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AI and Research in Marketing and Communications: How Are You Ensuring Quality and Truth Over Speed? As AI becomes embedded in our day-to-day marketing operations, from customer insight to content strategy, it’s clear we’re gaining speed and scale. But are we sacrificing veracity and quality of output in the process? One of the biggest challenges I'm seeing is the rise of hallucinated content in AI-generated research.   - How do we ensure AI systems are surfacing trustworthy, contextually accurate, and well-sourced information? - Are we building enough verification layers into our AI-driven content and research workflows? - And how are you educating teams (and execs) to challenge AI outputs, rather than accept them at face value? - What frameworks, tools, or governance models have you adopted to maintain integrity and depth in AI-led research and insight creation? - Have you had success (or struggles) balancing speed vs. substance in AI-powered strategies? Your thoughts, examples, and recommendations would be incredibly valuable in helping others try to navigate this shift responsibly.

According to a recent piece of research, the UK economy alone lost more than £257 billion ($327 billion) in potential output because of demotivated workers in 2023, adding to a productivity crisis that’s left the country falling behind other wealthy nations. With just one in 10 workers were classified as “engaged” at work. This isn't just aligned to the UK market and is effecting businesses across the globe, so how can companies respond and react to the 'quiet quitters' who have lost interest and passion or simply do the bare minimum to get by?

How do you ensure your customer insights tools remain aligned with how the rest of the business is evolving?

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How do you strike a balance between personalization and privacy while maintaining customer trust?

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