When interesting AI work often gets lost in translation

A few months ago, a little-known AI research lab built a model that could detect early signs of Alzheimer’s from voice samples with staggering accuracy. It never made headlines. Why? Because the story was buried under jargon, caveats, and language only experts could parse.

That disconnect has become sharper in 2025. AI systems now predict patient health deterioration up to 16 hours in advance with 67–94% accuracy, while neural machine translation tools are achieving unprecedented fidelity. Yet, many of these breakthroughs remain locked in academic papers and research decks. Meanwhile, Apple’s AI-generated news summaries have produced false headlines. This is proof that poor communication can distort understanding rather than improve it.

This is the paradox of our time: AI is rewriting how the world works — from healthcare to climate action — yet the way we communicate about it hasn’t caught up.

Recent research illustrates the growing trust deficit. Although 66% of people use AI regularly, only 46% actually trust AI systems.

The issue isn’t confined to consumers. Traditional marketing and communication frameworks, originally designed for FMCG, SaaS, or lifestyle brands, fall short in explaining complex, fast-evolving technologies. Public perceptions of AI scientists are now more negative than those of climate scientists or scientists in general, driven largely by fears of unintended consequences.

Even as 78% of organisations use AI in at least one business function, just 17% actively mitigate explainability risks. OpenAI’s leadership saga, Anthropic’s regulatory scrutiny, and Stability AI’s funding controversies underscore one truth: in AI, trust is fragile and reputation volatile.

Why the old playbook no longer works

For decades, technology communication has leaned on familiar tropes — productivity, efficiency, disruption. But when applied to AI, these clichés fall flat or, worse, provoke anxiety. “AI will take your job” is hardly a reassuring message.

Recent analysis of global media coverage, especially in the UK, reveals that nearly 60% of AI-related articles focus on product launches or industry announcements, with industry voices quoted six times more often than government or civil society sources.

This creates what researchers term a ‘pseudo-artificial general intelligence’ narrative. It portrays AI as a limitless force while ignoring ethical, social, or cultural nuances.

The communication challenge, however, runs deeper than buzzwords. Research from 2025 shows that AI systems trained primarily on English-language data impose Western narrative structures even when generating content in other languages. The result: an erosion of cultural diversity that cannot be solved through linguistic translation alone.

The traditional marketing playbook thrives on repetition and scale. AI communication, by contrast, demands nuance, agility, and humility.

An intelligent communication model

We don’t need to simplify AI, we need to translate it. That means making complex research accessible without diluting its scientific value.

Consider healthcare: studies show that AI can save providers 2.4 hours per day and reduce operational costs by INR 6,400 crore. But those numbers resonate far better when the story is told through patient outcomes rather than algorithmic precision.

The foundation must be trust. Transparency, accuracy, and clear boundaries matter more than slogans. Research consistently finds that people trust AI more when they understand both its strengths and its limits.

Equally, storytelling must be human-first. It’s not about neural networks or LLMs; it’s about the farmer improving yields, the student receiving personalised learning, or the patient diagnosed earlier.

The starting point must be trust. Transparency, accuracy, and clear boundaries matter more here than glossy slogans. Research shows people actually trust AI more when they understand both its capabilities and its limits.

AI communication must also evolve at the same speed as the technology. Static campaigns or six-month communication cycles no longer hold. The most effective strategies will be real-time, adaptive, and grounded in evidence.

Cultural sensitivity is another critical pillar. Different audiences require different lenses. While  data scientists may want technical granularity, executives seek clarity on risks and ROI. The way AI is framed in India will differ from Europe or the US. Hence, the one-size-fits-all messaging doesn’t work, and cultural context is critical for trust.

When trust meets storytelling

A striking example of trust-led storytelling emerged earlier this year, when OpenAI partnered with several Indian startups during its market expansion. Among them was Vahan.ai, India’s largest AI-powered recruitment platform. At a time when global discourse around AI focused on automation anxiety and job displacement, the collaboration reframed the narrative — showing how AI could become a bridge to livelihoods and inclusion.

The startup uses conversational AI to match blue- and grey-collar workers with verified employers, placing over 40,000 individuals each month across 920 cities. The partnership was used not merely for product integration, but as a storytelling opportunity. Together, OpenAI and Vahan crafted a narrative around ‘AI for Good’, highlighting workers who found their first jobs through an AI-led process.

The campaign resonated because it spoke to both trust and culture. It reflected India’s human-first approach to technology, one that views innovation as a force for empowerment rather than exclusion. In doing so, it provided a much-needed counterpoint to global fear-driven narratives around AI.

This example illustrates how communication can transform perception. By blending transparency, empathy, and local context, brands can make AI relatable and responsible — not abstract or intimidating. It showed that trust in technology isn’t built in labs but in lived experiences.c

The untapped white space

AI has created one of the most significant paradoxes of modern business: it is both the most transformative industry and the hardest to communicate. Adoption is soaring, yet trust continues to slide. This gap represents a critical opportunity for communicators, marketers, and brand strategists.

For brands and startups, this isn’t about ‘doing PR better.’ It’s about reimagining the very language of impact. Research indicates that organisations with robust AI governance and transparent communication strategies see 20–30% higher productivity gains compared to those with ad-hoc approaches.

For agencies and in-house teams alike, this means treating AI communication not as a subset of corporate PR, but as a core strategic discipline — one that translates innovation into human impact, builds credibility through clarity, and anchors messaging in ethics and context.

The challenge is not just to talk about AI, but to talk about it responsibly. This is about demystifying without overselling, to engage without exaggerating, and to inform without intimidating. The next decade will test how well communicators can evolve from storytellers to translators of technology.

Because if AI is transforming everything, the way we talk about it must transform too. The technology is ready. The question is whether we are ready to invest in the language that will unlock its full potential.

- Upasna Dash, Founder & CEO, Jajabor Brand Consultancy