Artificial intelligence (AI) is fast becoming an essential tool for the financial services industry. According to Insider Intelligence's AI in Banking report, most banks (80%) are highly aware of the potential benefits presented by AI. The opportunities in this space are legion, but AI solutions require careful governance, with the right checks and balances in place to ensure that they are robust and fair. The scope of possible uses for AI and machine learning in finance stretches across business functions and sectors. In the financial services space alone, AI tools are already used to refine customer service, client segmentation, fraud prevention and loan assessment, to name just a few.

We are well into the third wave of major investment in artificial intelligence. So it's a fine time to take a historical perspective on the current success of AI. In the 1960s, the early AI researchers often breathlessly predicted that human-level intelligent machines were only 10 years away. That form of AI was based on logical reasoning with symbols, and was carried out with what today seem like ludicrously slow digital computers. Those same researchers considered and rejected neural networks.

Building a machine learning model is an iterative process. For a successful deployment, most of the steps are repeated several times to achieve optimal results. The model must be maintained after deployment and adapted to changing environment. Let's look at the details of the lifecycle of a machine learning model. The machine learning lifecycle is the process of developing, deploying, and managing a machine learning model for a specific application.

The key to successful deployment is asking the right questions before making any investments. "AI is already prevalent in many business processes and applications used daily, and there are almost limitless other opportunities where it can be utilized," said Annette Taber, senior vice president for industry outreach and relations at CompTIA. "However, AI processes are complex. The key to a successful deployment is asking the right questions and understanding what's involved before making any investments." The guide identifies more than two dozen factors that should be thoroughly considered and addressed by business decision makers and AI practitioners.

This month, we discuss AI education. Joining the discussion this time are: Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Holger Hoos (Leiden University) and Oskar von Stryk (Technische Universität Darmstadt). Sabine Hauert: As we are starting the new term, the question is how should we do AI education and what should students be learning? Thinking more broadly, how should we rethink AI education for the general population? There will be huge swaths of the public that will need to gain an understanding of AI, or be trained in the use of AI.

While AI holds a lot of promise for marketers, a recent study from Blueshift found that while a majority of marketers see AI adoption growing, very few have implemented advanced AI techniques. The study found a strong correlation between successful deployments and marketers having direct access to customer data -- i.e., marketers accessing and activating the data without help from information technology (IT). The key to unlocking AI value is to choose a customer data and AI platform that marketers with limited technical knowledge can understand and operate on every channel. In a world of rapidly moving data and insights, marketers can no longer afford to go back and forth with IT. It is imperative to deploy platforms that bring advanced capabilities directly into the hands of marketers.

Our societies and economies are in transition to a future shaped by artificial intelligence (AI). To thrive in this coming era companies are transforming themselves by using machine learning, a type of AI that that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. This article investigates the role IT professionals play in enabling the AI-driven enterprise through machine learning model deployment. Imagine your company as an AI-driven enterprise. Embedded in core business processes, hundreds of machine learning models ingest streams of data as the company interacts with customers and suppliers.