IT Today Can’t Scale. Business Needs AI-driven IT Automation.

Computing in business has been a quest to increase productivity so work is done faster, more accurately, and with fewer resources. It’s time to recognize that IT work as done today is slow, not always accurate, and resource-intensive – it requires several technical disciplines, skilled resources, and too much time to perform. This way of working doesn’t scale, and CIOs, CTOs, and executives need to automate IT so it can.

Experience has shown that IT can’t adapt to meet workload and business demands. Deploying and managing infrastructure has been increasingly standardized and automated, which drives cloud infrastructure- and platform-as-a-service capabilities, but similar benefits have not yet happened for the development, deployment, and management of business applications.

Consequently, Forbes declares that legacy IT is “Digital’s Biggest Obstacle” and the Wall Street Journal reports “It’s Time to Get Rid of the IT Department.” Each technology and application IT adds to its portfolio increases technical complexity and skills required to use and manage them. This is the top risk, identified in a recent survey, board members and C-suite executives will face over the next several years: “Adoption of digital technologies requires new skills or significant efforts to upskill/reskill existing employees.”

The core factors behind this risk are the growing quantity of technologies in use, skills to utilize them, people needed to master the skills; time needed to deploy, manage, and sustain digital technologies; and effort required to integrate them to support a cohesive business operation. For this situation to change, IT work must be automated.

The Problem: IT Is Complex, Labor-Intensive, Time-Consuming, and Expensive

As it has been since the introduction of computing in business, applications are non-standard, technically diverse, and manually constructed. Each IT application is an independent, stand-alone system that operates with its own, distinct, data and business rules. IT today operates a disparate collection of applications used to in the business that are difficult to maintain, modernize, or replace. Every company is burdened with technical debt that constrains their ability to innovate or add new digital functionality and capabilities.

Complexity is a problem that is a consequence of IT’s focus on application technologies: “Today's tangled mess of technology hurts business agility and the bottom line” (CIOs In Search of IT Simplicity, CIO, link no longer available) and “Complexity is the sand that grinds all innovation to a halt”. Enterprise applications contain thousands of code and data objects, increase technical debt, consume IT resources, and are an obstacle to adding new functionality.

A significant amount of complexity in IT comes from collecting, correcting, standardizing, synchronizing, and integrating data for inter-application transaction processing, reporting, and analytics: “These bottlenecks in systems integration happen because each application has its own systemic database.”⁵ Applications, whether old or new, built or bought, in a data center or in a cloud, require labor-intensive, time-consuming, and expensive IT work.

IT methodologies, programming languages, and tools have not helped IT significantly accelerate delivery of business solutions, services, and outcomes. Transforming IT so it scales to meet its workloads and business demands quickly and effectively requires addressing:

• IT productivity, which must increase dramatically to meet dynamic business needs.
• Application efficiency, to operate in real time and not require data integration work.
• Ability to adapt, to keep application business functionality and technologies current and prevent accumulating technical debt.

These factors are useful for evaluating the potential impact of an investment in a technology might have on IT. They also serve as criteria AI-driven IT automation must meet to be successful.

IT Work Needs to Be Reinvented

IT work requires knowledge of technologies utilized, programming languages used, business problems being addressed, data used, and business rules applied. This knowledge must be sustained over time, for as long as an application is in use, so application problems can be fixed and new functionality incorporated quickly. The fundamental reason IT can’t scale is that people design and build application code and data objects according to individual judgements and expertise in a technical discipline.

More importantly, even when current IT methods or tools are effective, they do not help IT scale because they focus on improving a technical discipline or solving a particular IT problem. Each discipline (business analysis, architecture, engineering, programming, data management, and so forth) sets standards, practices, and artifacts for its own purposes and management needs, which may make other disciplines less productive. IT work needs to be reinvented and automated for IT to scale.

What About Agile, Microservices, Low-Code / No-Code Tools, and Cloud-Native Services?

IT vendors continue to provide tools to use in the development of applications and analytics, new programming languages, development methods, project management capabilities, and more capable computers, which have brought us to where we are today. But even with these, there is no doubt that relying on people to deliver technology-driven solutions has reached its limit in productivity and scalability.

Agile Development

Agile is an approach to application development predicated on teams working, in a cross-functional, collaborative process on a project of limited scope. Limited scope is characterized as a minimum viable product that delivers a set of business features that provides some, but not all, operational capabilities needed. Follow-on projects extend the MVP, refactoring programs and data as needed to incorporate additional functionality. As a CIO once summarized to me, “Agile means it isn’t done yet.”

Even though highly touted and can be an effective project management approach, Agile does not satisfy any of the criteria above: Agile does not dramatically increase IT productivity; Agile applications require data integration work because they create an application as its own silo; and Agile applications require people to refactor them to adapt them.

Microservices

Microservices are an approach to application design that replaces monolithic applications with a collection of operationally and technically independent services. Basically, this approach is the culmination of best practices for creating reusable, single-function units of code executable via APIs by those needing the specific service. Drawbacks to microservices include complexity and API management: “Despite the fact that APIs provide a standardized integration pattern, the massive proliferation of APIs is as disadvantageous as the growth of web services and even point-to-point interfaces in legacy architectures.”

Like Agile, microservices don’t satisfy the criteria above: microservices apply to a method of development and do not dramatically increase IT productivity; microservices require data integration work; and, although microservices require people to refactor them to adapt them, they localize refactoring. In general, microservices are best applied to new development and application modernization efforts.

