to an ability to upgrade or update or, more specifically, to include net-new business capabilities or offerings resulting from transformational ideas or innovation that harness reimagined business models. The infrastructural underpinnings for organizations that are modernizing are likely to include elastic environments that embrace a multicloud topology. Given the dynamic nature of AI, modernizing an organization means building a flexible information architecture to constantly demonstrate relevance.
THE BIG PICTURE
In agile development, an epic is used to describe a user story that is considered far too big to be addressed in a single iteration or a single sprint. Therefore, an epic is used to provide the “big picture.” The big picture provides an end-to-end perspective for what needs to be accomplished. The epic can then be decomposed into a series of workable stories that can be worked on. The epic serves to ensure the stories are threaded appropriately.
In the AI Ladder, the ladder represents the “big picture.” The decomposition is represented by rungs. The ladder is used to ensure that the concepts for each rung—collect, organize, analyze, infuse—are threaded appropriately to ensure the best chance to succeed and deliver value.
Constantly Adapt to Retain Organizational Relevance
As the famous adage goes, “If you dislike change, you're going to dislike irrelevance even more” (www.network54.com/Forum/220604/thread/1073929871). In a world of rapid change, the endgame is to remain relevant. Relevance is an ends. Accordingly, transformation and disruption can be viewed as a means. Knowledge of the means provides the how (a course of action and the directives). Knowing the ends gives you a target (the desired result) for which to aim, as shown in Figure 1-4.
Figure 1-4: Ends and means model
The ends and means model can be iterated upon to adjust to continuous change and continuous course correction to drive toward sustained and improved relevance.
If an organization can combine relevance with uniqueness, then the path forward may offer greater opportunities, as the organization is unlikely to be viewed as another commodity player in a given space.
Formulating the ends is potentially more difficult than formulating the means. Words that are substitutes for means include transformation (including digital transformation) and disruption. An organization wanting to transform might simply do so by focusing on select technologies or capabilities that are newer; for example, cloud computing, agile development, big data, consumer-oriented platforms, blockchain, analytics, and even AI. Regardless of which technologies and capabilities are picked as a means, the question would remain: but, to what ends?
Relevance can be more difficult to articulate in terms of an ends, especially in the light of newer technologies and capabilities. The struggle with articulation might result from subject-matter experts and end users having minimal insight or experience with how a new solution can actually be positioned and leveraged.
Disruption can be viewed through its two adjectives: disruptive and disrupted. A disruptive organization is more likely to garner a leadership position in handling change, while a disrupted organization is likely to have a lagging position. For example, traditional publishing companies and journalists were found flat-footed in the wake of the digital publishing era. For more than a decade, profits and viability have dwindled in traditional publishing due to social media, blogs, and the proliferation of web-based content.
Another example of a business type in the midst of being disrupted is the hospital. In the United States, the population size has grown by approximately 100 million people in the past 35 years and by nearly 200 million people in the past 70 years. It would not be unreasonable to assume that the demand for hospital beds has steadily risen alongside population growth, especially with an aging population. However, the demand for hospital beds is approximately the same now as it was in the 1940s. In the United States, the need for hospital beds peaked in the early 1980s.
Not all that long ago, a hospital was not exactly the safest of places to go and get well. In one account from the 1800s, it was noted that “hospitals are the sinks of human life” (www.archive.org/stream/proceedingsconn08socigoog). Through the use of anesthesia and the adoption of sterilization techniques, along with the advent of X-rays in 1895, hospitals turned a corner from being a highly risky place to get treated. But now, in a throwback to the 18th and 19th centuries, hospitals are once again seen as a less-than-desirable place to receive therapeutic medical treatment, as cited in a CDC report on hospital-acquired infections (www.documentcloud.org/documents/701516-cdc-hai-infections-deaths.html).
Facilities currently challenging the traditional hospital include walk-in urgent care centers, imaging facilities, surgical centers, physician offices, and so on. Hospitals are being forced to consider mergers and acquisitions as well as downsizing. Hospitals are being disrupted and need to seek nontraditional ways to remain relevant. Could the use of advanced analytics be part of the approach?
Ultimately, if AI is going to augment human intelligence, AI will be part of the means to transform or disrupt. While AI can potentially hypothesize about what can be relevant, AI is likely going to be challenged to convey a de facto direction as what needs to be done to remain relevant. For AI and for humans, collaboration is an augmented opportunity to address a defining issue of our times: relevance.
Therefore, the sole purpose of an information architecture for AI can be postulated, as an aid in the transformation and disruption of an organization that is on a ladder to achieve sustained or regained relevance, whereby each point of leverage is based on data and the organization is willing and capable of harnessing the insights that can be derived from that data.
ECONOMICALLY VIABLE
Without data there's no AI. Period. AI works well because organizations now have the means to economically collect and hoard immense quantities of digital information. Augmenting our work with machines and AI is the norm.
For example, when was the last time you went a day without Googling? Chances are, you don't give machine learning a second thought when you're interacting with AI-infused capabilities. We are augmenting our work with AI, and in large part, it feels totally natural.
Organizations still have a long way to go to fully realize how they can augment all of their processes with AI. For companies that are successful, the result should feel natural.
Data-Based Reasoning Is Part and Parcel in the Modern Business
Advanced analytics, including AI, can provide a basis for establishing reasoning by using inductive and deductive techniques. Being able to interpret user interactions as a series of signals can allow a system to offer content that is appropriate for the user's context in real time.
To maximize the usefulness of the content, the data should be of an appropriate level of quality, appropriately structured or tagged, and, as appropriate, correlated with information from disparate systems and processes. Ascertaining a user's context is also an analytical task and involves the system trying to understand the relationship between the user and the
