Despite unprecedented investment, most organizations still struggle to move beyond AI pilots. “Companies want results, not demos. AI becomes meaningful when it solves a strategic problem and delivers tangible business value,” says Adrien Le Gouvello, a recent partner at super{set} – an AI venture studio – and co-founder of Lucenn, a development studio specialized in helping Fortune 500 companies turn AI opportunities into production-ready software.
Start With the Problem, Not the Technology
A common mistake in AI adoption is allowing technology to dictate strategy. Many organizations move too quickly toward the latest models or automation trends without identifying where business friction truly exists. This can lead to fragmented pilots that fail to influence executive priorities.” AI should be the tool, not the starting point,” says Adrien, who has seen impact grow significantly when leaders begin with the core challenge.
While working with one of his clients, the company initially sought to deploy advanced AI agents, believing the tools themselves would unlock transformation. Once the discussion shifted to the client’s real operational bottlenecks, the core issue became visible: proposal generation inefficiencies consuming thousands of hours each year. By addressing that specific friction point, Adrien and his team implemented an AI-driven solution that delivered an 80% improvement in proposal throughput.
Define the Metrics Before the Model
Another major barrier to AI success is the absence of meaningful KPIs. Too many initiatives rely on model accuracy or experimentation milestones instead of financial and operational outcomes. This disconnect can quickly stall executive support.
“AI without KPIs is just experimentation,” Adrien says, who helped build a platform that enabled a client’s sales teams to prioritize commercial opportunities more effectively. By using AI to surface high‑potential targets, the system generated 10.6 million dollars in new bookings. “When AI is tied to business metrics, it becomes a strategic driver instead of a science project.” Doing this also strengthens cross-functional alignment, allowing teams to design models, processes, and operating routines that deliver against the metrics that matter most.
Build the Talent, Structure, and Governance to Operationalize AI
Even when strategy and metrics are sound, AI cannot scale without the right operational foundation. Effective operationalization requires cross-functional teams that combine technical expertise with business fluency. Data scientists, strategists, and operators each play a critical role in translating AI models into daily impact. Without this interdisciplinary structure, insights remain isolated and fail to produce tangible outcomes.
Equally important is governance. Ethical use, data security, transparency, and consistency are not obstacles to innovation; they are prerequisites for adoption at scale. “Democratizing AI use across the business doesn’t mean losing control. It means creating systems that work at scale and do so responsibly,” says Adrien. With the right guardrails, organizations build confidence among teams and leaders, enabling broader and more sustainable AI deployment.
From Buzzword to Bottom Line
Adrien’s track record demonstrates that AI’s real business value comes from discipline. Identifying the right problem, defining measurable outcomes, and operationalizing with robust teams and governance form the foundation of impactful AI adoption. “AI can deliver extraordinary results, but only if it is grounded in strategic realities and supported by the people and structures required to make it real.”
To connect with Adrien Le Gouvello, visit him on LinkedIn.
