Where Should Companies Start Their AI Transformation? An 8-Step Roadmap

Where Should You Start Your AI Transformation?

Artificial intelligence is no longer a topic limited to technology teams. From marketing and human resources to software development and customer experience, AI-powered tools are becoming part of everyday workflows across many business functions.
However, the main question for companies is no longer:
“Should we use artificial intelligence?”
The real question is:
“Where should we start our AI transformation?”
Because starting to use an AI tool is not the same as reshaping the way a company operates around artificial intelligence.
Recent global research shows that AI adoption is rapidly increasing across businesses. However, the number of organizations that can turn this technology into measurable business outcomes at scale is not growing at the same pace.
In the coming period, the real difference will not be between companies that have access to AI tools and those that do not. It will be between companies that can successfully integrate AI into the right business processes and those that cannot.
So, what steps should companies take when starting their AI transformation?

AI Adoption Is Growing Rapidly

A large share of companies worldwide now use artificial intelligence in at least one business function.
With the rapid adoption of generative AI tools, the use of AI has expanded significantly across areas such as content creation, data analysis, customer communication, software development, and operations.
However, using AI in everyday tasks does not necessarily mean that a company is undergoing a true transformation.
A company may enable employees to use AI tools, but if its processes, workflows, and decision-making structures remain unchanged, the result is often an increase in productivity rather than a genuine transformation.
Real transformation begins when artificial intelligence becomes an integral part of how work is done.

AI Adoption Is Also Increasing in Türkiye

The use of artificial intelligence among companies in Türkiye has increased significantly in recent years.
AI adoption is particularly more common among large enterprises compared with small and medium-sized businesses.
Marketing, sales, production, service processes, R&D, and innovation are among the areas where companies most frequently explore AI applications.
This trend shows that artificial intelligence in Türkiye is moving beyond being a technology that companies simply follow and is increasingly becoming part of real business processes.
However, this raises an important question:

Where should a company actually begin its AI transformation?

1. Start With the Problem, Not the Technology

One of the most common mistakes in AI transformation is starting with tool selection.
“Should we use ChatGPT?”
“Should we develop a custom AI system for our company?”
“Which AI tool should we invest in?”
These are important questions, but they should not be the first ones.
The starting point should be:
Which business problem are we trying to solve?
For example, a company may spend too much time categorizing customer requests.
Another team may repeatedly prepare the same reports manually.
Software teams may spend significant time on testing, documentation, or code review.
Marketing teams may struggle with time-consuming research, first-draft creation, or adapting content for different channels.
The first step in AI transformation should therefore not be choosing the technology, but identifying repetitive and measurable business problems.

2. Start With a Small and Measurable Use Case

Trying to transform an entire organization with AI at once can be both costly and difficult to manage.
For this reason, starting with a limited and clearly defined use case can be a more practical approach.
An ideal pilot project may have three main characteristics:
It involves a repetitive process, creates a meaningful time or cost burden, and produces measurable outcomes.
For example:

• categorizing customer requests,
• summarizing long documents,
• structuring meeting notes,
• searching internal knowledge sources,
• supporting software testing,
• creating first drafts of reports

can all be suitable starting points.
The goal should not be to achieve a large-scale transformation from day one, but to determine in a controlled way whether artificial intelligence can create real value.

3. Do Not Measure Success Only by Usage Numbers

When AI tools begin to spread across an organization, one of the first metrics companies often track is adoption.
How many employees are using AI?
How many teams are experimenting with new tools?
How many processes are being completed with AI each month?
These indicators can be useful, but they do not fully reflect the real impact of transformation.
What matters most is the business outcome.
For example, after introducing AI:

• Did customer request resolution times decrease?
• Did report preparation become faster?
• Did software development processes improve?
• Were repetitive tasks reduced?
• Did output quality improve?
• Did costs change in a meaningful way?

The answers to these questions are much more important when evaluating the real value of an AI investment.

