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AI can process information and generate outputs at speed, but people still have to decide what matters, whose interests count, and what to do with the result. In her 29 September 2026 opinion article for The AI Journal, Kristine Genovese argues that more capable technology makes human judgment—not less of it—central to decisions about purpose, values, and consequences.
What Genovese means by Soul Intelligence®
Genovese, identified as CEO and Creator of the Soul Intelligence Method, uses “Soul Intelligence®” or “SQ” for inner awareness and discernment: noticing intuition, considering what feels aligned, connecting choices with purpose, and looking beyond external data. It is her conceptual framework, not an established IQ- or EQ-like measurement. The article offers no validated scale or independent evidence that SQ is a recognized psychological construct.
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Her central distinction is between processing information and interpreting its meaning. AI may help analyze patterns or produce options; people must still make judgments in context and take responsibility for the ends those systems serve. This is Genovese’s argument, not a demonstrated rule about what every AI system can or cannot do.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchHow that distinction changes a decision
Genovese’s leadership example makes the division of work concrete. AI might help examine market conditions, projections, customer behavior, trends, and outcomes. A leader still needs to ask whether a proposed decision reflects the organization’s values, who could be affected, what blind spots remain, and whether a promising data pattern points to an action worth taking.
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The useful question is not simply whether a system can produce an answer. It is who interprets that answer, who is affected by acting on it, and who remains accountable for the choice. Data can inform a decision without settling its purpose or consequences.
Healthcare and education are illustrations, not proof
Healthcare
Genovese suggests that AI may assist clinicians with analysis, pattern recognition, and diagnostic support, while patients still need to be heard and treated with compassion. These are illustrative possibilities in an opinion article, not findings from a clinical evaluation or evidence that AI improves health outcomes.
Education
She similarly points to AI’s possible use in personalized learning and immediate feedback, alongside teachers’ roles in encouraging curiosity, recognizing potential, and building confidence. The article does not evaluate educational results; the example illustrates the author’s view that technical assistance and human relationships can serve different purposes.
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Use intuition as an input, not a verdict
Genovese does not recommend accepting every internal signal as truth: “This doesn’t mean treating every intuitive feeling as fact.” Instead, she encourages curiosity about intuition and considering it alongside evidence, experience, expertise, and reason.
- Notice the signal. Identify what feels concerning, promising, or misaligned without treating that feeling as proof.
- Check it against evidence. Look for relevant facts and test assumptions, including assumptions behind an AI-generated recommendation.
- Bring in experience and expertise. Ask what informed people with relevant context see, and what they may know that a dataset does not capture.
- Make and own the decision. Weigh the inputs, explain the rationale, and remain accountable for the choice and its effects.
What leaders should examine when deploying AI
Genovese contrasts using AI mainly to cut costs and improve efficiency with using automation to create room for innovation, relationships, meaningful problem-solving, and service. Her article supplies no measured comparison showing that either approach produces better outcomes. Organizations can make the choice more concrete by examining four questions:
- Purpose: Is the deployment aimed at reducing costs or increasing throughput, freeing time for human work, or both?
- People affected: How might employees, customers, patients, or students experience the change?
- Human responsibility: Who interprets system outputs and owns consequential decisions?
- Evidence: What observed outcomes support claims that the deployment is beneficial, and for whom?
Efficiency alone does not establish that people have gained meaningful work or better service. Those are outcomes to assess rather than assume. The distinction matters because any time or capacity an organization hopes to free has to be directed somewhere deliberately.
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Why the argument is about choices, not a forecast
The article asks, “As technology becomes increasingly intelligent, what does it mean for us to become more deeply human?” Its answer is a call to give human judgment, relationships, and purpose a deliberate place alongside AI—not a measured prediction about future jobs or proof that a particular human ability will become more valuable. A 2023 scholarly review, “Rise of artificial general intelligence: risks and opportunities,” discusses AI capabilities, potential risks, and unresolved questions, but does not validate Soul Intelligence® or establish those labor-market claims.
Genovese captures her position this way: “AI can help us move faster. Soul Intelligence® can remind us to ask where we’re going.” The practical implication is to judge AI not only by what it can process, but also by the human purposes, responsibilities, and observed outcomes built around its use.
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