Across industries, businesses of all sizes are embracing digital transformation (DX or DT). Using advanced technologies to improve operations and delight customers has become a source of competitive advantage. And companies spare no expense in these efforts. Worldwide spending on DX is forecasted to reach $2.3 trillion in 2023, according to the International Data Corporation (IDC), a global market intelligence provider. The COVID-19 pandemic is set to only further accelerate this trend.
But succeeding in DX requires more than massive budgets. Artificial intelligence (AI) technologies, such as machine learning (ML), are important factors that allow companies to produce actionable insights, deliver immediate impact, and drive ROI, from their newly digitized assets. And there are many ways in which ML can extract more value out of your digital transformation efforts.
Various capabilities of ML algorithms
Today’s companies are collecting vast amounts of data. But analyzing it is beyond an individual person’s capabilities, and that’s where ML algorithms take over. They study data to detect patterns and learn from that experience, adding value in various areas.
For instance, ML can categorize and catalog different types of information, including transactions, accounts, companies, and people. Also, it can predict likely outcomes and decide on actions by analyzing various parameters. And whether you want to identify new relationships or anomalous behaviors within the data, ML can be of help.
Furthermore, AI and DX work well together. According to a report published by the IT consulting giant Infosys, 98 percent of respondents who used AI-supported activities to achieve digital transformation generated additional revenue, with more than half of high-growth enterprises making over $10 million.
AI supports DX projects related to machinery and factories
You can use ML in various ways to extract value out of DX. Predictive maintenance is an especially effective way of driving ROI and involves collecting, digitizing, and analyzing sensor data. A PwC study shows that the adoption of this technology among manufacturers is expected to increase by 38% because of its ability to increase profit margin.
Smart algorithms, for instance, can predict machine failures. Your team can then use this insight to fix the problem before it escalates and causes trouble. Doing so will also prevent incidental damaging of surrounding components and additional financial costs. And by repairing machines only when suggested by the algorithm, you’ll be able to function with less permanent maintenance staff.
AI and the Industrial Internet of Things (IIoT) solutions can also monitor pieces of machinery scattered around the globe. ThyssenKrupp, a German industrial conglomerate, collects sensor data from 1.1 million elevators worldwide and feeds it into ML models. Its teams receive real-time updates on the status of their equipment and warnings if the maintenance is required. Similarly, Rolls-Royce collects trillions of data points from engines for predictive maintenance purposes.
The use of smart robots in manufacturing is another area where ML adds value. Known as cobots, these AI-powered machines work alongside humans and can perform sophisticated operations such as quickly inspecting numerous items for flaws, automating the transportation of materials through a plant, and avoiding obstacles using predictive intelligence.
AI transforming sales and marketing processes
Besides machines and factories, digital transformation extends into other fields as well. Many hotels, for example, use ML to automate and personalize the process of recommending upgrades and add-on experiences for guests. AI-driven chatbots help improve customer service operations by providing your customers with answers to simpler questions or routing difficult inquiries to agents.
Companies can also analyze customer data to improve their sales and marketing operations. Data can reveal which products shoppers are likely to buy and at what time. And companies such as Netflix and Amazon have taken AI-powered product recommendations and price optimization techniques to a new level and use them to boost sales and expand customer base.
Challenges of implementing ML in DX
Companies looking to use ML in their DX initiatives will need to keep in mind several challenges, though. For one, they need to have a clear business case for using ML. If you’re dealing with predictive maintenance, for example, it’s recommended you define what machines or plants you want to modernize and what the success criteria are.
Finding the right partner with the right specialty is equally important. Some tech partners may take a lot of time to show value, which is why you need to work with those that deliver clear benefits as fast as possible.
Getting the most out of your DX projects
Many companies are turning to ML to speed up their DX journey, cut costs, and boost profit. Smart algorithms have also proven to be an effective way of gaining a competitive edge with solutions such as predictive maintenance delivering immediate impact. This is especially important during the ongoing COVID-19 pandemic when ROI is vital and businesses can hardly afford delays and failures. Keeping these considerations in mind and teaming up with a fast-moving tech partner will ensure you get the most out of your AI-powered digital transformation.