Why Enterprise AI Is Still Making Its Way to Mainstream

The Barriers to Artificial Intelligence

The technology world is abuzz with an exciting concept: the future of AI and how machines could transform our lives. Today, it seems as though every aspirational company, is already launching their own project for digital transformation, complete with a focus on the potential of artificial intelligence.

Of course, despite the reach of the AI bug, the truth is that this technology is still stuck somewhere between excitement and adoption. Around 85% of companies believe that AI could help their organisation to establish a competitive advantage according to MIT, but only 1 in 5 have begun to incorporate AI into their workings.

So, what is it that’s holding the modern enterprise back?

Artificial Intelligence and The Education Gap

Often, the teams responsible for driving AI adoption programs are innovative groups brimming with data scientist knowledge and a strong understanding of machine learning. Unfortunately, while the average enterprise might have a few machine learning engineers and data scientists on board, the ratio isn’t always ideal for ensuring instant adoption.

Many enterprises still lack the skills, tooling, methods, and systems required to bridge the gaps between workflow in the data science space, and performance in software systems. While AI companies like Google and Amazon are taking steps to address this issue, there’s still a lot of extra education required.

Finding a Place for Artificial Intelligence

As any company who has explored the potential of digital transformation will know, it’s not enough to simply push the most innovative technology into any open space in your business. AI is about solving specific problems, but a lot of companies don’t know which issues to address first. This means that they invest in intelligence before they know how to apply it.

Alternatively, best practices would suggest that the best strategy is to find your issue and work backward. Pinpoint an area the enterprise needs help with, then look at ways that AI might be able to help. For instance, if you’re having trouble providing personalised customer service in a contact centre, AI could automatically deliver contextual information about a customer to your agents.

Unlocking the Resources for Success

Finally, one of the biggest hurdles facing AI adoption in the enterprise is linked to the level of choice that today’s organisations have. Solving business problems with AI means making sure all the right building blocks are in place, from the perfect technology partner to the right platform. Of course, the sheer number of options available all the way from Watson to AWS means that enterprises have a lot of work to do.

What’s more, since every company is different, with its own data sets and regulatory requirements, one machine learning model might not be enough to overcome every issue. Instead, the path to success may need to be taken one step at a time, with an industry focused approach supported by business KPIs.

It may take some time for enterprises to discover the perfect way of adopting AI, but it’s safe to say that we’re on our way.

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