New Study Reveals 6 AI Usage Modes, Misses Judgment Value

Drexel University researcher Tim Gorichanaz published a peer-reviewed study, presented at the 2025 ASIS&T Annual Meeting, identifying six distinct modes of AI usage based on an analysis of 205 real-world ChatGPT applications. This research highlights a critical disconnect between current AI deployment strategies and the technology’s full potential, particularly within professional domains like SEO and GEO. Senior practitioners are increasingly integrating AI into their daily workflows for tasks such as drafting content and summarizing data, yielding tangible productivity gains. However, this prevalent approach largely confines AI to the execution layer, missing the deeper value proposition available at the judgment layer. Understanding this distinction is paramount for professionals seeking to maximize their substantial investments in AI tools and licenses right now.

Key Developments

• A recent study by Tim Gorichanaz at Drexel University analyzed 205 real-world ChatGPT use cases, identifying six distinct modes of AI interaction.
• The research suggests that many professionals, particularly in fields like SEO and GEO, are primarily using AI for execution-level tasks such as content drafting and summarization.
• These execution-layer applications, while delivering productivity benefits, fall short of the strategic value AI can provide.
• The study’s findings point to a “mode problem” rather than a “tool problem,” indicating that the issue lies in how AI is utilized, not in the capabilities of the AI itself.
• The core insight is that significant untapped value resides in leveraging AI for judgment-centric activities, moving beyond mere task automation.

What Happened

Academic research presented at the 2025 ASIS&T Annual Meeting has illuminated a significant trend in how professionals engage with artificial intelligence. Tim Gorichanaz, a researcher at Drexel University, conducted an in-depth analysis of 205 actual ChatGPT use cases. This extensive dataset, primarily sourced from Reddit and therefore skewed towards Anglophone users, allowed Gorichanaz to categorize the diverse ways individuals interact with AI systems.

The study specifically identified six distinct operational modes: Writing, Deciding, Identifying, Ideating, Talking, and Critiquing. While many professionals readily embrace AI for tasks falling under the ‘Writing’ category—such as generating initial content drafts, summarizing lengthy documents, or performing first-pass analyses—the broader potential remains largely unexploited. This current application pattern, while boosting immediate productivity by streamlining routine tasks, represents only a fraction of what AI is capable of delivering.

The core finding indicates that organizations, having invested significantly in AI tools and licenses, are largely deploying AI at the “execution layer.” This means AI is being used to perform predefined tasks more quickly and efficiently. The real opportunity, as highlighted by the research, lies in shifting AI utilization towards the “judgment layer,” where its capabilities can inform strategic decisions and critical analysis rather than just automate output.

Why It Matters

This distinction between AI at the execution layer and the judgment layer is not merely academic; it has profound implications for businesses and individual practitioners across numerous industries. Companies are spending considerable capital on AI subscriptions and integrations, expecting substantial returns on investment. If AI is primarily used for basic content generation or data summarization, the ROI will inevitably plateau, failing to meet the strategic ambitions that drove the initial investment.

For senior professionals, particularly in data-intensive fields like SEO and GEO, understanding this nuance is critical for career development and organizational impact. The ability to move beyond basic AI prompts to architect systems where AI augments human judgment will differentiate top performers. This shift will enable more sophisticated decision-making, competitive advantage, and ultimately, more significant business outcomes.

The implication is clear: those who master the art of integrating AI into their critical thinking processes, rather than just their task lists, will be best positioned to navigate the evolving digital landscape. It challenges the prevailing “more output, faster” mindset, urging a re-evaluation towards “smarter decisions, better outcomes.”

Industry Impact

The implications of this “mode problem” extend far beyond individual professional workflows, reshaping expectations and strategies across the broader AI and technology ecosystem. Industries heavily reliant on content, data analysis, and strategic decision-making—such as marketing, finance, legal, and healthcare—are particularly affected. In marketing, for example, using AI to generate ad copy drafts is an execution-layer application. Using AI to analyze market sentiment across vast datasets, identify emerging trends, and recommend strategic shifts in brand positioning represents the judgment layer.

Enterprise software vendors are also taking note. The demand for AI tools that facilitate higher-order cognitive tasks, such as complex data synthesis, predictive modeling for strategic planning, and nuanced ethical review, will intensify. This will likely drive innovation in AI interfaces and prompt engineering frameworks, moving beyond simple text generation to more interactive, analytical platforms. The current market, saturated with tools focused on basic automation, will need to evolve to meet this demand for deeper integration into decision-making processes.

Furthermore, this perspective influences talent acquisition and development. Companies will increasingly seek professionals who possess not just technical AI skills, but also critical thinking and strategic acumen to guide AI effectively. The focus shifts from merely operating AI tools to designing AI-augmented workflows that enhance human intellectual capacity, fostering a new generation of “AI strategists” rather than just “AI operators.”

Expert Analysis

The findings from Gorichanaz’s study offer a crucial reframing of AI adoption, moving the conversation from mere technological capability to strategic utility. Many organizations have approached AI integration with a “lift and shift” mentality, automating existing processes without fundamentally rethinking the nature of work itself. This has led to a significant ceiling on the value derived from AI investments, as the technology is confined to tasks that, while important, do not leverage its full analytical and generative power for strategic insight.

