Artificial Intelligence, Quantum Applications

OpenAI’s 5 Insights: Building Trust for AI Adoption in Enterprises

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May 11, 2026 by Ivy Delaney

Executives at Philips, BBVA, Mirakl, Scout24, Jetbrains and Scania are sharing insights into how enterprises are scaling AI, prioritizing trust and adoption over rollout speed. OpenAI presents findings that organizations successfully scaling AI aren’t simply moving faster; they are building deliberate systems grounded in workflow design and governance. Early involvement of security, legal, compliance, and IT teams consistently enabled faster implementation with fewer setbacks. “Scaling AI is less about rolling out AI and more about building the conditions where people trust it, adopt it, and improve it over time,” the research states, demonstrating that durable gains come from hybrid workflows enhancing, rather than replacing, expert human judgment.

The organizations demonstrating success aren’t necessarily the fastest, but those creating environments where individuals feel empowered to experiment with AI safely. This focus on safe experimentation allows for more effective integration and broader adoption of AI tools within the enterprise.

The organizations pulling ahead aren’t simply moving faster; they’re moving more deliberately, prioritizing thoughtful implementation over speed.

scaling AI is less about “rolling out AI” and more about building the conditions where people trust it, adopt it, and improve it over time.

Interviews with executives at Philips, BBVA, Mirakl, Scout24, Jetbrains and Scania

AI scaled when teams could redesign workflows and build with AI, not just use it as a feature. Where security, legal, compliance, and IT were involved early as design partners, teams moved faster later, with fewer reversals and more trust.

Quality before scale The organizations that earned trust defined what “good” meant early, invested in evaluation, and were willing to delay launches when the bar wasn’t met

Organizations are consistently moving beyond individual productivity toward AI embedded in end-to-end workflows, with human oversight in place. Sustained impact requires trust, ownership, and quality built in from the start, ensuring that AI serves as a valuable and reliable component of business processes.

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Avatar of Ivy Delaney

Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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