How Hypebeast Reached 97% AI Adoption Without Fear or Layoffs
How Hypebeast Reached 97% AI Adoption Without Fear or Layoffs
AI adoption is at the top of every data leader’s agenda. Yet most attempts stall. Leaders flood their teams with new tools, staff get overwhelmed, and adoption drops. In some cases, AI is seen as a threat rather than an enabler.
At Hypebeast, it’s different. 97% of staff now use AI agents in their daily work. Not because they were forced, but because they wanted to.
In this episode of Data Matas, I spoke with Sami Rahman, Director of Data & AI at Hypebeast, about how he made AI adoption stick. His story offers grounded lessons for any data leader trying to balance hype with reality.
Meet Sami Rahman
Sami leads Data & AI at Hypebeast, the global fashion and lifestyle brand. His career spans psychology, counter-terrorism, and data science — giving him a rare perspective on how people, systems, and trust interact.
That unusual career path means he doesn’t just see AI as technology. He sees it as part of a wider human system — where behaviour, incentives, and culture matter just as much as code.
“We’re a creative company. We don’t want to replace journalists or designers. But we can use AI as a force multiplier — speeding up research, consolidating information, and helping people make decisions faster.”
That human-first but pragmatic outlook shaped every decision in Hypebeast’s adoption journey.
Why Most AI Adoption Efforts Fail
AI is no longer optional. Boards and executives expect adoption. But many teams fail to deliver value. Why?
Sami points to fear — not of the technology, but of being abandoned.
“The reason people are fearful around AI isn’t the tech. It’s because they don’t trust governments or institutions to look after them if jobs disappear.”
This distinction matters. If AI is framed as replacement, staff feel threatened. If it’s framed as empowerment, they engage.
Psychology backs this up. People resist change when they fear loss of control or status. The solution isn’t just better tech — it’s better framing. Data leaders need to talk about AI as a tool that supports their teams’ value, not one that makes them redundant.
Lessons from Hypebeast’s Adoption Journey
1. Frame AI as a Force Multiplier
At Hypebeast, AI is not a substitute for creativity. It’s an assistant. Tasks like research, summarisation, and trend monitoring were made faster and easier, while final judgement stayed human.
“We’re not trying to replace jobs. We might automate manual tasks, but we won’t remove the human side.”
This framing reassured staff that their value remained central — and made AI a welcome tool, not a competitor.
Implementation guidance:
- What to do first: Communicate clearly that AI enhances, not replaces.
- Tools: Introduce AI where speed and consolidation matter (e.g. research, summarisation).
- Watch-outs: Don’t oversell — focus on real, modest gains.
- Benefit: Higher trust and willingness to experiment.
2. Focus on “Unsexy” Use Cases
Flashy AI demos rarely translate into real value. Sami leaned into the unglamorous but high-impact tasks: scanning social feeds, packaging intelligence, automating logistics and finance.
“We leaned into use cases that aren’t super sexy but free up time.”
By cutting drudgery, staff had more time for meaningful creative work.
Implementation guidance:
- What to do first: Audit manual processes that drain time.
- Tools: Simple AI agents for monitoring, reporting, logistics.
- Watch-outs: Avoid over-investing in “showcase” projects.
- Benefit: Faster results, more trust in AI.
3. Create a Curiosity Gap
Perhaps the boldest move was delaying access. For 10 weeks, Sami drip-fed teasers: short demos showing what AI could do.
“It was 10 weeks before we gave anyone access — on purpose… By the launch, adoption went from 3% to 95% in three weeks.”
Scarcity created FOMO. Instead of pushing adoption, Hypebeast created pull.
This contrasts with most change management programmes, which over-prepare staff with slide decks, training, and handholding. Sami flipped the playbook — and in his industry, it worked.
He’s quick to note it wouldn’t fit everywhere. In banking or pharma, where regulation and compliance demand rigour, leaders may need a heavier hand. But in fast-moving creative industries, curiosity was the lever.
Implementation guidance:
- What to do first: Hold back, and drip-feed examples.
- Tools: Short demo videos or snippets to spark curiosity.
- Watch-outs: Don’t launch too early before demand builds.
- Benefit: Rapid, voluntary adoption.
4. Kill Zombie Agents Without Sentimentality
Hypebeast set strict benchmarks: daily or weekly agents had to hit 80% usage. If they weren’t used, they were deleted.
“If an agent isn’t used, we delete it. No sentimentality. It’s not failure — it’s iteration.”
That unsentimental approach kept adoption high and avoided wasted energy.
This is another overlooked lesson. Too many teams keep “zombie tools” alive because someone invested time or money. Sami’s product mindset — measure, test, delete — freed his team to focus only on what added value.
Implementation guidance:
- What to do first: Define usage thresholds per agent type.
- Tools: Usage dashboards, adoption metrics.
- Watch-outs: Don’t cling to underused tools.
- Benefit: Consistently high adoption, leaner portfolio.
Measuring Adoption: Beyond Usage
Usage was Sami’s primary metric, but adoption measurement can and should go deeper. Data leaders can track:
- Time saved on manual tasks
- User satisfaction with AI support
- Error reduction in workflows
- Frequency of repeat use across teams
By triangulating usage with impact, leaders can see not just whether AI is being used — but whether it’s making a difference.
This level of measurement is critical for building trust with executives and avoiding the “we bought AI, but what did it achieve?” backlash.
Real-World Impact
Hypebeast reached 97% adoption within three weeks of launch. Staff now use AI agents daily across journalism, retail, logistics, and design.
The before state was one of downsizing, high pressure, and manual workloads. The after state is a team with more time for creativity, backed by systems that take care of drudgery.
Instead of creating fear or resistance, the approach built curiosity and trust. AI is now embedded in workflows, freeing staff to focus on creative and strategic tasks.
Putting It All Together
Hypebeast’s success was not built on hype or heavy-handed change management. It came from reframing AI as support, solving practical problems, creating demand through scarcity, and cutting what didn’t work.
The results speak for themselves: adoption consistently above 90%, and a team that sees AI as an enabler, not a threat.
For data leaders, the playbook is clear:
- Start with framing — AI enhances, it doesn’t replace
- Automate boring tasks first
- Create demand with curiosity, not forced adoption
- Be ruthless with underperforming tools
Your Next Move: A Leader’s Checklist
Before your next AI rollout, ask yourself:
- Have I made it clear AI is here to support, not replace?
- Am I solving everyday pain points first, not chasing flashy demos?
- Can I create demand by showing value before rolling out access?
- Do I have clear benchmarks for adoption and usage?
- Am I willing to cut what doesn’t deliver?
Tick those five boxes, and you’ll be far closer to adoption that actually sticks.
Dig Deeper
This article is based on Data Matas Episode with Sami Rahman, Director of Data & AI at Hypebeast.
📺 Watch the full conversation: https://www.youtube.com/@matatika
🎙️ Listen to the podcast: https://www.matatika.com/podcasts/