4. Develop Employees’ AI Skills

AI transformation cannot be achieved through technology investment alone.
Employees also need to understand when, why, and how to use new AI tools.
For this reason, companies should avoid limiting AI training to prompt writing alone.
Employees should also learn:

• which tasks can benefit from AI support,
• how to review AI-generated outputs,
• how to identify incorrect or misleading responses,
• which types of information should not be shared with AI tools,
• how to verify AI-generated content.

AI transformation is therefore also a skills transformation.

5. Review Your Data Infrastructure

Accurate and accessible data plays a major role in helping AI tools create real business value.
Information within a company may be stored across different systems.
Documents may be outdated.
Departments may store the same information in different formats.
Access and authorization processes may not be clearly defined.
These issues can make it more difficult for AI projects to deliver the expected results.
Data quality becomes particularly important when AI systems are connected to internal knowledge bases, product data, operational records, or corporate documents.
For this reason, companies should evaluate not only the AI model or tool they plan to use, but also the readiness of their existing data infrastructure.

6. Make Personal Data and Information Security Part of the Transformation

One of the most important considerations in enterprise AI adoption is protecting personal data and internal company information.
Companies should clearly define what types of information employees are allowed to share with AI tools.
Customer information, employee data, internal documents, and commercially sensitive information may create different levels of risk depending on the system being used.
For this reason, companies may benefit from establishing internal AI policies and guidelines.
For example, organizations can clearly define:

• which tools employees are allowed to use,
• what information can be entered into these systems,
• when AI-generated outputs require human review,
• how access permissions should be managed.

The goal is not to prevent the use of artificial intelligence, but to ensure that it is used in a controlled and responsible way.

7. Do More Than Add AI to Existing Processes

The first stage of AI adoption often focuses on making existing tasks faster.
Writing a text more quickly, summarizing a report, or receiving coding assistance are common examples.
However, as companies move to a more advanced stage, they should begin asking a different question:
“If we were designing this process from scratch today, how would we use artificial intelligence?”
For example, in a customer service process, AI does not have to be limited to generating a response draft.
A system could:

• categorize the request,
• find the relevant source of information,
• prepare a recommended solution,
• present it for employee review,

and support the creation of the necessary records at the end of the process.
At this point, artificial intelligence stops being an additional tool and becomes part of the workflow itself.
This is often where true transformation begins.

8. Follow AI Agents, but Move Forward Carefully

One of the most prominent developments in artificial intelligence is the rise of AI agents.
Traditional generative AI tools generally respond to a prompt provided by the user. Agent-based systems, on the other hand, can plan multiple steps toward a specific objective and interact with different tools.
These systems can create significant opportunities, particularly for automating repetitive workflows.
However, autonomous systems also introduce new control and governance requirements.
Before adopting AI agents, companies should clearly determine which tasks can be performed automatically, which actions require human approval, and what data these systems are allowed to access.

An 8-Step Starting Plan for AI Transformation

For companies trying to determine where to begin their AI transformation, the process can be summarized in eight steps:

1. Identify the business problem

Determine which processes create the greatest time, resource, or operational burden.

2. Identify potential use cases

Evaluate where artificial intelligence could create value within these processes.

3. Choose a small pilot

Instead of trying to transform the entire organization, start with a single clearly defined process.

4. Define success criteria

Establish measurable indicators such as time, cost, quality, or productivity.

5. Develop employee capabilities

Do not limit AI literacy to technology teams.

6. Evaluate data and information security

Define what information can be used in which systems.

7. Measure the results

Analyze whether the pilot project has created meaningful business value.

8. Scale successful use cases

Expand proven AI use cases to other teams and processes in a controlled way.

What Should Be the First Question in AI Transformation?

Access to artificial intelligence tools is becoming easier every day.
New models are being developed, use cases are expanding, and AI is becoming part of everyday work across many business functions.
However, the competitive advantage companies gain in the coming years will not simply come from using the newest AI tools.
The real difference will emerge among companies that:
identify the right business problems,
develop their employees,
prepare their data,
manage risks,
measure results,
and scale successful use cases.
For this reason, the first question when beginning an AI transformation should not be:
“Which AI tool should we use?”
Instead, it should be:
“Which business problem can we solve better with artificial intelligence?”