Babak Hodjat’s view is that AI should earn the right to act on its own: ethical rules need to be built into systems and monitored after launch, while delegation should depend on whether a system can be trusted in unfamiliar situations. Two interviews published in 2025 and 2026, alongside a 2025 Cognizant Investor Day demonstration, show how that principle connects to his work on evolutionary AI, land-use decisions and enterprise agents.
What Hodjat says AI should be trusted to do
In a February 26, 2026 interview with The Meridian Dialogue, Hodjat defines intelligence as the ability to make decisions that support survival in contexts not yet known. For AI, the important test is therefore not only whether a system performs a familiar task, but how it behaves when it encounters circumstances its designers did not anticipate.
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He argues that ethical and moral considerations should be made explicit throughout system development and deployment, with monitoring and safeguards continuing after launch. “Ethical and moral considerations should be codified unambiguously,” he says. That places responsibility on the people and organizations building and operating a system, rather than treating ethics as a feature that can be assumed from capable technology.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteHodjat does not argue that a person must make every decision. “Preserving human agency in all decisions is not desirable,” he says, pointing out that people already delegate decisions to non-AI technologies. His position is that the appropriate boundary depends on context, culture and trust—not that all decisions should be automated.
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He describes trustworthiness as autonomous action in unforeseen situations that produces acceptable outcomes. That is his proposed standard, not an independently established consensus or a guarantee that any particular system meets it.
How evolutionary AI differs from generative AI
In a separate interview published October 17, 2025 by Unite.AI, Hodjat discusses evolutionary algorithms in the context of a land-use decision-support project. The distinction that matters in this example is the kind of problem being addressed: generative AI is associated with producing content, while evolutionary search explores candidate solutions against objectives that may conflict.
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For land-use choices, a recommendation cannot be judged by a single target. A policy may affect carbon storage, food supply, habitat preservation and economic disruption at once. The interview describes evolutionary methods as a way to explore combinations of choices and trade-offs; it does not provide a quantified comparison of accuracy against generative AI or other methods.
The land-use project: scenario exploration, not proven policy impact
Hodjat describes work with the University of Texas at Austin using Cognizant Neuro AI Decisioning technology and the Project Resilience platform. As presented in the interview, the aim is to help decision-makers examine land-use trade-offs rather than predict one outcome in isolation.
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- Competing objectives: The system considers carbon storage alongside economic disruption, food supply and habitat preservation.
- Regional context: The interview says recommendations can reflect historical land-use and carbon data, including differences in land type and latitude.
- Scenario exploration: Hodjat says the framework explores thousands of scenarios. This describes the project’s design; it is not a measured impact or accuracy result.
- Climate-policy exploration: The project is described as connecting to Climate Interactive’s En-ROADS simulator to examine climate-policy incentives.
The interview presents the project as decision support for exploring possible policy choices. It does not establish that recommendations were adopted, that environmental or economic outcomes improved, or that the system outperformed another approach in a measured evaluation.
What Cognizant’s Chief AI Officer works on
Cognizant identifies Hodjat as its Chief AI Officer and says he leads Cognizant AI Lab. The lab’s remit includes advancing AI research, developing capabilities for Cognizant offerings and leading AI-for-good initiatives. That spans research and applied work; the title does not by itself mean he personally manages every AI project or deployment across the company.
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His earlier work offers context for his interest in agents. Hodjat’s career includes Dejima, Sentient Technologies and Sybase iAnywhere. Unite.AI’s profile says Dejima’s agent-oriented technology for intelligent interfaces was behind Apple’s Siri. In Cognizant’s profile, the company reports that Hodjat has more than 50 published papers and 39 issued U.S. patents, and holds a Ph.D. in Artificial Intelligence from Kyushu University. Those career and publication figures are Cognizant’s biographical claims.
What an enterprise-agent demonstration does—and does not—show
At Cognizant’s March 25, 2025 Investor Day, Hodjat described a prospective agent for scoping eligibility for Small Business Administration loans. The process identified a possible use case, data requirements and objectives. The example illustrates an early design and scoping workflow: defining a task and what information an agent might need before implementation.
Hodjat explicitly said the example was not implemented at KeyBank. He also said its online research and return-on-investment estimates relied on public information and guesswork. It should therefore be read as a demonstration of how a potential agent might be scoped, not as evidence of a customer deployment, realized savings or validated financial returns. At the event he used the title “CTO AI for Cognizant”; Cognizant’s current profile identifies him as Chief AI Officer.
How to read the two interviews
The conversations are distinct, not parts of one interview called “A Return Conversation.” Unite.AI published its interview on October 17, 2025, focusing on evolutionary AI and the land-use project. The Meridian Dialogue published its interview on February 26, 2026, addressing intelligence, ethics, trust and delegation. Read together, they show how Hodjat connects technical approaches to decisions people may eventually delegate, while keeping his views and project descriptions attributed to him rather than treating them as independent proof of outcomes.
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