Across industries, the organizations that learn fastest, adapt best, and create the most value share a common engine: creativity. In the scholarly literature, creativity is not a vague flourish—it has a precise meaning. Creative work must be original and useful. Among these two pillars, originality (novelty) is the non-negotiable requirement: without novelty, an idea is merely competent replication, not creativity. Utility matters because it determines which original ideas take root in a particular context, but originality is the gate through which every creative contribution must pass (Runco & Jaeger, 2012; Stein, 1953). In an era shaped by AI and rapid tool turnover, this originality-first view offers a rigorous way to evaluate talent, forecast who will embrace and exploit new technologies, and predict downstream performance.

This white paper develops four assertions. First, it clarifies creativity’s definition and emphasizes originality as the essential pillar. Second, it synthesizes evidence that creativity predicts interest in—and effective use of—novel tools, including AI, primarily through well-studied pathways of personal innovativeness and openness that drive technology adoption. Third, it reviews links between creativity and other organizationally valuable attributes—tolerance for ambiguity, optimism/psychological capital, self-direction, and resilience. Fourth, it integrates research connecting creativity to performance in education and in business—at the level of individuals, teams, and firms. Throughout, the tone is pragmatic: originality is measured, cultivated, and translated into value through process and context, not mystique.

Assertion 1: Creativity has two outputs—originality and utility—with originality as the primary and essential pillar

The definitional backbone of creativity has changed little since the mid-20th century. Stein (1953) formulated the classic statement: “The creative work is a novel work that is accepted as tenable or useful or satisfying by a group in some point in time” (p. 311). Contemporary summaries converge on the same core: creativity requires novelty (originality) and appropriateness (usefulness) (Amabile, 1988; Amabile & Pratt, 2016; Plucker, Beghetto, & Dow, 2004; Runco & Jaeger, 2012). Crucially, these dimensions are not symmetrical. Originality is necessary: if a product or idea is not novel relative to its context, it cannot be creative, regardless of how efficient or well-executed it might be (Runco & Jaeger, 2012). Utility is then the contextual qualifier that determines which original contributions count as creative in practice—what Stein called acceptance as “tenable or useful.”

Organizational theories embed this primacy of originality. In the componential model, creativity in organizations is the production of ideas that are both novel and useful, with original ideation as the initial gate and contextual factors (expertise, motivation, and work environment) determining whether those ideas are refined and implemented (Amabile, 1988; Amabile & Pratt, 2016). Educational and psychological frameworks reiterate the same dual criterion (Plucker et al., 2004), and operational measures—from divergent thinking tasks to consensual assessment of products—explicitly score originality and then evaluate usefulness/appropriateness.

Emphasizing originality matters for practice. Assessments that fail to isolate originality cannot credibly claim to measure creativity as the field defines it. Conversely, once originality is established, judging utility becomes a matter of fit to goals, users, or constraints. In short: no originality, no creativity; originality plus utility yields creative value (Runco & Jaeger, 2012; Stein, 1953).

Assertion 2: Creativity predicts interest in—and ability to use—tools, including AI

Direct longitudinal studies tying creativity scores to AI proficiency are only beginning to appear, but there is a robust, convergent body of research that explains why creative people are more likely to embrace and exploit new tools. The core mechanism runs through personal innovativeness and openness to experience—dispositions consistently linked to technology adoption.

In information systems, Personal Innovativeness in IT (PIIT) is defined as an individual’s willingness to try out new information technologies. PIIT reliably predicts intentions to adopt and actual usage across contexts, and it moderates the Technology Acceptance Model (TAM) pathways from perceived usefulness and perceived ease of use to behavioral intention (Agarwal & Prasad, 1998; Venkatesh & Davis, 2000). Large-scale reviews and meta-analyses of TAM/UTAUT show that these belief structures explain substantial variance in adoption and use across hundreds of studies, and that personality dispositions—notably openness—feed into perceived usefulness/ease of use (King & He, 2006; Schepers & Wetzels, 2007; Venkatesh, Morris, Davis, & Davis, 2003; Venkatesh, Thong, & Xu, 2012). Independent of TAM, technology readiness research likewise finds that traits capturing optimism toward and innovativeness with technology predict adoption (Parasuraman, 2000; Parasuraman & Colby, 2015).

