Responsible AI adoption is actually faster AI adoption
Women are slower to embrace AI because of a proven fear of judgment. Zehra Chatoo explains why agencies need to tackle culture, not just capability.
Responsible AI adoption is actually faster AI adoption
Women are slower to embrace AI because of a proven fear of judgment. Zehra Chatoo explains why agencies need to tackle culture, not just capability.
It is no longer a question of whether or how much agencies should use AI, but how they can embed it across every part of the business. As organisations race to integrate AI into workflows, they are also under pressure to encourage employees to adopt the technology confidently and consistently.
Zehra Chatoo
Founder, Code For Good Now
One of the biggest challenges is that women are adopting generative AI tools at around 25% lower rates than men. While this gender adoption gap has often been framed as a skills issue, research from Harvard Business School suggests it is largely a cultural one. Among the biggest barriers women report are trust, ethics and fear of judgment.
For Zehra Chatoo, founder of Code For Good Now, who has also held senior leadership roles at Omnicom and Meta, the phrase ‘fear of judgement’ raised an important question:
is the fear of being judged for using AI a perception or is it rooted in reality?
To find out, Code For Good Now conducted a nationally representative study of 1,000 UK adults. Participants reviewed one of two identical AI-assisted CVs for a marketing role. Both disclosed the use of AI. The only difference was the name at the top: Emily Clarke or James Clarke.
The findings were striking. Reviewers were 22% more likely to question Emily's trustworthiness, twice as likely to doubt her competence, and male Gen Z respondents were three and a half times more likely to describe her CV as “weak”.
Chatoo has coined this phenomenon the ‘AI Judgment Penalty’: "the additional scrutiny and lack of credibility that certain people face for using AI compared with others, even when the output and the AI use are identical."
Her consultancy, Code For Good Now, was founded to help agencies and brands adopt AI responsibly and effectively. Through strategic advisory work and its ‘Permission to Prompt’ programme (of which a day course is being run by the IPA on 8 October), the organisation focuses on removing the barriers that prevent people from embracing AI, rather than simply teaching them how to use it.
For Chatoo, this is about far more than fairness. "When I talk about responsible AI adoption, I mean bridging that adoption challenge and making sure this technology is used by everyone," she says.
"AI is a technology where the input shapes the output, so the user base has a direct impact on the outputs we all see. That's why I think it's critical that women's voices are represented and that they play a meaningful role in shaping how AI develops."
So, if AI skills are not the real barrier, what is? And how can agencies create a culture where everyone feels confident adopting the technology? We caught up with Chatoo to discuss why responsible AI adoption is becoming a leadership issue, what the ‘AI Judgment Penalty’ means for agencies, and how ‘Permission to Prompt’ aims to help organisations close the gap.
Is there a danger that agencies pushing to adopt as quickly as possible could unintentionally widen existing inequalities within the workforce?
Zehra Chatoo: Yes. I think the AI Judgment Penalty highlights that the gap in AI adoption is a rational response to a real penalty. Let's fix that penalty first. If organisations accelerate AI adoption without addressing the culture around its use, they risk scaling the very barriers holding adoption back.
The opportunity is to remove those barriers.
Responsible AI adoption isn't slower adoption. Done well, it enables more of your workforce to participate, which ultimately means faster and more effective adoption.
You argue that AI adoption has become a leadership challenge rather than simply a technology challenge, with culture now just as important as capability. Many businesses are investing heavily in AI training. Why won't AI skills training alone solve the adoption gap?
Zehra Chatoo: AI skills training is important, but capability alone doesn't create adoption.
Capability determines whether people can use AI. Culture determines whether they will.
AI adoption is a social behaviour. People take their cues from the culture around them. Is experimentation encouraged? Do leaders role-model AI use? Are people learning openly from one another, or is AI use still quietly judged?
Our AI Judgment Penalty research shows why that matters. You cannot upskill someone out of a structural bias. So the leadership challenge isn't simply, “Can our people use AI?” It's “Have we created a culture in which they will?”
Do you think this problem is actively being addressed enough?
