How To Implement AI Into Your Product If You Are Taking The First Leapby@mniczyporuk
592 reads
592 reads

How To Implement AI Into Your Product If You Are Taking The First Leap

by Marcin NiczyporukJune 17th, 2018
Read on Terminal Reader
Read this story w/o Javascript
tldt arrow

Too Long; Didn't Read

<em>This article was written by </em><a href="" target="_blank"><em>Marcin Niczyporuk</em></a><em>, the director and head of Cognitive Services at </em><a href="" target="_blank"><em>intive</em></a><em>. intive is a software company focused on digital product development with more than 18 years of experience and 150+ apps.</em>
featured image - How To Implement AI Into Your Product If You Are Taking The First Leap
Marcin Niczyporuk HackerNoon profile picture

This article was written by Marcin Niczyporuk, the director and head of Cognitive Services at intive. intive is a software company focused on digital product development with more than 18 years of experience and 150+ apps.

Many say 2018 will be the year AI finally hits the mainstream, helping organizations and employees unleash their real potential. However, look around, and you’ll realize many companies have already taken the first big leaps.

Thanks to exponential technologies such as machine learning, mobile solutions and the cloud, companies around the world have the resources to invest in AI solutions and augment their processes. In fact, according to PwC, 54 percent of businesses claim AI has already increased productivity within their organizations.

However, for those who have yet to take the much-needed jump into the AI world, the idea of adopting the technology can be, frankly, daunting. Especially considering the amount of data required for AI to be impactful. But finding this data is not as complicated as you might believe. Here’s how to implement AI in your product — even if you don’t think you collect enough data in the first place:

Check to see if there are existing datasets at your disposal

Tools that leverage recommendation technology, natural language processing, image recognition or deep learning algorithms undoubtedly require a vast amount of data to train machine learning models. However, when compiling data, it’s not always necessary to start from scratch. In the early stage of product and service development, it’s important for companies to verify what out-of-the-box data services already exist on the market.

Tech giants like Amazon, Microsoft, Google, IBM and Oracle all boast PaaS cloud offerings, which feature existing public data sets and pre-built AI services for companies to leverage. Dig a little deeper, and there’s actually a whole range of free public data sources available in unsuspecting places — like World Bank Open Data or the Bureau of Justice, for example. In fact, this Forbes article lists 30 places companies can begin the search for data.

There’s also a handful of organizations that sell data for this very purpose. Companies such as DAVEX, Quandl, DatastreamX and Qlick Data Market are data market platforms that have already done the hard work of collecting the data and are now trading it through an API. Other organizations like Kaggle and Drivendata actually crowdsource data and data science services — so if a company needs something in particular, it can be created for them.

However, remember: these public data sources might not work if you have a genuinely disruptive model — we’re talking something like, which claims to be the first GDPR compliant voice assistant, as it doesn’t rely on the cloud. So in cases like these, a company might need to put significantly more work into getting the data they need.

Your back office software could be a ‘treasure island’

Ask any company that hasn’t formally invested in AI if they have valuable data on customer behavior, and they’ll likely say no chance. The thing is, however, many companies don’t realize they’re collecting this data through everyday processes — which in turn, can be used to develop powerful AI tools.

Customer emails can provide a huge wealth of data; for example, they can be examined to extract information on how the customer’s ‘mood’ changes over time. Invoicing systems can provide great data, too. This year, for instance, Canadian accounting software company Freshbooks measured how polite citizens are by extracting data from business invoices. So can CRM notes, customer survey responses, meeting minutes, chat transcripts, and more.

Now, imagine being able to measure 10 years of email and invoice history, for example, together. A company could combine the data to predict what actions a customer might take once they start communicating a certain way. If the AI predicts a customer is not going to pay, for example, it would enable the company to react quickly — and maybe even save the contract.

So how can a company find out whether their once overlooked data is treasure or mere fool’s gold? Find a vendor. A vendor will consult companies on their current data structure processes, and explore ways in which the data can be extracted — think extracting data from text, for example. They’ll also present the data to a company in a digestible way, through modern infrastructures like Hadoop, Hortonworks, Tableau or Power BI.

A good vendor will have experience working with a range of client domains, as this diversity enables them to learn quickly. However being specialized in certain vertical is also valuable, so try to find a vendor which clearly markets what they’re best at. And finally, a good vendor should be able to offer packaged data science solutions — such as pre-built models, frameworks, and reusable components from previous projects — to speed up the entire of process of ‘mining for data.’

Start logging

If a company has already checked out public data sets to no avail, and the back office software data doesn’t quite meet expectations — it’s time to start logging. This means starting to collect literally everything; of course, with an experienced vendor’s help. Yes, companies should look to start compiling CRM, chat and invoicing data etc. However, they should also look to public datasets such as from social media, their real-time business insights from places like Twitter for sentiment analysis, and even the weather. This will all go into a data warehouse. You never know what you might need the data for — that’s why it’s important to log everything in sight.

Keep in mind, though, that creating a data warehouse is no trivial task. That said, it’s not particularly easy to find experts with strong analytical skills on the market. So when searching for an expert vendor, be sure to request case studies, testimonials from previous clients, or proof of concept tests to ensure the data scientists can really collect the data efficiently, and help produce valuable insight from it.

Many companies new to AI might feel apprehensive to take the first leap. However, if companies know where to look for help — whether it be to public datasets, existing customer information, or innovative data scientists — the whole process doesn’t seem as daunting. And even if it does, it’s prudent for companies to pull themselves up by their bootstraps, anyway. Because if they don’t invest in AI sooner or later, they’ll most certainly be left behind.