AI in Digital Transformation: A Simple Guide for Businesses

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AI in digital transformation means using artificial intelligence to change how a business works, not just which software it runs. Earlier digital projects moved paper processes onto computers and into the cloud. AI goes a step further by reading documents, answering customers, forecasting demand and drafting content, so the real gains come when companies redesign their workflows around those abilities.

This guide explains what that looks like in practice as of 2026, with real company examples, the risks to plan for and a simple roadmap any business can follow. For more plain-language technology explainers, see our tech section.

Digital Transformation vs. AI Transformation

Digital transformation is the broad shift from manual and paper-based work to digital systems: online sales, cloud software, digital records and data dashboards. AI builds on that foundation. Without clean, digital data, AI tools have very little to work with.

Area	Traditional digital transformation	AI-driven transformation
Main goal	Digitize processes and records	Automate judgment-based tasks and predict outcomes
Typical tools	Cloud apps, CRM, ERP, e-commerce	Machine learning models, generative AI, AI agents
Customer service	Online forms and help center articles	Chat assistants that answer and route questions
Data use	Reports on what already happened	Forecasts and recommendations on what to do next
Main risk	Poor adoption, cost overruns	Errors, bias, privacy and regulatory exposure

Where Businesses Actually Use AI

AI use is now common, but deep change is not. McKinsey’s State of AI survey, published in November 2025, found that 88 percent of respondents said their organizations regularly use AI in at least one business function, up from 78 percent a year earlier. Only about one third said their companies had started scaling AI across the enterprise, and 39 percent reported any impact on earnings before interest and taxes from AI (McKinsey).

The most common use cases fall into a handful of areas:

• Customer service: chat assistants that answer routine questions, summarize conversations and pass complex cases to staff.
• Knowledge search: tools that let employees ask questions of internal documents, policies and research.
• Document processing: extracting data from invoices, contracts, claims and forms.
• Forecasting: predicting demand, inventory needs, cash flow or customer churn.
• Content and marketing: drafting product descriptions, emails and reports for human review.
• Software development: coding assistants that suggest, explain and test code.
• Security and fraud: spotting unusual transactions or network activity faster than manual review.

The rise of AI agents

The newest step is the AI agent: a system that can plan and carry out several steps of a task, such as checking an order, updating a record and sending a reply. In the same McKinsey survey, 62 percent of respondents said their organizations were at least experimenting with AI agents, with 23 percent scaling them somewhere in the business (McKinsey). Agents can save time, but they also act with less human oversight, which raises the stakes for testing and permissions.

Real Examples and What They Teach

Klarna: big savings, then a correction

In February 2024, payments company Klarna said its new AI assistant was handling two-thirds of its customer service chats in its first month, doing work it compared to 700 full-time agents and cutting average resolution time from 11 minutes to 2 minutes (CX Today). By May 2025, CEO Sebastian Siemiatkowski told Bloomberg the company would hire more human support staff, saying customers should always be able to reach a person (Entrepreneur).

Lesson: automation can handle volume, but quality and customer trust need to be measured alongside cost savings.

Morgan Stanley: AI on top of company knowledge

In March 2023, Morgan Stanley Wealth Management announced an internal tool built on OpenAI’s GPT-4 that lets financial advisors query the firm’s own research library and get answers with links back to the source documents (Morgan Stanley).

Lesson: some of the most practical AI projects start with information a company already owns, and they keep sources visible so staff can check the answers.

What high performers do differently

McKinsey found that the small group of companies reporting the largest financial gains from AI were far more likely than others to redesign workflows and to have visible commitment from senior leaders (McKinsey). Adding a chatbot to an unchanged process rarely produces the same results.

Benefits of AI in Digital Transformation

• Faster service: customers get answers to routine questions at any hour.
• Less manual data work: staff spend less time copying information between systems.
• Better planning: forecasts can draw on more data than a spreadsheet model.
• Easier access to knowledge: new employees can find policies and past work quickly.
• New products: AI features can become part of what a company sells, not just how it operates.

Risks and Rules to Plan For

Accuracy and oversight

Generative AI can produce confident but wrong answers. Any output that affects customers, money, health or legal matters needs human review or strict limits on what the system can do.

Data privacy and security

Check where data entered into AI tools is stored, whether it is used for training and who can access it. Many business plans from major AI vendors offer settings that keep company data out of model training, but you need to confirm this in the contract.

Regulation

In the United States, the NIST AI Risk Management Framework, first released in January 2023, gives organizations a voluntary structure built around four functions: govern, map, measure and manage (NIST). Companies that serve customers in Europe also need to follow the EU AI Act, which entered into force on August 1, 2024. According to the implementation timeline tracked by the Future of Life Institute, most of its provisions apply from August 2, 2026, with requirements for many high-risk systems phased in later (EU AI Act timeline).

People and skills

Staff need training on what AI tools can and cannot do. Under the EU AI Act, AI literacy obligations for providers and deployers began applying in February 2025. Clear communication about how roles will change also reduces resistance.

A Simple AI Transformation Roadmap

1. Pick a business problem, not a tool. Choose a slow, repetitive or error-prone process with a clear owner.
2. Check your data. Confirm the information the AI will need is digital, accurate and legally usable.
3. Start with a small pilot. Test with a limited group, with humans reviewing outputs.
4. Measure the right things. Track time saved, error rates, customer satisfaction and cost, not just usage.
5. Redesign the workflow. Decide which steps the AI handles, which stay with people and how handoffs work.
6. Set governance. Write simple rules for approved tools, data handling and escalation, using a framework such as NIST’s as a guide.
7. Scale what works. Expand only after the pilot shows clear results, and keep monitoring after launch.

Tips for small businesses

Small companies do not need custom models. Many get value from AI features already built into tools they pay for, such as email, accounting, customer support and office software. Start with one use case, set a budget and review the results after 60 to 90 days before adding more.

Putting It Into Practice

AI can make digital transformation faster and more useful, but it works best on solid digital foundations, clear goals and redesigned processes. Start small, measure honestly, keep humans in charge of important decisions and treat governance as part of the project rather than an afterthought. Our research and sourcing standards are described in our editorial policy.

Frequently Asked Questions