: Predicting analytics, root cause analysis, and solution recommendation and useful for enterprises to lower-cost improve productivity. This could also be for System and Application Monitoring/Management tools.
- Optimization Platform: Optimization is a huge problem everywhere from supply chain to manufacturing, sales to marketing. Most of these would still be an industry vertical solution platform for the next 2-3 years.
- Data quality Platform: Data quality is a huge problem in enterprises, it has a lot of missing and inaccurate data, esp manufacturing. And too many data sources to look for info.
- Real-time Online ML Platform: An online machine learning platform for real-time data learning for ever-changing data. It’s not a single learning algorithm: in fact, lots of algorithms can learn online. An example would be stock market price/volume data which might never follow a pattern of the past.
- Digital Twin Platform: A digital twin is a digital representation of a physical object or system. The technology behind digital twins has expanded to include large items such as buildings, factories, cities, people, and processes.
An example would be to simulate a cricket bowler's actions and bowling style.
However, I think it will take a longer time for such platforms to evolve. It is not as simple on the ground to prepare different solutions from a common platform, unlike the standard platforms that exist today (say a Data Quality platform tool that could be used across different verticals). Tons of variations exist and manually connecting the dots is sometimes not possible as people who have knowledge move on. And a lot of system being introduced, continuity is lost and correlation is not clear at all. This why a software company cannot build generic tools/platforms for some of the real-world cases.
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