We’ve all heard that artificial intelligence (AI) is poised to disrupt every industry — but where does the hype end and true AI step in? Companies that have already implemented AI and machine learning into their systems, or are taking actions to build them in now, will thrive as markets move into a new era of data-driven results. On the other side of the coin, hesitation to adapt by companies sticking to tried-and-true operating methods may turn out to be a fatal mistake. The AI washing trend is getting out of control as companies fear being pushed into irrelevancy.
So what exactly is AI washing? To call yourself a true AI company, you need to engineer and utilize an AI product — it’s as simple as that. AI washing sees many companies serving enterprises calling themselves “AI” companies when they aren’t actually AI at their core. Instead, they are using alternative/old data analytics tools or acting as a consulting company under the pretense of AI, but without the technological bandwidth, task efficiency and overall intelligence to produce AI results. This marketing stunt helps these companies to align with winning AI companies, thereby inflating their product and in turn, their revenue.
This year, we’ve seen investors doubling down on AI in Silicon Valley; however, no one is being held accountable for any real implementation. While some companies are building off of this momentum by integrating AI into their systems to increase efficiency and positive growth, others are playing the AI card to appear more advanced than they are. Many lack the actual bandwidth to produce machine learning or AI-level results.
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These culprit companies confound AI with simple data processing technologies, which is not only holding the companies themselves back from more significant development, but is also problematic for limited partners, shareholders and partners looking to invest in AI-centered companies. Similar to the adverse effects of greenwashing on overall corporate progress in measures of sustainability, AI washing disincentivizes companies from building out real AI systems.
Luckily, it’s not all hype. According to Gartner, by 2020 AI will be among the five biggest priorities for investment for CIOs and customers will manage 85% of their relationship with the enterprise without interacting with a human. But for now, with no set standards, how can investors avoid wasting millions when the companies they invest in can’t live up to the hype?
Here are some ways that shareholders can sift through the noise:
- Look for employees who can build. The company’s core team should be made up of machine learning and AI engineers, data scientists and mathematicians. These are the team members who are actually building AI products.
- Take a closer look at the company numbers. The AI system should be intaking terabytes of data, with the capacity to cleanse enormous amounts of data instantly. If the company is releasing marketing and advertising surrounding the company’s AI capacities, there should be numerical data to back it up.
- Identify the use case or AI application being used. What does the company’s metric of success look like? Even Google and Facebook’s research vision labs had an ultimate end goal, and improved computer vision led to a new medium of search. When there’s no use case, this work is simply research for research’s sake, rather than a commercial venture.
- Look for progress. AI is not stagnant, but constantly learning and developing as it receives data. Therefore, the system should be getting smarter over time, and the longer a company has implemented AI, the more it should be able to accomplish.
The combination of a strong team of engineers and data scientists and the capacity to run extensive machine learning models can help drive any retailer to success. A push for AI developments in retail is what will push the industry to adapt to the needs of current and future customers.
Kerry Liu, Co-Founder & CEO at Rubikloud, leads people, sales, and technology disruption. Rubikloud is the world’s most advanced machine learning platform for large retailers. By delivering in-market recommendations on loyalty and merchandising campaigns, Rubikloud significantly lifts revenue and saves operational time. In his role, Liu works to manage and maintain a thriving company culture that recruits the best and brightest in the industry, while also maintaining relationships with global retailers and investors. Liu is passionate about machine learning and big data, and enjoys providing enterprise retailers with the tools and knowledge needed to enhance their overall business goals.
