Ideation-Execution Gap

• The ideation-execution gap is the challenge of converting promising and novel ideas into executable, validated outcomes amid cognitive and process constraints.
• Empirical studies reveal that while AI-generated ideas may score high on initial novelty, human-conceived ideas consistently outperform them in effectiveness and execution.
• Bridging the gap requires structured systems, adaptive AI assistance, and rigorous evaluation frameworks to transform ideation into successful implementation.

The ideation-execution gap refers to the challenge of transforming creative insights or promising concepts (ideation) into realized, operational, or evaluated outcomes (execution). This gap is recognized across multiple domains, from artistic creation and engineering design to research ideation and automated scientific discovery. Fundamentally, it arises from the differences between generating potentially valuable ideas—often assessed as exciting, novel, or promising at first glance—and the practical difficulties or performance drops that occur during implementation, evaluation, or deployment.

1. Theoretical Perspectives on the Ideation-Execution Gap

The cognitive underpinnings of the ideation-execution gap are sharply delineated in the creativity literature, notably in the contrast between traditional search-select theories and honing theory:

• Search-Select Models: These frameworks posit that creativity involves generating multiple, distinct candidate ideas (akin to “pre-inventive structures” in the Geneplore model or variants in Darwinian/BVSR accounts). Execution in this context is the process of evaluating and selecting the most promising candidate from a well-defined option set. The ideation-execution gap is conceptualized as the selection bottleneck or as losses due to incomplete evaluation of generated ideas.
• Honing Theory: In contrast, honing theory asserts that creativity often begins with a single, ill-defined “seed” idea existing in a superposition state—a potentiality analogous to a quantum system. Here, ideation is a process of iterative refinement triggered by internal or external context shifts, rather than the distinct evaluation and selection of fully formed alternatives. The transition from ideation to execution is cast not as a “choice among” but as the “gradual actualization” of an ambiguous, high-potential mental representation (Carbert et al., 2014, &&&1&&&, Scotney et al., 2019).

The formal representation of idea potentiality in this view employs state vectors:

$$|\psi\rangle = c_1|idea_1\rangle + c_2|idea_2\rangle + \dots + c_n|idea_n\rangle$$

with contextual “collapse” yielding an executable form.

2. Empirical Evidence and Manifestations Across Domains

Recent experimental and applied studies indicate that the ideation-execution gap is pervasive and multifaceted:

• Art and Analogy-Making: Experimental data show that mid-process creative states are better characterized by descriptions of ambiguity, “jumbled” details and emergent properties than by clear alternate choices. Responses by artists and analogy solvers are significantly more consistent with honing theory than search-select accounts; e.g., chi-square and t-tests illustrate statistical dominance of the potentiality narrative (Carbert et al., 2014, Scotney et al., 2019, Gabora, 2015).
• Research and Scientific Innovation: Controlled studies in research ideation confirm that LLM-generated ideas, although judged initially as more novel or exciting, undergo steeper declines in quality scores (novelty, excitement, effectiveness, overall) after execution, relative to human-generated ideas. Aggregated review tables reveal a “flip” in rankings post-implementation, with human-conceived projects ultimately outperforming AI-originated ones after execution (Si et al., 25 Jun 2025). Specific differences measured:

| Metric | Δ Human–AI Gap | Significance (FDR-adjusted p) | |------------------|---------------|-------------------------------| | Novelty | 1.039 | .025* | | Excitement | 1.835 | <.05* | | Effectiveness | 1.827 | <.05* | | Overall Rating | 1.348 | <.01* |

• Engineering and Startups: In technology innovation, the gap manifests when strategic intentions (such as rigorously validating product-market fit) are not faithfully realized in practice. Behavioral frameworks model this as the squared sum of discrepancies across multiple dimensions (product, team, market, business): $G = \sum (S_i - A_i)^2$, with higher G indicating larger misalignments (Giardino et al., 2017).

3. Causes and Cognitive Mechanisms

Several mechanisms underlie the ideation-execution gap:

• Potentiality and Emergence: Early-stage ideas are amorphous, containing both relevant and irrelevant features. Execution requires disambiguation and “collapse” of cognitive superpositions into actionable representations (Scotney et al., 2019).
• Loss of Context and Implementation Details: As shown in LLM-assisted research and AI Scientist frameworks, the initial “promise” of an idea—often based on surface novelty—fails during planning, implementation, or empirical testing, due to missing experimental rigor, poor reproducibility, and lack of resource or context awareness (Si et al., 25 Jun 2025, Zhu et al., 2 Jun 2025).
• Human Cognitive Limitations: The ever-expanding concept space increases the “burden of knowledge,” making it harder to identify and execute highly original ideas amidst a vast repository of prior art (Sarica et al., 2023).
• Premature Convergence and Over-Reliance on Automation: In both LLM-assisted ideation and iterative creative processes, introduction of AI tools too early can reduce autonomy, creative self-efficacy, and originality, as measured by higher overlap with AI outputs and lower ownership of ideas (Qin et al., 10 Feb 2025).

