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5 Reasons Not to Jump Into AI

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5 Reasons Not to Jump Into AI

While incorporating AI into your office, for example, is undoubtedly indicative of a forward-thinking philosophy, the rest of the world just isn’t completely ready yet.

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Artificial Intelligence is more than a buzzword; it’s the stuff of science fiction brought to life, and it is set to change the way we live, work, and exist forever.

The inevitable end point of developments in data science, analytics, and relentless technological improvement and innovation, AI is set to be so revolutionary that some people are suggesting that companies skip traditional smart data technology and invest in the end result (AI).

However, there are a few reasons why investing in analytics is still the best call in 2017.

1. AI Isn’t Perfect

As with any potentially huge development, loads of investors, venture capitalists, start-ups, and business leaders are ready to get ahead of the game and integrate AI into their workplaces and lives right now.

The issue is that AI is still a work in progress. Like a child, AI technology needs time to learn and improve itself. Remember what happened with the AI Twitter account? Even the most advanced AI on the market will seem ordinary three years from now.

You also need to consider the fact that while incorporating AI into your office, for example, is undoubtedly indicative of a forward-thinking philosophy, the rest of the world just isn’t ready.

You may have an incredible presentation system driven by an AI engine, but what good is that if your investors cannot connect their laptops?

The frustration with AI right now is that as exciting as it is, it’s still niche — and until it becomes common, any efforts to integrate could end up excluding those who are not early adopters.

2. Analytics Still Have Tremendous Impact

According to Simbla, analytics is still in its infancy, and the true value of the field is yet to be understood. As a funnel of data and information move into AI algorithms, the goal is to perfect the analytics software and algorithms currently available to the point where they become as close to autonomous as possible.

Apart from basic digital functions such as URL crawling and social media reports, companies are starting to give analytics the room it needs to flex its muscle within other digital fields, such as marketing and search engine optimization.

Operating within the binary space that is the foundation of all things digital means that the analytics tools of tomorrow will not only offer reports and pattern recognition within a market but move into a predictive space.

The ability to forecast market-specific movements with accuracy alone is enough reason to stick with analytics and wait for AI to mature in the next two to three years.

3. Machine Learning Is a Process

Building a business sector is a linear process, and we have yet to align every piece of the puzzle needed to deliver flawless, integrated AI.

The funnels feeding AI are, of course, data and analytics — two fields that are still being pushed to their logical and undefined conclusion.

For us to truly experience AI, we must collect the volume of data and craft the predictive analytical tools needed to interpret information in an intuitive way.

Jumping straight into AI is, for lack of a better metaphor, like trying to fly before you are comfortable walking. It has been done, but not nearly on a level to get excited about.

4. Potential Division

Although it is difficult to understand exactly what the future of business is, it is highly likely that instead of working towards a unified intelligent industry, the cooperative fields of analytics, data, and AI are likely to fragment and develop in isolation.

Industry fragmentation does bring with it several interesting opportunities, particularly for those who would rather invest in a more mature market than pin their hopes on inexperienced AI.

Investors and business owners alike are faced with the difficult question of spending the next few years with high-impact, functional analytics, or the more uncertain field of Artificial Intelligence.

5. Consumer Perception

For the business owner, deciding between analytics and AI should be influenced by the current state and mindset of their primary audience.

Companies like Luxury Link with a younger audience is likely to trust and test an intuitive digital solution. However, if you are marketing to a more mature generation, it may be better for the business to allow the power of analytics to improve business performance in the background.

The temptation to leap into AI is overwhelming, but neither this nor analytics are going to vanish; if anything, they will both grow and improve. There appears to be a stronger case for the proven record of accomplishment of analytics — at least for now.

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Topics:
artifical intelligence ,analytics ,big data ,data science

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