Artificial Intelligence
David Cross Explains AI-Native Thinking On The CAIO Connect Podcast With Sanjay Puri
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On the CAIO Connect Podcast, David Cross discusses with Sanjay Puri AI-native, rethinking workflows, and focusing on real impact over experiments.
David Cross explains that many companies make the mistake of “bolting on” AI to existing systems. This is similar to how some companies moved to the cloud without changing how they worked. Instead, businesses should aim to become AI-native. This means building systems and workflows with AI at the core, not as an afterthought. The focus should be on making tools better for users, not just adding AI features.
According to David Cross on the CAIO Connect Podcast, AI opens the door to completely new ways of working. For example, developers used to handle many small, separate tasks. Now, AI can bring those tasks together, automate them, and make the process faster. This idea applies to all knowledge workers, not just developers.
The discussion between Sanjay Puri and David Cross highlights an important point: experimentation is good, but it is not enough. Many companies run small AI pilots. These are like “snacks.” They may be useful, but they do not solve big problems. Real success comes when AI is used to solve meaningful business challenges. AI success should not be measured by small productivity gains. Instead, companies should look at the whole system. For example, AI might generate code, while humans review it. Together, this creates better results than either working alone.
Culture plays a big role in AI adoption. David Cross says teams should feel supported, not controlled. Security teams, for example, should act like helpers. Their goal is to make developers successful by adding guardrails, not by blocking progress.
Even with powerful AI, humans still matter. Some decisions, like large financial transactions, need human review. AI should act as a helper, not a replacement. This balance reduces risk and builds trust.
One of the biggest challenges in AI is not the technology—it is the data.
David Cross points out that companies must organize and label their data properly. Without this, AI systems can create confusion or even share information by mistake. AI agents are becoming more common. These are like digital workers that act on behalf of humans. Companies must treat them like real users. This means managing their access, tracking their actions, and keeping them secure.
Looking ahead, the conversation on the CAIO Connect Podcast suggests that success will depend on three things:
* Becoming AI-native
* Embedding AI into workflows
* Managing how systems work together (orchestration)
The conversation between Sanjay Puri and David Cross makes one thing clear: AI is more than just a new technology. It is a chance to change how we think and work. Companies that embrace this mindset will move ahead. Those that only experiment may fall behind.
Upasana Das
Knowledge Networks
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EIN Presswire provides this news content "as is" without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
Frequently Asked Questions
What is the core message about AI for businesses?
The core message is to become AI-native, meaning AI should be at the core of systems and workflows, not just an add-on. The focus should be on making tools better for users and rethinking how work gets done.
Why is experimentation with AI alone not sufficient for success?
Experimentation is good, but small AI pilots are like 'snacks' that don't solve big problems. Real success comes from using AI to tackle meaningful business challenges and looking at the entire system for better results, not just small productivity gains.
What are the key challenges and future factors for successful AI adoption?
A significant challenge is organizing and labeling data properly. Future success will depend on becoming AI-native, embedding AI into workflows, and effectively managing how different systems work together (orchestration).
First published on Tue, Apr 7, 2026
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