Artificial Intelligence (AI) is everywhere. When we call a bank, a robotic voice guides us through the menu based on our inquiry. When shopping online, we get personalized product offerings. Or when searching for something on the Internet and then corresponding advertisements start following us everywhere. AI is truly all around—changing our lives and the way we do business.
But if the consumption of AI-optimized processes is seamless and smooth, being on the other side and actually implementing all of those advanced analytics solutions is not an easy task. You would need the criteria to select the right AI Vendor to improve your business with predictive analytics. You would need the keys to AI for Everyone—and you can find them in this post.
If you found this post on the web it might mean two things: first, you don’t need an explanation of what Artificial Intelligence is, and second, our marketing team works great. Why not three things, you may ask? We would say that nowadays even companies that have professional Data Science teams are still in the market for AutoML.
However, selecting among hundreds of vendors is not an easy task, especially in a market with so many old myths.
Challenges and myths to consider when choosing an AI Vendor:
Data is the new Oil. The more you have, the more investments in processing them you’ll need in order to extract valuable insights for your business. Machine Learning does its thing, it helps businesses with cost optimization, risk mitigation, achieving smooth operations, and frictionless customer service. But in a world where only big money makes money, it’s hard for small and medium businesses to keep up with all the digital transformation “must haves.’’
At ML Studio, we don’t want to frustrate business owners and executives with the unrealistic claim bar that AI behemoths are creating just to justify their excessive prices. At this point, hardly anybody underestimates the potential of Data Science or what kind of competitive advantages it provides businesses with. We want to facilitate the access of small and medium businesses to AI. We believe that each business should have access to an AI platform without the need of breaking the bank.
Pointing out how many companies have already invested in AutoML platforms or how many business leaders cited AI as their top priority initiative for the coming years just puts more stress on an organization’s leaders. It’s not a shock that every company wants to get better over time to get better results and AI is mandatory to compete for a bigger market share.
Who else likes to create extra urgency to unsettle you? Just think of the last security training you’ve had at work and that might help you with the answer. We believe that nobody knows better what clients need than the clients themselves, but they definitely don’t need spooky stories—we already have enough news channels for that.
Big software companies are trying to crush their competitors with the statistics on how many tech startups couldn’t survive more than 3 years, even though they work harder and would do anything to delight their customers. But who opposes empires and countries? Singapore became an independent sovereign country in 1965 and yet few countries can compete with it when it comes to innovation, transformation, and results.
Company growth over time is a good criterion to test the viability of someone’s business model. However, major layoffs in big tech companies throughout 2020–2022 have proven that they shouldn’t throw stones at smaller organizations that didn’t hire a bunch of salespeople just to see if they can scale and get more funding. Shrinking companies are a risky game because even if big AI brands are claiming to have many different teams to provide you with the best in class service—they might lose control over this promise.
If the age of the company would have been a relevant indicator of success for the Data Solutions you get from them, there wouldn’t be so many sad cases. For example, company X bought an extremely costly product annual subscription, which it couldn’t really afford, from a legacy tech giant, and in the end, the only thing this company got out of it was repackaged legacy software. Famous brands are nice to have, but if your house is on fire, would you look for Evian to snuff it out?
Startup acquisitions on the other hand really might cause disruption in customers’ workflows. In case an acquiring vendor shuts down the client support with all the following problems, users may have to migrate their projects and it’s unpredictable and hence costly. In order not to get service quality declines and any other losses, it’s always better to choose vendors with a passionate approach that treat their AutoML platforms as a native child.
There’s no doubt that AI is sort of a secret society you can’t easily join. But it’s not “Skull & Bones,” and you don’t have to steal the Dean’s Oxford Vocabulary to be accepted. It’s true that all the specialists engaged in an End-to-End AI journey have their own language, as well as their own processes, tools, and metrics. And domain experts do want to talk on their own terms about the problems of their own. But AutoML platforms should not be just a Google translator that bridges the gap between executives and AI experts. Isn’t it better for business owners to have an actual bridge between themselves, their domain expertise, and AI that helps to leverage it without fancy tags?
When businesses struggle due to unprecedented uncertainties all around the world it’s unethical to distract their leaders with something that doesn’t solve their current pain and requires even more investments. For example, many AI products require Data Science expertise and constant support in order to even operate—without bringing tangible value to the company. These products are expensive and difficult to use in every part of usage and implementation, and they require constant and time-consuming training for all skill-level users.
Good employers surely invest in development opportunities for their staff, however, it’s critical not to mix two different strategic needs. Why pay more if you’re in the market to fulfill Data Analytics needs, rather than qualification upgrades for your teams? In fact, money is not the only resource businesses lose in such a scenario—time is impossible to reimburse. More than that, lost momentum for solving critical business problems over time leads to an end.
The Machine Learning Landscape truly faces a rapid explosion of various solutions and tech giants obviously don’t like it. This problem is probably known by any SME in the world—it’s hard to compete with someone who has unlimited power and resources. Even if a business has its own unique soul, it can be kicked out of its niche by massive corporations that set the tone and disagree with equal rights and opportunities for everyone.
If a given company knows that investment in artificial intelligence and machine learning will allow it to leverage all the data it’s sitting on and get all the hidden insights to navigate a business more efficiently, it’s already the first step to success.
