Communicating with others about a new architectural vision of the future requires techniques such as illustrations and presentation design, graphic recording and facilitation, and storytelling. Storytelling in particular reaches people on a human level, as we explore in this Advisor.

In our studies of medical decision making under stressful conditions, we found that individual differences between users, such as experience level or familiarity with the technology, must be recognized as part of the context (i.e., individual user characteristics). In addition, local conditions such as time pressure or uncertainty provide another construct for context (i.e., environmental characteristics). We share a model for technology embeddedness in this Advisor.

Following the popularization of Agile in tech, educators in a small New York City startup who got word about it began experimenting with creating “agile classrooms” in an effort to increase student engagement and collaboration in otherwise conventional environments. 

Regulated industries should not pull back in their quest toward digital transformation but should instead find a way to embrace it. To begin, regulated industries should consider the questions explored in this Advisor.

Innovation management processes do not follow a prescriptive, single formula. Each organization can decide and define what works. This Advisor provides a basic outline of the common elements/steps for a process to manage innovation with disruptive technologies.

The advent of big data technologies with an emphasis on the ease and speed of ingestion of large amounts of data into a data lake — as opposed to the often-complex traditional ETL processes for load­ing into a data warehouse — has meant far less focus on defining schemas or structures. The focus now shifts toward how to achieve an adequate level of governance of such data lakes. This is where the data catalog provides a central canonical reference point of business meaning to underpin any data governance activities of the data lake.

New commercial applications are now available that utilize artificial intelligence, machine vision, Internet of Things connectivity, and camera-based security technologies to detect and interdict active shooter incidences before they become deadly.

A North American energy-producing company was seeking to understand how to change its status-quo pattern of finding new solutions only when it was abundantly clear it would be unable to meet its yearly market targets. In short, it was looking for a different approach to conducting its operations organization. To that end, I encouraged leadership to focus on several areas, including the topic of this Advisor: fostering a mindset of challenging the status quo on everything.

In the digitized world of financial data, there are truly enormous volumes of heterogeneous structured and unstructured data across siloed data stores. Managing such complexity is beyond human cognitive abilities or comprehension; hence, AI-based smart machines are required to assist human cognition and decision making. We believe that if AI is to achieve its promise, financial institutions are going to have to address a range of challenges.

We don’t want to design in one large lump — ever! Instead, we want to create a layer or certain amount of architecture (services, plumbing, back-end functionality, etc.) and then build something on top of it. 

In this month’s issue of Cutter Business Technology Journal, we examine the latest advancements in technologies related to Industry 4.0 and the impact of these technologies on work, business, and organizations by featuring six articles that cover a range of topics.

This Advisor describes some of the notable challenges facing the evolution of blockchain.

We strongly believe our self-organizing model based in full transparency has been key to our success. In this Advisor, we take a look at each of the main positive results our company has been able to achieve thanks to this model.

To show what is being implemented with currently available drone technologies and within the current regulatory framework, this Advisor offers examples from three verticals: construction, engineering, and precision agriculture.

Most executives say agility is critical for the future of their business, yet only a small amount of their transformation efforts are delivering agility. This gap is caused by entrenched legacy culture and general resistance to change. Responding to these challenges and delivering business agility require more than Agile practices. Many organizations are discovering “solution focus” to be the missing piece of their transformation puzzle.

This Advisor shares five practical tips on rolling out “industrial,” scalable robotic process automation solutions based on my experience at a multinational organization.

For most organizations implementing customer experience (CX) management practices, it is still too soon to tell if their practices are living up to expectations.

Our company’s Agile journey across a 2,000+-person product development unit in 10 locations in Sweden, Poland, and China, resulted in a quadrupling of value throughput; a doubling of speed; a tenfold increase in quality; and happier, more engaged people who are, ultimately, more innovative. The company made major shifts in a few areas. In this Advisor, we explore its shift from resource efficiency to flow efficiency.

People’s increased mobility, facilitated by air travel, has resulted in the increased spread of contagion across geopolitical boundaries. A growing awareness that bioterrorism agents could spread in the same way has raised the level of concern even more. Many practitioners and researchers agree that contact tracing, which is the identification and locating of people who may have been in contact with an infected person, represents an important factor in mitigating the spread of a pandemic.

About a quarter of surveyed organizations have appointed a “chief customer officer” (CCO) to ensure that the organization provides a unified and seamless customer journey/experience across all customer channels.

Encouraging and developing data-based decision support is an organization-wide effort and requires many resources, including people, money, and technologies. Building an effective decision support capability can help improve decision making, but meeting that goal is a challenging task. So how can senior managers increase the chances of the successful implementation of an enterprise-wide data-based decision support, analytics, or BI project? The answers to the five pivotal questions explored in this Advisor provide some insight.

Financial institutions have digitally transformed their business processes and products, creating vast sources of structured and unstructured data. AI offers the means to complete this transformation in radical ways — across the front, middle, and back offices, while also addressing the big data problem. In addition, AI is also shaping the fintech and regulation (“regtech”) landscapes, particularly in addressing what has become known as “Big Regulation.” However, AI’s promise must be balanced with current limitations to the application of enabling technologies like machine learning (ML) and natural language processing (NLP). This Advisor looks at the promise, potential, and limitations of AI in the financial industry.

Being able to show that your organization actively considers and cares about its impact on the world is sure to become one of the most important levers for talent and investment attraction and retention. Expectations now go beyond having great-sounding values on your website; people want to practice those values every day and be proud of what they are creating. Investors want to know that they can count on pro­active mitigation. Users want to see those values in the products they use. Consequence scanning requires that participants know what the product or service is intended to do, be able to determine whether the potential consequences of the product or service are positive or negative, and know what their organization considers positive.

Scientific-based modeling systems contain extraord­inary amounts of data and produce mountains of output. These systems offer almost limitless options for providing the user with informed knowledge. This Advisor seeks to use the concept of technology embeddedness to delineate factors that are prohibiting emergency managers from harnessing the capability of scientific modeling systems when responding to disasters.

This Advisor explores the complexities and challenges of big data–based analytical processing and storage.