Moving from “What” to “How”: Why Businesses Must Shift the AI Conversation from 'what' to 'why'.

As a digital technology consultant, I have spent the last few years working closely with businesses and organisations across Africa, guiding them through the fast-evolving landscape of artificial intelligence (AI).

From leading strategy workshops and staff training sessions to supporting implementation of AI-powered tools, I have witnessed a common pattern: Most conversations begin with excitement around what AI is and what it can do.

Yet the real transformation only begins when these discussions shift to how AI can be meaningfully applied to solve actual problems and unlock growth.

Many organisations are already convinced of AI’s potential. They’ve read the reports, attended the webinars, and seen the demos. What they need now is a structured path to action—a clear way to move from abstract understanding to real-world implementation.

The organisations that will win in the years ahead are not those who merely know about AI, but those who use it effectively across functions—from marketing to operations, from HR to finance. It is time for leaders to move the conversation from fascination with the technology itself to mastery of its application in context.

Why the Shift from “What” to “How” Matters

1. Information Abundance, Application Deficit

There’s no shortage of information on AI. Every industry event or executive meeting features AI on the agenda. But very few organisations are asking the next crucial question: How do we embed AI into our work to make it easier, faster, and smarter?

2. Competitive Edge Lies in Execution, Not Exploration

According to McKinsey, companies that rapidly test and adopt AI at scale outperform peers in productivity and profitability. AI is no longer a futuristic differentiator—it is a present-day enabler. The advantage lies in how you apply it to simplify processes, improve experiences, and create value.

3. The Workforce Wants Practical Tools, Not Theories

From top executives to front-line employees, teams are now asking: “How can AI help me work better, not harder?” This demand for practical relevance must shape how organisations integrate AI.

Practical Steps to Shift from “What” to “How”

1. Begin with an AI Readiness Assessment

Before introducing tools, assess your current workflows and data maturity:

• Where are the inefficiencies?
• Which tasks are rule-based and repetitive?
• Where are data points underutilised?

Case example: In working with a Ghanaian property firm, we mapped customer service queries and discovered that over 70% were predictable and repetitive. Implementing a simple AI chatbot on WhatsApp improved customer response time and freed up staff for more complex queries.

2. Design Context-Specific Use Cases

Don’t approach AI as a one-size-fits-all solution. Instead, identify use cases tailored to departmental goals:

• Marketing: Content generation, campaign analytics, and personalised communication
• HR: Talent acquisition automation, sentiment analysis, and training needs assessment
• Operations: Inventory optimisation, predictive maintenance, and workflow automation

Case in point: I supported a retail chain to use AI for demand forecasting, which improved stock availability while reducing waste by 25%.

3. Launch Low-Risk Pilot Projects

Start small. Prove success. Scale gradually. Recommended pilots include:

• Chatbots for customer service
• Meeting transcription and summarisation
• AI-driven data analytics dashboards
• Automated content creation for blogs or reports

These pilots serve as internal proof of concept, generating both momentum and measurable outcomes.

4. Establish an Internal AI Task Force

Form a cross-functional team of AI “champions” to:

• Identify internal pain points
• Explore and test tools
• Collect feedback and document learnings
• Drive organisation-wide awareness

Example: A client in the logistics sector created an AI Task Force made up of young analysts and department heads. Within six weeks, they identified seven use cases—four of which were rolled out within the first quarter.

5. Upskill Your Teams Continuously

Provide ongoing learning opportunities:

• Subscribe to AI courses (e.g., Coursera, LinkedIn Learning)
• Host internal knowledge-sharing sessions
• Provide prompt libraries and toolkits customised to different teams

You don’t need AI experts across your workforce—what you need are AI-literate professionals who are confident using tools to enhance their work.

6. Implement an AI Governance Framework

As adoption grows, establish policies around:

• Data privacy and compliance
• Ethical usage and transparency
• Bias detection and mitigation
• Accountability for AI-driven decisions

This ensures that your organisation scales AI responsibly, especially in regulated industries like finance, health, or education.

Final Thoughts: Curiosity Is Not Enough

AI’s potential is clear. The question is no longer what it can do—but how you will use it to transform your organisation.

This shift from curiosity to capability is what separates early adopters from laggards. In my consulting work, I have seen how even simple applications of AI can drive measurable improvements in efficiency, accuracy, and innovation—when deployed with clarity and purpose.

Let’s move from fascination to functionality.