Everyone’s an AI student
In schools and companies alike, getting the most from AI comes down to confidence and judgment. Both have to be built deliberately.
In this issue, I’m reading the curve to examine:
Why confidence matters when it comes to who gets ahead with AI
How co-learning turns AI use into a two-way skill
What schools can teach every organization about readiness
Reading the Curve
I’ve been teaching graduate-level business courses for over two decades now, and I’ve seen how each generation of students arrives ready to make sense of whatever technological shift defines their moment. Back then, we were exploring the possibilities of the internet. Today we’re doing the same with AI.
The technology and tools might change, but what’s constant is how eager people are to make the most of them. I saw this with my most recent class, where I ran a small experiment with students—mostly with international backgrounds—at Sciences Po, a French university where I teach corporate strategy. For the midterm exam, I allowed AI resources in line with the school’s guidelines and on the condition that students explain how they used them. For the final, they were on their own.
Most students naturally gravitated toward LLMs, which isn’t surprising since it’s something that’s already part of their daily life. What struck me in the results was there was no consistent gap in overall grades between the two exams. But there were differences: The AI-assisted essays tended to be better structured but more generic, while the final exams had richer, more idiosyncratic content, even if some students ran out of time to finish.
An interesting observation was how the rankings shifted. Except for a small group of consistently strong students, the order changed completely between the two exams. It’s as if the technology reshuffled the deck, and those who used it particularly well gained a real advantage, all else being equal.
AI didn’t make the class better or worse. It changed who came out ahead, rewarding those who used it well rather than lifting everyone equally.
That advantage won’t disappear after graduation. Employers are already signaling that AI fluency will shape what entry-level jobs look like. In a recent survey of C-suite leaders across the world’s largest organizations, we found that 74% say their companies are likely, or very likely, to create new entry-level roles centered on working with AI over the next one to two years.
Now, my classroom story is just one tiny example, but it points to something larger, which is why this issue looks at a new report from my colleagues Mike Moore and Babak Moussavi, alongside the British nonprofit Teach First, on how school education leaders in England are approaching AI. Their central finding is that confidence, not technology, is the real constraint. Schools have the tools. What they’re missing is the shared understanding and training to use them well.
I also spoke with two researchers behind Learning, reinvented: Accelerating human–AI collaboration, Apurva Soumya and Deeksha Khare Patnaik. Between the classroom and the workplace, which this report examines, the same question kept coming up: how do we get the most out of this technology without losing the human judgment that makes us good at what we do?
This topic is one I care about deeply. I started teaching because I wanted the chance to pay back what my French public education gave me. Some of my former students are now colleagues, which is the quiet reward that keeps me at it after all these years.
What I hope they carry forward, more than any particular skill, is a sense that using AI responsibly is a personal and social choice. With the advancement of the internet, my generation opened up access to knowledge. We didn’t always get responsible use right. This is a real chance to do better, and for the next generation of leaders and creators to take these tools further than we ever imagined.
Being graded along the AI curve
The reshuffling that I saw in my classroom, where the rankings shifted depending on how well students used AI, begs the question: What determines who ends up ahead? It also underscores the importance of AI readiness for our future workforce.
The two reports I’ll reference here, AI in schools in England and Learning, reinvented, suggest that access to AI alone doesn’t determine the outcome. What matters just as much is how people engage with the technology. At the institutional level, it’s leadership modeling and confidence-building; at the individual level, it’s how actively and critically someone engages with the tool, rather than deferring to it.
These examinations matter because no matter how you may feel about AI, students and employees are going to use it whether or not they get the proper training. One English school’s own internal survey found that more than 80% of its sixth-formers (this would be 11th and 12th grade in the US) were already using AI, regardless of whether the school had a policy for it. So banning the technology outright doesn’t stop that. It just means the use goes ungoverned—what’s known as shadow AI—where some students will figure out how to use it well and others won’t.
So let’s start where it often begins, with the teachers and leaders whose own comfort with AI ends up shaping everyone else’s.
Confidence in two parts
Babak helped me break down what confidence with AI looks like on the ground, among teachers and school leaders. “One is being unsure about how to use it and also the lack of trust in the outputs,” he told me, as some teachers question if what’s coming out of AI is trustworthy.
In other words, confidence isn’t a single switch. Someone can feel comfortable typing a prompt and still not trust a word of what comes back. Someone else might trust the tool completely, yet not know how to get good results from it. Either gap is enough to stall adoption.
