As organisations rush to adopt artificial intelligence, many are building sophisticated systems that solve imaginary problems, Nedbank’s group technology executive for product management Makopi Nkopodi has a clear message: AI initiatives must be explicitly linked to measurable business outcomes, or they risk burning resources without delivering value.
“Too often, organisations jump into AI initiatives driven by hype rather than clarity,” Nkopodi explains. “Agentic AI should not exist for its own sake, it must solve meaningful problems, not imaginary ones.”
For Nkopodi, the foundation of successful AI implementation lies in rigorous upfront questioning. Before any AI initiative begins, teams must ask: Why are we doing this? What problem are we solving? Who will use it? What outcome do we want to achieve? And critically, how will we measure it?
“Without this level of clarity and discipline upfront, organisations risk building sophisticated technology that solves imaginary problems,” she notes. “AI done right doesn’t start with technology, it starts with intentionality. It begins with why and ends with measurable impact.”
At Nedbank, this philosophy has driven a deliberate transition from project-focused to product-focused operations. Every technology the bank adopts or builds, including AI tools, is linked to clear business outcomes and objectives.
“We are intentional about not implementing tech just from excitement,” Nkopodi says. “The product mindset is helping us avoid isolated technology deployment that does not tie to strategic goals.” The shift is already yielding results, with technology and business teams working closer together with shared goals.
Nkopodi argues that agentic AI represents more than an analytics upgrade, it’s a fundamental rethink of how decisions are made. “It’s the difference between analysing what happened yesterday and acting on what’s needed tomorrow,” she explains.
The results speak for themselves. Nedbank’s retail digital transaction values grew by 16%, with digital sales now accounting for around 70% of total retail sales. Hyper-automation systems are freeing teams to spend more time with clients, proof that when technology aligns with purpose, performance follows.
“AI needs to transition from the lab to leadership conversations,” Nkopodi insists. “When we apply product discipline, set goals, measure outcomes, and iterate quickly, AI becomes a living part of how a business performs, not an isolated experiment.”
Beyond technology, Nkopodi emphasises that extracting value from AI requires new skills and mindsets across entire organisations , not just technical teams.
Data literacy is becoming a core capability for all roles. But Nkopodi is quick to clarify: “Data literacy doesn’t mean everyone must become a data scientist. It means understanding what data is, how it’s used, and how it informs decisions.”
She challenges the common refrain that organisations lack data access. “Often, we say ‘we don’t have access to data’, but in reality, we interact with data every day through customers, operations, conversations, and experiences,” she notes. “When we truly understand data, we start to see that we already have more data than we think.”
Problem-solving emerges as another foundational competency. “The ability to define problems clearly and ask the right questions is foundational,” Nkopodi explains. “If you can’t define the problem, even the smartest AI won’t give you the right answer.”
Regarding AI itself, Nkopodi’s message is clear: “AI is not something to fear , it’s a tool for solving problems. You don’t need to build the model to use the power of AI , but you do need to understand what it can unlock.
“Don’t just experiment with AI, institutionalise it. Build systems that don’t just predict, but decide, act, and learn. Because the real test of leadership isn’t whether you use AI , it’s whether your business can think and move at the speed of intelligence.”
In a landscape crowded with AI pilots and proofs of concept, Nkopodi’s approach offers a blueprint for transformation grounded in purpose, discipline, and human capability, ensuring AI solves the right problems, not just the most technically interesting ones.