As more companies integrate artificial intelligence into their products, services, processes, and decision-making, the definition of what AI is and where it can be most effectively applied is evolving as rapidly as techniques themselves. What started as algorithms used to determine loans, select new hires, and hold chatbots accountable (with mixed success), are now deeply rooted and used in everything from weather risk forecasting to prospect selection. The question is no longer whether a company should use AI, but where it provides the greatest competitive advantage.
In our work with businesses, we see three areas where AI has now moved from being ‘nice to have’ technology to ‘must have’ technology. Companies that push the boundaries of AI to refine predictions, increase efficiency, and optimize real-time pricing or inventory control for their products are moving faster and further than their competitors who are still cautiously hesitant about where to go. wisdom to use AI for these purposes.
In recent years, AI has moved from a technology that finds relationships in data and predicts existing trends more accurately, to one that detects future changes in everything from leisure spending and spending habits. trip to the solvency of the company by analyzing preferences and feelings in large quantities. data, including text, voice, images, digital news feeds and social media.
AI can now recognize disruptors on the horizon by making connections between built-in features, allowing businesses to more effectively prepare for disruptive events. Early AI fraud alert systems can now detect bots, making them increasingly essential to stay ahead of evolving tactics from hackers, national players, malware and ransomware. Machine learning algorithms adapted to market shocks help major banks to predict not only the performance of their investments, but also the potential vulnerabilities caused by disruptors such as Covid-19.
This helps banks and large corporations alleviate the impact and potential bankruptcies of their investment portfolios. For example, a bank was able to predict in weeks, instead of months, which loans would be unlikely to be repaid and reduced their number by 70%, thus increasing the returns on its overall loan portfolio by dozens. millions of dollars. Likewise, AI enabled an aerospace parts distributor that suffered from excess inventory and cash flow shortages during industry downturns to more accurately forecast declining demand for its products. parts when Covid-19 would strike. As a result, the company was able to reduce its working capital by hundreds of millions of dollars and double its on-time deliveries.
In areas such as insurance, human resources, and conduct monitoring, machine learning reads forms and examines voice and video recordings to highlight where the examiner’s attention needs to be focused, how a call should be routed or simply if an attachment has been forgotten. The development of so-called “attention” approaches that learn which parts of the input are most critical, has accelerated the use of natural language processing, allowing AI to more reliably link concepts seemingly without report and work faster.
As a result of these advancements, over the next few years, automated processes and filters will become more and more ubiquitous in departments and processes that are traditionally not seen as data-driven, spanning every step, from the interaction to ‘a customer processing an order, for example. Advances in quantifying fairness and mitigating bias also allow AI-based approaches to be more fair, transparent, and objective than our previous human endeavors, although quantifying fairness can sometimes be helpful. a painful first step.
The gains from new AI-based efficiencies add up quickly. Bank saved tens of millions of dollars after using AI to improve decision speed and consistency in customer service. Even with fewer staff, the bank has significantly reduced customer waiting times, while maintaining the same level of vigilance and detecting three times as many potential frauds.
AI also allows businesses to grow faster by freeing up staff for more skilled tasks and upgrading skills when needed. Until recently, AI was considered too delicate to be used for traditional data processes, like cleaning up duplicates in data sets. But now AI is widely used for this laborious task. In one bank, algorithms were found to be much more effective at identifying duplicates and removing them in weeks rather than years. As a result, regulators approved the bank’s plan to create dozens of branches.
Likewise, AI enables companies to perform tasks and change strategies in real time. Machine learning algorithms now instantly and automatically increase sales promotions or, on the other end of the spectrum, delay the launch of products that could cannibalize the profits of other product lines. In retail, AI can recalibrate these types of decisions to drive additional sales, even other un-promoted products.
One of the reasons this breakthrough is now possible is “cutting edge computing”, a distributed computing technique that allows data storage and machine learning models to remain more local. By eliminating the need to send data to the cloud, AI can change strategy instantly, while maintaining data privacy and security, avoiding cross-border data flow issues, especially since many jurisdictions are beginning to seek to reduce data transfer.
Real-time optimization has an immediate, and often dramatic, impact on business bottom lines. A retailer we worked with increased their profit margins by 50% after using AI to instantly optimize their offerings. It increased commodity sales with AI-powered margin-generating promotions and ended margin-destroying specials, all without investing a lot of time in creating and testing new data.
This was important because the low frequency of transactions precluded traditional testing and the overlap of promotions and coupons added complexity.
Insurance company grows profits by more than tens of millions of dollars by using AI to tailor health insurance policy pricing to individual customers in real time. As soon as clients improved their lifestyle by quitting smoking or exercising more, their premium was reduced.
AI is often presented as an investment rather than an accepted fixed cost. Adopting or increasing the use of AI requires resources to tackle governance, transparency, skills upgrading and generally tech debt as well. But companies can no longer afford to treat something so pervasive and powerful as optional.
Modern customers are hyperconnected. They require quick decisions and responses. AI can understand the language and dynamics of multiple channels, such as Twitter, WhatsApp, TikTok, or a chatbot, and can scale and change code to become more and more likable.
As the “explainability” of AI models improves, as well as more reliable means of monitoring performance, robustness and fairness, these more complex models will become even more reliable, their methods and results more reliable. understandable, and therefore their more creative and feasible applications.
With these capabilities, the flexible and modern remote workforce should be able to skip boring and repetitive tasks. By not getting bored or distracted, an AI is likely to do a more reliable job. By moving staff to higher value-added areas, managers don’t just make the best use of their team, they have a happier business. Combine that with the ability to spot and mitigate disruptors and dramatically increase profits, and your business can thrive for years to come.