We have recently discussed the way AI is being increasingly adopted by companies across all industries, permeating all of their business areas from internal operations to customer service. With our economy currently undergoing a turning point in more than a few of its aspects, AI is set to be one of the main allies in the struggle to tackle some of the most urgent challenges, from the ongoing supply chain crisis that started amid the pandemic, to the worldwide effort to significantly reduce greenhouse gas emissions in the next years.
It is no surprise then that all companies seeking to thrive in a competitive and challenging market are trying to adopt AI strategies as efficiently and fast as possible. However, this vast Enterprise AI adoption has not, of course, been equally successful for every company. So what we would like to explore here is what are the most effective AI adoption strategies that we can identify so far.
Why lessons from leading companies can help new AI adopters
The e-Commerce industry has recently shown us a great example of the importance of being at the vanguard of AI. When Wayfair invested in machine learning and made a great effort to shift all its data to the cloud, there weren’t many other companies focusing on this type of digital innovation. Then came Covid-19 and massive lockdowns all over the world, which produced a fast and dramatic change in consumer habits. But Wayfair had made itself more than ready to adapt to these new demands, as logistics had been dramatically optimized through the use of AI tools. The result? Their inbound logistics costs went down by 7,5%.
But the thing is, when it comes to the outcome of AI adoption, Wayfair is more an exception than a rule. Many companies are still struggling to get the most of it in terms of revenue growth, cost reduction, improved commercial strategies, and optimized operations. Best practices approaches of companies such as Wayfair that are currently leading the way should offer valuable lessons for companies that are seeking to enhance their AI strategy.
What makes an AI strategy successful
For a while now, most of the leading companies have considered machine learning as one of their strategic priorities. With centralized support functions aimed at keeping track of the progress of every digital program across different areas, they have a clear definition of how digital innovation is linked to the wider, long-term business strategy. They are also more prone to adopt the most advanced approaches, leveraging ML for a wide variety of purposes, from improving logistics and operations to areas that require more complex processes, such as quality assurance.
Another key point is, obviously, budget. Leading companies have been steadily raising their budget to intensify AI adoption, and most of them are announcing further raises of 10% to 15% for the immediate future.
But such a challenging field as AI requires much more than vision, a strong budget, or hard work. In the end, it is fueled by talent and knowledge, and today’s leading companies have stood out in their effort to attract and recruit talent, both internally and through partnerships and alliances. But they have also stood out at training from the early stages those employees that are not specialists in the fundamentals of ML, making data accessible and AI tools easily available for them. And this is part of a long-term goal to deeply embed AI in their business operations in the fastest and most effective manner. More sooner than later, this same goal is set to be one of the most important for any thriving company.
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