Zehra Chatoo: Not enough yet, but I'm encouraged by what I'm seeing. One of the most common questions I get from agencies is: “We've invested in AI. Why aren't our people adopting it at the rate we expected?”
That's an important shift. Leaders are recognising that investing in the technology and skills training alone doesn't automatically translate into adoption.
A growing part of my work with agencies is advising them on the cultural conditions that turn AI investment into adoption: trust, accountability, role-modelling and permission to experiment. We're moving beyond simply building AI capability to creating the conditions for high-performing teams in an AI world.
You’ve said that "speed without accountability doesn't close the gap; it scales it." What does accountability actually look like when agencies are embedding AI across their organisations?
Zehra Chatoo: Accountability starts with measurement. Overall adoption rates can mask significant gaps in who is actually adopting AI and benefiting from it.
Then establish clear norms. Where should AI be used? How should its use be disclosed? What does responsible experimentation look like? Ambiguity creates space for bias and judgment.
And finally, look at what leaders role-model. People need to see that responsible AI use is recognised and valued across the organisation.
What gets measured, normalised and role-modelled gets adopted.
Some leaders might see this primarily as a DEI issue. Why is addressing the AI Judgment Penalty also a commercial and competitive advantage for agencies?
Zehra Chatoo: Because this is fundamentally a performance issue.
If significant parts of your workforce are using AI less, you're not getting the full return on your AI investment or your talent. And in an industry where competitive advantage comes from the quality and diversity of ideas, narrowing who shapes AI-assisted work ultimately narrows the work itself.
The agencies that create the conditions for their entire talent pool to use AI confidently will have more people experimenting, learning and producing better work.
Inclusion isn't adjacent to AI performance. It's one of the conditions for it.
What do you hope agency leaders and teams will take away from the ‘Permission to Prompt’ course?
Zehra Chatoo: Permission to Prompt is designed to overcome the barriers that stop people adopting AI, so organisations can maximise the potential of the technology.
The course gives participants a practical framework for building a high-performance culture with AI, where the entire talent pool can participate and the social conditions support people and AI working effectively together. Crucially, it moves beyond training to accountability, giving leaders a way to measure adoption and progress over time.
The competitive advantage isn't simply having AI. It's creating teams that perform better because of it.
If an agency wanted to assess whether it already has an ‘AI Judgment Penalty’, what warning signs should leadership be looking for?
Zehra Chatoo: I think the key thing is measurement. Look at AI adoption across your agency and your teams. Are there differences by gender that your overall adoption numbers might be masking?
Then look at who you're role-modelling when it comes to AI best practice. Is it always your CTO or transformation lead? They should absolutely be part of the picture, but AI is becoming embedded across every part of the agency model, so our role models need to be much broader too.
We need to role-model AI best practice across different departments and audiences, so people can see how this technology can be used effectively throughout the organisation.
What advice would you give to individuals who worry they'll be judged differently because they're using AI? How can they navigate that without holding themselves back?
Zehra Chatoo:
I don't want to put the burden of solving a structural problem onto the person experiencing it.
Individuals should absolutely build their AI capability, experiment and share what works. But they shouldn't have to become individually responsible for overcoming a judgment penalty they didn't create.
That's why I keep bringing this back to leadership. If two people can use the same technology in the same way and be judged differently for it, that's an organisational culture problem, not an individual confidence problem.
Looking ahead, what do you think will separate the agencies that adopt AI responsibly from those that simply adopt it quickly?
Zehra Chatoo: The distinction I would make is between moving fast and moving your organisation forward.
You can mandate AI use, deploy tools and set adoption targets very quickly. But if the same small group of people are doing most of the experimenting while others hold back because of trust, judgment or uncertainty, you haven't transformed the organisation.
The agencies that win won't simply be the fastest to deploy AI. They'll be the ones that create the conditions for their entire talent pool to move forward with it.
Published by the IPA, the IPA AI Magazine helps member agencies make sense of the fast-changing AI landscape, with practical insights, expert perspectives and real-world applications of generative AI.
Managing Editor: Conor Nichols
Design: Jenna Betts
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