_In an era where Artificial Intelligence (AI) can generate answers in seconds, one Nigerian-born Product Leader and 2025 JCI Aberdeen President argues that the real competitive advantage lies not in having the information, but in knowing what to do with it. Dapo Richards, who shared these insights at the Galvanized Leaders Conference ’26, is challenging professionals across industries to rethink their relationship with AI—and to recognise that judgment, not access, is the new currency of leadership._

As artificial intelligence reshapes industries and professional work, Nigerian-born product leader Dapo Richards has argued that the traditional advantage of simply possessing information is rapidly disappearing, leaving professionals to compete increasingly on judgment, evaluation and sound decision-making.

 

Richards, a product leader and Immediate Past President of Junior Chamber International (JCI) Aberdeen, made the argument while speaking at the Galvanized Leaders Conference ’26, where he examined how artificial intelligence is changing work and the skills professionals need to remain effective.

 

Drawing on his experience in payments and fintech, Richards said the era when access to information alone gave leaders a competitive advantage was giving way to one in which the ability to interrogate information and determine what to do with it would matter more.

 

“For a long time, the leaders who won were the ones who knew first,” Richards told the audience. “They’d read the report, seen the data and understood the market before anyone else. Access to information was the edge.”

 

But with AI systems capable of producing information and analysis almost instantaneously, he argued, that advantage has been substantially weakened.

 

“Information is no longer scarce; judgment is,” he said. “AI will hand you an answer in seconds, and the answer will usually sound confident whether or not it’s correct. The skill that matters now is knowing whether that answer is right, whether it’s complete, and what to do with it.”

 

Richards illustrated the shift with an example from his early career in payments, when he spent the first hour of each working day preparing a transaction report involving the manual pulling, cross-checking and formatting of data.

 

The process has since become largely automated, he said, freeing time for higher-value work. The experience also changed his understanding of productivity, making him question whether completing a task necessarily amounted to creating value.

 

For Richards, the lesson is not simply that AI can make workers faster, but that organisations must redirect the time saved by automation towards analysis, communication and decision-making.

 

He identified three skills as particularly important for professionals working with AI: clarity, evaluation and knowing when not to use the technology.

 

Clarity, he explained, begins with understanding precisely what one is trying to achieve. Poorly framed questions, rather than deficiencies in the AI model, can often be responsible for disappointing results.

 

Evaluation, however, is central to his argument. Richards advised professionals to use AI extensively on areas they already understand well. Existing expertise, he said, gives users the ability to recognise where AI performs reliably and where it can fail.

 

Using AI primarily for unfamiliar tasks, he warned, can create dependency without necessarily building competence.

 

“The failure mode is rarely a wrong answer that looks wrong,” he cautioned. “It’s a wrong answer that looks entirely reasonable, arrives quickly, and gets forwarded.”

 

The warning is particularly relevant in professional environments where AI-generated material can move rapidly through organisational systems before its accuracy has been properly established.

 

Richards also stressed the importance of recognising situations in which AI should not be used. Regulatory judgment calls, sensitive customer information and decisions where the consequences of an error could outweigh any time saved, he argued, require particular caution.

 

“Mastery isn’t using AI everywhere; it’s using it well, in the right places,” he said.

 

He cited his experience navigating a regulatory change that required communicating a technical specification to three different audiences as an example of where AI could enhance rather than replace human judgment.

 

AI helped him produce an initial draft quickly, he explained, allowing him to concentrate on the more consequential questions of what to communicate, what to omit and how firmly to frame timelines.

 

“AI didn’t make the decision,” he said. “It cleared the runway so I could spend my energy on the decision.”

 

The distinction, according to Richards, is central to responsible AI adoption: the technology can assist with preparation and execution without assuming responsibility for the decisions that follow.

 

Beyond individual productivity, he addressed the organisational challenge of introducing AI into workplaces, arguing that resistance to new technology often stems less from the tool itself than from poor communication, inadequate training and the failure to involve employees in the change process.

 

He urged organisations to approach AI adoption as a change-management exercise, supported by clear communication, proper training and feedback mechanisms.

 

Technology alone, he argued, cannot transform teams; leadership determines how it is introduced, governed and incorporated into everyday work.

 

Richards also drew firm ethical boundaries around AI, particularly in payments and fintech, where decisions can involve sensitive financial and personal information.

 

Accountability, he stressed, must remain human. Where AI contributes to a decision, responsibility for that decision cannot subsequently be transferred to the technology.

 

Data protection is equally important, he said, warning against putting customer information into AI systems simply for convenience. His engagement with information-security governance and standards such as ISO 27001, he added, reflects the need for technological innovation to be matched by appropriate safeguards.

 

Asked which single AI skill young professionals should prioritise, Richards chose evaluation rather than prompting.

 

He advised beginners to identify one repetitive task they dislike, use AI consistently on it for about two weeks and gradually turn the experiment into a routine. The next step is to provide the system with genuine context rather than relying on generic prompts, he said.

 

But experimentation, he stressed, must always be accompanied by verification. Young professionals should check AI-generated output consistently and seek mentors who are further ahead in their understanding and use of the technology.

 

Richards ultimately placed AI adoption within a broader conception of leadership, arguing that professionals should not measure their readiness by how many tools they know today but by their willingness to continue learning as the technology evolves.

 

“The leaders who do well from here won’t be the ones who know the most today,” he said. “They’ll be the ones still learning tomorrow.”

 

That is the central challenge Richards poses for professionals entering an AI-driven workplace: as technology makes information increasingly abundant, the premium may shift decisively towards those who can question it, evaluate it, apply it responsibly and recognise when human judgment must prevail.