Data and Analytics: Cross the AI Chasm

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Artificial intelligence is a manna sent from digital heaven. That’s how blessed your business can get if you immerse into the AI of things. Compared to traditional data and analytics approaches, one cannot argue enough about the broad use and substantial economic potential of advanced AI techniques. This transformational technology can drive new sources of customer, and add product and operational value to all industries.

While we could not be more excited about how artificial intelligence is shaking and moving things up, some tread into the AI frontier with skepticism.

Chasm is real (and so are your business goals)    

AI and its practical application in the business economy is growing full tilt. At the rate things are going, you might not be able to keep in pace with the newer “deep learning” techniques. And while you see the potentials of the said platform, you can also discern some of its limitations and obstacles.

Your doubts are real. It is important to highlight that, even with future opportunities in sight. As AI technology continues to advance, the use of data and analytics must always take into account several concerns including data privacy, security, and the probable issues of bias. The lack of understanding of end markets can also impede.

Does your business have the capability to harness the full potential of artificial intelligence? You have all the reasons to be skeptical because from traditional analytics to AI lies the chasm.

How do you cross over?

So, in the stage of artificial intelligence development, where exactly is the AI chasm?

After coding algorithms that seem to work and presenting a sleek prototype using data sets, and just when your team thinks you’re all confident to deploy, there lies the AI chasm. A lot is to be blamed on the all-important data (or the lack of it), such as samples of customer transactions, requests, and their accurate responses. It should also be presented in an organized manner but in a fixed format, much like a comma-separated values file.

New data

Real-world artificial intelligence, which powers machine learning algorithms will frequently require fresh data to drive your AI models. Think of data as fuel to artificial intelligence. You should capitalize timely in a good machine learning platform. It should uninterruptedly gather fresh data and apply it to routinely update your AI models.

The AI chasm doesn’t have to be a dreadful period in the stage of development, after all. New data churns in and there will be revisions and re-engineering of things. It can be too technical and overwhelming, but it can be exciting when you think of the meaningful, albeit automated, human-AI interactions. All in the name of good business.

What’s in it for you on the other side?

Let’s delve into the seeming depth and complexity of handling artificial intelligence and what’s in it for you. The benefits far outweigh your doubts.

  • To be able to identify and capture new sources of customer and market value creation, AI needs detailed, granular data (also known as “big data”).
  • In using data and analytics to monitor the business, you have to shift your mindset — from an organizational mentality to predicting/assessing what’s likely to happen, and setting actions to avert or monetize that prediction.
  • To control the costs of storage and data management, you have to go beyond simply aggregating data to a “greedy” mentality of accumulating every bit of detailed historical data of every customer — down to every individual bit, every product, and every device and asset. Complement all these with a wealth of external data sources.
  • To expand data access from your current restrictive method of accessing data (because you don’t want to add a new data source to your data warehouse every so often), you need to allow access to all data that might have value. This serves well in your efforts to optimize organizational and operational decisions.
  • To be able to process and analyze data in real-time, you need to transition from batch data processing to an operational model. This way, you’ll be able to prescribe new prevention guides and create new monetization opportunities.

Building a Bridge: What AI technology providers can do

You should make it your goal to adopt AI in your business. You can start with machine learning and performing artificial intelligence experiments across your departments by launching testing solutions. You see, it is your company’s ability to execute AI technologies that creates value.

But just as important is about who does the AI models for you. Many companies that provide or develop AI models have sufficient knowledge in the technology itself; some may have data scientists needed to make it work but they might, like you, also lack a thorough understanding of end markets.

AI technology providers must have an astute understanding of the value of artificial intelligence across sectors. On the technical side, they should be able to map problem types and techniques. You want them to help you determine your specific areas of expertise and guide you on where to focus. 

Before going the AI way…

It is wise to step back and think of the challenges ahead. Crossing the AI chasm not only requires you to pick up organizational buy-ins along the check points, but more importantly, it will also demand continuous learning on your part.

You need a full understanding of what artificial intelligence feeds on, what it asks from you, and what it is good at — for you. It is one thing to realize that AI is good at automating complex, tedious manual work, but you need to be able to apply its benefits more directly to where it matters, to the real-world organizational and operational business models.

While your business tries to capture all those basic elements of understanding about the data and analytics world, Four Cornerstone can help you cross the AI chasm and manage the foothold. Call us today at 1-(817)-377-1144.

Photo courtesy of Deepak Pal.

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