Today, innovation-driven businesses are investing significant resources in artificial intelligence (AI) systems to advance their AI maturity journey. According to IDC, worldwide spending on AI-centric systems is expected to surpass $300 billion by 2026, compared to $118 billion in 2022. In the past, AI systems have failed more frequently due to a lack of process maturity. About 60-80% of AI projects used to fail due to poor planning, lack of expertise, inadequate data management, or ethics and fairness issues. But, with every passing year, this number is improving.

Enterprise deployment of artificial intelligence (AI) is positioned for tremendous growth. Artificial intelligence is set to change the business world by improving predictive analytics, sales forecasting, customer needs, process automation and security systems. IBM's Global AI Adoption Index revealed that a third of those surveyed will be investing in AI skills and solutions over the next 12 months. The latter group might include people in leadership, sales, finance, human resources and operations. This is where AI will shine, empowering business teams to make AI-driven decisions.

As a digital strategist, one question I'm asked all the time is "How do we deliver better user experiences?" I always answer with a question: "What are you doing today?" Most often, they'll respond, "Well, we recently updated our UI design with new design assets (e.g. Most organizations still think a better user experience is achieved simply by improving the look and feel of their applications (web, mobile, social media). But this is just one small part of the equation.

Some people throw these terms around like they're disparaging. As if there's something wrong with staying home on prom night to write code. I was one of those nerds in high school, and I've built a career as a technologist. But there comes a time when every nerd needs to grow up. I'm not talking about moving out of your parents' basement -- I'm talking about moving beyond thinking of technology for technology's sake to become what we at West Monroe call commercial technologists.

With high-volume transactional data, historical insights, accessible computer power and new analytic tools, few industries are better suited for using artificial intelligence and machine learning than banking. Yet, few organizations have fully leveraged the many ways machine learning can improve back office operations or the consumer experience. Subscribe to The Financial Brand via email for FREE!Financial services organizations realize they have the potential to apply advanced analytics for both internal and external benefits since they have large data sets and experience with analytical tools. From payment services to everyday banking, insight is captured that can make machine learning more powerful. The good news is that banks and credit unions state that they are going to apply the data at their disposal to improve the customer experience, first and foremost.

Navigating the turbulent waters of Artificial Intelligence (AI) and Machine Learning (ML) can seem like a daunting task to the uninitiated. In fact even the question of how AI relates to ML is answered differently depending on who you ask, as evidenced by the numerous articles about on these topics. In this area, confusion abounds – for example with ML being linked to predictive analytics, including Monte Carlo Simulations, which have nothing to do with ML! Some of this comes from the breadth of subjects that are related to these concepts. For example Natural Language Processing, Random Forest, Dimensionality Reduction, Neural Nets and Deep Learning do not fit into a nice grouping structure or hierarchy which can defined as just an instance of that overall class of technique. The next layer of complexity comes from the fact that any given use case can actually use a fairly arbitrary combination of these tools to achieve its aims.

The'AI in Banking' Digital Banking Report surveyed banks and credit unions globally to determine the extent of development of artificial intelligence functionality in the banking industry. The findings indicate that industry-wide deployment is far behind other industries. The door is wide open for forward-thinking financial institutions to leverage the data, insights and advanced analytical tools at their disposal to improve back-office operations and contextual personalization. That said, research indicates that most banks and credit unions – and the industry as a whole – have not kept pace with consumer expectations around digital capabilities or digital engagement compared to what other industries are providing. Nowhere is this more evident than with the banking industry's deployment of artificial intelligence (AI).

The first AI World Conference and Expo, an event specializing in artificial intelligence, kicked off in San Francisco in early November. This niche conference included demonstrations of language mapping and image tagging technologies, natural language processing applications, and, of course, bots. Presentations and exhibits were not focused on the front-end applications such as speech recognition or connected devices that are typically discussed as part of enterprise communications. AI, the technology itself, is more about the back end. There is a tremendous synergy between artificial intelligence and speech recognition technology and the Internet of Things, but at AI World such subjects are viewed as the data collection endpoints.

We have seen the birth to a generation of enterprises that are data-rich and analytically driven, eagerly following trends in big data and analytics. Let's take a closer look as I provide some use cases demonstrating how IBM is helping clients find innovative big data solutions. Enterprises that use data and sophisticated analytics turn insight into innovation, creating efficient new business processes, informing strategic decision making and outpacing their peers on a variety of fronts. According to the International Data Corporation (IDC), rich media (video, audio, images) analytics will at least triple in 2015 to emerge as a key driver for big data and analytics technology investment. And such data requires sophisticated analytics tools.