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Published: 23 May 2024
Contributors: Molly Hayes, Amanda Downie

What is AI transformation?

AI transformation is a strategic initiative whereby a business adopts and integrates artificial intelligence (AI) into its operations, products and services to drive innovation, efficiency and growth. AI transformation optimizes organizational workflows by using a range of AI models and other technologies to create a continuously evolving and agile business.

AI transformations employ machine learning and deep learning models—for example, computer vision, natural language processing (NLP), and generative AI—together with other technologies to create systems that can:

  • Automate manual tasks and repetitive administrative work
  • Modernize apps and IT with code generation
  • Provide data-driven insights and decision-making support
  • "Learn" from data to improve accuracy and performance over time
  • Improve the customer experience with personalization and chatbots

As advancements in AI accelerate, AI transformation has become a significant factor in a business’ long-term success. According to Augmented work for an automated, AI-driven world, a recent report from the IBM Institute for Business Value, organizations that integrate AI into their transformation journey more frequently outperform their competitors.

Typically, an AI transformation is a more holistic endeavor than the simple replication of existing business processes with new technologies. A well-crafted AI transformation strategy has the capacity to create entirely new ways of doing business, increase productivity and facilitate sustainable growth. To best realize and scale the technology, AI transformations require broad changes to business strategies and cultures. 

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Technologies in AI transformation

An AI transformation strategy can involve any number of technologies, often requiring a broad toolkit of solutions. Some of the most common include:

Natural language processing

NLP enables computers to process human language in text or audio form. It can be used to facilitate intelligent search, analyze consumer sentiment on social media, convert material from one language to another, summarize content, or extract relevant information from large data sets.

Computer vision

With computer vision, systems can glean meaningful information from digital images or videos by using algorithms and other technologies. Applications include image classification, image-based search, and object detection and search. Examples of using computer vision include identifying machinery that requires maintenance or automatically tagging images with relevant metadata.

OCR and digitization

Optical character recognition (OCR) recognizes printed or handwritten texts and converts them into a machine-readable format. OCR is widely used in digitization efforts to make unwieldy document collections simpler to edit, store, and search. OCR-converted data sets can support training and tuning AI models.

IoT integrations

IoT integrations include geolocation, which identifies the longitudinal and latitudinal location of a connected device. Geolocation supports location-specific customer interactions like zone-based pricing or targeted marketing. In an operational capacity, it can facilitate AI-assisted route planning or supply chain optimization by tracking assets and goods that are outfitted with sensors and connected to the internet-of-things (IoT).


Through automation, machines perform repetitive tasks and processes with little to no human input. Intelligent automation, or AI-assisted automation, has a wide variety of uses in a business context, including AIOps and complex business process management.

Expert systems and decision support

A decision support system helps decision-makers solve unstructured problems, while an expert system solves a particular and often difficult problem. Both provide organizations with rapid, data-driven insights based on large datasets that are difficult for a single person to absorb.  

Generative AI

Generative AI is a set of AI technologies that create original content—such as text, images, video, audio, or software code—in response to a user’s prompt or request. Gen AI relies on deep learning models that simulate the human brain. In consumer-facing applications, generative AI can create personalized content in real-time. Back-office uses include employee-facing AI assistants, code-generation software, and product development and testing.

Big data analytics

Big data analytics uses large amounts of data, requiring advanced analysis techniques like machine learning and data mining to extract meaningful information and value. Big data is used to train AI models, and is typically processed in a data lakehouse, where it is collected, cleaned, and analyzed. 

How to develop a targeted AI strategy 

Organizations that embrace an AI-first mentality, rather than digitizing their business process, stand to gain a significant competitive advantage in the rapidly changing business ecosystem. And while no single standard playbook for an AI transformation exists, common considerations during the early planning stages of an AI transformation include:

  • Strategy and value: What are the use cases and objectives for an AI transformation? Which workflows will be augmented, and what are the internal metrics for success?
  • Technology and data: Which models, data and deployment strategies are best suited to the organization’s strategy?
  • Experience design: How will users, both internal and external, interact with AI?
  • Operating models: How will an organization scale new technologies across their business?
  • Talent and culture: How will an organization embrace a culture of AI through its training, upskilling and hiring?
Stages of AI transformation

AI transformation is a dynamic process, and implementation looks different for every company. But before an organization trains and deploys an AI, it typically follows the following planning processes to ensure the effectiveness of its strategy:

Information gathering: During this stage, an organization performs research to gain an understanding of such tools as generative AI, machine leaning, computer vision, and other technologies. During this exploratory phase, stakeholders might list business problems AI can address and outline what benefits might be gained. 

