Artificial Intelligence (AI) is rapidly transforming the way organizations of all sizes plan and operate. While the availability of machine learning solutions custom-tailored to solve various business problems is poised to grow dramatically, the reality is AI already is transforming the enterprise landscape.
AI’s growing impact is evident in the increasing adoption of AI by large organizations for decision-support and business process automation. Meanwhile, smaller enterprises are taking advantage of a recent boom in AI-as-a-Service offers. Accessible machine learning tools bundled into low-cost subscriptions are fueling a new market for service vendors, helping small business be more competitive with bigger companies.
AI is also having a measurable impact in data- and research-intensive industries like financial services, life sciences, and retail, but other industries are seeing rapid AI adoption as well. For example, in patient care, predictive models can now identify certain diseases much sooner, enabling earlier intervention and treatment. Researchers developed AI systems to be able to predict a patient’s likelihood of developing Alzheimer’s up to six years before any symptoms appear. In manufacturing, machine learning on the plant floor can dramatically reduce unplanned downtime and improve productivity. An automotive manufacturer recently implemented an AI-based quality control system that reduced product defect rates by 90 percent and increased production levels by 300 percent.
Applying AI to the right problems and having access to high-quality data in sufficient quantity are both critical for successful AI adoption. Unfortunately, these two key elements are often wrongly perceived as inherent limitations of AI. But AI is like any other business tool: garbage in, garbage out. For instance, if an organization seeks to better predict customer behavior, the fundamental question to ask is whether this is the kind of problem appropriate for an AI solution. Is the right data available to train the AI models? If not, then significant up-front work to collect, clean up, and categorize the data is usually necessary. And these fundamental questions are pre-requisite to the actual building, training, testing and deployment of the AI models themselves.
The current shortage of skilled workers who can model, train and successfully deploy solutions is actually the biggest practical barrier to AI adoption, not technology. But the good news is the number of trained AI professionals is steadily growing, with people of all backgrounds increasingly taking advantage of accessible, high-quality AI-related training and education. This shift will open new career opportunities beyond the traditional computer sciences, with hiring practices following the trend. In fact, data science is poised to become one of the most highly-sought-after career fields as data-driven automation and predictive analytics expand across the entire business spectrum and transform entire industries.
Business and public sector use of AI also brings with it significant security and ethical considerations. The recent popularity of large language model tools like ChatGPT prove why organizations must be strategic in their plans for AI. They must protect against theft or manipulation of sensitive employee and customer data, while also ensuring their AI infrastructure is fully secured against exploitation and misuse. Organizations must secure AI as they would any other critical business function and not cut corners for the sake of expediency. Public trust demands that every organization implement safeguards against inadvertent bias in their AI models and continually consider possible ethical ramifications so AI systems are fully transparent and accountable.
AI isn’t the Hollywood science fiction of the future, at least not in a practical sense. The AI revolution is already here, and it is transforming the way organizations of all sizes plan and operate. While the real and potential benefits are clear, AI adoption does come with significant challenges, including a need for skilled workers, complex ethical questions, and always-evolving cybersecurity threats. But as AI capabilities continue to advance at breakneck speed and adoption rates accelerate, organizations that are strategic in their use of AI and don’t cut corners for short-term gain will be the most successful in the long-run.