Artificial intelligence is becoming increasingly accessible to businesses of every size. But access to AI does not mean that companies are equally ready to use it effectively.

A 2025 study, “Unveiling Barriers and Enablers: A Study on AI Adoption in Business Management,” by Hassan Arif Siddiqui, Arman Khan, and Shafqatullah Shaikh, examined AI adoption across manufacturing, healthcare, finance, IT, and retail.

The study found that some of the biggest barriers were not technical. Financial constraints were reported by 48% of respondents, organizational resistance by 45%, privacy and bias concerns by 41%, and inadequate IT infrastructure by 26%. At the same time, leadership, modern technology systems, cost-saving opportunities, and governance were identified as important enablers. (pdfs.semanticscholar.org)

The important point is that AI adoption is not simply about buying a tool.

A company may have access to powerful AI systems, but still struggle to use them if its internal processes are unclear, its data is fragmented, employees are uncertain about how AI should be used, or management has not decided where AI actually fits into the business.

In that sense, AI adoption is closer to business transformation than ordinary software implementation.

Leadership and Organizational Readiness

The study also found that leadership commitment and a clear vision for innovation were positively associated with AI adoption.

That is important because many of the questions created by AI are management questions:

• Which processes should be changed?

• What information can be used?

• Who is responsible for reviewing AI-generated work?

• What risks are acceptable?

• Which tasks should remain human?

• How should employees be trained?

Without clear answers, AI adoption can easily become fragmented, with different employees using different tools without a common business objective.

The Readiness Gap

There can also be a gap between what a business wants AI to do and what the organization is actually prepared to support.

For example, a company may want AI to analyze internal information, while its data is spread across emails, spreadsheets, documents, and disconnected systems.

It may want to automate a workflow that has never been properly documented.

It may encourage employees to use AI without establishing clear rules around privacy, security, or accountability.

In these situations, the problem is not necessarily the AI.

The organization itself may not yet be ready.

AI Should Solve a Business Problem

This also suggests that companies should not try to add AI everywhere.

A better approach is to start with a specific business problem and ask:

• Is the task repetitive or time-consuming?

• Is the required information available and reliable?

• Can the result be measured?

• What happens if the AI makes a mistake?

• Can the process be tested on a small scale?

• Does AI actually improve the workflow?

Sometimes the right answer may be to improve the existing process before introducing AI.

From AI Strategy to Business Strategy

Ultimately, AI affects operations, staffing, technology, risk, governance, and customer experience. These are already core business issues.

The better question may therefore not be:

“What AI should we use?”

but:

“What are we trying to improve in the business, and can AI help us improve it?”

The study by Siddiqui, Khan, and Shaikh suggests that successful AI adoption depends on much more than technology. Leadership, culture, financial capacity, infrastructure, governance, and organizational readiness all matter. (pdfs.semanticscholar.org)

In that sense, AI adoption may be less a test of the technology and more a test of how prepared the organization is to change.

Source: Hassan Arif Siddiqui, Arman Khan & Shafqatullah Shaikh, Unveiling Barriers and Enablers: A Study on AI Adoption in Business Management, Journal of Social & Organizational Matters, Vol. 4, No. 1 (2025), pp. 193–209.