Getting useful results from AI takes more than technical skill. In a framework proposed by Dr. Jonathan Costa, CTOs also need emotional, social, diverse and data intelligence: capabilities for leading people through change, bringing different perspectives into decisions, and understanding the information AI relies on. This is an author’s leadership framework, not a standardized or scientifically validated taxonomy.
For finance teams and other organizations weighing AI adoption, Costa’s framework shifts attention from the technology alone to the leadership and information practices around it. The four capabilities are complementary: people skills help leaders understand the effects of change, varied perspectives broaden discussion, and data knowledge helps teams assess what their systems are being asked to do.
Costa’s May 30, 2024 article for BetaNews presents these ideas as recommendations for technology leaders, not as proven guarantees of successful AI adoption.
1. Emotional intelligence: lead through the human consequences
Emotional intelligence means recognizing and managing your own emotions and responding thoughtfully to the emotions of others. In AI adoption, that can help a CTO notice how decisions affect teams, handle uncertainty and reconsider roles as the work changes.
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Costa emphasizes self-awareness, self-regulation and empathy. As practical leadership habits, these can help a CTO pause before reacting to concerns, communicate with care and account for the human consequences of introducing new systems. They do not remove disagreement or guarantee that a change will be welcomed.
2. Social intelligence: listen and respond to the team
Social intelligence is the ability to read a situation and judge when to listen, what to say and what action fits. Costa connects it to relationship-building, active listening and reverse mentoring, in which leaders learn from employees with different experience or expertise.
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Those practices can surface questions about job changes, reveal where employees need training and give leaders a clearer view of how AI-related decisions are landing across the organization. Listening is useful only if it informs a response: leaders need to distinguish between a knowledge gap that training can address and a concern that calls for a different decision or clearer explanation.
3. Diverse intelligence: widen the perspectives in the room
Costa uses “diverse intelligence” to describe the range of backgrounds, ages and skills represented on a team. His argument is that a wider range of perspectives can help teams generate ideas and consider ethical risks they might otherwise miss.
He suggests reviewing job requirements with HR, broadening candidate pools where appropriate and diversifying interview panels. These are recommendations for building a team with varied perspectives; the BetaNews article’s discussion does not independently establish that any particular hiring practice will produce better AI outcomes.
4. Data intelligence: understand the information behind AI
Data intelligence means understanding the “who, what, where and when” of an organization’s data: who it concerns, what it contains, where it comes from and when it is collected or used. Costa argues that organizations need a data-first culture and capabilities for collecting, preparing or cleaning, and analyzing data.
This capability matters because AI systems depend on the information organizations provide and manage. A team with varied perspectives cannot, by itself, ensure that an AI system is equitable if the organization does not understand the data it collects, stores and uses. Data knowledge is therefore part of responsible leadership, not just a technical task to delegate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the four capabilities fit together
The framework is most useful as a set of prompts for leadership rather than a scorecard. Each capability points to a different question a CTO can ask when considering or managing AI adoption:
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| Capability | Leadership action | Problem it is intended to address |
|---|---|---|
| Emotional intelligence | Practice self-awareness, self-regulation and empathy. | How AI-related change affects people and team dynamics. |
| Social intelligence | Build relationships, listen actively and learn through reverse mentoring. | Employee concerns, communication and training needs. |
| Diverse intelligence | Review hiring requirements and broaden perspectives in recruitment and interviews. | Ethical blind spots and a limited range of ideas. |
| Data intelligence | Understand data collection, preparation and analysis. | Whether the organization understands the information AI relies on. |
The table summarizes Costa’s proposed actions and intended concerns; it does not imply that the framework has been independently validated or that using these practices guarantees a particular result.
What adoption forecasts do—and do not—show
Gartner’s October 11, 2023 forecast said that more than 80% of enterprises would have used generative AI APIs or models and/or deployed generative-AI-enabled applications in production by 2026, up from less than 5% in 2023. That was a forecast about a defined measure of enterprise use, not a report that the projected level was later achieved. It also says nothing by itself about whether adoption was well managed or valuable.
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