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Getting more from AI takes more than technical expertise. A framework proposed by Dr. Jonathan Costa, published by BetaNews on May 30, 2024, points to four leadership capabilities: emotional intelligence, social intelligence, diverse intelligence and data intelligence. It is a practical lens for thinking about AI adoption—not a standardized or scientifically validated taxonomy.
Why CTOs need more than technical expertise
AI adoption changes how people work as well as what systems can do. CTOs have to make technical decisions while helping teams respond to new tools, shifting responsibilities and questions about how data is used. Costa’s framework focuses on the human and organizational capabilities that can complement technical implementation.
The business context is moving quickly, but one widely cited adoption figure is a forecast, not a confirmed outcome: in October 2023, Gartner predicted 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 definition is narrower and more precise than simply saying that 80% of enterprises would have “adopted AI.” Gartner’s 2023 forecast does not establish what adoption actually reached in 2026.
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The four types of intelligence in the framework
| Capability | What it means | Leadership action | Problem it is intended to address |
|---|---|---|---|
| Emotional intelligence | Recognizing and managing your own emotions and responding to other people’s emotions. | Build self-awareness, self-regulation and empathy; consider how AI adoption affects people and roles. | Team friction, uncertainty and the human consequences of organizational change. |
| Social intelligence | Reading social situations and judging when to listen, what to say and what to do. | Use active listening, relationship-building and reverse mentoring to learn what employees are concerned about and where they need training. | Unheard concerns about AI and job change, and support needs that leaders might otherwise miss. |
| Diverse intelligence | Drawing on a team with varied backgrounds, ages and skills. | Review job requirements with HR, widen candidate pools where appropriate and diversify interview panels. | Narrow perspectives that can limit problem-solving or leave ethical risks unexamined. |
| Data intelligence | Understanding who, what, where and when a data asset involves, alongside how data is collected, prepared and analyzed. | Promote a data-first culture and build capability in collection, cleaning, preparation and analysis. | Poorly understood or managed data that can undermine AI work, including efforts to produce equitable outcomes. |
The actions and intended benefits in this table summarize Costa’s recommendations; they should not be read as independently proven effects of each capability.
#1 Best Overall
How to put the framework into practice
Lead change with emotional intelligence
Make space to notice how AI-related decisions affect employees, not only whether a system works as designed. Self-awareness and self-regulation can help CTOs respond constructively when concerns or disagreement arise. Empathy helps leaders consider how changing tasks and roles may affect the people doing the work.
Use social intelligence to hear what teams need
Listening should be an ongoing management practice, not a one-time announcement. Active listening and relationship-building can surface questions about job changes and reveal where employees may need training. Reverse mentoring—learning from colleagues with different experience or perspectives—offers another way to hear concerns that may not reach senior leaders through ordinary reporting lines.
Rank #2
Widen perspectives in hiring and review
A team with different backgrounds and skills may bring a broader range of questions to AI design and use. Costa recommends examining hiring requirements with HR, broadening candidate pools where appropriate and using more varied interview panels. These are proposed steps, not a guarantee of fairer systems: representation alone cannot establish that an AI system is equitable.
Build data understanding into AI work
Data intelligence means more than knowing that an organization has data. Leaders need to understand what information is collected and stored, whose information it is, where it comes from and how it is prepared and analyzed. A team can be diverse and still produce poor or inequitable AI outcomes if the organization does not understand the data informing its systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the framework does—and does not—establish
Costa’s four-part model is a useful way to organize leadership questions around AI, but it is an author-proposed framework rather than a recognized scientific classification. The source presents recommendations and arguments; it does not establish that applying them causes better AI performance. The BetaNews article also includes other statistics whose original studies and methods are not identified there, so those figures cannot responsibly be treated as verified evidence in support of the framework.
For a CTO, the practical value is as a checklist of distinct responsibilities: understand people’s responses to change, listen for workforce needs, bring broader perspectives into decisions, and know the data behind AI systems. These capabilities complement technical work; they do not replace it.
Source: Dr. Jonathan Costa’s original BetaNews article, published May 30, 2024.
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