This article draws your attention to the design, devel­opment, deployment, and refinement of cognitive computing systems (CCSs). While CCSs are deployed in a variety of fields yielding benefits exceeding expectations, there are also major failures. Lack of appreciation for the differences inherent in developing a CCS versus a traditional software system is key to these failures. To assist in developing successful CCSs and to derive benefits from them, the authors offer a set of nine key recommendations based on their examination of over two dozen systems. They conclude that CCSs will be a dominant technol­ogy that will permeate all business operations for the foreseeable future.

This article draws your attention to the design, devel­opment, deployment, and refinement of cognitive computing systems (CCSs). While CCSs are deployed in a variety of fields yielding benefits exceeding expectations, there are also major failures. Lack of appreciation for the differences inherent in developing a CCS versus a traditional software system is key to these failures. To assist in developing successful CCSs and to derive benefits from them, the authors offer a set of nine key recommendations based on their examination of over two dozen systems. They conclude that CCSs will be a dominant technol­ogy that will permeate all business operations for the foreseeable future.

Cutter Fellow Steve Andriole presents a brief, multi­faceted overview of AI — “the good, the disruptive, and the scary” — and sets the backdrop for further explor­ation. He outlines some recent advances in and key limitations of AI and explores how AI could disrupt several domains, such as insurance, banking, law, real estate, and education. He then discusses the impact that the deployment of intelligent systems will have on jobs and the profes­sional opportunities that will arise.

Cutter Fellow Steve Andriole presents a brief, multi­faceted overview of AI — “the good, the disruptive, and the scary” — and sets the backdrop for further explor­ation. He outlines some recent advances in and key limitations of AI and explores how AI could disrupt several domains, such as insurance, banking, law, real estate, and education. He then discusses the impact that the deployment of intelligent systems will have on jobs and the profes­sional opportunities that will arise.

We hope the articles in this issue present perspectives and ideas on fulfilling the promise of artificial intelligence and that you’ll find them interesting, insightful, and practical. The articles will help you “imagine the new possible” and inspire and encourage you to harness advances in AI in your domain of interest, addressing any concerns and limitations, for good. 

We hope the articles in this issue present perspectives and ideas on fulfilling the promise of artificial intelligence and that you’ll find them interesting, insightful, and practical. The articles will help you “imagine the new possible” and inspire and encourage you to harness advances in AI in your domain of interest, addressing any concerns and limitations, for good. 

“The purpose of a system is what it does” — referring to “system” as the company as a whole — means that a company’s statements of intent (“we are an innovative, digital native company”), or even its market analysis or the initiatives it has undertaken, are secondary to what a company actually does.

The industry is ripe for transformation in areas including customer service and marketing, claims management and fraud detection, and underwriting.

This Advisor explores one approach that has been particularly effective in making enterprise architects understand the realities of organizational change in their own context.

In this on-demand webinar, featuring Cutter Consortium Senior Consultant Philip O'Reilly and 4 of his colleagues, you'll discover how data harmonization — via the marriage of open industry standards and semantic database technologies — results in improved outcomes in data quality, analytics, risk management and governance.

A strategic approach around big data not only includes the multiple analytical, architectural, project, and technical elements in a synergistic manner, but also pays due attention to the financial and people aspects, resulting in business value.

Artificial intelligence (AI) in all its various forms — machine learning (ML), natural language processing (NLP), speech recognition, cognitive systems, intelligent agents, chatbots, and robotics — is generating intense interest across every industry — from consumer electronics, banking, and finance to automotive, insurance, healthcare, government, aerospace, big pharma, retail, and manufacturing.

Artificial intelligence (AI) in all its various forms — machine learning (ML), natural language processing (NLP), speech recognition, cognitive systems, intelligent agents, chatbots, and robotics — is generating intense interest across every industry — from consumer electronics, banking, and finance to automotive, insurance, healthcare, government, aerospace, big pharma, retail, and manufacturing.

In this Advisor, we consider what leaders are expected to do within Agile organizations, then see how these duties translate to a set of desired skills and personality traits.

The existence of a digital backbone in an organization means that anyone aspiring and planning to transform different parts of the enterprise will be able to leverage the digital backbone in a consistent and sustainable way, ensuring that each effort connects to and leverages a common platform. 

Even radical approaches to change management, such as business process engineering or Lean, assume there is a desirable endpoint (however temporary) toward which change is directed. However, what if change happens so continuously that no fixed endpoint exists for any initiative, but instead organizations must constantly change, not just to succeed, but even to survive?

Governments are starting to embrace the possibility of creating their own cryptocurrencies.

If electric cars and trucks are the vehicles of the future, then that already suggests a massive change in the industrial infrastructure — new modes of manufacturing, new modes of refueling and repair, and, perhaps, new roads. Given the key role of autos in society, such a transition will be a major story. However, it is really only a small part of the changes facing the automobile industry. As we explore in this Executive Update, other technological changes in the auto industry include: self-driving cars, intelligent auto production processes, and cars as entertainment centers.

If electric cars and trucks are the vehicles of the future, then that already suggests a massive change in the industrial infrastructure — new modes of manufacturing, new modes of refueling and repair, and, perhaps, new roads. Given the key role of autos in society, such a transition will be a major story. However, it is really only a small part of the changes facing the automobile industry. As we explore in this Executive Update, other technological changes in the auto industry include: self-driving cars, intelligent auto production processes, and cars as entertainment centers.

As a second-order effect of the Internet, digital transformation can be broken down across the three major areas that it will transform: customer experience, operational processes, and business models. Although transforming the customer experience is the most visible manifestation of digital transformation, as I discuss in this Advisor, transforming internal processes through digitization, worker enablement, and performance management can also show great benefits.

Both the explosive growth of Agile adoption and the return to collaborative and experiential learning are part of a larger global transformation whereby people are seeking to cast off authority in exchange for autonomy and peer collaboration.

There is an ingrained optimistic spirit infusing the enterprise, creating a bias toward action, toward change, toward better things. And, a resulting need for speed: speed to market, shorter cycles, quicker turnaround, and more throughput. Faster, faster, faster! More Agile. But speed kills. Even avid practitioners of Agile — if we pay attention to some of the current conversations — are generally of the view that Agile is not merely about speed, and that breakneck speed can break necks and more, if “Agile” is simply an excuse to hurtle mindlessly into space.

This Advisor discusses the risks and challenges of implementing prescriptive analytics in the context of machine learning.

In many market sectors, the journey to become more Agile is a strategic imperative. This Advisor looks at some of the reasons some organizations find this transition difficult. These challenges point to the practices, processes, and cultures of the past as potential hurdles.

Researching and writing about the digital backbone has strengthened my initial thesis of the concept due to an overwhelming response to the research and from opportunities to engage with audiences worldwide on the topic. In this Executive Update, I’d like to share some of my own discoveries and insights from conversations with CEOs, chief architects, CIOs, market and sales leaders, as well as innovation leaders across many industries and geographies — what I refer to as my “digital backbone learning lab.”