Getting Left Behind with A.I.

When it comes to Artificial Intelligence it can seem like everyone else is embarking on a grand voyage towards the shimming promise of a machine learning future while you’re left waving from the shore. It’s a classic case of FOMO – fear of missing out.

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We’ve been here before with Big Data, which is fitting because both are connected. Data is the lifeblood of any business and organizations looking to take advantage of A.I. need to overcome the challenge of accessing all their data no matter where it resides. Data just keeps growing and there are no signs of it slowing down. Frankly, humans just aren’t equipped to deal with this much data.

Moving from Big to Smart Data

Right now there’s a gap between the ingestion of massive amounts of data and the outputs of actionable insight.

  • Information is fragmented
  • Processes to deal with incoming information break down, data gets muddied, replicated and hidden away.
  • Privacy issues surrounding personal data and compliance rules further complicate the processing of information
  • An army of knowledge workers working around the clock could barely make a dent in this “info-mess” and their extracted analysis is still prone to human error.

NEWS FLASH: We don’t  just need data, we needs smart data. For organizations to take advantage of future opportunities and cost savings of A.I. they need to determine how they’re going to need help to intelligently wrestle with data issues facing them now. The companies that find themselves ill equipped to handle, clean, store and use the massive influx of data coming their way are going to miss out. And, with Artificial Intelligence quickly moving from potential to proven, you don’t want to miss this boat.

Learning to Run

Machine learning, as its name implies, learns as it goes. As it starts ingesting data, it slowly begins to make decisions based on that data. Those decisions fuel additional insights that lead to better decisions. This isn’t linear growth but exponential. Small investments compound over time making the best time to start – now. Organizations that take advantage of the disruptive forces currently in play will emerge clear winners. Those who fail to react shouldn’t just fear missing out but being left behind entirely.

Start on the right foot by looking at the data needed to fuel organizational A.I. To train machine learning algorithms, organizations need to get a handle on their fragmented and growing data, ensure that it’s clean, and do it all with minimum biases.

Intelligent automation makes sense of your organizational data at scale. It’s less prone to error and machines see patterns that aren’t immediately obvious to the rest of us mere mortals. You’re supporting human decision making – not replacing it. By accessing enterprise data wherever it exists, from file shares to user desktops; by purging duplicates, ditching obsolete files and filtering out trivial information; by extracting entities, categorizing content and driving workflows, companies can keep the machines adequately fuelled with smart data.

Intelligent automation is the enabler not the destination. Retiring the old systems and the old ways of apprehending business intelligence requires investment and vision. Those with the willingness to make those investments will see significant future opportunities and cost savings. By being in a position to take advantage of the rapidly accelerating advancements in Artificial Intelligence, companies can establish a market lead and be on the right side of the chasm that separates the winners from the losers.

There’s no need to be missing out – we’ve got your ticket in. Let’s work to build the foundation on which your company’s future will be built.

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