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As shown in Figure 1-1, the rungs of the ladder are labeled Collect, Organize, Analyze, and Infuse. Each rung provides insight into elements that are required for an information architecture.

Collect, the first rung, represents a series of disciplines used to establish foundational data skills. Ideally, access to the data should be simplified and made available regardless of the form of the data and where it resides. Since the data used with advanced analytics and AI can be dynamic and fluid, not all data can be managed in a physical central location. With the ever-expanding number of data sources, virtualizing how data is collected is one of the critical activities that must be considered in an information architecture.

These are key themes included in the Collect rung:

 Collecting data with a common SQL engine, the use of APIs for NoSQL access, and support for data virtualization across a broad ecosystem of data that can be referred to as a data estate

 Deploying data warehouses, data lakes, and other analytical-based repositories with always-on resiliency and scalability

 Scaling with real-time data ingestion and advanced analytics simultaneously

 Storing or extracting all types of business data whether structured, semistructured, or unstructured

 Optimizing collections with AI that may include graph databases, Python, machine learning SQL, and confidence-based queries

 Tapping into open source data stores that may include technologies such as MongoDB, Cloudera, PostgreSQL, Cloudant, or Parquet

The Organize rung infers that there is a need to create a trusted data foundation. The trusted data foundation must, at a minimum, catalog what is knowable to your organization. All forms of analytics are highly dependent upon digital assets. What assets are digitized forms the basis for what an organization can reasonably know: the corpus of the business is the basis for the organizational universe of discourse—the totality of what is knowable through digitized assets.

Having data that is business-ready for analytics is foundational to the data being business-ready for AI, but simply having access to data does not infer that the data is prepared for AI use cases. Bad data can paralyze AI and misguide any process that consumes output from an AI model. To organize, organizations must develop the disciplines to integrate, cleanse, curate, secure, catalog, and govern the full lifecycle of their data.

These are key themes included in the Organize rung:

 Cleansing, integrating, and cataloging all types of data, regardless of where the data originates

 Automating virtual data pipelines that can support and provide for self-service analytics

 Ensuring data governance and data lineage for the data, even across multiple clouds

 Deploying self-service data lakes with persona-based experiences that provide for personalization

 Gaining a 360-degree view by combing business-ready views from multicloud repositories of data

 Streamlining data privacy, data policy, and compliance controls

The Analyze rung incorporates essential business and planning analytics capabilities that are key for achieving sustained success with AI. The Analyze rung further encapsulates the capabilities needed to build, deploy, and manage AI models within an integrated organizational technology portfolio.

These are key themes included in the Analyze rung:

 Preparing data for use with AI models; building, running, and managing AI models within a unified experience

 Lowering the required skill levels to build an AI model with automated AI generation

 Applying predictive, prescriptive, and statistical analysis

 Allowing users to choose their own open source frameworks to develop AI models

 Continuously evolving models based upon accuracy analytics and quality controls

 Detecting bias and ensuring linear decision explanations and adhering to compliance

Infuse is a discipline involving the integration of AI into a meaningful business function. While many organizations are able to create useful AI models, they are rapidly forced to address operational challenges to achieve sustained and viable business value. The Infuse rung of the ladder highlights the disciplines that must be mastered to achieve trust and transparency in model-recommended decisions, explain decisions, detect untoward bias or ensure fairness, and provide a sufficient data trail for auditing. The Infuse rung seeks to operationalize AI use cases by addressing a time-to-value continuum.

These are key themes included in the Infuse rung:

 Improving the time to value with prebuilt AI applications for common use cases such as customer service and financial planning or bespoke AI applications for specialized use cases such as transportation logistics

 Optimizing knowledge work and business processes

 Employing AI-assisted business intelligence and data visualization

 Automating planning, budgeting, and forecasting analytics

 Customizing with industry-aligned AI-driven frameworks

 Innovating with new business models that are intelligently powered through the use of AI

Once each rung is mastered to the degree that new efforts are repeating prior patterns and that the efforts are not considered bespoke or deemed to require heroic efforts, the organization can earnestly act on its efforts toward a future state. The pinnacle of the ladder, the journey to AI, is to constantly modernize: to essentially reinvent oneself at will. The Modernize rung is simply an attained future state of being. But once reached, this state becomes the organizational current state. Upon reaching the pinnacle, dynamic organizations will begin the ladder's journey anew. This cycle is depicted in Figures 1-2 and 1-3.

Figure 1-2: The ladder is part of a repetitive climb to continual improvement and adaptation.

Figure 1-3: Current state ⇦ future state ⇦ current state

These are key themes included in the Modernize rung:

 Deploying a multicloud information architecture for AI

 Leveraging a uniform platform of choice across any private or public cloud

 Virtualizing data as a means of collecting data regardless of where the data is sourced

 Using DataOps and MLOps to establish trusted virtual data pipelines for self-service

 Using unified data and AI cloud services that are open and easily extensible

 Scaling dynamically and in real time to accommodate changing needs

Modernize refers 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.