Cloud computing data is getting stronger and stronger, and AI will give you how to change SaaS, AI+SaaS is the future development model. With the exponential growth of AI in the SaaS industry, many large companies are already one step ahead.
In recent years, leading companies in the SaaS industry have invested heavily in artificial intelligence research and development and acquired many AI companies in order to take the lead in the competition. Shopify introduced machine learning to drive fraud defense, and Salesforce launched Einstein, an artificial intelligence platform.
As cloud computing services offer the possibility of acquiring artificial intelligence, we are in a new era, and SaaS vendors are beginning to introduce AI applications that truly solve consumer problems.
At this year's SAAS NORTH summit, I discussed the development of artificial intelligence in the SaaS industry with many experts. Here are the futures of AI+SaaS in their eyes:
The first wave: big companies are one step aheadThe traditional SaaS model is based on a large monthly subscription fee, which means that SaaS needs to continuously improve and develop customer relationships to ensure that customers continue to pay.
Leo Lax, founder of the SaaS accelerator L-Spark, said: "AI is helping SaaS companies reduce the labor associated with building customer relationships and helping SaaS companies communicate with customers in a more meaningful way."
In the past few years, only the truly well-funded SaaS giant has the resources to hire the right talent and invest in meaningful AI R&D. But only funds are not enough to implement useful artificial intelligence applications. The main basis is data, a lot of data.
Building your own platform SaaS has a good start. One of the biggest obstacles to training machine learning systems is getting a large enough data set. David Lennie, senior vice president of data and analysis at Shopify, explained:
“The biggest value is getting the largest sample size as quickly as possible, and when you have a large group of users doing the same thing as you do, this is more likely to happen. SaaS usually offers a solution Enter the market to get users and finally get more data.
Lennie believes that SaaS tools that focus on solving a specific problem rather than an "integrated" solution can better capture the right data to train machine learning applications. Once a company has access to “cleanâ€, large data sets from millions of users around the world, they can begin to solve problems. However, Kerry Liu, CEO and co-founder of Rubikloud, believes that the best success stories in artificial intelligence are still within the company.
"Whether it's Google Optimized Search, or Salesforce uses Einstein to help sales managers determine the best use cases, most of the successful applications to date have been to improve internal efficiency and internal product development.
Although most of the applications of leading companies have been internal to date, they are moving in the right direction. Experts say that artificial intelligence will improve automation, personalization, voice input and user security.
The second wave: cloud computing to level the competitive environmentUntil recently, some emerging companies in the SaaS industry really used advanced AI applications. Ardi Iranmanesh, founder of Affinio, said: "AI is heavily abused for marketing purposes. Many small companies use only basic applications such as chat bots or linear regression to claim to be AI.
However, in the past few years, the use of cloud computing services such as AWS, Microsoft Azure, Google Cloud and Oracle, using the "Artificial Intelligence as a Service" cloud tool, opened the door for smaller companies to use more advanced applications such as machine learning.
In other words, the support provided by these cloud services is at the bottom of the computing level. Cloud computing services have changed this situation, enabling small businesses to get the computing power they need to build meaningful AI applications without having any hardware or worrying about data security, and can be deployed anywhere in the world.
This more inclusive "second phase" of SaaS AI evolution has led to the emergence of many professionally segmented AI SaaS companies that address vertical market issues rather than compete with big companies for productivity or communications.
As Mobify co-founder and CEO Igor Faletski pointed out, "AI has been around for a while. The new change is that he is opening the door to developers, and more and more small startups can use AI."
Companies such as Beanworks and Mindbridge AnalyTIcs focus on the emerging vertical industry, automating "white-collar" tasks such as auditing and accounting. So far, the SaaS giant has largely ignored these areas.
EnergyX SoluTIons CTO Alex Corneglio confirms this trend. “I saw a new vertical product that can be tailored to specific market roles, and all the subtle qualities are now embedded in the products and services.â€
However, the biggest challenge in developing meaningful AI applications is to get proprietary data sets. At the SAASNORTH summit, David Lennie emphasized that the value of AI lies not only in powerful algorithms, but also in the data sets acquired by the company. He cautioned that companies should fully understand how to use data before building an AI-based solution.
Lennie suggested that in order to overcome the AI ​​data "chicken and egg" problem, emerging AI companies will have to share more data, including cooperation with traditional companies. "Maybe you can exchange some data through cooperation."
Iranmanesh expects more traditional companies to open data to AI startups. He mentioned open data companies like MasterCard and Visa. He said: "Although data regulations are always a must, but companies always want to improve their bottom line, simply storing data does not solve the problem."
However, Eli Fathi, CEO of Mindbridge AnalyTIcs, argues that when dealing with tasks such as auditing, the algorithm can be trained with public data and small data samples from companies. Beanworks CEO Catherine Dahl said that accounting tasks are very repetitive and ideal for training machine learning algorithms.
The third wave: AI's exponential growth in the SaaS industryMore mature SaaS companies have collected a large amount of user and operational data, so the intelligence of their machine learning systems has grown exponentially, and in the near future, we are likely to see more attention and solve real business problems.
Forrester predicts that by 2018, the SaaS giant will increasingly compete at the platform level, running some of its services on cloud computing services to handle the need for custom applications and more advanced AI applications to achieve a range of Automation of core business functions.
At the same time, with the popularity of artificial intelligence in all walks of life, smaller, more professional players will be able to acquire more customers and gain more data sets to train artificial intelligence. Lennie believes that if small companies focus on solving specific problems one by one, this will enable them to turn to solving similar problems and develop their platforms to the level of the SaaS giant. He said: "You can rinse and repeat this model. If you have enough time to rinse and repeat, you get a very developed platform."
Faletski predicts that companies such as Amazon, Microsoft and Google will invest heavily in the development of strong AI, build a platform ecosystem, and become the largest "AI as a service" provider. This will further open the door for small companies to use cloud services to use cloud AI applications or to develop their own algorithms.
Liu agrees: "The US technology giants (Apple, Microsoft, Google, Facebook, Amazon) realize that there will be more artificial intelligence applications in the market, and there will be more cloud computing options. Technology giants can use labor from enterprises. Profiting in smart solutions because these data and applications can be deployed on an elastic computing platform that can scale indefinitely."
The SaaS giant has developed intelligent platforms that are growing at an exponential rate, and cloud services have provided a platform for smaller vertical market players. Liu believes that people underestimate the speed of development. While some predict that Fortune 500 companies will take 10 to 15 years to apply AI SaaS products to their core business functions, he predicts that this will happen in the next five years.
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