The AI Component of Digital Transformation

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David Jensen
July 8, 2026
7 mins
In August of 2024, Kaiser Permanente announced its implementation of an artificial intelligence (AI)-based clinical documentation tool that supports physicians and other clinicians with securely capturing clinical notes during visits with patients. The assisted clinical documentation tool uses AI to securely summarize relevant medical information from spoken, natural conversations. The impetus behind the use of this technology is to elevate medical professionals’ interaction with patients. The bottom line is the technology enables clinicians to spend more time talking with patients rather than on documentation or administrative tasks. 
This is one of thousands of case studies across multiple industries showing that AI is woven into nearly every aspect of society and people’s lives. As it is, public- and private-sector companies across the globe recognize that they risk extinction if they don’t pursue a digital transformation and adopt modern technologies. Now that AI has become a nucleus of mainstream technology, digital transformation is even more pertinent.
AI technologies are used in many business sectors, including healthcare, government, manufacturing, and education. With the ability to process and analyze large amounts of data, companies that embrace AI in their digital transformation have a wider range of possibilities in terms of growth, innovation, and competitiveness. AI embeds intelligence into the core functions of any business—decision-making, resource allocation, and workflow management.

AI Functionality

By definition, AI is a simulation of human intelligence in machines. The technology enables the machines to perform tasks that typically require human involvement. This includes abilities such as learning, reasoning, problem-solving, perception, and language understanding. AI functions by using algorithms that provide a finite sequence of operations to yield a desired result. Ultimately, the technology adds automation to the digital transformation equation, which elevates efficiency in various aspects of business operations.

Different Types of AI for Business Applications

  • Generative. AI that creates new, previously unseen content or data based on patterns learned from existing data. It analyzes the data and produces relevant outputs.
  • Predictive. AI that uses statistical analysis to identify patterns, anticipate behaviors, and forecast future events.
  • Autonomous. AI programs trained in using large amounts of data to make and act on independent decisions.
  • Natural Language Processing (NLP). Enables computers to understand and interpret human language. A subset of NLP is Large Language Models (LLMs), which are designed to generate human language.
  • Machine learning (ML). A subset of AI that enables computers to improve their performance of tasks by processing data and learning from it without being specifically programmed.

Business Use of AI Across Industries

Implementing AI yields a variety of benefits for enterprises of all sizes, such as improving efficiency by taking on repetitive tasks and saving time by reviewing and analyzing large amounts of data. It can be used to streamline supply chains, simplify demand forecasting, and make inventory control more accurate. Still, to become useful, it needs to be adopted by all internal and external stakeholders.
According to McKinsey & Company’s State of AI in 2025 report, the surveyed companies were in the following phases of AI usage:
  • 88% report using AI in at least one business function
  • 7% have fully deployed AI across the company
  • 31% were scaling AI across the organization
  • 30% were piloting AI for a first use case in business
  • 32% were in early testing of AI
For leadership, it entails building teams that are comfortable working with data, open to new tools, and skilled in reviewing and validating outputs. It also means pacing the transition so that everyone learns and progresses equally.​
Employees need a practical understanding of when and when not to use AI. They need to see AI as an assistant that enhances their judgment rather than replacing it. Training, iteration, and ongoing engagement all play a role in the successful use of AI.​

Healthcare

Historically, the healthcare industry pursued adoption of advanced technology in a gradual fashion. However, the industry’s AI transformation evolved quickly. The technology is capable of analyzing records from EHRs and other data and identifying patients at risk of chronic conditions or those likely to experience complications. It can detect diseases faster and determine treatments with more effective care pathways. 
Healthcare personnel currently use AI technology and data for patient diagnosis. Clinicians use AI algorithms to analyze medical imaging data, such as X-rays, MRIs, and CT scans, to assist in making swift and accurate diagnoses. For example, a UK study found that an AI tool can successfully detect 64% of epilepsy brain lesions previously missed by radiologists. Trained on the MRI scans of over 1,100 adults and children globally, the AI tool was able to spot lesions more quickly than a doctor and discover tiny or obscured ones that humans overlooked.

