Founder and CEO of FortySeven Software Professionals, with over a decade of experience advising F500 companies and growth-stage startups.
Artificial intelligence (AI) is gaining a lot of traction lately. Apparently, the majority of AI services and products will be in high demand for the next few years. According to Gartner, worldwide AI software revenue is forecast to total $62.5 billion in 2022, and one-third of organizations with AI technology plans said they would invest $1 million or more in the next two years.
And when we talk about AI, there is always another topic to discuss —machine learning (ML) methods.
The upheaval of 2020 forced companies to be laser-focused on their most important priorities — among them, of course, are AI and ML initiatives. According to an Algorithmia report, 83% of organizations have increased AI or ML budgets year-over-year. It’s no surprise when you consider ML models can generalize and perform complex tasks.
But businesses are struggling when it comes to building AI solutions that can quickly scale. When implementing ML models across different industries, they allow current businesses to scale even faster. ML helps to automate everything, including decision-making, pricing, customer support and more tasks.
When it comes to AI, more and more companies are facing a choice: whether to develop a project using a traditional approach (predefined rules) or with the implementation of ML (teaching machines to do something not by instruction or logic but by examples or some kind of feedback). When choosing the traditional approach, it’s more reliable with a full and clear view of a road map. With the implementation of ML models, sometimes it’s risky and hard to do it right because it requires a lot of experience and judgment to build it properly. It’s a kind of an art, really, and it’s not a straightforward process.
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This is why more and more clients from all industries are looking for proven ML experts. In a 2020 report, it was found that data science jobs will increase by 38% over the next 10 years, while demand for machine learning jobs will rise by 37% over the same time period.
When talking about the implementation of this technology in our daily lives, a great example is self-driving cars. The self-driving car sector is growing at a rapid rate, and the market is expected to be worth $400 billion by 2025.
Other early adopters of ML are those in the e-commerce industry and financial institutions. Because they have a lot of data and manual processes, ML can optimize these processes.
Think about financial transactions. When you pay with your credit card, it’s an ML model that decides if the operation is suspicious or not. Another example from e-commerce is dynamic pricing — hundreds of times per day, a system decides what price to put on a specific product, predicting future demand trends.
Our advice for companies that want to start the journey of implementing AI by using ML models is first to establish a clear use case with a lot of manual processes, define success goals (such as reduction of 80% of manual labor, for example), and then find a good expert to help to build an initial implementation and measure its impact on the business. Usually, it’s hard to find them, though we do have such specialists. Then rinse and repeat this process for other use cases. It’s also significant to periodically update ML models with fresh training data in order to keep the same performance metrics.
The most important aspect of building ML models is that we teach them, we do not code them. ML models are like having an army of robots performing work simultaneously in under a matter of hours. It’s our job as humans to provide high-quality training data for the continuous improvement of ML models.
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