Content Generation Has Turned Into a Commodity — There's a New Business Skill in 2026 Every Leader Needs

Filtering out the BS has never been more critical in 2026. With the adoption rate of AI tools accelerating at unprecedented rates, organisations and business leaders are noticing something peculiar. After years and multiple phases of "increasing productivity" strategies, employees are finally starting to deliver the throughput requested by management.

Thanks to AI, the ability to churn out specific but at the same time generic output is at the fingertips of every employee. Need to create a marketing strategy for a new product launch? Let's take a guess — the strategy goes something like pre-launch validation > go to market channels > building traction > post-launch follow up.

Need a budget plan to reduce costs by x%? Does the plan look something like people costs > technology and licenses > office operations > vendor & outsourced services?

The plan looks ok, there's enough content unique to the organisation, but dig deeper and it's clear another cookie cutter "strategy" has been proposed.

This is where true business experience comes in. With AI enabling easy content generation, a manager's task of supervising and monitoring their team's output has exponentially increased. As the manager, suddenly you've gone from chasing your team on actions to them putting it back on you to review their work — it's like the teacher having to go through bunches of exam assessments.

What to do then? Put it back on the LLM and start saying no. Your ability to critique and reject is a skill the LLM cannot replicate because you are using years of experience and context to come to a conclusion that is at odds with what's been delivered.

This is good. Because the LLM doesn't have enough context, by saying "no, this is incorrect" actually forces the LLM to narrow its focus as well as come up with alternatives.

The LLM cannot tell you the proposal is wrong because it doesn't know your history with this client, your CFO's risk appetite, or the fact that you've seen the same similar proposal three times in the previous month. That context — accumulated across good decisions, bad decisions, and many, many meetings over the years — is exactly what makes your rejection of mediocre AI output valuable.

Not just to your organisation but to the AI itself. Push back, and watch it narrow its focus, recalibrate, and try again. The irony is rather satisfying: the skill that AI was supposed to make redundant, i.e. making judgement calls, turns out to be the very thing that makes AI work properly.