Government

AI transformation is already being deployed across government sectors for a wide range of uses, largely to improve how organizations deliver public services. Some processes include drafting policy documents, analyzing and summarizing global legislation and policies, simulating urban planning scenarios, and engaging with the public. According to the 2026 State of Digital Government Trends in Websites and Customer Service report:
  • 55% of government organizations now use AI.
  • 64% of government call centers use AI to operate as consolidated hubs, streamlining operations.
  • 78% of local governments plan to use AI to improve service delivery in 2026.
The United States Department of Agriculture (USDA) Forest Service needed to make faster, more informed decisions in combating wildfires. Yet the available data was fragmented, incomplete, and constantly changing. It was difficult to determine how to optimize crews and equipment. Eventually, the Forest Service teams leveraged a variety of AI-based tools and applications in their fire management efforts, including the Risk Management Assistance Dashboard, which is a set of science-based tools for wildfire incident managers. For example they use one of the tools to predict where downed or weakened trees might injure or trap firefighters, daily fire weather, and resource deployments that enable responders to adapt to changing conditions.

Manufacturing

Manufacturers are proactively exploring strategies to incorporate AI into their operations to streamline processes and be more cost-effective. AI introduces a fundamentally different architecture across this value chain. By embedding real-time sensing and predictive intelligence into execution, it enables operations to shift from reactive, scheduled execution to adaptive, predictive and learning-based systems that respond dynamically to changing conditions. 
According to a survey conducted by the National Association of Manufacturers (NAM), 72% of the companies deploying AI have reduced costs and improved operational efficiency.
Artificial intelligence (AI) tools are key to effective data collection and data management solutions in manufacturing. They offer robust data acquisition techniques from a wide range of data sources, including sensors, equipment, automation, and control systems.
While automation and data analysis are significant contributions of AI, manufacturers are implementing the technology in ways that keep workers as the central drivers and decision-makers in AI processes. AI systems can quickly process large amounts of data, identify patterns, and automate repetitive tasks. However, the systems lack humans’ intuition and context-specific problem-solving skills.
Some of the ways manufacturers are putting AI to use include:
  • Supply chain. AI enhances supply chain efficiency and resilience by analyzing real-time data and trends and recommending improvements or contingency plans to mitigate risks.
  • Predictive maintenance. Generative AI proactively addresses equipment issues by analyzing sensor data and usage patterns to predict potential malfunctions and schedule maintenance before the issue causes downtime.
  • Quality control and defect detection. Companies are using machine learning (ML) to inspect and maintain product quality.

Education

In the academic arena, the global pandemic prompted educators and policymakers to adopt digital technologies to support online instruction and other off-campus educational options. AI technology has now made its way into classrooms. A survey of more than 1,100 U.S. students across all levels of the academic spectrum revealed that 90% have used AI academically.
  • Many of the respondents cited using the technology to brainstorm, refine ideas, draft outlines, and improve their writing.
  • Given the high levels of usage among students, colleges and universities are recognizing the need for AI fluency, which includes guidance on using AI responsibly, critically, and effectively.
  • The demand for skilled AI workers in industries is growing, so many universities are incorporating AI fluency programs into their learning curricula.

A Faster, Smoother Approach to Technology Transformation

A common and understandable hurdle organizations in all industries face in the adoption of advanced technologies is moving on from legacy systems. This is difficult because the systems are deeply integrated in the company’s operations, and the notion of lengthy downtime and retraining on new systems to complete the transition doesn’t seem feasible. However, there are more downsides to maintaining a legacy infrastructure. Outdated technology presents a significant roadblock to a company’s innovation initiatives. Furthermore, legacy technology infrastructures can drain the organization’s IT budget in record time.
A company’s IT infrastructure has many disparate technologies that need to coordinate well across the organization. However, this is more difficult when companies have several single-purpose servers that need frequent attention and maintenance. The print infrastructure is no exception. Staff tend to take printing functions for granted until there is an issue with one or more of the print servers that creates a costly bottleneck in the supply chain. 
By eliminating print servers and implementing direct IP printing, companies reduce IT costs and unplanned downtime while enjoying the confidence of having an efficient, secure, and reliable print infrastructure.
Digital transformation does not require significant downtime, new processes, and staff retraining. Vasion is committed to making digital transformation and reliable printing easily accessible to all organizations in all industries. Learn how Vasion solutions can transform your business. Schedule a demo.
Watch for part 2 of this two-part series, which discusses the challenges of AI, including data bias, ethical considerations, hallucination, the balance between humans and AI technology, and AI governance, and how AI experts are striving to remedy the issues and make AI a valuable contributor to the digital infrastructure.
The AI Component of Digital Transformation | Vasion