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.

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.