Client: Late-stage EdTech startup, across Marketing, Sales, and Implementation functions.

Challenge:

The company was under pressure to become AI-first. Leadership (particularly investors) wanted teams to use AI to increase productivity and efficiency so that more work could be done with leaner teams. The CEO was supportive and willing to fund multiple AI subscriptions, but tool access alone was not translating into better business results. Different teams were using different tools in different ways, often inconsistently, and there was no common way of building on shared work or knowledge.

People did not clearly understand how to use the available AI tools well. Even where they had some familiarity with the tools, they did not understand where those tools would be most useful in their actual workflows or where AI could solve meaningful productivity bottlenecks. As a result, adoption was fragmented, workflow quality varied by team, and the organization was not realizing the value of its AI investment consistently.

What Coucal did:

• Coucal began by working directly with the CEO to define what the organization actually wanted to achieve through AI adoption. First, clarifying the business outcomes that needed to change, especially higher productivity and efficiency across target functions, and next, by identifying the problem areas where AI could make a measurable difference.

• From there, Coucal helped the business understand its current work structure more clearly. That meant documenting workflows, surfacing where productivity was low or processes were haphazard, and identifying the most relevant pain points and early use cases to target first.

• Coucal then ran leadership alignment sessions on AI value, guardrails, and role expectations across Marketing, Sales, and Implementation. These sessions clarified what good AI-enabled work should look like by role, what outcomes mattered, and how teams should think about responsible and useful adoption.

• This was followed by bespoke, role-based learning for leaders and teams, built around real work and practical exercises. People were not only introduced to tools, but shown which tools were relevant to them, how those tools could support their role, and how to apply them in meaningful use cases rather than experiment in disconnected ways.

• To improve cross-functional consistency, Coucal also introduced workflow patterns supported by Retrieval-Augmented Generation (RAG). This helped teams move beyond generic AI use and generate outputs grounded in more specific, useful internal context for analysis, drafting, review, and quality assurance.

• Finally, Coucal established an adoption rhythm supported by dashboard-led measurement. This helped the organization see which subscriptions were being used, where usage was ineffective, how well the value stream was performing, and which points in the workflow should be improved next.

Outcomes:

• Reduced cycle time for selected workflows in Sales and Implementation by 50%+

• Improved perceived usefulness of AI and employee confidence in using it

• Significantly enhanced analysis capabilities across Marketing and Sales through RAG-supported workflows

Capability built:

• Internal AI enablement playbook

• Role-based training kits for leaders and teams

• Documented workflow and value-stream maps to identify high-impact AI use cases

• A dashboard approach to track subscription usage, workflow effectiveness, and future improvement priorities