Cutter Expert Paul Clermont helps us understand how and why capable, experienced people often make poor choices. He describes familiar pathologies like confirmation bias, groupthink, and the tendency to double down on failing courses of action in which we have invested heavily. Clermont argues that challenging market conditions sometimes exacerbate our tendency to make these types of mistakes, rather than bringing out the best in us. He provides four guidelines for enhanced critical thinking, arguing that leaders need to be more inquisitive, humble, diligent, and skeptical.

Dave Martin, Tony Ponton, and Kim Ballestrin explain that humans crave certainty, and we desperately want to be right the first time. They argue that trying to be “right” can lead us astray in highly complex environments. Thus, we must become comfortable with ambiguity, especially with the notion of arriving at a partially correct decision. Next, we need to select the appropriate measures to track our progress. Are we on the right track or not? Once we have additional information, we can adapt our decision and refine our course of action. This iterative process serves us well in complex situations for which our predictive powers are simply far too limited.

Cutter Fellow Noah Barsky outlines 10 unconventional rules for helping us identify critical risks. He argues that we should not relegate risk management to our compliance or legal departments. On the contrary, we need to transform the mindset that says risk management and mitigation are the responsibility of specialists within specific functional areas. Instead, it must become everyone’s responsibility. Many risks remain stubbornly hidden in organizations. Barsky closes his article by suggesting, quite astutely, that we need to “listen to the kids” in our enterprises.

October is “Cybersecurity Awareness Month.” To create better cybersecurity awareness, this Advisor series examines the evolving cybersecurity landscape. Here in Part I we explore why cybersecurity remains a perpetual concern and challenge and what we can do about it.

The best leaders recognize that they don’t have all the answers. They acknowledge the limits of their expertise and understand the need to marshal the collective intellect of their teams. As Peter Drucker once said, “The most common source of mistakes in management decisions is the emphasis on finding the right answer rather than the right question.” So what can leaders do to gather input from a diverse array of sources? How can they generate and critically evaluate options? What does it take to uncover and assess hidden risks? In this issue of Amplify, we explore these questions in depth.

Lori Silverman says we need to rethink our focus on data analytics. She points out that many enterprises are not achieving the ROI that they would like from their data science and analytics efforts. Silverman argues that we need to think more broadly about optimizing the decision-making processes throughout our organizations, rather than continuing to pour resources into hardware, software, and human resources in hopes of better data-driven insights. She explains how to design an end-to-end process that helps people throughout the enterprise define key questions, mine data for crucial insights, and develop recommendations based on those insights. Finally, she describes how we can communicate our conclusions to others much more effectively through narrative storytelling.

One of the most important knowledge graph (KG) functions is creating linkages across multiple data sets. By providing a visual representation of the underlying connections between data nodes, KGs help leaders advance their understanding of their environment so they can make intelligent business choices.

In Part I of this Advisor series on virtual reality (VR) in healthcare and medicine, we examine how companies are using VR technologies to develop applications for healthcare educational and training purposes.

This Advisor reviews the book Future Ready, which should be in every leader's library because it lays forth the issues and risks of each path forward for successful digital transformation.

Biases in AI models can crop up due to data bias, and biases in algorithms can result from a developer’s viewpoints. Natural intelligence (NI) provides a countermeasure to these biases, challenging the data and algorithm biases with its understanding of the context in which decisions are made.

Innovative Leadership originates from the idea that we must regularly innovate how we lead as our organizations change in a continually more complex and faster-paced world. As with contin­uous improvement, leaders who follow Innovative Leadership regularly elevate their leadership to meet current and anticipated conditions, challenges, and opportunities. 

AI art generators combine machine learning (ML) and natural language processing to generate images from natural language text prompts input by users. These tools offer exciting possibilities for novices and professionals to create incredible art for many uses. They also raise several issues for business and society as their use becomes more mainstream.

This Advisor offers two “mini-starters” to explore the DEI issue in more depth: (1) a critique of leadership programs for Black executives with suggestions for improvement and (2) the threat that “me over we” poses to both DEI programs and the business sector at large.

Companies can’t choose between growth and sustainability — they must have both. But what kind of technology-led business model transformations will ensure “sweet spots” between the pursuit of growth and sustainability?

Incorporating your customers’ reality into your product development processes via rigorous voice of the customer (VoC) solutions can help your company grow with significant and sustainable margins. In this Advisor, we explore various benefits and challenges of VoC solutions.

By its very nature, digital transformation involves a bimodal process during which an organization must launch changes at two levels: tangible and intangible. Both types of processes could lead to a disruptive transformation. But there is a second set of actions that must occur along with the tangible and intangible changes: (1) linear, organized exploitation processes and (2) chaotic, disruptive exploration processes. This second pair of changes has increased potential to impact an organization’s path to meeting its goals. As we explore in this Executive Update, the Bimodal Management™ model offers a lens that organizations can use to examine the processes and outcomes as they work toward digital transformation.

Examples of deep fakes range from videos of politicians or celebrities saying or doing things they never actually said or did and so-called “revenge porn” to fake photos of soldiers appearing to commit atrocities they didn't commit. However, as we explore in this Advisor, this trend is changing: hackers now use deep fake techniques to target commercial enterprises and government agencies for nefarious purposes.

Without question, complexity is a business inhibitor and slows down the wheels of business. But to be fair, not all business complexity is on IT’s shoulders. However, reducing the complexity instantiated by IT can dramatically improve business agility. And with agility, businesses can create the foundation to win in the digital world. In this Advisor, Myles Suer shares some CIO insights on reducing this business complexity.

If the world’s big data is a virtual mountain of dots, how can you connect them to extract their value? Knowledge graphs will help. The authors showcase several real-world KG applications, detail how they designed a KG to ensure vertical traceability in a systems engineering context, and offer specific advice on using KGs.

Andy E. Williams looks at how human-centric functional modeling (a way to allow computers to solve general problems) could be used to create KGs capable of providing compete semantic models of systems, enabling us to transition to Industry 5.0. He defines Industry 5.0 as a world in which far greater integration is possible, including functional computing approaches like GCI. Although the emergence of GCI isn’t guaranteed (it could end up in a technology gravity well), it would bridge type 1 and type 2 reasoning and lead to a radical increase in our ability to solve every problem.

George Hurlburt details how a KG was used to assist a regional center of a major university system in its course-selection process. The KG helped leaders more clearly see the array of educational pathways from K-12 to community college (CC) coursework that are the results of articulation agreements between universities and CCs. Hurlburt shares five figures from the KG that demonstrate its meaningful visualizations. He also explains how the KG was built, including limiting the number of arcs and emphasizing node unambiguity. Finally, Hurlburt concludes with five key academic relationships and trends that are clearly demonstrated by the regional center’s KG.

Cigdem Z. Gurgur looks at KGs in the context of blockchain. The article begins with background information on how KGs have been used in advanced analytics and their role in helping AI developers. Gurgur then shows how blockchain’s immutability and verifiability offer designers a way to advance KGs to produce more reliable results. The blockchain/KG combination is an ideal one to build more explainable AI systems, she says. Finally, Gurgur explains how KG-enabled information systems can be used in industrial settings to enhance product development lifecycles, improve factory safety, and enhance information systems to the point where employees need less technical knowledge to perform their duties.

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.