MGT 726 Week 6 Creativity and Adaptive Approaches Example

Creativity, Experimentation and Generative AI: Adaptive Approaches to Innovation at Cedar Valley Instruments

[Student Name]

University of Phoenix

MGT/726: Emerging Managerial Practices

Week 6 Assignment

[Instructor Name]

[Date]

Cedar Valley Instruments and all pilot results are composites written for a model paper.

Competitors have been launching more new products than Cedar Valley, our composite instrument maker with engineering centers in Iowa and India. Its engineers are skilled, but an internal review found that most projects improve existing instruments rather than create new ones. In focus groups, engineers said they have ideas but no time to explore them, and that proposals go through months of review. Leaders also wonder whether generative AI tools could help engineers spend less time on documentation and more on design. Creativity rarely fails for lack of talent; it usually fails because the environment leaves no room, time or permission for people to use the talent they have. This paper examines creativity and adaptive approaches at Cedar Valley.

The Work Environment for Creativity

Amabile et al. (1996) developed and validated an instrument for assessing the work environment for creativity and showed that the most creative projects tended to sit in settings marked by organizational encouragement, supervisory encouragement, work group support, sufficient resources, challenging work and freedom, while workload pressure and organizational impediments were associated with lower creativity. The environment, not just individual talent, shapes creative output.

Assessing Cedar Valley's Climate

A survey of 240 engineers adapted to these dimensions found high scores for challenging work and work group support, moderate scores for supervisory encouragement and low scores for freedom, resources and workload. Organizational impediments, such as long approval processes and risk-averse project reviews, scored high.

Adaptive Approaches

Adaptive management treats decisions as experiments: implementing changes on a small scale, measuring results and adjusting before broad rollout. This approach suits uncertain areas such as creativity programs and new technologies, where outcomes are hard to predict in advance.

Evidence on Generative AI

Noy and Zhang (2023) ran a preregistered experiment with college-educated professionals completing writing tasks and found that access to a generative AI tool reduced time taken and increased output quality, with larger benefits for lower-ability workers, compressing differences among them. Brynjolfsson et al. (2025) studied the rollout of an AI assistant to customer support agents and found productivity gains averaging about 14 percent, with the largest gains for novice and lower-skilled workers and little effect for the most experienced.

Interpreting the AI Evidence

These studies examine specific tasks, writing and customer support, over limited periods. Engineering design involves technical judgment and safety requirements that differ from these settings. The evidence suggests AI may help with documentation, reports and some idea generation, but claims beyond those tasks should be tested.

An Adaptive AI Pilot

Cedar Valley ran a three-month pilot in which 30 engineers used an approved AI assistant for documentation, test reports and early idea generation, while a comparison group of 30 did not. Engineers using the tool spent about 25 percent less time on documentation, and reviewers rated report quality similar between groups. Less experienced engineers reported larger time savings. Idea generation results were mixed: AI produced many ideas, but engineers judged few as feasible.

Learning From the Pilot

The pilot supports expanding AI use for documentation, with review requirements, and suggests limited value for idea generation without engineers' judgment. It also revealed risks: two engineers pasted confidential specifications into an unapproved tool before guidelines were issued.

Why Approval Processes Matter

The climate survey's strongest finding concerned organizational impediments. Engineers described a review process in which any new idea needed a full business case, three signatures and a slot in a monthly committee meeting, taking three to five months. By then, enthusiasm had faded or customers had moved on. Research on creativity identifies such impediments as among the most damaging factors, and they are also among the easiest for leaders to change, since they are rules the company wrote itself.

Creativity Across Sites

Creativity climate differed by site. Pune engineers scored higher on freedom, partly because their center was newer and less bound by legacy processes, but lower on resources, since prototype equipment sits mostly in Iowa. Shipping prototype kits to Pune and giving Pune teams direct access to experiment budgets would use the creativity already present there.

Skill Development and AI

The research finding that AI helps less experienced workers most is encouraging but raises a question for an engineering company: if junior engineers use AI to write test reports, will they still learn to write them well? Cedar Valley will require junior engineers to complete a set number of reports without AI assistance during their first year and will review AI-assisted work with them, treating the tool as a coach rather than a substitute.

Practice One: Protected Exploration Time

Engineers will receive four hours a week, freed partly by AI time savings, for exploring new ideas, with a short quarterly showcase. Protected time addresses the freedom and workload dimensions, and the showcase gives engineers an audience of leaders who can fund promising ideas.

Practice Two: Small Experiment Budgets

Teams can access up to $25,000 for prototypes with a simple one-page proposal approved by a director within two weeks, reducing organizational impediments and replacing the three-to-five-month process engineers described.

Practice Three: Supervisory Encouragement

Supervisors will be trained to respond to ideas with curiosity, help engineers develop them and recognize experiments that fail but teach something. Their own reviews will include how many team ideas reached prototype.

Practice Four: AI Guidelines

Guidelines will specify approved tools, prohibit entering confidential data into unapproved systems, require human review of AI-generated content and clarify that AI use will not be used to evaluate individual performance.

Leadership's Role in Adaptive Change

Adaptive approaches require leaders who accept that some experiments will fail and who judge pilots by what they teach. Cedar Valley's executives will review experiment results quarterly, celebrate useful failures alongside successes and decide which practices to expand. Their visible patience matters: if the first failed prototype leads to budget cuts, engineers will stop proposing ideas.

Risks

Risks include overreliance on AI leading to errors, skill erosion among junior engineers who rely on AI for writing they should learn, unequal access across sites and exploration time being absorbed by urgent work. Monitoring and supervisor attention address these.

Measures

Measures include the number of new ideas proposed and prototyped, the share of revenue from products launched in the past three years, climate survey scores on freedom and workload, time spent on documentation and quality of AI-assisted documents.

Research Questions

Questions include how AI tools affect skill development among early-career engineers, whether time saved by AI is reinvested in creative work or absorbed by other tasks and how climate changes affect creative output over time.

Conclusion

Cedar Valley's engineers face a climate with challenging work but too little freedom, too much workload and slow approvals. Research on the creative environment points to protected time, resources and encouragement. Evidence on generative AI supports targeted use in documentation, tested through an adaptive pilot. Combined and measured, these practices can increase creative output while managing new risks.

References

Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Academy of Management Journal, 39(5), 1154-1184. https://doi.org/10.2307/256995

Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889-942. https://doi.org/10.1093/qje/qjae044

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. https://doi.org/10.1126/science.adh2586