How to Set Your AI Project Up for Success

An industrial AI platform is a powerful tool that can help enterprises digitize their processes, increase efficiency and reduce costs. But how do you get started? AI projects require specific steps to set up for success, especially if your organization is a global giant with many hoops to jump through and tons of data to sort through. However, this should not stop you from investing in AI. As a matter of fact, by utilizing a software like Machine Learning in an operation this large should only encourage you to get started right away. When Fortune 500 businesses use AI and ML tools to improve operations by 5% or cut energy consumption by 10%, they will not only save millions of dollars every year, but they will also have a significant global impact in sustainability and reduce their carbon emissions. Our objective is to assist you in filtering past the marketing noise of AI and figure out which platform is best for your needs so that you may see results fast and start reaping the benefits of AI and ML. That's why we've put up this list of seven questions everyone should ask before starting their AI journey.

Before we begin, you should first assess your present position to determine if you are prepared to adopt AI/ML SaaS solutions.

1. What are the main problems you would like AI to help you achieve: Are your machines constantly breaking down and repairs are taking too long to fix? Are your machines consuming a ton of energy and you think you can become more energy efficient? Are product recalls and defects hurting your brand and costing you millions in wasted product? Are you looking to improve your employees work environment and prevent catastrophic human-error actions from happening? Or perhaps, you want to optimize your entire manufacturing process (from start to finish) and would like to know where and how you can improve your operations?  
2. What data is available, and what needs to be collected? Now that you have an idea of what you want to improve, the next step is to make sure you have enough data for the Machine Learning algorithm to learn from. Let's say you are looking to catch anomalies in your bottle operations by catching dents in the bottle caps. In order to catch these anomalies, you will need a HD camera equipped to take millions of pictures to then funnel these images into the AI software to analyze and alert you if it catches any issues. Take it one step back, and if you want to prevent the bottle cap from even creating any dents in the first place, you might want to look into the machine that is creating and pressing the cap on the bottle and using devices that can collect this data (i.e. vibration, temperature, or even speed). The biggest hurdle in this step is making sure you have the right infrastructure -- hardware devices -- that are collecting the information you need, you have "clean" data that can be used to train a ML algorithm, and these devices are compatible with the AI software you are looking to invest in. Pro Tip: if you are a global company where security is very important, you will want to make sure the AI software can operate at "The Edge" of the device and also the "cloud".
3. Are your team members also invested in making this AI project work or do you need a pilot program to convince them on this? It's important to make sure everyone is on board with the new technology, from executives down through engineers and managers. If there is any resistance or hesitation by those in power within your company then this project will never see completion due lack of commitment from top brass - which means you could lose out big time!

So now what?

Now that you have a clear idea of your goals, the right data in place and everyone's on board with investing... what's next? It's time to find the right AI/ML partner.  

There are a lot of AI/ML providers in the market, so how do you choose? Here are the 7 questions you must ask the next AI software provider:  

1. What's your Industry focus? - Make sure the provider has experience and expertise in your industry or sector. Don't try to reinvent the wheel here - partner with someone who knows your business inside out and ask for specific case studies to see the value they have provided. The industrial industry is more complex and has many moving parts, so make sure they are experts in this field.
2. Are you an end-to-end solution platform? This is a fantastic question if you have a process like a manufacturing plant with numerous stages. An end-to-end solution is simply a way for the model to learn all of the steps between the start and finish phases. This is a deep learning method in which all of the various components are simultaneously trained, rather than one at a time.
3. Are you an open platform? The implementation of an AI software is dependent on data, so by having an open platform, the AI provider can connect to any machine. Automated AI pipelines opens up a lot of possibilities for data scientists because it allows them to utilize more modern machine learning algorithms and capabilities. Services that are cloud agnostic and developed according to open common standards give businesses more control over their corporate cloud solutions. This enables enterprises innovate faster and improve their products at the forefront of safety and innovation.
4. Can you create REAL-TIME ML models deployed at the edge of the device? When it comes to security and fast results, you should ensure that the AI provider has the ability to deploy ML models at the device's edge with the options of leveraging a hybrid cloud infrastructure.
5. Do I own my own data? This is by far the most crucial thing to ask from the start! This is YOUR machine and YOUR data, so be sure you have full access to it! The AI supplier should give you complete access to this information and shouldn't bind you into a contract where you lose control of it. Check how open they are about this information or read the fine print.
6. What are the optimization costs and do I need data scientists to derive value from the data? In the last few years, AI and ML have advanced dramatically, and a PhD or Data Scientist is no longer required to implement these algorithms. Hiring a team of these people can be costly and time-consuming, with no assurance that they will be able to help you accomplish your objectives. Instead, choose an AI software platform that is democratizing these solutions, AUTOMATICALLY. If your organization already has a data engineering staff - fantastic! Their work will be made much easier when they use this tool, and you'll be able to get higher results.
7. Who is behind the company and do they know how to build and present data? Are they experts in Automation and data Analytics? Knowing the team behind the software is the last question. The size of the firm isn't nearly as important as the talents of each team member. I'm sure you'd choose to work with a small business that employs PhDs in automation and has experience working in your field (whether it's pulp and paper, food and beverage, pharmaceuticals, or even water waste management). Each of these sectors has its own unique problems, so having an expert on your side who understands how to apply AI and ML to optimize your operations is significantly more beneficial than having a rookie working for a major corporate behemoth.

Regardless of where you are in your AI journey, these questions can help you sort through the firms that are rushing to get on the AI bandwagon but wind up delivering bad outcomes. The best thing you can do is set up a demo, request case studies that are relevant to your sector, and ask if they offer any free trials of their software. Within 30days the results should be apparent. If not, you might want to keep looking for the best software for you. Schedule a call with an expert on our team to get the full demo of our software if you're interested in learning more about the SORBA.ai Industrial ML platform.

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