Speed Isn't the Measure of AI's Value. Judgment Is.
AI has made work faster. Whether it makes work better depends on the choices that sit around it-the questions asked before running the tool, the standards applied when reviewing output, and the intellectual honesty to distinguish between something that looks polished and something that is actually good.
A team lead recently submitted a client strategy document in under two hours. It was well-structured, covered all expected ground, and polished. It was also almost entirely generic. The AI had done its job. The thinking had not.
The productivity numbers are real
Research from MIT and Harvard Business School shows AI-assisted knowledge workers complete tasks 20% to 35% faster, often with measurable quality improvements. Across communications and marketing, output volumes have surged. Agencies that once needed full teams to produce a campaign can now do it with a fraction of the headcount.
Clients are asking for more, faster, and at lower cost. The market is obliging.
Output inflation is the hidden cost
As the cost of producing work drops, so does its perceived value. When content is abundant and cheap to generate, differentiation becomes harder. The premium shifts away from production capability toward something far more difficult to automate: judgment, interpretation, and the ability to navigate genuine complexity.
Yet commercial pressure moves in the opposite direction. More deliverables. Faster turnaround. Shorter thinking cycles.
This is a leadership problem, not a technology problem
Organisations embedding AI into workflows are making implicit choices about what they value. If the metric is speed and volume, then depth and originality are quietly deprioritised. Nobody announces this. It simply happens as the incentive structure shifts.
Over time, it hollows out the very capability that justified the premium in the first place.
South Africa faces dual pressure
AI adoption is uneven-not just across industries, but within them. Some organisations are accelerating rapidly, deploying tools across research, content, and client service. Others are still building foundational digital infrastructure.
The result is pressure to catch up quickly, often without the governance frameworks or strategic clarity needed to do so responsibly. An organisation that uses AI to replace thinking rather than enhance it is not becoming more competitive. It is becoming more fragile, dependent on tools it does not fully understand, producing work that is difficult to defend when it matters.
The communications industry needs to examine itself
Communications professionals have never competed on volume. The value proposition rests on reading a situation accurately, shaping narrative under pressure, and exercising judgment when facts are contested and stakes are high. These capabilities cannot be automated. But they can be eroded through neglect, underinvestment, and the slow substitution of speed for thought.
The risk is not only reputational. It is structural.
Where to draw the line
Organisations that succeed in this phase are not those using AI most aggressively. They are the ones most deliberate about where it stops. That means protecting time for thinking, even when technology makes faster movement possible.
It means being explicit about where human judgment is non-negotiable, and building that distinction into how work is structured, priced, and evaluated. It means resisting the temptation to let a client brief become a prompt, and then mistake the output for strategy.
The questions you ask before running the tool matter more than the tool itself. Learn more about prompt engineering and how to integrate AI into communications work through AI for PR & Communications training.
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