Save your precious business resources with an AI platform

Until recently, implementing AI solutions has been a resource-heavy investment. But things have changed.

We have now entered the era of the self-serve SaaS AI business platform, developed in such a way as to allow any business user to execute an AI project from A to Z. This era has flipped traditional implementation and engagement models on its head.

At this point, the barrier to entry is so low that AI can be leveraged in any organization, offering access to the benefits of machine learning to any business user.

We’re going to take a look at why traditional AI projects are being scrapped, and how AI platforms can help you to save your most precious business resources: time and money.

The true cost of traditional AI deployment

If you’ve ever been involved with the planning and vision stage of an AI project, you already know that it is a lengthy, resource-intensive process.

According to this article, How Much do AI Projects Really Cost, one of the things that one must consider first when considering the budget for an AI product is to actually create two separate budgets. You’ll need one for proving the technological goal and another one for productization. And while both are geared towards ensuring the end goal is reached, you’ll find that the latter is the costliest of the two.

From that same article:

Developing an initial AI model can be as simple as putting a handful of data engineers and data scientists in a room with a CSV file full of training data, a Jupyter notebook, and two pizzas, and letting them pull an all-nighter.

But taking their model and integrating it with your products and revenue streams involves many more resources, both financial and personnel. What’s more, you’ll need quite a bit more technological infrastructure.

To even get to the point where you might have a working product is a long and iterative process. It looks something like this:

  •       Determine the business problem
  •       Assess the viability of the data
  •       Train machine learning models
  •       Document and explain the solution
  •       Testing
  •       Feature engineering
  •       Pipeline generation
  •       More testing
  •       More coding

Your essential technical talent is on the line and involved in every step of this process. Testing. Coding. Testing again. And you still only have a Minimum Viable Product that may or may not actually make it to the production phase. According to TechRepublic, 85% of AI projects eventually fail to bring their intended results to the business.

Then, if your shiny new solution goes into production, it has been built by (and designed for) data scientists. Those individuals now sit in the unique position of being able to use the solution.

As so few people within the organization can actually use the finished product, the costs to your organization now include both actual costs of hardware and highly exclusive labor, but also the opportunity costs associated with the exclusivity of the product.

The majority of enterprise AI teams have fewer than 10 members, according to this article, The Cost of Machine Learning Projects. Just the proof of concept phase could end up costing up to $15,000 and it hasn’t even gone into production. It goes on to say:

The steep price of machine learning makes it less accessible for individuals, small teams and startups that want to tackle a new problem or automatize (sic) their processes and decision making.

Throughout this entire process, you’re consuming business resources that could be directed towards solving your actual business problems with Automated Machine Learning (AutoML) processes that already exist.

Here’s how it works.

Reclaim time and generate value with AutoML

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. That’s not the case in this model.

From here, you decide how deeply you’d like to train your machine learning model. And once you’ve done that, you’ve jumped to the point of “explanability” and interoperability. The simplified process will allow you to focus more fully on what you’re trying to solve. The results will 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.

Equal access to AI means better business decisions 

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 make touchy trade-offs in an era when so many decisions can be pivotal for your business.

Enter equalized 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.

An enterprise AI software 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 the traditional method of development, the advent of SaaS AI business platforms has opened up the playing field in ways that just weren’t possible in the past.

Equalized 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.

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