A Data Dilemma - Is Your Business Really Ready for AI - Part 2

If your business isn’t equipped to use artificial intelligence (AI), you won’t be able to seize numerous opportunities coming down the pipeline. But no need to fret if you’re feeling imposter syndrome. In this article, we’ll explore some of the most important steps every data-driven company takes to successfully get their AI endeavors off the ground.

In my previous post, we discussed what makes a truly data-driven business. We also touched upon some of the immense benefits that becoming one unlocks! In case you missed it, you can read it here.

Before we dive into this article, I want you to take a moment to get your ideal objectives with AI in order; this was a major talking point of my last post, and for good reason — every successful data-driven organization is actually an outcome-driven organization.

Getting Your AI Endeavors Off the Ground

Got your desired upshots in order? Great! Now follow up with these steps. They’ll go a long way towards ensuring the success of your AI efforts.

Establish Your Infrastructure

The promise of AI will never pay off unless the data systems of your business are properly prepared to handle it. Odds are good that some (if not all) of your information is locked away in inaccessible, poorly structured silos. And you can’t connect the dots in your data if you can’t even connect the data.

If data is the new oil, then it must be refined and processed correctly to get the right results.

Going back to our oil analogy, data is the fuel that makes AI work. So your business needs to establish an efficient pipeline that refines and processes it correctly to get the right results. To do this, you must create an ontology. Essentially, this is a comprehensive overview of how your business’s information flows through the architecture you’ve constructed. Think of an ontology as the key to bring together all of your business’s data.

It’s crucial to note that the best way to build an ontology differs for each business. Simplify this task by turning to your ‘why’ — this will inform how you structure, normalize, and get a sense of your data. Not only does this help drive your results in the right direction, but it also allows you to tailor your infrastructure for future projects. There’s less chance of inefficiencies in the process and fewer opportunities for the injection of unfiltered, invalidated anomalies making their way into your data flow.

For more tips on how to build an effective ontology, check out the four steps that authors Seth Early and Josh Bernoff included in a great Harvard Business Review article on the topic.

Take Correct Cybersecurity Precautions From the Get-Go

If data is the new oil, then it must be protected like the invaluable asset that it is. I’m a big proponent of proper cybersecurity measures. AI and data are no exceptions. Information security is destined to play an even more integral role in our lives as AI is integrated into society. So get ahead of the curve now.

This really comes down to two factors: Educating your people and protecting your processes. As with our discussion about infrastructure, your approach to information security is circumstantial; it depends on your business endeavors.

At the very least, each of your team members should have an adequate understanding of how to implement proper data security measures such as strong passwords, two-factor authentication, etc. But if you’re working in a regulatory field like finance, there are specific aspects of information security you must attend to. And it’s imperative that you follow and stay up-to-date with the latest guidelines and developments.

Let’s look at healthcare, for example. You can’t just simply dump your data into a centralized source, give everyone access, and hope for the best. You have to ensure that anyone who accesses an electronic medical record or any other patient information has a right and need to do so — not just permission.

Take the right precautions to obfuscate your data. Often, this information contains a level of detail that isn’t necessary for AI. In the case of healthcare, you usually don’t need to know patient names, addresses, or any other identifiable information when running analyses with a machine learning model. So why leave it available? In such a state, if your data is compromised, so is your organization.

At Realware, we always apply tokenization to our sensitive information. Essentially, this means that all relevant features for data analysis and AI are still there, but any personal stuff is removed. I highly recommend you do the same.

Be pre-emptive; prepare for the worst. It can save you from that situation ever happening. And it’s easier to establish proper security guidelines at the beginning of your AI endeavor than later down the line.

Start Small

Many organizations see AI implementation as a lofty ambition. Consequently, they assume that they should shoot for the stars with their first initiative in this technology. There are numerous sources arguing for both starting with small endeavors or beginning with monumental moonshots. Personally, I think there’s more merit to the former category.

Large companies can invest more resources in experimental endeavors like AI without flinching if things fall through. But for most businesses, You don’t want to throw all your eggs into one basket and say “Let’s go for broke!” It’s just not a reasonable approach for self-preservation.

Instead, start with a narrowly-focused project. Honestly, it’s best if you begin with an initiative in which you know what the result should be. This shines a light on how well your AI approach is connecting the dots in your data between point A (now) and point B (your ideal outcome).

Think of it this way: It’s better to get your feet wet than to dive into the deep end. If you believe that applying a certain AI process with a particular dataset should lead to a specific outcome, test it in a low-stakes scenario. At best? You’ll be able to drive that specific outcome over and over again, and eventually, expand this to a broader range of applications. At worst? You’ll be able to refine your approach.

Start small and measure. Iterate on what you learn.

Be Patient — The Future Comes Faster to Those Who Persevere

Incorporating AI into your business may seem like a tall order. But it’s an endeavor that can pay in dividends. The important thing is that you don’t lose sight of your “big picture.”

AI is different for everyone. To me, it’s a bunch of math problems. But unlike the hundreds of algebra equations we had to solve in middle school, these math problems have vast potential to improve the world.

It’s important to remember that AI isn’t just some piece of software; it’s a process. You define your outcomes, collect your information, organize that data, train your models, and run an analysis. Sometimes, the outcome from all of this work delivers significant results. Sometimes, it doesn’t. But even that can yield useful insights.

In closing, all data-driven organizations are actually outcome-driven organizations. Those who adhere to the series of steps involved in AI are much more likely to steer their process in the right direction. And if they keep going, they’re destined to reap some serious rewards.

Focus on your outcome. It’s the first step towards getting your business AI-ready.

At Realware, we take an outcome-driven approach to make your vision a reality.

Reach out today to see how we can help you!