Until recently, implementing AI solutions has been a resource-heavy investment. This has changed thanks to the era of the self-serve (so easy to use, most any business user can execute an AI project from A to Z) SaaS AI business platform, ushering in a new way to look at AI that flips traditional implementation and engagement models on its head. In this article we’ll look at how such a platform can save time for both users and organizations by lowering the barrier to entry that comes with traditional AI projects and providing an easy-to-use method to leverage AI in your organization–all while offering democratized access to the benefits of AI for any business user.
Why traditional AI deployment costs you so much.
If you’ve ever been involved with the planning and vision of an AI project, you’ll have been exposed to the lengthy, resource-intensive process of “traditional AI.”
To even get there, the process is a long and iterative one, stewarded by data scientists. From determining which business problems you want to solve, determining the viability of your data, training the machine learning models, documenting and explaining the solution, testing, feature engineering, pipeline generation, your essential technical talent is on the line and involved in every step along the way. Testing. Coding. Testing again…
Then, finally, when your new solution goes into production, it has been built by (and designed for) data scientists who now sit in the unique position of being able to use the solution. The costs to your organization includes both actual costs of hardware and highly exclusive labor, as well as the opportunity costs that arise because so few people in the organization can use the finished product, the AI solution itself.
Reclaim time and generate value with a self-serve SaaS AI business platform.
If the process we just ran through seems arduous to you, you aren’t alone. Up until now, it has really been the only way to approach AI. But there is a new way, brought about by SaaS AI business platforms. These platforms are built as self-contained AI implementation solutions, no coding required.
With a self-serve SaaS AI platform, the project process goes something like this:
You begin by identifying your business challenges to determine the use case(s). That doesn’t change from any traditional approach. If you’re not sure about what you want to solve yet, it’s of little use to push forward with an unfocused solution.
When you’ve understood what you’d like to solve, a no-code AI platform will generally let you get to work on the next steps quickly, regardless of technical know-how. Different challenges will require different types of computations. And in a self-contained platform that uses automated workflows, you can simply decide which one to use–no building required.
Then you’re already at the stage of inputting data. If that seems a bit too fast, remember that in the traditional method, someone needs to code and test most everything along the way. Here, that just isn’t the case.
From here, you decide how deeply you’d like to train your machine learning model. And once you do that, you’ve jumped to the point of explanability and interoperability.
The simplified process will have allowed you to focus more fully on what you’re trying to solve and the results explain themselves. As for what’s going on behind the scenes, the platform has that taken care of by implementing a version of what is known as AutoML. This philosophy of providing a quick way to point, click, and choose the types of computation processes you’d like to carry out–without needing to code anything–is at the core of the new outlook of platform AI.
Once the model is placed into production, it is time to think about how future data is going to be input. Upload data manually in batches? Connect via an API to get real-time data inputs? That really depends on your goals, types of implementations, and who is going to be leading the initiatives.
Democratized access to AI decision making returns multiples.
The stewards of AI projects–data scientists–are essential talent. And there will always be a limit to how many projects they can deal with at any given time. The problem is that there are myriad key functions whose task load has grown substantially due to the accelerating change in many industries. This can leave technical talent spread too thin or making touchy trade-offs in an era when so many decisions can be pivotal for your business. Enter democratized access to AI for every business user, regardless of their technical know-how.
Automating business processes and decision making is important for so many aspects of keeping up with new operating rhythms. And augmenting human decision making is gaining ground due to a new, more human-centered way of looking at AI.
By putting this power in many different areas of your business at once, you free up overburdened technical staff to work on more pressing projects while business users leverage AI in their everyday functions. There are minimal new practices to develop. You merely need to know your data and know what you want to do with it.
A SaaS AI business platform can lower the barrier to entry for AI-augmented decision making for everyone.
The uplift that AI and machine learning can bring organizations can’t be disputed. And yet, many still feel that these powers are out of their reach. While this might be the case for those still considering AI in terms of what was, the advent of a SaaS AI business platform like Kepler has opened up the playing field in ways that just weren’t possible in the past. Democratized access to carry out a vision for AI implementation is a vision in itself–a vision that is sure to help foster better business across the globe.
To learn more about how our self-serve SaaS AI business platform Kepler can offer better, data-driven decisions to anyone in your organization, contact us.