Assessing current resources and limitations: Before making a comprehensive plan, an organization typically audits its existing business, reviewing the capacity of its IT department and data practices. 

Defining objectives: During this phase, the organization identifies which specific problems it hopes to solve, and how success will be measured during implementation. 

Building a roadmap: In creating a roadmap, the organization chooses AI projects based on practical needs, determining what kind of support might be required — and which partners or vendors with AI-specific expertise should be involved. 

Once these strategic planning phases have been completed, the designing, building, training, validating, and tuning of an AI model can begin. Some stages that facilitate a responsible and effective AI deployment include: 

  • Collecting and managing data
  • Organizing data 
  • Building, training, and tuning AI models
  • Automating workflows and adding AI to applications
  • Infusing AI across an enterprise 
Collecting and managing data

The first phase of AI transformation identifies and harnesses the raw data that is used to train and tune an AI. It also involves determining what third-party data might be used. Often, organizations are limited by rigid architectures and data silos that require a foundational reorganization. This process might include pulling data from various departments and subdivisions, digitizing existing records or implementing a more robust data management system. As this process requires fluency with data science, it might require hiring specialists or upskilling in-house employees.

Organizing data

Data quality and strong data governance practices are the backbone of a successful AI transformation. During this process, an organization helps ensure the accuracy and cleanliness of its data pipeline along with its findability and governing rules. This might involve automating select workflows with DataOps tools, optimizing data warehouses and infrastructure, and investing in data management solutions such as a data lakehouse. During the organizational phase, business leaders also determine who owns the data, the data security measures in place, and the conditions for using the data. This process creates a self-service pipeline making data accessible to the right people at the right time.

Building, training and tuning AI models

Using this clean and organized data, a business can build, train, validate, and tune its AI models. With sufficient internal AI engineering talent, this process can be completed in-house. Many organizations opt to collaborate with third-party vendors with a track record of success. During this phase, AI models “learn” from large data sets and are fine-tuned on smaller, task-specific data sets. After this initial development and testing period, validation and testing workflows are ongoing, facilitating consistency as the model continues to learn.

Automating workflows and adding AI to applications

When the AI is ready, it’s integrated into previously identified workflows and applications across an enterprise. Typically, AI is used with other technologies and techniques, and deploying AI involves collaboration between IT, engineering, and infrastructure teams along with other stakeholders. As AI augments routine business processes and becomes part of a business’ day-to-day operations, a strong change management strategy might be necessary as roles shift across an organization.

Infusing AI across an enterprise

With the foundation of a strong automation and intelligent application practice, organizations can build AI more deeply into their business and transform how the company works. As employees expense less time on routine tasks, organization-wide changes might be required to encourage more creative and valuable labor from human partners. And at this level, more complex workflows can be entirely replaced by a combination of AI-powered tools.

The AI transformation might also include AI-assisted analysis of enterprise-level business practices, for example through delivering insights about consumer behavior or advanced forecasting. With AI fully baked into the business, an organization can also automate the AI lifecycle, increasing the speed of experimentation and building purpose-specific models faster.

AI transformation use cases

An AI transformation can improve performance across every aspect of a business. Adoption allows organizations to automate administrative tasks, facilitate hyperpersonalized customer experiences and modernize the IT process by automatically generating code. 

Some use case examples include: 

  • IT modernization
  • Customer service workflow
  • Supply chain
  • HR and talent management 
  • Sales and marketing 
  • Core business operations 
IT modernization

AI models have a vast number of applications in IT processes and operations. AI can rapidly increase IT agility and address complex processes like app modernization and platform engineering. For example, generative AI can generate code, convert code from one language to another, reverse-engineer code, and drive transformation planning. These tools can also provide augmented site reliability engineering for developers and automate testing processes—ultimately streamlining the IT process and allowing employees to focus on more creative and human-centric tasks.

Read more about AI in app modernization
Customer service workflows

Generative AI can transform the way customer experience is delivered, differentiating a business and giving it a competitive edge. AI tools can present customized recommendations, handle customer support at any hour of the day, and seamlessly create personalized content such as social media posts, personalized messages, or website copy.