Where does creativity fit? Personality research shows that creative achievement and potential are most strongly associated with openness to experience—the trait that underwrites curiosity, cognitive exploration, and tolerance for novelty (Feist, 1998). Field studies in MIS connect the Big Five to technology usage, with openness often emerging as a positive predictor of adoption and use intentions (Devaraj, Easley, & Crant, 2008; McElroy, Hendrickson, Townsend, & DeMarie, 2007). Put simply, people who generate original ideas tend to be the same people who seek out, tinker with, and integrate unfamiliar tools.

Beyond dispositional pathways, studies have begun to connect creativity to adoption of novel data and digital platforms. For example, creative users have been shown to be more apt to reuse and adopt open government data platforms, a domain that—like AI—rewards exploratory engagement with unfamiliar, information-rich tools (Alexopoulos, Saxena, Rizun, Matheus, & Janssen, 2024). In organizational settings, educational and workplace research repeatedly finds that individuals high in creative thinking are earlier adopters and more versatile users of digital tools when usefulness and ease of use are acceptable (Devaraj et al., 2008; Venkatesh et al., 2003, 2012).

AI adds two wrinkles. First, AI can amplify productivity and output quality when paired with human ideation and judgment; second, timing and process matter for originality. Human–computer interaction and organizational creativity literatures caution that workflows that overvalue AI outputs in human-AI collaboration can increase fixation—converging too quickly on familiar patterns—which can dampen originality (Shalley, Zhou, & Oldham, 2004; Baer, 2012). Translating that to best practices, creative individuals are likely to adopt AI rapidly and extract more value from it, provided workflows encourage human ideation in partnership with AI augmentation. That process design keeps originality intact while leveraging AI for search, synthesis, or refinement.

Assertion 3: Creative individuals are more likely to demonstrate tolerance for ambiguity, optimism/psychological capital, self-direction, and resilience

Creative work unfolds in uncertainty. It is therefore unsurprising that creative individuals tend to score higher on tolerance for ambiguity (Zenasni, Besançon, & Lubart, 2008). Reviews across psychology and management agree on a positive, if variable, association between ambiguity tolerance and creative thinking, while also noting that measurement quality matters (Furnham & Marks, 2013; McLain, 2015). In practice, this means creative employees are less rattled by poorly specified problems or shifting constraints—exactly the conditions that characterize AI adoption and digital transformation.

A second, complementary strand concerns optimism and psychological capital (PsyCap). PsyCap comprises hope, efficacy, resilience, and optimism; a comprehensive meta-analysis links PsyCap to better attitudes, behaviors, and performance at work (Avey, Reichard, Luthans, & Mhatre, 2011). Multiple field studies show that positive states and agentic beliefs facilitate idea generation and creative persistence (Amabile & Pratt, 2016; Rego, Sousa, Marques, & e Cunha, 2012). This is not naïve cheerfulness; it is a resource that sustains experimentation through setbacks and criticism—core realities of innovation.

Values research adds self-direction to the profile. In Schwartz’s refined theory of basic values, Self-Direction (thought/action) sits within the openness-to-change cluster that promotes exploration and independent judgment (Schwartz et al., 2012). Cross-cultural studies associate these values with everyday creativity across domains (Lebedeva, Schwartz, Van de Vijver, Plucker, & Bushina, 2019). Self-directed employees do not wait for permission to test the new tool, run the pilot, or build the first prototype. They initiate.

Finally, resilience—both as a PsyCap component and as a standalone construct—shows positive links with creative performance through mechanisms such as reappraisal and efficacy (Avey et al., 2011; Amabile & Pratt, 2016). In large change programs, resilient creative contributors stay engaged when goals, data, or tools shift mid-stream.