Low-Code / No-Code Tools

Low-code / no-code tools provide automation for some programming tasks. They begin with developing underlying data and/or object models, use proprietary programming or scripting languages to specify actions using them, and operate in their own runtime environments to execute. Consequently, they are best suited, like microservices, to new development and application modernization efforts.

Low-code / no-code tools improve programmer productivity but don’t satisfy the criteria above: Low-code / no-code tools increase IT productivity for new development and application modernization; low-code / no-code tools build applications that require data integration work; and low-code / no-code tools require people to refactor them to adapt them.

Application architectures and data models have been used for decades and have not improved IT scalability. Low-code / no-code tools suffer the same limitations.

Cloud-Native Services

Cloud is becoming the dominant operating infrastructure for business information processing, so cloud-native services are increasingly popular. IMHO, cloud should be used as infrastructure, and applications and business functionality need to be implemented in containers and ready to run on a cloud platform. While cloud-native services are best for developing or modernizing an application to operate in the cloud, they rate like microservices, and don’t satisfy our criteria.

Summary of Agile, Microservices, Low-Code / No-Code Tools, and Cloud-Native Services

This summary chart shows why investments in these methods and tools, while offering some benefits, do not help IT scale:

Method / Tool	IT productivity Increase the ability to meet workload and business demands.	Application efficiency Don’t cause work for using, sharing, or integrating data.	Ability to adapt     Easily keep business functionality and technologies current.
Agile Development	N/A	N/A	N/A
Microservices	N/A	N/A	Low (they isolate and localize refactoring)
Low-Code / No-Code	Medium (for new development and application modernization)	N/A	Low (they depend on the degree of change to underlying data and/or object models)
Cloud-Native Services	N/A	N/A	Low (they depend on the degree of change to underlying data and/or object models)

The Solution: AI that Makes IT Automated, Instant, and Economical

What does AI-driven IT automation look like? To consider an analogy, look at how 3D printing is changing manufacturing and construction. 3D printed houses are being built in the US, Europe, Mexico, and Asia, and 3D printed automobiles are running on test tracks. These examples show how automation fundamentally changes labor-intensive processes used to construct houses and manufacture cars. They eliminate manual activities in constructing products and dedicate human work to designing objects for a 3D printer to print / build.

AI-driven IT automation is the same. Technical specialists are removed from the development of application functionality. Instead, effort is focused on “users’ experience” and their interactions with data and rules needed to perform work tasks. Businesses need applications that support operational workflows, interact with enterprise data, and consistently use business rules that perform operational work – a business design that is a precise, easy-to-change description of a business operation.

What Instant IT^® AI-Driven IT Automation Does

IT automation uses AI to generate complete, ready-to-use applications from business designs instantly, giving companies the speed and flexibility needed for adaptable and agile business operations. AI-driven automation increases IT productivity 100-fold and significantly lowers application TCO.

IT automation changes the methods, practices, procedures, and tools IT uses to deliver business applications. Data and rules-based business design, and automated application development, transforms IT from being labor-intensive, time-consuming, and expensive into an automated, instant, and economical operation.

My company, InfoNovus Technologies, offers Instant IT® AI-driven automation that generates zero-defect, ready-to-use applications to satisfy operational business needs, no matter how unique, instantly.

Instant IT AI-driven IT automation satisfies all the criteria above: Instant IT increases IT productivity for new development because it automates application design and construction tasks; Instant IT generates applications that use a single set of unified data and business rules, eliminating the need for data integration work; and Instant IT does not require refactoring – updated applications can be generated for updated busines designs or to take advantage of a new, more capable technology – eliminating technical debt.

How Instant IT accomplishes this is too long to add to this piece, so I will address it further in a separate entry. For now, I will point out a few of Instant IT’s key features:

• Business design is better and faster for eliciting operational business requirements.
• AI-driven application design is performed, without any object or data models, when a business design is complete, so its form and content reflect and support all use cases.
• Computer-generated applications eliminate programming and increase IT productivity a hundred-fold and lower application TCO by 60%.
• Management, governance, and control are built in because business designs become business assets instead of programs and databases.

This summary chart shows why Instant IT satisfies the three criteria above and helps IT to scale:

Method / Tool	IT productivity Increase the ability to meet workload and business demands.	Application efficiency Don’t cause work for using, sharing, or integrating data.	Ability to adapt     Easily keep business functionality and technologies current.
Agile Development	N/A	N/A	N/A
Microservices	N/A	N/A	Low (they isolate and localize refactoring)
Low-Code / No-Code	Medium (for new development and application modernization)	N/A	Low (they depend on the degree of change to underlying data and/or object models)
Cloud-Native Services	N/A	N/A	Low (they depend on the degree of change to underlying data and/or object models)
Instant IT® AI-Driven IT Automation	10,000% increase in IT productivity for application delivery	All applications share one unified set of data and rules	High (IT automation easily generates updated applications)

When IT is transformed through automation, it can scale easily and enable a company’s agility and adaptability. With Instant IT, business agility relies on operational readiness and change management capabilities, not IT’s speed of application delivery.

The pressure on companies to be more competitive, better respond to customers’ preferences, and deliver engaging customer experiences drives digital transformations efforts. Consistent, correct, data and uniformly applied rules that enable analytics and AI, without redundancies and inconsistencies, are the cornerstones of digital business. IT that can scale is what a digital transformation is meant to produce: organizations able to respond to market changes, add digital capabilities, and create operational innovations quickly and easily.