Artificial Intelligence (AI) probably makes you think of sentient robots from sci-fi movies who rise up to end the human race. Or you might think of Amazon’s Alexa, who seems to be on the aforementioned path. But what you might not know is that AI or machine learning isn’t just some untouchable futuristic technology for the top FANG companies. In fact, CEOs of companies all shapes, sizes, and industries have been looking at machine learning as a legitimate business solution.
How AI is beneficial to your company
The possibilities for machine learning are infinite, and so there are no boundaries as to how you can implement it into your business, only that of you and your developers’ imaginations. Companies have used AI to detect fraud, improve customer experience through chatbots, automate work processes, and even increase sales.
According to a survey done by the Harvard Business Review in 2017 of more than 3,000 executives, about 30% of early artificial intelligence adopters say that their revenue increased by utilizing AI to gain market shares or expand their products and services. A different report stated that AI had already directly improved profits with ROI comparable to that of big data and advanced analytics.
Small businesses are adopting AI at a faster rate
Machine learning is costly to implement and a relatively new technology that hasn’t been well established yet, which is why many businesses have been slow to adopt it wary of its risks. But recently as Facebook and Google have open sourced their machine learning technology with Fasttext and Tensorflow, respectively, it has become more accessible and affordable. These open sourced tools allow for companies’ in-house developers to study AI development and begin planning for the adoption of AI without high financial risk.
According to Dmitry Matskevich, CEO and Cofounder of Dbrain, due to the high-speed growth of AI technology, it is more likely that startups and small businesses will be able to adopt AI into their business before larger corporations.
Generally this can be due to the fact that startups have to jump through fewer hoops to overhaul their tech infrastructure and have fewer employees to retrain in the new technology compared to larger corporations. Well established corporate leaders are also more complacent in obtaining new knowledge, with AI having a steep learning curve it’s harder for these leaders to quickly engineer a plan of implementation. On the other hand, startup leaders are consistently learning and researching new technologies out of necessity and are willing to take larger risks.
However, the adoption of machine learning is still in its infancy. Although startups now have easy access to open source tools and larger companies have the funds to afford skilled AI developers, many companies have only just begun planning their implementation and not many have successfully executed them yet. Due to the complex nature of the technology and the scarcity of AI developers, there is still some ways to go before AI is common practice amongst businesses.
How to integrate Machine Learning into your business
So how should you start to apply AI into your business? The first and most important thing to do is assess why and how you would need to implement machine learning and whether it’s worth the risks. AI isn’t like liking the famous world_record_egg, don’t do it just because it’s trending!
Do you need to provide 24/7 customer service, but can’t afford the manpower? Might you need a more accurate predictive analysis that can offset human error? Or perhaps there are clunky processes that slow down your business operations and your developers can use machine learning to streamline those processes.
Once you’ve determined what you need, it’s important to have the right developers for the job. Developers trained in AI are scarce since it’s a very new industry. But luckily there isn’t a brand new language that your current developer needs to learn. Instead, think of AI as a poem — it’s a form of writing but not an independent language. There are five major languages that are used for AI development: Java, Python, Lisp, Prolog, and C++. Depending on your business, it is likely more cost efficient to pay for the education of a current top developer in your company to get up to speed on AI development than hire one of the few unicorns that have already mastered it.
The last thing to keep in mind is to start small. Adnane Charchour, President and CEO of Exous, recommends to first “integrate first-party apps that facilitate workforce productivity and predictive client experiences. When you’re ready, cloud systems, open-sourced AI and flexible workforce models all make for cost-effective AI development and infrastructure.” By starting small, testing applications, and focusing on infrastructure, you can ensure that your business continues to operate smoothly during this transition period.
At the end of the day, AI is just another piece of technology your business can utilize to grow and become more efficient. It’s not going to come to life and rule the world with its artificial brain (hopefully), nor is it some untouchable technology sent from the gods. Implement it as you would with any other business tool, with rationale and data backing it up.