Digital transformation accelerated very quickly during the global pandemic, leading to 10 years of innovation in just three months. The abrupt changes came mere weeks after the rules of the California Consumer Privacy Act (CCPA) went into effect and just a few months before they were enforced. As the risk of new fines piled up – at a cost of up to $750 per person, per incident – businesses also endured an increase in cybersecurity incidents and expenses. On top of that, CIOs had to reconsider their strategy for digitization. For example, it wasn’t uncommon for accounts receivable and accounts payable to be mostly digitized while still relying on some aspect of physical paper. Many ERP providers had to catch up quickly and were forced to prioritize eliminating gaps in fully digitizing these processes.
As black swan events increased in frequency – including the Suez Canal blockage, delays at the Port of LA, and the Texas polar vortex – economic pressures continued to mount. Some businesses responded by taking their digital investments one step further and by implementing advanced automation capabilities to reduce ongoing operational costs. Amid the Great Resignation, businesses inevitably focused on how they could use artificial intelligence (AI) and machine learning (ML) to do the same amount of work with fewer people.
Both technologies could drastically transform the future of business and the future of work, but the reality is that AI might not be immediately beneficial to the end user. However, you can expect to see more investment and innovation in using AI and ML with the goal of surfacing better data to help inform business decisions. Neither technology will serve as an auxiliary brain or an automated set of hands to solve all problems, but it can – and will – contribute to more educated voices in the room.
The Promise and Potential of AI and ML
Businesses are looking toward AI and ML to drive efficiency in their supply chain. Their goal is to gain complete visibility to build in redundant suppliers, eliminate common choke points, and ultimately avoid delays. The promise and potential cannot be denied – according to a report by McKinsey, AI-enabled supply chain management has allowed early adopters to improve logistics costs by 15%. Better still, inventory levels improved by 35% and service levels by 65%.
Other businesses are taking notice. MHI’s 2022 Annual Report shows that 73% of supply chain and manufacturing leaders plan to use AI in the next five years, up from just 14% today. That’s a massive increase, but supply chain AI is still largely in its infancy, so there aren’t many case studies to prove or disprove its effectiveness.
Agility and Resilience Depend on Deep Analytics
While AI and automation show promise, businesses cannot pin their hopes on one innovation alone, especially one that is still being refined. And even if they could, AI wouldn’t help manufacturers if they still couldn’t get the parts they need to finish assembly. The same could be said for the talent shortage, though many organizations hope automation can solve at least some of those problems. In both cases, they want software to do more than it’s ever done before to help propel the business forward.
These efforts are leading businesses down a path of intelligent decision-making, but there is still work to be done. As new technologies are developed to serve our evolving needs, innovations in both automation and deep analytics will be instrumental to any business looking to build a more agile and resilient supply chain.
The True Value of AI Is Still to Come
AI holds a lot of promise – according to a report by IDC, AI investments will reach $120 billion by 2025. This highlights the support that businesses have thrown behind the technology, which could (per PwC) contribute $15.7 trillion to the global economy by 2030.
But AI and ML are not a magical solution that will instantly solve all problems out of the gate. That’s why these investments are so important – to unearth the innovations that can reveal better, more actionable data and inform smarter decisions. These investments stand to reveal the true value of AI and drive business results.
This article was originally published on Dataversity.net.