AI (Artificial Intelligence) concept. Deep learning. GUI (graphical user interface).
Established in 2005, Rimini Street is an enterprise application support service provider for companies such as SAP, Oracle and Salesforce.com. The goal is to achieve sales of $ 1 billion by 2026.
To achieve this, the company evaluated a variety of AI solutions. But they all fell short.
“In the end, we used a combination of best-in-class open source technology and tools to build our own AI platform that is very powerful, intelligent, extensible and scalable,” said Brian Slepko, executive vice president of Global Service Delivery in the Rimini Street. “Our self-built AI platform and components give us the flexibility that we need along with the data intelligence. Therefore, three patents have now been registered.”
It was a risky strategy. After all, many AI projects do not go beyond the proof-of-concept phase.
However, in order to stay competitive, companies often have no choice but to make big bets with AI. “Building your own AI solution takes a huge budget, research, time and resources,” said Swapnil Bhagwat, vice president of marketing at Atlas Systems. “But AI capabilities for any company have the potential to become a huge competitive advantage.”
Then what factors need to be considered when considering whether to create AI?
Let’s take a look:
Is AI Really Needed? Yes, the temptation is to assume that AI is some kind of elixir for any kind of problem. But that’s a big mistake. While AI is very powerful, simpler technology can be a better approach.
“Identify your business challenges first – don’t jump on the AI / ML bandwagon because it’s the topic du jour,” Slepko said. “Put together a very structured plan that outlines your vision, strategy, how you want to use the technology, and the desired business outcomes that you want to address.”
Experience: If your company has little real-world experience with AI, the first thing you should do is look at standard products. For example, a chatbot could be a good place to start, for example with basic customer support services.
Another idea is to look at your company’s IT applications and apply the AI functions. They can be very powerful, especially since they are already built into your data sources.
Don’t do anything: If you decide to build your own system it is still wise to look for ways to get help from other companies. An example would be to outsource data labeling (the process can be difficult and tedious).
Skills: Is your organization AI ready? Usually the answer is no.
According to Mingkuan Liu, Senior Director of Data Science at Appen, here are a few questions to ask:
- Is AI critical to your business or project?
- What is the investment level for this project?
- What is leadership visibility and involvement?
- What is the focus on bias, risk, governance, and ethics?
Note that Appen has a useful free AI Readiness tool to help you out.
Data: This is often underestimated. However, data is essential to the success of AI.
“It’s important to understand that generic AI platforms require massive data integration efforts and an army of data scientists to implement,” said Bill Scudder, vice president and general manager of AIoT Solutions at AspenTech. “Organizations should consider finding solutions that seamlessly integrate data sources and deliver actionable insights. That way, hundreds of cases can be run within minutes to identify the best ways to increase the margin. “
Tom (@ttaulli) is a Startups Consultant / Board Member and author of Artificial Intelligence Fundamentals: A Non-Technical Introduction, The Robot Process Automation Guide: A Guide to Implementing RPA Systems and Implementing AI Systems: Transform Your Business in 6 steps. He has also developed various online courses, for example for the programming languages COBOL and Python.