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The Race for AI: Embed Artificial Intelligence in all Business Application by 2019 or Risk Irrelevancy

The Race for AI: Embed Artificial Intelligence in all Business Application by 2019 or Risk Irrelevancy

Artificial Intelligence (AI) emerged as a hot topic in 2017. Although scientists have been working on the technology and heralding its numerous anticipated benefits for more than four decades, it’s only in the past few years that society’s AI dreams have come to fruition.

The impact AI applications stand to have on both consumer and business operations is profound. For example, a New York-based Harley Davidson dealer incorporated the Albert Algorithm AI-driven marketing platform into his marketing mix, resulting in 2,930% increase in sales leads that helped triple his business over the previous year.

Unfortunately, success stories like this aren’t as common as the more prevalent failed AI pilot projects. However, with growing volumes of raw data about people, places and things, plus increasing compute power and real-time processing speeds, immediate AI applicability and business benefits are increasingly becoming a reality.

In fact, according to a survey by Cowen and Company, 81 percent of IT leaders are currently investing in or planning to invest in AI, as CIOs have mandated that artificial intelligence needs to be integrated into their entire technology stack. Another 43 percent are evaluating and doing an AI proof of concept, and 38 percent already have operational AI applications and are planning to invest more.

Additionally, McKinsey research estimates tech giants spent $20 to $30 billion on AI in 2016, with 90 percent of it going to R&D and deployment, and 10 percent to artificial intelligence acquisitions. Industry analyst firm, IDC, on the other hand, predicts AI will grow to be a $47 billion market by 2020, with a CAGR of 55 percent. Of that percentage, IDC forecasts some $18 billion will be spent on software applications, $5 billion will be invested in software platforms, and another $24 billion on services and hardware.

If analysts’ forecasts indicate enterprises plan to invest $18B in software application development and deployment, if your business doesn’t already have a strategy for how to incorporate artificial intelligence (AI or machine learning (ML) into your development efforts by 2019, then you are likely already behind the pack.

The Artificial Intelligence Race is Heating Up

Google and Amazon currently lead the AI race, with Microsoft Corp. investing a lot of time and resources to catch up. These companies already have thousands of researchers on staff and billions of dollars set aside to invest in capturing the next generation of leading data scientists—giving them a huge head start vs. the rest of the market. For example:

  • Of Google’s 25,000 engineers, currently, only a ‘few thousand’ are proficient in machine learning—roughly 10 percent—but Jeff Dean, Google Senior Fellow, would like that number to be closer to 100 percent.
  • In its first year of operation, the AI and Research group at Microsoft grew by 60 percent through hiring and acquisitions.

These Tech Giants only account for a few of the ‘serious’ AI contenders in the market today and there is only so much ML/AI talent to go around. This doesn’t just impact recruiting efforts, but also the time and existing talent required to conduct new employee onboarding, training and supervised learning to effectively scale AI programs.   

Most companies lack the connected, analytical infrastructure and general knowledge needed to apply AI and ML to its fullest extent. Engineers must be able to securely access data without having to deal with multiple layers of authentication, which is often the case if a company has several siloed data warehouses or enterprise resource planning application systems. Before IT leaders attempt to successfully deploy or conquer an enterprise-wide AI strategy, they must have the ability to bring large data sets together from several disparate and varied data sources into a centralized, scalable and governed data repository.

Looking Ahead: The AI Services Marketplace

While it’s clear that the use of AI is becoming more prominent, not all companies have the IT budgets needed to recruit the highly skilled talent required to build AI-fueled applications in-house. Thus, what we can expect to see more immediately is the emergence of an AI services marketplace.

Examples of this are already starting to be revealed with many companies beginning to offer artificial intelligence baked into self-service marketing tools that have become both easier to use for the non-data scientist and less expensive to acquire. Much like mobile app stores, these new AI marketplaces will resell specialized AI services and algorithms that companies can instantly buy and implement within their business. This model makes it easier for companies lacking the cash to recruit and retain the talent required to keep some skin in the game when it comes to their ability to keep up in the broader competitive race for AI leadership.  

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