That split shows up clearly in the interviews behind the report. School leaders fell into roughly three groups: eager adopters, who trusted both their own ability and the technology’s output; a cautious middle group, broadly optimistic but aware of the risks, and still working out what to think; and skeptics, who doubted both. Babak described the eager adopters as “confident at trying new things, but also confident that what they were getting back was useful, helpful, efficient.” The skeptics, by contrast, “did not trust the output” and “often admitted that they weren’t sure how to get the best out of it either.”
Sixty-three percent of school leaders cite a lack of staff confidence or skills as the top barrier to AI adoption in their schools, far outweighing the 16% who point to cost. The tools are there. What’s missing is the shared understanding of how to use them.
The report mentions several ways teachers can learn AI skills and support one another that don’t require a large budget or formal curriculum. Some schools have found success naming “AI champions” within departments (teachers who experiment early and share what they learn with colleagues). Others have built small peer groups where staff review AI use together.
On the more formal end, one multi-academy network surveyed its staff to understand where confidence stood, then ran an all-school training day exploring the “good, bad and ugly” of AI.
Co-learning and taking AI “off days”
Business shows a version of the same pattern, from a different angle. Eighty-four percent of executives expect AI-human collaboration to be standard within three years, but only 26% of employees say they’ve actually been trained to do it.
That’s a readiness gap rather than a direct confidence measure, but it’s hard not to see the connection. People rarely feel confident doing something they were never taught to do in the first place.
That’s where co-learning comes in.
Co-learning, as Apurva described it to me, is less like training and more like a working relationship. “The AI teaches the human, the human teaches the AI, and they both learn from each other in the flow of work,” he said. It’s not a course you complete once. It’s a continuous adjustment between a person and the technology they’re using, each one shaping the other over time.
Deeksha broke the idea down into three parts:
The first is co-creation. “The employees are actively shaping the work,” she explained. “They write the prompt, challenge the output.” Rather than accepting whatever the AI produces, the person stays in the loop, questioning and refining it.
The second is holding on to enough independent skill to catch AI when it’s wrong. As Deeksha put it, this means an employee is “being able to explain why a decision was made, defend it and spot where AI went wrong.” That’s a different bar than simply knowing how to use the tool. It requires knowing enough, independent of the tool, to recognize when it’s failed.
The third, and maybe the least intuitive, is deliberate reinforcement: intentionally exercising judgment so it doesn’t quietly atrophy. Some companies build this in directly. Deeksha mentioned organizations that hold regular “AI off days,” where employees complete their usual work without any AI assistance at all. “That’s deliberately taking the brain to the gym,” she told me, borrowing a phrase from one of our colleagues, Accenture Chief Learning and Research Officer Majd Sakr. It’s a small, almost old-fashioned practice, but it’s a sign that a company takes this seriously. “This is where long-term competitive advantage comes from,” she said.
Taken together, these three habits describe something narrower and more demanding than simply “using AI.” They describe staying as an active participant in your own thinking, even while a very capable tool is standing by, ready to think for you.
A signal worth watching
There’s a reason a study of English secondary schools is useful well beyond the classroom. Schools are at the forefront of how the next generation is being most immediately impacted by AI, in terms of how students and teachers are using the technology.
If AI can reshape how one institution operates, it’s a meaningful signal for the rest of us. “Schools provide a bit of a test bed,” Mike told me, “in understanding how small organizations can use the technology more effectively.”
Worth Your Attention
AI-ready graduates don’t emerge by chance, research from Pearson and AWS: This is a global study finding that AI readiness among graduates doesn’t fail for lack of trying, it falls apart at the handoff between what schools teach and what employers need.
The smartest kids in the world, by Amanda Ripley: An American journalist looks at what countries like South Korea, Finland and Poland are doing differently to consistently outperform on global education benchmarks. Babak told me the book offers an interesting way to see just how different education systems are around the world, with different norms and priorities. “It makes me think that the way AI will be handled could differ quite significantly based on things like prevailing educational structures and the sense of urgency about reform,” he said.
Exam nation, by Sammy Wright: Babak also recommends this book from a UK headmaster. He said, “It’s about rethinking the education system in the UK, but it’s also applicable elsewhere because it starts with the fundamental question: What is school for?”
Go deeper on how humans and AI are reshaping the way we work. The Accenture Research Journal lets you query our full body of research and get answers grounded in empirical findings.
Overheard
“Be humble and be curious and become a beginner again.”
— Amy Coleman, Chief People Officer, Microsoft, on building an AI-first, human-centered culture
The insights above are made possible by 350 researchers, editors and AI agents across Accenture Research, as well as by the Accenture business leaders who sponsor and shape our agenda and by my colleagues in marketing and communications who help bring these insights to life.