We have proven the impact of generative AI on the front lines of 37+ companies and 2,500+ professionals, including listed enterprises. From chemicals and pharma to IT and construction, we share case studies that engage deeply with each industry's operational context, told through Before→After and the numbers.
"We rolled out the tools — but no one on the front line actually uses them." Across every industry, the entry-point challenge looks remarkably similar. We organize each operation's context into a form AI can use, and build a state where results follow — from R&D decision-making to company-wide rollout.
In new-business exploration, only 3 of every 1,000 ideas succeed. There was neither enough internal time nor a large budget for external research. New-material use-case discovery relied on researchers' experience and intuition, making it hard to combine speed with scale.
We built a generative-AI method for new-material ideation. We designed prompts that score ideas quantitatively on three axes — market potential, technical difficulty, and fit with company strategy — and systematized the process from divergence to convergence.
Not merely cost reduction, but a case that fundamentally changed the speed and quality of R&D decision-making. We redesigned the intellectual-production process itself with AI.
Disease and drug market research was outsourced to external consultants, generating millions of yen in fees each year. Because only the deliverables were handed over, research know-how never accumulated in-house, and the "can't quite scratch where it itches" feeling had become the norm.
We drove the in-house adoption of market research. We ran multiple in-person training sessions built around Deep Research capabilities, and combined prompt BPO (business process outsourcing) with hands-on support to create an environment where employees can run research independently.
Beyond cutting outsourcing costs, the company acquired "research capability" as an organizational competency in itself — a shift to a structure where its own decisions no longer depend on outside partners.
The corporate contract for generative-AI tools was already in place, but only a handful of employees were using it in their work. "What can it actually be used for?" had not spread to the front line, and the return on investment was not visible.
We ran hands-on training tailored to each department's operational context for all 300 employees, designed so people use AI already understanding "how to apply it in my own work."
For an organization of 300 people, rather than one-size-fits-all training, we designed around operational context — turning it into "AI that actually gets used."
Leadership had set AI adoption as a priority, but operational characteristics differed greatly across departments, leaving no clear path to a uniform, company-wide rollout.
We are providing hands-on support for AI adoption designed across multiple departments. We formulated a rollout plan reflecting each department's operational characteristics and are advancing a phased company-wide rollout.
Aiming to embed AI adoption across the whole organization, we continue hands-on support to turn "one department's success" into "the company standard."
We have a track record across 7+ industries. With a deep understanding of each industry's operational context, we design the optimal way to put AI to work.
Start with 30 minutes — tell us about your current challenges.
With methods honed across 37 companies and 2,500 people, we'll map out the best way to begin, right there.