Stocks to sell

There are winners and losers in every industry. So are you making the right decision when it comes to avoiding the worst AI stocks? Since ChatGPT was released earlier this year AI overtook blockchain as the trendy buzzword in the investment world. This has made it difficult to see past the hype in AI, as well as to spot the stocks that offer legitimate growth opportunities for the right price.

When one looks past the marquee brands in AI such as Nvidia (NASDAQ:NVDA) or Alphabet (NASDAQ:GOOG), it’s possible to unearth potential winners. The risk is that these more obscure stocks offer more speculative returns, although their share price might be cheaper. Still, it’s possible to find a few diamonds in the rough. But the worst AI stocks won’t be included in the mix.

So to save you some time and potential heartache, I’ll uncover the worst AI stocks that investors should avoid in this column. I’ll be approaching this task by examining companies’ fundamentals as well as technical indicators.

Veritone (VERI)

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Veritone (NASDAQ:VERI) is a speculative penny stock in the AI space that is worth considering. The company stands out due to its development of aiWARE, an ‘artificial general intelligence operating system.’ By utilizing a stack of AI tools, Veritone transforms raw data into actionable insights for large enterprises.

The company’s premise is solid, as business intelligence tools are increasingly prevalent, and the use of AI in business settings is expected to become even more widespread. However, the adoption of Veritone’s platform has been slower than anticipated. Rather than experiencing the explosive revenue growth typically associated with tech companies, Veritone’s growth is significantly lacking.

Last quarter, Veritone’s revenue growth decreased by 11.90%. Looking ahead, its forward growth rate is only 11.11%, barely surpassing the median of its sector peers. This is an atypical performance for a tech stock that is investing heavily in acquiring new customers. On a deeper level, it also doesn’t offer anything innovative or disruptive. So this, combined with weak business execution, is why it’s one of those worst AI stocks to avoid.

BlackLine (BL)

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BlackLine (NASDAQ:BL) specializes in providing financial automation solutions. As a SaaS company, it seeks to streamline and modernize financial close processes for organizations across various industries. For example, it provides software for accounts receivable automation, accounts receivable automation, and more.

BlackLine is in a diametric position to Veritone. BlackLine’s pivot to AI is starting to see some validation with strong revenue growth. However, its valuation may offer insufficient risk to reward. EPS for BL stock surged 75% this year while revenue rose 21.16% year-over-year.

Its forward P/E ratio is 42.76, which is significantly overvalued compared to the mean of its industry peers. Each unit of the company’s revenue is equally pricey. It has a forward P/S of 5.98, which is 105.96% above the median of 2.91.

The company also carries an enormous long-term debt burden. Its debt/equity ratio is 11.86 and has almost as much long-term debt as cash on its balance sheet. This added financial risk, combined with its easily duplicable solutions means there’s little reason to buy BL stock.

Appen (APX)

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Appen (ASX:APX) has been a hot stock on the Australian stock exchange, but recently it has fallen out of favor. The business provides training data for artificial intelligence and machine learning. It crowdsources a huge number of people to complete tasks like data labeling, transcription, and image annotation.

APX stock has been on struggle street in recent times. Some headwinds it faces include declining revenue growth year-over-year and a negative net income. Its financial woes came to a head in the form of diluting existing shareholders with 32.2 million new shares hitting the market last month.

I think The Motley Fool summarized the company’s woes the best. Namely, the data APX stock provides to its customers will be overtaken by the rise of synthetic data created by machines by 2030. The business therefore may have a limited shelf life unless it can dramatically revamp its business model.

On the date of publication, Matthew Farley did not have (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.

Matthew started writing coverage of the financial markets during the crypto boom of 2017 and was also a team member of several fintech startups. He then started writing about Australian and U.S. equities for various publications. His work has appeared in MarketBeat, FXStreet, Cryptoslate, Seeking Alpha, and the New Scientist magazine, among others.

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