In an industry as dynamic and exciting as the IoT (Internet of Things), it can difficult to separate hype from reality. One sector that often falls victim to hype is AI (artificial intelligence). For many decades, machine intelligence and autonomous decisionmaking have grabbed ahold of people’s imaginations, simultaneously bringing about utopian and dystopian predictions for the future AI-driven world. Practically speaking, the hype surrounding AI technologies can make it tough for investors and businesses to get a handle on exactly what to expect from this growing space and how they should try to harness it. How can companies avoid getting caught up in the AI hype cycle? How can they set their AI projects up for success?

Gartner made an astonishing prediction back in 2018, saying 85% of AI projects will fail through 2022. Why? Gartner said the top reason for these failures will be bias in data, algorithms, or the teams responsible for managing them. Last summer, IDC released research suggesting most organizations report AI failures, with a quarter of respondents reporting up to a 50% failure rate among AI projects. The top reasons for these failures, according to IDC, include a lack of skilled staff and unrealistic expectations.

Worldwide spending on AI systems is on the rise. IDC estimates the market reached $35.8 billion last year, an increase of 44% over 2018, and even more growth is predicted for 2020. Lux Research data suggests $30 billion in VC (venture capital) funding went to AI startups last year alone. And yet, AI remains a nebulous concept for many in the business world. Companies too often use “AI” as a branding strategy, Lux says.

Interestingly, MMC Ventures released a report examining AI startups in Europe and found that of the 2,830 purported AI startups in the 13 EU countries involved in this study (Austria, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Portugal, Spain, Sweden, and the United Kingdom), just 1,580 of them actually showed evidence of AI material being critical to a company’s value proposition. The other 40% of AI startups apparently showed no evidence of AI.

What’s more, many companies struggle with how to successfully integrate AI into their businesses. Lux Research released a report called “Artificial Intelligence: A Framework to Identify Challenges and Guide Successful Outcomes” that analyzes the market, outlines several challenges companies face in integrating AI, and hones in on several factors businesses should consider before investing in AI. The four factors the research firm suggests to help businesses make wise AI investments and decisions include: clearly understanding the outcomes implementing AI will provide for their businesses; focusing on an AI product’s capabilities instead of flashy marketing; knowing when the technology is mature enough to mitigate risk; and identifying practical challenges to both implementation and maintenance of the technology once it is in place.

There’s no doubt that AI technologies can be impactful in helping companies achieve digital transformation, but there is also a lot of hype that is not necessarily helping the space and the players within it. Many AI project fail due to unrealistic expectations, unprepared or ill-equipped staff or leadership teams, and bias in data that makes it unreliable. However, many AI are successful, and businesses can leverage best practices to augment their own chances for success.

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