The distinction between execution and judgment layers forces a necessary introspection into organizational workflows and decision-making hierarchies. True value accretion from AI comes not from simply doing the same things faster, but from doing fundamentally different, more insightful things. This requires a cultural shift where AI is seen as a co-pilot for strategic thought, rather than just a digital intern for routine tasks. It demands a higher level of prompt engineering, where the focus is on framing complex problems for AI to assist in analysis, rather than just instructing it to produce content.

This perspective also highlights the importance of data quality and context. For AI to contribute meaningfully at the judgment layer, it requires access to rich, contextualized data and sophisticated frameworks for interpretation. This means organizations must invest not only in AI tools but also in robust data governance, knowledge management systems, and the development of internal expertise to guide AI in complex problem-solving scenarios.

Competitive Landscape

The revelation that many enterprises are underutilizing AI at the execution layer presents a significant strategic opening for more forward-thinking competitors. Companies that recognize and actively pursue the judgment layer will gain a substantial competitive edge, particularly in knowledge-intensive sectors. For instance, in the SEO industry, while many agencies use AI for keyword research and content outlines, a competitor leveraging AI to predict algorithm shifts, identify nuanced user intent signals, and strategically map content clusters for long-term authority building is operating at a different level entirely.

Major AI platform providers like OpenAI, Google DeepMind, and Anthropic are already beginning to emphasize advanced reasoning capabilities in their model development, signaling a market shift towards more sophisticated applications. Their focus on improving factual accuracy, contextual understanding, and multi-modal reasoning directly supports the transition from execution to judgment. This competitive drive will push the capabilities of foundational models, making them increasingly adept at complex analytical tasks.

Furthermore, smaller, specialized AI startups are emerging with solutions specifically designed to augment human judgment in niche areas, such as legal document analysis for strategic litigation or medical diagnostic assistance. These companies are not just offering faster ways to do old tasks; they are offering new ways to think about complex problems, thereby disrupting incumbents who remain stuck in execution-layer automation. The market is subtly but surely segmenting into those who optimize for speed and those who optimize for insight.

Future Implications

Near-term (3–6 months): We will observe a heightened focus on “AI literacy” within professional development programs, emphasizing prompt engineering for analytical tasks rather than just generative output. Enterprise AI solution providers will begin rolling out more advanced analytics dashboards and contextual reasoning features in their platforms, moving beyond basic automation. Organizations will initiate pilot projects specifically designed to test AI’s capabilities in strategic planning and complex problem-solving rather than just content creation.

Medium-term (1–2 years): The market will see a clear bifurcation between AI tools optimized for execution and those tailored for judgment augmentation, with premium pricing for the latter. Job descriptions for senior roles across various industries will explicitly require experience in leveraging AI for strategic decision support and critical analysis. Companies that fail to transition AI usage to the judgment layer will experience diminishing returns on their AI investments, losing ground to competitors who successfully make this shift.

Long-term (3–5 years): AI will become an indispensable partner in high-stakes decision-making across all major industries, from corporate strategy to scientific research. Human-AI collaboration models will evolve to seamlessly integrate AI’s analytical power with human intuition and ethical oversight, creating entirely new paradigms for problem-solving. Educational institutions will fundamentally redesign curricula to prepare students for a workforce where augmenting human judgment with AI is a core competency, not an optional skill.

Actionable Insights

• Audit Current AI Use Cases: Systematically review how AI is currently deployed within your organization. Categorize each use case as either “execution layer” or “judgment layer” to identify areas for improvement.
• Invest in Advanced Prompt Engineering Training: Shift training focus from basic command-giving to crafting sophisticated prompts that elicit analytical insights, comparative analysis, and strategic recommendations from AI models.
• Redesign Workflows for Judgment Augmentation: Identify critical decision points in your processes and explore how AI can provide data-driven insights, risk assessments, or alternative scenarios to inform human judgment.
• Pilot Strategic AI Initiatives: Launch small-scale projects where AI is specifically tasked with assisting in strategic planning, competitive analysis, or complex problem-solving, rather than just routine content generation.
• Foster a Culture of AI-Enhanced Critical Thinking: Encourage teams to view AI as a tool for intellectual expansion and deeper analysis, rather than merely a productivity hack for repetitive tasks.
• Evaluate AI Tools for Analytical Depth: When considering new AI investments, prioritize platforms and models that demonstrate strong capabilities in data synthesis, reasoning, and contextual understanding over sheer output speed.

Key Takeaways

• Current AI adoption largely confines the technology to execution-level tasks, limiting its strategic value.
• Drexel University research identifies six distinct modes of AI usage, highlighting the prevalence of basic automation.
• The significant untapped potential of AI lies in its application at the judgment layer, augmenting human decision-making.
• Organizations must transition from using AI for faster output to leveraging it for smarter insights and strategic advantage.
• Mastering AI for critical analysis and complex problem-solving will define future success for professionals and enterprises.