A balanced view includes edges. Meta-analytic personality profiles indicate that highly creative people are, on average, more open and independent, and sometimes less agreeable—traits that can produce friction in bureaucratic or highly standardized environments (Feist, 1998). The managerial task is not to “smooth out” creativity but to design context—clear goals, psychological safety, and progress cues—so that ambiguity tolerance, optimism, self-direction, and resilience translate into executed ideas rather than conflict (Shalley et al., 2004).

Assertion 4: Creativity predicts performance across business contexts

The education literature provides a clear signal: creativity predicts achievement. A meta-analysis of 120 studies (N ≈ 52,000) reported a reliable, positive association between creativity and academic achievement across decades, subjects, and measures (Gajda, Karwowski, & Beghetto, 2017). Classic longitudinal work on the Torrance Tests of Creative Thinking found that early creative potential forecasts later accomplishments, complementing (and in some analyses exceeding) the predictive utility of IQ for real-world creative achievement (Cramond, Matthews-Morgan, Bandalos, & Zuo, 2005; see also reviews summarized in Plucker et al., 2004). These findings matter for business because they identify creativity as a foundational capability in the talent pipeline.

At work, meta-analytic evidence shows that creative/innovative performance relates positively to task performance and citizenship behaviors, and negatively to counterproductive behaviors (Harari, Reaves, & Viswesvaran, 2016). In other words, people who generate and develop original, useful ideas tend to be the same people managers rate as strong performers and reliable colleagues. Classic organizational studies found that personal and contextual factors—such as learning orientation, challenging work, and supportive leadership—elevate employee creativity and, in turn, performance (Oldham & Cummings, 1996; Gong, Huang, & Farh, 2009; Shalley et al., 2004). Importantly, creativity can be trained: quantitative reviews report that well-designed creativity training programs produce meaningful gains in creative problem solving, especially when they teach process skills and involve real problems (Scott, Leritz, & Mumford, 2004).

Creativity is the seed; innovation is the harvest. A team-level meta-analysis covering three decades identified climates and processes that translate idea generation into implemented innovation—the step that yields measurable business value (Hülsheger, Anderson, & Salgado, 2009). Complementary studies emphasize that implementation behaviors (championing, coalition building, experimentation) are distinct from idea generation and must be intentionally supported (Baer, 2012). The managerial levers are well known: set challenging goals, grant autonomy, ensure resources, and model openness to novel suggestions (Amabile & Pratt, 2016; Shalley et al., 2004). When these conditions are present, team creativity is more likely to become shipped features, better customer experiences, and improved processes.

Marketing and innovation research links creativity to new-product success and sales growth, including in high-technology firms (Im & Workman, 2004). At the firm level, large industry studies connect creative capability with financial outperformance and market leadership. IBM’s Global CEO Study concluded that creativity is the top leadership competency for navigating complexity (IBM Institute for Business Value, 2010). McKinsey’s multicountry analysis reported that companies with higher creativity scores were more likely to post above-average revenue growth, total return to shareholders, and innovation metrics (McKinsey & Company, 2017). Forrester’s “Creative Dividend” study found that firms that foster creativity were more likely to achieve double-digit revenue growth and to lead their markets (Forrester Consulting, 2014). While these industry reports are not randomized experiments, they triangulate with the academic evidence: where originality is generated and utility is realized, business value follows.

Conclusion

Originality is the front door to creativity. People who generate original ideas are more inclined—and better prepared—to explore new tools, including AI, because the same dispositions that underwrite creativity (openness, innovativeness, agency) also drive technology adoption. They more often bring the adaptive attributes organizations need during change—tolerance for ambiguity, optimism/psychological capital, self-direction, and resilience. And when environments are designed to translate ideas into implementation, creativity predicts performance: higher individual ratings, stronger team innovation, better new-product outcomes, and superior firm-level results. If you are hiring, upskilling, or leading AI transformation, start by measuring and enabling originality, then build the context that converts originality into utility—and utility into business value.

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