Am I Solving a Business Problem—or Simply Following the AI Trend?

Durban, 14 August 2026

Artificial intelligence may be the proposed solution. But has the business clearly defined the problem?

By Arnold Muscat, Director of College Africa Group

CAG Executive Question of the Week

If the words artificial intelligence were removed from the proposal, would the project still make business sense?

A manager walks into the CEO’s office with an exciting proposal. The company should purchase a new AI platform before its competitors get ahead.

The presentation looks impressive. It promises faster reporting, better customer service, lower costs and higher employee productivity.

The CEO asks how much it will cost. Finance asks whether there is enough money in the budget, while IT begins examining security and integration.

But nobody asks the most important question.

What business problem are we trying to solve?

AI is creating pressure in the boardroom

CEOs are under pressure to demonstrate that their companies are embracing artificial intelligence.

Competitors are announcing AI projects, software vendors are adding AI to existing products and employees are already experimenting with ChatGPT and other tools.

Doing nothing may feel risky. However, rushing into AI without understanding the business need can be equally dangerous.

AI is not a business objective. It is one possible tool for achieving an objective.

The objective may be to reduce the time required to prepare management reports. It could be to respond to customers more quickly, improve project delivery or reduce repetitive administration.

Until that objective is clear, management cannot judge whether AI is the right solution. The company may simply be following the trend.

Would I approve the project without the word AI?

This is a useful test for any CEO.

Remove the words AI, automation and digital transformation from the proposal. Then examine what remains.

Does the proposal still describe a genuine business problem? Does it explain who is affected, what the current process costs and what measurable result the company expects?

If the proposal becomes weak without the technology language, it may not contain a proper business case. It may only contain enthusiasm for a new tool.

A strong proposal should be able to explain the problem in plain business language. The technology discussion should come later.

Have we examined the existing process?

A company may believe that a report takes three days because employees lack the right AI tool.

The real problem may be that six departments submit information in different formats and nobody agrees on which figures are correct.

Automating that workflow will not necessarily solve the problem. It may simply move inconsistent information faster.

Before approving an AI initiative, management should follow the work from beginning to end. Who starts the process, where does the information come from and who checks the final result?

The review may reveal duplicated work, unnecessary approvals, unclear responsibilities or spreadsheets that require repeated correction.

These are process problems before they are technology problems.

This is where AI Workflow Discovery becomes valuable.

It helps the organisation understand how work is actually being done before deciding where artificial intelligence can make a measurable difference.

Are we solving the right part of the problem?

Executives often see the visible delay but not the cause behind it.

A sales quotation may take two days to prepare. Management may decide that AI should write quotations faster.

However, the delay may have little to do with writing.

Employees could be waiting for pricing approval, searching for the correct product information or trying to establish which version of the customer agreement is current.

AI may produce the document in minutes and still leave the two-day delay untouched.

The question is not simply whether AI can perform a task. The question is whether that task is responsible for the business problem.

An AI Strategy Consultant should help management separate the visible symptom from the underlying cause.

That prevents the company from spending money on an impressive solution to the wrong problem.

Do our employees have the underlying skills?

AI can help an employee analyse a spreadsheet, draft a report or prepare a presentation. It cannot compensate indefinitely for weak business knowledge and poor workplace skills.

An employee who cannot recognise an incorrect Excel formula may accept a convincing but inaccurate AI explanation.

Someone who does not understand the business process may approve an AI-generated document that overlooks an important control.

Better workplace skills create better AI outcomes.

Employees still need to understand Advanced Microsoft Excel, Word, PowerPoint, Outlook, Teams and the wider Microsoft 365 environment.

They also need the judgement to recognise when an answer is incomplete or does not make business sense.

AI training should therefore go beyond teaching employees how to write prompts.

It should show them how to use AI within real business workflows, protect sensitive information and validate the output.

Are we buying software or building capability?

Purchasing licences is relatively easy. Changing how people work is much harder.

A company can give employees access to ChatGPT or Microsoft Copilot and still see very little improvement.

Some employees may not use the tools, while others may use them without suitable guidance.

