Thoughtworks Technology Podcast · Thoughtworks

What does an AI strategy with humans at the center look like?

·25 min·2 clips

Tiankai Feng proposes a 'return on ethics' metric beside ROI.

Prem Chandrasekaran opens by introducing Tiankai Feng and framing the conversation around Tiankai's latest book on humanizing AI strategy. Tiankai explains that he works on data and AI strategy and governance on the global side. He ties his writing to a fairly simple view of the field. Most of the problems people blame on technology are rooted in human factors. Bad decisions, weak collaboration, and lack of knowledge keep showing up beneath the technical surface. That framing sets the tone for the rest of the discussion. The point is not to celebrate AI as an isolated capability. The point is to ask what kind of organization can actually use it well. The discussion then turns to creativity and the question of what AI is allowed to learn from. Tiankai argues that consent matters when data and creative work are used for training, even if there is plenty of gray area. He also draws a line between human creativity and machine-generated output. Human beings, in his view, can produce genuinely original ideas. AI tends to work by recombining what it has already seen. That makes its creativity incremental rather than disruptive. The conversation takes that distinction seriously. Tiankai suggests that human creativity is not less important in an AI world. It is more important. He argues that people need to stay more human, not less, if they want AI systems to remain genuinely useful. The episode then shifts from creativity to communication. Prem asks about transparency and the effort required to make AI work visible to non-technical stakeholders. Tiankai treats transparency as a balancing act. Too little visibility creates confusion and risk. Too much documentation creates overhead and slows the work down. He notes that transparency often matters most when something goes wrong. That is when people suddenly want to know how the system made its decisions. The conversation lands on the practical tension between efficiency and accountability. The show does not reduce the issue to a simple rule. It keeps asking what level of explanation is enough for the organization involved. Near the end, the discussion moves toward the present challenge of going agentic. Tiankai says he is working internally with colleagues on frameworks that identify specific blockers and help solve them for clients. That closing note keeps the episode close to the show's usual operating style. It treats AI as a business and organizational problem as much as a technical one.

As heard by us

A grounded look at human-centered AI strategy, from governance and consent to the tradeoffs of transparency.

Prem Chandrasekaran and Tiankai Feng keep the focus on human-centered AI strategy, returning to governance, creativity, consent, transparency, and the human factors that decide whether AI holds up in practice.

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If you want a human-centered AI strategy conversation that stays practical, start here.

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