Artificial intelligence, the internet of things and digital transformation have been popular subjects over the last year. A quick scan of your favorite tech publication will likely result in multiple stories covering all three of these concepts as companies across the globe embrace them.
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Most recently, AI has driven a bulk of the excitement. According to a recent survey conducted by MIT Sloan Management Group, nearly nine in 10 business executives believe AI will serve as a key competitive advantage in the near future, helping them explore new opportunities and potential revenue streams. Yet, a lot of question marks still hang in the air — less than two-fifths of these same respondents even have an AI strategy in place, while only 16% understand the costs of AI development in the first place.
Of course, it’s hard to talk about AI without also talking about machine learning. As a current popular application of AI, many companies across the globe are trying to identify ways of applying machine learning to provide intelligence that will boost their business. But doing so is beyond the reach of most organizations due to the complex and resource-intensive data science lifecycle. So why not apply AI to AI and reduce the number of expensive and scarce human resources needed to teach AI everything it needs to know to be effective? This approach can effectively democratize machine learning, standardize it across the company and make it accessible to more people. In fact, business giants like Uber are already taking this approach.
IoT is a key driver of both the machine learning and AI craze. With the volume of data produced by machines and people on a daily basis becoming unmanageable, it has become increasingly difficult to make use of this information — and the proliferation of connected sensors only serves to further up the ante. Without AI and machine learning, making heads from tails of this data is downright difficult and creates problems for businesses looking to use their data.
With digital transformation at the forefront of many business initiatives, applying AI to IoT can help drive the innovation and business effectiveness that many companies are hoping to achieve. Applied correctly, companies can change the way they operate through the use of these digital technologies to realize key competitive differentiators.
Some industries are more proactive in this regard than others, which is best observed in Constellation Research’s recently released “Business Transformation 150.” This list recognizes some of the global leaders of digital transformation — people who have minds for productive disruption and experimental technologies, and are spearheading digital initiatives at their companies. Nearly one-quarter of the people on the list are from the manufacturing industry, which should come as no surprise when you consider that the sector is one of the pioneers when it comes to using IoT data to fuel machine learning and AI initiatives.
The convergence of today’s digital trends and what it means for you
AI, IoT, digital transformation — these key concepts will be important to the success of businesses in the near future, if they aren’t already. Business leaders already see the potential advantages they can gain by capitalizing on AI, IoT and digital transformation, now they just need to devise the right approach. This starts from the top, and is one of the many reasons why organizations have created centers for excellence or added new job titles, from chief mobility officer to chief data officer. It may be time to broaden this focus that some organizations are doing via a chief digital officer, but the definition and scope depends on the business priorities. Regardless, it makes sense to determine your organization structure and executive leadership based on your digital goals — including the role of IoT.
Digital transformation means a lot of different things to a lot of different businesses. While there is this notion that these efforts are led by the desire to improve the bottom-line customer experience, that doesn’t mean digital transformation can’t impact behind-the-scenes operations either. IoT, machine learning, AI — these technologies all touch the customer experience in one way or another, whether it’s by speeding up manufacturing time, improving product design, streamlining customer service or anything else.
IoT remains a key driver that will impact nearly every industry in the future. Regardless of whether you’re tracking packages as they’re shipped or looking to identify machine performance anomalies, being able to collect and sort all this data and then act on it will be pivotal to success. Data lies at the foundation of AI, so businesses looking to take advantage of these technologies will need to learn how to manage and use their data effectively.
While the manufacturing sector is pioneering advancements in this space, other industries should use them as a case study and learn by example. From a digital standpoint, many business leaders have a tendency to look inwards as they shape their own strategies — they react to and imitate the leaders in their own sectors. However, in a time of such fast-moving digital innovation, the best approach is often looking outside the industry, where you get can find new insights and strategies. Driven by the Industry 4.0 revolution, manufacturing has done a lot of the early digital transformation groundwork. It’s not a bad starting point for other industries — particularly those making heavy use of machines that need to be optimized and managed effectively. Then you can extend those efforts to other predictive use cases.
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