A few enthusiastic employees may develop useful methods that are never shared with the rest of the team. Different departments may then create their own practices, standards and risks.

The business should decide what employees need to do differently after the investment. It should also determine what training, support and management oversight will be required.

ChatGPT can support drafting, summarising, research, process documentation and meeting preparation.

Microsoft Copilot can assist employees within familiar Microsoft 365 applications such as Outlook, Word, Excel, PowerPoint and Teams.

These capabilities only create business value when employees know how to apply them to approved work. Practical training turns access to software into usable organisational capability.

What result must the project deliver?

“Improved productivity” is not a sufficient measure of success.

Management should define the expected result before approving the project.

The objective could be to reduce report preparation from three days to one, shorten customer response times or remove a specific number of repetitive administrative hours each month.

The starting point must also be measured. If the organisation does not know how long the existing process takes, it cannot prove that the AI initiative improved it.

Not every benefit will appear immediately in the financial statements. Better consistency, stronger knowledge sharing and faster access to information can still create real value.

However, the measures should be agreed upon in advance. Otherwise, the organisation may continue paying for a tool because people say it is useful, even when nobody can demonstrate what changed.

Every AI investment should earn its place in the business.

Who owns the outcome?

AI projects often involve several departments.

IT considers security and technical integration. Legal and compliance teams examine risk, while HR considers employee policies and training.

Finance monitors the cost. Operational managers focus on whether the tool improves daily work.

Each department sees part of the picture, but somebody must own the complete business outcome.

That person should have the authority to make decisions, address problems and stop the initiative if it is not delivering value.

Ownership cannot be passed to the software vendor or an AI-generated recommendation.

Clear ownership does not mean creating unnecessary bureaucracy. It means ensuring that somebody remains accountable after the initial excitement has passed.

Where will human judgement remain essential?

AI can generate a polished answer without understanding the consequences of that answer. It can also make an incorrect response sound completely credible.

This creates a dangerous gap between appearance and accuracy.

Before approving an AI initiative, the CEO should know which outputs require human review. The company must decide who checks the result, what evidence is required and who gives final approval.

A customer communication may require a manager’s review. A financial analysis may need to be checked against the source data, while an HR recommendation may require legal and ethical consideration.

Sensitive business information must also be protected. Employees need clear guidance about which tools are approved and what information may be entered into them.

AI produces language. People remain responsible for decisions.

Start with a small business problem

Companies do not need to delay every AI initiative until they have developed a perfect strategy. They do need to start with discipline.

Select one clearly defined business problem. Understand the existing workflow, establish a starting measure and identify a practical area where AI may help.

Train the employees involved and agree on the rules for human validation. Then measure the result before expanding the initiative.

A small project that solves a genuine problem is more valuable than a large AI programme built around vague promises. It also gives management evidence that can guide the next investment.

AI Consulting Services can help the organisation identify suitable opportunities, prioritise them and establish the governance needed for responsible adoption.

The objective is not to slow innovation, but to prevent wasted expenditure and unnecessary risk.

The CEO’s final approval test

Before signing the proposal, the CEO should be able to explain the business problem without referring to the software.

Management should know how the current process works, why it is underperforming and what result must improve.

The company should also know whether employees have the required skills and who will remain accountable for the outcome.

Only then should the discussion move to ChatGPT, Microsoft Copilot or another AI platform.

The best AI initiative is not necessarily the one with the most advanced technology. It is the one that solves a real business problem, improves the workflow and helps employees perform better.

Before approving another AI purchase, ask the question directly:

Am I solving a business problem—or simply following the AI trend?

College Africa Group helps South African organisations examine their business processes, identify practical AI opportunities and strengthen the workplace skills required for successful adoption.

Through AI Strategy Consulting, AI Workflow Discovery, AI Consulting Services and practical ChatGPT and Microsoft Copilot training.

CAG helps organisations move from experimentation to measurable business value.

Better Business Processes. Stronger Workplace Skills. Smarter AI. Greater Business Productivity.

Contact College Africa Group to discuss an AI Workflow Discovery session, business process review or practical AI training for your team.

www.collegeafricagroup.com