Lila Rajabion provides four examples of how KGs can help leaders advance their understanding of the business environment in which their company sits. These include merging data silos to create a company overview across divisions, connecting different types of data in meaningful ways, aiding informed decision making by narrowing searches and contextualizing information, and showing interconnections that help leaders gain perspective. Next, she dives into how Google, LinkedIn, eBay, and IBM are using KGs and explains how other companies could follow suit. She then addresses four challenges currently faced by companies looking to leverage KGs, followed by a look at specific business efficiencies enabled by KGs, including making data more accessible for employees, helping leaders make data-driven decisions, and assisting companies in deploying AI technology.

The increasing realization that deep learning alone cannot be the solution to build robust, reliable artificial intelligence (AI) systems, coupled with the ever-increasing need to make use of heterogeneous data sources for decision making, has led to a recent resurgence of knowledge graphs (KGs). KGs are now playing a seminal role in the emergent field of neuro-symbolic AI, which aims to integrate domain knowledge into AI systems. By combining AI’s statistical/machine learning (ML) side with KGs, we get more effective, more explainable cognitive results and begin creating logic-based systems that get better with each application. In other words, we can build the next generation of AI models, ones that support better human-machine collaboration.

Effectively managing AI’s carbon footprint requires a shift to a system like regenerative capitalism or doughnut economics that does not emphasize continuous growth or increased consumption. However, the novel opportunities AI offers society make it difficult for many to accept the idea that data consumption related to AI must be managed. The 3Rs framework presents an alternate system grounded in regenerative capitalism and doughnut economics as a way to reduce the carbon footprint of data.

In a recent issue of Amplify, Torgeir Dingsøyr and his coauthors presented a teamwork effectiveness model aimed at co-located Agile development teams working on a single product. Since the publication of that issue, Dingsøyr has been asked to present the team effectiveness model to a number of organizations. In this Advisor, he addresses a recurring question from those experiences: how does the team effectiveness model adapt to post-pandemic work?

Explainable AI (XAI) is the discipline of going deeper within the AI system, identifying the reasoning behind the recommendations, verifying the data, and making the algorithms and the results transparent. XAI attempts to make the analytical recommendations of a system understandable and justifiable — as much as possible. Such explainability reduces biases in AI-based decisions, supports legal compliance, and promotes ethical decisions.

True strategy execution takes a village. To really succeed, we need to consider the entire process to move an idea into action — from innovation and strategy formulation through the delivery of results. A formalized and cohesive process is necessary to develop strategies, architect changes, plan initiatives, execute solutions, and measure success. It requires integrating many different teams and creating end-to-end transparency and accountability for the results.

Companies need to make swift materiality and disclosure decisions on a plethora of topics, from environmental impacts to executive health concerns. This interactive virtual bootcamp taught by Cynthia Clark, PhD, will give you in-depth knowledge about materiality and disclosure in general, and you will also discover the risks of being on the wrong side of the disclosure decision.

In this Advisor, we identify a number of important trends involving the application of artificial intelligence (AI), big data analysis, collaboration, mobile, and other technologies in insurtech.

This Executive Update explores CIO lessons learned from the COVID-19 pandemic and how we can better prepare for the next crisis.

In the shift toward data-driven healthcare, focusing only on new technologies is not sufficient. Indeed, how data is gathered and how stakeholders’ interests are managed can enable or hinder the transformation. This Advisor explores eight drivers of data-driven healthcare.

Cutter Expert Esther Derby, Dave Martin, and Tony Ponton present an interesting comparison between organizations that adapted during the pandemic and those that did not. They also suggest remote governance based on the SEEM model (steering, enabling and enhancing, and making).

Lori Silverman explains how leaders can use stories to create greater engagement with staff. She says that seeking different communication patterns enhances relationship building and reverses stress levels in some team members. Silverman demonstrates how executive storytelling can be used to establish direction and motivate teams.

Al Shalloway’s article outlines how leadership’s focus must switch from the people to the processes — the value streams by which the organization operates. He believes leaders can enhance customer experience, increase innovation, and reduce costs with this approach.

In this edition of Amplify, our contributors discuss the effects of the pandemic from their viewpoints. With such a dramatic disruption to business worldwide, many leaders found they had to quickly unlearn how they had operated over the past 20 or 30 years and adapt to the new normal.

It’s one thing to talk about new ways of working and cultural change, but the actions of the leader count. Bob Galen asks, “How are you showing up?” Galen suggests that changing yourself is a more decisive action to enable your people. He asserts that culture shaping happens whether by design or not, so it’s far better for leaders to be aware of this fact and act intentionally.

The authors suggest that although none of us would choose a 10-year-old computer for our work today, many executives persist in using outdated leadership models and behaviors. They say leaders who want to elevate their capabilities must start by knowing why they lead, then update their behavioral algorithm using the Innovative Leadership framework.

Teamwork effectiveness models are based on accum­ulated empirical observations and reasoned arguments, and identify and describe key factors necessary for effective teamwork. Our model, tailored for Agile practitioners, offers insights into effective Agile teamwork and explains how certain Agile practices support it.

Customers are more demanding than ever. In this Executive Update, we explore two questions related to meeting customer expectations: To what extent are you incorporating your customers’ reality into your product development processes? How can a rigorous voice of the customer (VoC) solution help your company grow with significant and sustainable margins?

As we explore in this Advisor, machine vision systems employing machine learning and other artificial intelligence techniques are now bringing major benefits to automakers, dealers, and repair shops in the form of camera-based automated vehicle inspection systems.

In this Advisor, we discuss the idea that managers can and should use softer, psychological skills to build and maintain successful teams.

This Executive Update describes the strategy execution challenges companies face today and shares some ideas for bridging the gap. With a new vision for strategy execution, leadership, and a commitment to action, organizations can transform their end-to-end strategy execution from a disconnected set of activities to an organizational capability.

This Advisor looks at the key roles that AI can play in cybersecurity operations: behavioral analytics, threat intelligence, ransomware attack detection, smart identity governance, strengthening security of cloud applications, online fraud detection, defending against deepfakes, and risk assessment.

Cigdem Z. Gurgur describes how a blockchain-based Internet of Things (IoT) can push market systems toward sustainability. Blockchain offers new opportunities relevant to systems design. It connects stakeholders with multiple sources of verified information, generates a richer informational landscape for executing business processes, and enables secure transactions between untrusted actors. Trusted networks can reduce transaction costs, simplify processes, and reduce resource intensity compared to traditional transaction technologies. Gurgur explores the conditions needed to facilitate blockchain deployment in the next generation of supply chains, specifically through IoT technologies that have attractive applications for creating, monitoring, and enforcing sustainability standards.

Rohit Nishant and Thompson S.H. Teo discuss how to limit the negative impacts of artificial intelligence (AI) adoption by using concepts from both regenerative and doughnut economics. These two approaches seek to reconstruct economic systems so they operate within the sustainable operating limits of natural systems. Pointing out that AI adoption threatens to exceed sustainable boundaries by increasing aggregate demand for energy and new materials, Nishant and Teo put forth a 3Rs framework with which AI adopters can keep the impacts of AI within sustainable boundaries.