Introduction

I attended the a.tv Business Forum 2026 in Augsburg. The central theme was “The Future of Truth—Between AI Creativity and a Return to Authenticity.”

For me, it wasn’t a tech update. It was a market update: many talk about AI, few apply it properly. Right now, the gap between “tool users” and “process thinkers” is widening fast.

What I observed in the market

The mood was split between excitement, uncertainty, and a strong desire to stay “real.” That’s understandable. But it often distracts from the core point: AI is not a goal in itself. It’s a tool—and it only delivers when goals, workflows, and quality standards are defined.

The real problem: AI is used the wrong way

In day-to-day work I keep seeing the same patterns. Many companies either don’t use AI at all, or they use it in a way that produces volume without identity.

The outcome is predictable: generic output, weak positioning, and no real value for customers or teams.

Who needs to act now

This is not just a marketing topic. Leadership, marketing/sales, operations, and IT are all involved. AI changes workflows. Without ownership, AI becomes a playground. With ownership, AI becomes a productivity engine.

From when: it’s no longer “if”

From my perspective, the timing is already here. AI is no longer “slow progress”—it simply happens. While companies debate, models and capabilities keep improving in the background.

The practical consequence: companies should embed AI now into selected workflows—with rules and measurable outcomes.

Exceptions: where I limit AI on purpose

There are areas where I don’t “let AI run” and I apply strict control—or skip it:

• Legally sensitive content without a clear approval flow
• High-reputation-risk communication (e.g., crises, sensitive HR topics)
• Core positioning and key arguments: my thinking must lead, not the model

AI can support, but accountability stays with humans—especially for tone, stance, and final decisions.

Why cost-benefit is the deciding factor

AI pays off only when it’s used efficiently and purposefully. Not “more AI,” but “better placement.”

The biggest misconception remains: more AI does not automatically mean better results. Without guardrails, you just scale mistakes.

Action steps: how I apply AI in a structured way

• 1) Define the goal: what improves (time, quality, consistency, conversion, support)?
• 2) Pick one workflow: a clearly bounded process, not “everything at once.”
• 3) Assign roles: who creates, who reviews, who approves?
• 4) Set quality standards: what’s acceptable—and what’s off-limits?
• 5) Measure: before/after comparison (time spent, error rate, output quality, business metrics).

Consequences: what happens with unstructured AI use

If you experiment without a plan, you pay twice: first through weak output, then through rework and lost trust. Typical consequences:

• Brand dilution due to inconsistent tone and claims
• Teams lose time through rework instead of gaining time
• Tool silos emerge because everyone uses “their own stack”
• Competitors pull ahead by scaling processes faster

My conclusion

The question is no longer whether you use AI, but how well you understand and apply it. Companies that hesitate or test randomly will struggle in the coming years. Those who think clearly, understand processes, and embed AI with intent will stand out. That’s my approach with scinet: no hype—measurable value.

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