By analyzing large volumes of data and analyzing sentiment, AI can identify patterns to make predictions about consumer behavior in the future. For example, a bank might provide personalized, automated portfolio management services, or a government might automatically convert correspondences into multiple languages.  

Read more about AI in customer service
Supply chain

Using AI, businesses can automate the source-to-pay process and manage resource needs, reducing inefficiency and waste. For example, AI tools can triage deliveries, selecting the most cost-effective and environmentally sustainable ways to fulfill orders, or analyze historical data to predict demand. And AI-driven order intelligence systems have the capacity to provide rapid insights into order management workflows, allowing business leaders to identify potential disruptions or identify problems before they arise. When combined with digital twins that replicate real-world processes or pieces of equipment, AI can optimize processes like maintenance and scheduling for increased efficiency.

Read more about generative AI in supply chains
HR and talent management 

AI capabilities can increase efficiency and employee experience across the HR lifecycle, from improving the candidate experience to providing personalized high-quality career development advice. Using AI, businesses can automate repetitive but critical talent acquisition tasks such as job postings and interview scheduling. For current employees, AI can offer personalized feedback like performance reviews or manage requests for time off through chatbots, allowing HR leaders to focus on higher-value work.

Read more about AI and talent transformation
Sales and marketing

In sales and marketing, AI can deliver personalization at scale, automatically generating product recommendations and consumer communications based on purchase history and other data. The technology can forecast future trends and customer behavior, allowing marketing teams to allocate resources more efficiently across the content supply chain and enhance the overall customer experience. With the use of these tools, sales professionals are empowered to dedicate time to higher value work, improving decision-making and increasing productivity.

Read more about AI in marketing
Core business operations

AI adoption at the enterprise level has the capacity to streamline and augment a business’ core operations. AI can help product development. For example, a healthcare company might expedite new drug discovery with the assistance of an AI model that is trained to infer molecular structure. A product team might use AI to test and optimize a product through its lifecycle. The technology can also be applied to threat management and decision support. These functionalities reduce incident response times and helping business leaders proactively plan for and manage future risk. 

Explore more AI for business use cases
Overcoming AI transformation challenges

A strong, responsible AI project can improve performance and give businesses a significant competitive advantage. But as in all digital transformations, successful adoption is far from guaranteed.

According to McKinsey, while 90% of businesses the consultancy surveyed started some form of digital transformation. However, only one-third of the expected revenue benefits had been realized.1 AI brings a particular set of challenges to these processes including: 

Scope of transformation and scaling AI

Scaling AI across a business can present a challenge, requiring decision-makers and stakeholders to invest significant time and energy to outline how the technology will integrate into their organization. As part of an AI transformation, businesses might find themselves managing large volumes of data and needing significant computing power to meet their goals. Successful implementations typically involve extensive research into which AI models are a right fit for the organization, and significant investment in infrastructure to power AI solutions. Increasingly, organizations are considering hybrid cloud models to support wide-scale adoption and deployment.  

Learn more about scaling AI
Data governance and security

Good data governance requires that the data that is used in AI training is clean, consistent, and secure. This means organizations that intend to adopt AI will become data companies as well. The inputs used to train large language models (LLMs), for instance, must be properly organized and stored—and sourced in a way that doesn’t use biased or proprietary data. Good data governance also ensures that the model outputs are observable and explainable. Organizations that are involved in a successful AI transformation typically monitor data activity and continuously audit their cybersecurity practices. They also encrypt sensitive data in compliance with local regulations. This phase might involve multiple processes to increase data security on-premises, in the cloud, and in software-as-a-service (SaaS) apps.

Learn more about data governance
Change management

Integrating AI systems with existing IT infrastructure, workflows and business processes can be complex and time-consuming. And adopting AI involves significant organizational change and cultural shifts. Businesses might opt to invest in change management initiatives, work closely with stakeholders, and embark on partnerships with trusted third parties to foster a culture of empowerment and education.

Learn more about change management
Upskilling and talent management 

AI projects can involve various highly skilled professionals, including data engineers, data scientists, and data analysts. Some organizations might decide to improve existing employees' skillsets, while others may need to hire significant new talent to ensure a smooth and responsible AI transformation. This may involve labor from human resources departments, or carefully managed transition programs.

Learn more about human resources and AI
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