As AI becomes more visible as a corporate strategic tool, organizations will have to incorporate issues surrounding AI as part of corporate strategy. Pavankumar Mulgund and Sam Marrazzo help us by providing a framework for developing an AI strategy. The authors discuss the “minimum viable model” approach to the development of the underlying AI/ML models, along with the platform on which these models run and the inevitable tradeoffs. They conclude their piece by examining some best practices for the successful implementation of AI initiatives.

One way of getting an off-course system (or person) back on track is by nudging. This concept can be particularly useful in goal-directed systems. But, to reiterate, errors will occur. In his article, Richard Veryard describes technologically mediated nudging; the possible unintended consequences; and the need to consider the planning, design and testing, and operation of the system for robust and responsible nudging.

One way of getting an off-course system (or person) back on track is by nudging. This concept can be particularly useful in goal-directed systems. But, to reiterate, errors will occur. In his article, Richard Veryard describes technologically mediated nudging; the possible unintended consequences; and the need to consider the planning, design and testing, and operation of the system for robust and responsible nudging.

Experienced IT practitioners know that errors will occur. A big part of building and managing complex systems is dealing with risk management (which includes identification and mitigation strategies). This is hard enough when documentation and source code exist. But the current state of ML-based AI tends to result in opaque black boxes, which make this activity, um, challenging. David Biros, Madhav Sharma, and Jacob Biros explore the implications for organizations and their processes.

Experienced IT practitioners know that errors will occur. A big part of building and managing complex systems is dealing with risk management (which includes identification and mitigation strategies). This is hard enough when documentation and source code exist. But the current state of ML-based AI tends to result in opaque black boxes, which make this activity, um, challenging. David Biros, Madhav Sharma, and Jacob Biros explore the implications for organizations and their processes.

This article takes us to outer space (well, low Earth orbit, actually) to examine the issues around AI (in its ML incarnation) employed in a NASA system to track orbital debris. William Jolitz, the inventor of OpenBSD (open source Berkeley Software Distribution), makes the case for organization-wide awareness and alignment around ML and suggests that, like security, transparency cannot be bolted on later; it must be addressed at a project’s origin.

This article takes us to outer space (well, low Earth orbit, actually) to examine the issues around AI (in its ML incarnation) employed in a NASA system to track orbital debris. William Jolitz, the inventor of OpenBSD (open source Berkeley Software Distribution), makes the case for organization-wide awareness and alignment around ML and suggests that, like security, transparency cannot be bolted on later; it must be addressed at a project’s origin.

The contributions in this issue of CBTJ will help us get up to speed with the current state of AI and to think about some of the issues raised when we look beyond systems that appear to work as intended. Our contributors span industry and academia, and their commentary provides a good way to gain an overview of the problem.

The contributions in this issue of CBTJ will help us get up to speed with the current state of AI and to think about some of the issues raised when we look beyond systems that appear to work as intended. Our contributors span industry and academia, and their commentary provides a good way to gain an overview of the problem.

Artificial intelligence (AI)-based cybersecurity solutions offer significant advantages with respect to threat detection, response time, and, most important, reduction in false positive alerts.

Artificial intelligence (AI)-based cybersecurity solutions offer significant advantages with respect to threat detection, response time, and, most important, reduction in false positive alerts.

Many aspects of software have changed in the last few decades, particularly since the advent of personal computers in the 1980s, the World Wide Web in the 1990s, and the widespread use of mobile and cloud computing in the last decade or so. Other key changes have been the massive trend toward outsourcing and offshoring in the 2000s and the widespread use of social media beginning in the 2010s. We explore some of these trends in this Advisor.

This Executive Update provides insights into how the cognitive enterprise can proactively protect itself from risks and security incursions by building business-driven safeguards into its underlying DNA.

Recently, we described the application of Scrum at Riccardo’s Restaurant in London. This environment is similar to that of Lean hardware Scrum, in that shifts are repeatedly creating and delivering products in short cycles with high quality. Process efficiency and cycle time become the key metrics for production.

Digital transformation has hit a wall. The need for reinventing how we think about and approach archi­tecture is becoming ever more prevalent, especially if an organization is to truly become Agile.

Aligning strategic objectives and tactical demands is critical to successfully execute your strategy and drive change. Cutter Consortium Senior Consultant Brian Cameron explores the ways you can structure your business architecture to effectively facilitate strategy execution in this on-demand webinar. (Not a member? For a limited time, you can watch the webinar on demand.)

Aligning strategic objectives and tactical demands is critical to successfully execute your strategy and drive change. Cutter Consortium Senior Consultant Brian Cameron explores the ways you can structure your business architecture to effectively facilitate strategy execution in this on-demand webinar. (Not a member? For a limited time, you can watch the webinar on demand.)

Many Agile experts have written about integration and testing. Some debate about the goals, while others explore the tactics taken to achieve them. Most agree that integration and testing should be performed continuously and in an automated manner, if possible. However, this seems to be the extent of agreement. Debate arises over “who does the integration and testing — when, where, and why?” along with discussion about the most efficient and effective way to get the job done. 

In this edition of The Cutter Edge, you'll explore a multi-pronged approach to creating a strategic incident response plan focusing on continuous process improvement, learn what a team has to do to span the gap between its work and the customer market, and more!

It’s clear that organizations are going to have to extend their enterprise data protection practices to be more transparent and flexible if they hope to comply with the various (changing) requirements of data protection and privacy legislation.

Here in Part VIII, we discuss the social and emotional cognitive aspects of proj­ects.

Cybersecurity incidents lead to huge loss or severe damage to industrial assets. To mitigate cybersecurity threats, it is essential to understand the cycle of infor­ma­tion security governance and control: preparation, prevention, detection, response, and learning. This Advisor closely examines the security management cycle.

Cybersecurity incidents lead to huge loss or severe damage to industrial assets. To mitigate cybersecurity threats, it is essential to understand the cycle of infor­ma­tion security governance and control: preparation, prevention, detection, response, and learning. This Advisor closely examines the security management cycle.

Digital transformation is not an end point; it is just a beginning. By going digital, your organization is only entering the game. In this Advisor, we share some laws of software evolution and the market forces at play.

The communication to design teams of new information that causes rework results in design oscillations. Since knowledge about how to complete a project is incrementally “consumed” by team members, design osc­illations are a natural metabolic byproduct of knowledge-foraging behavior. Design oscillations represent the key engine of the project metabolism. The problem lies in the timing of and team cooperation in synchronization of efforts related to design oscillations.