4. Quantitative and Computational Models

Quantitative frameworks and computational tools have been developed for systematically characterizing, diagnosing, and mitigating the gap:

• High-Dimensional Embedding Analysis: Ideas are mapped to high-dimensional vectors (e.g., using TE3 embeddings) and analyzed via UMAP, DBSCAN, PCA. Objective measures such as cluster sparsity, idea sparsity, and dispersion of PCA eigenvalues quantify the diversity and uniformity of ideation sessions. For example:

$$\text{Cosine similarity} = \frac{\mathbf{A}\cdot\mathbf{B}}{\|\mathbf{A}\|\|\mathbf{B}\|}$$

and

$$\text{Cluster Sparsity} = 1 - \frac{\sum_{i=1}^{N_c}A_i}{A_t}$$

where $A_i$ is the area of cluster $i$ and $A_t$ the total area (Sankar et al., 11 Sep 2024).

• Behavioral and Process Models: Discrepancy metrics such as $G = \sum (S_i - A_i)^2$ assess misalignment between intended and actual execution in startups (Giardino et al., 2017).
• LLM-Aided Frameworks: Multi-agent research ideation systems (e.g., IRIS) employ Monte Carlo Tree Search (MCTS) to systematically explore research hypotheses, with UCT-based selection balancing exploration and exploitation. Reward feedback is returned both automatically and via human-in-the-loop corrections, enabling iterative refinement of ideas before implementation (Garikaparthi et al., 23 Apr 2025).
• Latent Space Exploration: Model-agnostic methods navigate embedding manifolds using interpolation and perturbation (e.g., $e_{\text{new}} = \lambda e_i + (1-\lambda) e_j$ or $e_{\text{new}} = e_i + \epsilon$), providing a basis for scalable, controlled divergence in ideation (Bystroński et al., 18 Jul 2025).

5. Strategies and Systems Bridging the Gap

Recent work has shifted from merely recognizing the ideation-execution gap to actively scaffolding the transition:

• Structured Interactive Systems: Tools such as IdeaSynth, IRIS, and FlexMind externalize the ideation process into visual structures (e.g., node-based canvases, idea trees) and enable iterative development and branching. These systems provide feedback grounded in literature, allow direct manipulation of idea facets, and support trade-off analysis and mitigation strategies (Pu et al., 5 Oct 2024, Garikaparthi et al., 23 Apr 2025, Yang et al., 25 Sep 2025).
• Adaptive AI Assistance: Empirical studies show that delaying the introduction of LLM-generated ideas (allowing for an autonomous “incubation” phase) preserves originality, autonomy, and creative self-efficacy while mitigating idea fixation and loss of ownership. Mediation analyses substantiate that autonomy $\to$ ownership $\to$ creative self-efficacy $\to$ higher idea quantity and quality (Qin et al., 10 Feb 2025).
• Automated Evaluation and Visualization: Integration of quantitative diversity metrics and visual clustering supports rapid, unbiased identification of promising ideas, especially for novice designers, thereby improving the transition from ideation to implementation (Sankar et al., 11 Sep 2024).
• Scalable Automation in Practice: Platforms such as IDEIA demonstrate that real-time integration of trend analysis with generative AI significantly reduces time and cognitive burden in domains such as journalism, with empirical productivity gains of up to 70% in the ideation stage. Modular architectures, robust API integrations, and CI/CD practices underpin these systems (Santos et al., 8 Jun 2025).

6. Remaining Limitations and Future Directions

Despite these advances, significant challenges persist:

• Execution Bottlenecks in Automated Science: Even advanced AI Scientist platforms achieve low rates of successful verification (e.g., ~1.8% code execution accuracy in PaperBench), with execution, debugging, and evaluation cited as core limitations. The time, resource, and cognitive demands of the implementation phase far exceed that of the ideation phase (e.g., $46,900$ seconds per full cycle vs a few hundred seconds for pure reasoning) (Zhu et al., 2 Jun 2025).
• Shortcomings of LLM-Generated Proposals: While LLMs routinely produce ideas with high subjective novelty, empirical studies reveal consistently larger performance drops post-execution, especially in metrics requiring experimental design rigor and empirical substantiation (Si et al., 25 Jun 2025).
• Cognitive and Strategic Fragilities: Long-horizon reasoning, agent coordination, and dynamic memory remain active bottlenecks which current LLM-based or agentic systems do not overcome without human-in-the-loop planning, reinforcement learning acceleration, or modular protocol developments (Zhu et al., 2 Jun 2025).
• Evaluation and Synthesis Deficits: LLMs are under-utilized in stages such as scope specification, multi-idea selection, and rigorous evaluation, despite excelling at divergent generation and refinement. The Hourglass Ideation Framework provides a taxonomy highlighting this imbalance (Li et al., 2 Mar 2025).

Ongoing directions include tighter human-AI integration for execution feedback, more granular evaluation protocols incorporating automatic execution and reward models, expansion of quantitative metrics for idea selection, and development of frameworks generalizable to synchronous, multimodal, and group ideation.

7. Broader Significance

The ideation-execution gap is not merely a theoretical artifact but a quantitatively observed, systematically characterized limitation in both human and AI-driven creative processes. Its roots span cognitive theories (e.g., honing theory’s potentiality states), operational failures (e.g., misaligned startup behaviors), and empirical drop-offs in LLM proposal implementation. Addressing this gap requires an integrated response: combining theory-driven frameworks, robust computational models, adaptive process scaffolding, and iterative feedback that traverses the continuum from abstract ideation to practical, validated output. The scalability, objectivity, and user-adaptiveness of new AI-augmented paradigms are poised to reduce—but not yet eliminate—the ideation-execution gap in complex, creative, and scientific domains.