But there are many things that need to be considered prior to choosing an AI vendor. We already reviewed all the myths and tricks that the noisy AutoML field habitues might project on different players on the market. So hopefully the burden of choosing is getting lighter.
What is it that you want to achieve with AutoML? What is it you want to get from an AI vendor? Before answering these questions it’s probably not the best idea to start looking for a vendor as you will be swamped with too much material to digest. The first step would be discussing with your senior management whether your visions for solving some business-critical use cases with AI are aligned and if they are ready to consider implementations of such kind.
There are many opportunities to create business performance improvements and AI helps organizations address them in a fraction of the time. But only AutoML platforms that bring noticeable and measurable benefits to your business should be on your list. At this step, finalize with the team what resources you have for achieving your goals and what else you need.
Whether it’s a machine learning application or deep learning algorithms to leverage the data already prepared with your analytics team—you will need a vendor to play a leading role in the whole architecture of the AI project as well as data collection and preprocessing. It’s good to have a flexible client-oriented vendor who is capable of listening to understand your needs, not preaching to you. If your business doesn’t experience a lack of domain specialists, AutoML platforms can help you hit your targets.
It’s better to make sure that you’ve identified the data problem, the question you’re trying to solve, or the metrics that are suffering in case this problem won’t be solved as soon as possible or in case you don’t get additional capabilities for your team to keep up with the goals behind your business strategy.
For example, no matter how hard you try and how many Data Scientists you have, the number of use cases from other departments just doesn’t allow your marketing team to squeeze into the Data Science projects queue. At the same time, you desperately need to enhance your lead scoring process, otherwise the sales team will start pointing out that they aren’t getting quality leads to sell to. You know that at some point when there will be a decline in the Sales Plan, everyone will agree on how crucial this problem was, but you don’t want to get to that point.
Preventing illness is always easier than healing afterward and it takes less effort. Putting up all the numbers together will help you analyze the potential impact of AI implementation and what business value you can get in the end. It will also allow vendors to understand what it is you want to get as output and whether there’s a fit between your needs and what a potential vendor can offer. To calculate what the ROI would be of a potential vendor, you should understand your business case metrics.
Make sure that your vendor uses a Data-Centric approach because even if the platform’s algorithms are best in class they won’t give you accurate and trustworthy predictions. As a very limited amount of businesses store their data in one place, It’s a must to have an accessible and flexible data import as you don’t want to be stuck importing and preparing your data from different sources for two to three months (which is the average time for data preparation for a medium to large organization).
If you understand that the most impactful use case that you have requires some data that you don’t have, you can find a vendor who offers public dataset access—as well as ready templates for various use cases within your industry. Don’t hesitate to invite your colleagues from technical teams for your calls with vendors at the early stages of communication, as they can make sure whether a given product has all the needed capabilities for data prep and can save you some time.
If you’d like to engage your business domain specialists into solving complicated business problems with AI, the smooth integration of business know-how into data via extensive feature engineering drives the best results. So choosing a company that is Data-Centric is another point. Most machine learning platforms on the market focus more on the model-building part and lack the right support for data-wrangling operations.
ML Studio fills this gap by incorporating a visual drag-and-drop canvas for quick and easy data manipulation. New features can be added to enrich the datasets as well.
Once analysts made all the needed manipulations with the data and used their domain expertise to identify the most impactful variables to make a prediction, it’s important to have a platform that is intuitive enough to start with the model building. If you have a few Data Scientists on board and they start working on a project without a chance to continue, it’s a necessity that this platform would allow other users to finish it.
We all know how many projects are left behind when someone leaves a company. And given the very high demand for Data Scientists all over the world, this is sadly the usual case. One of the reasons why the Roman empire was so strong was all of the politicians there had experience in a variety of different domains—hence making everything extremely interchangeable. If your Data Scientist has started something and then your analyst can pick it up from there, your company will be strong enough for any competition.
But here it’s also very important to have visibility through all of the processes. If a platform is transparent enough and you can get all the needed information on what was done to your data, it’s a good indicator. And if you can use the platform with or without coding you can relax about the future of your company’s projects. It usually takes from two to four months to build a single model, so AutoML helps speed up your processes.
Vendors should clearly show that they build their platform using the best practices so that your freshly AI-empowered teams could produce viable models. Working with open source is a must since it brings speed and innovation to everything you do.
If you’re happy with the model that you’ve built, smooth and frictionless deployment can save you two to six months to finally benefit from all your work.
Choose a vendor that supports regular retraining of your models as well as keeping you up-to-date with all the changes. Easy integration with all your existing workflows and the ability to get single predictions makes life much easier. Such an approach will help you focus on your business more and dedicate more time to other interesting and strategic initiatives.
AI can bring tremendous value to your company by reducing costs, optimizing various processes, and getting new revenue streams. However, it should be chosen wisely. By following these five simple steps, you’ll be able to choose the right AI Vendor and avoid costly mistakes while transforming your business.
Here at ML Studio we make AI accessible for organizations that need to solve real-world business problems by providing our clients with a robust and ready-to-use End-to-End AI platform that doesn’t go off budget.
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