In December 2025, Nikkei AI Leaders brought together business leaders and tech strategists to tackle a crucial question for Japan's future: how can enterprises move beyond a cautious "follower mindset" and harness AI for genuine, non-linear growth? As a panelist, I highlighted that, winning in today's AI landscape requires much more than simply tweaking existing workflows or cutting operational costs. True transformation relies on looking past incremental metrics to focus on creating uniquely differentiated "Only One" experiences and building collaborative data ecosystems. That said, I offered closiing comments on human resource development, a new generation of "Synthesis Talent", professionals who don't just analyze data in isolation, but synthesize complex context to drive meaningful customer value and bold enterprise vision.
Beyond Linear Growth: Rethinking Japan’s AI Strategy and Experience Design
Recently, I joined the panel discussion at the AI Leaders Conference 2025 Autumn. The conversation was featured in the December 2025 issue of Nikkei Construction.
Our main topic was clear and urgent: "To Boost Japan's AI Power, We Must Aim for Non-Linear Growth, Overcome the 'Follower Mindset,' and Rethink Talent."
Atsushi Nakada (Head of Nikkei BP AI Data Lab) moderated the session. The panel brought together experts from industry, startups, and consulting, including Naoki Uramoto (Kao Corporation, our Adobe customer too), Yukinori Shimada (SyntheticGestalt), a brilliant startup!!, Kiyohiro Terada (Deloitte Tohmatsu Consulting), and Kiyoshi Matsuoka (FIXER). Here is a summary of our discussion, the main points, and my personal insights.
The Core Problem: The "Follower Mindset" and Slow Growth
Japan currently ranks around 10th in global AI competitiveness rankings. The country has world-class IT infrastructure. However, enterprise AI adoption still lags behind global leaders like the US, China, and the UAE.
During our panel, we pointed out three big reasons for this gap:
The Follower Mentality: Many Japanese companies are risk-averse. They focus on low-risk metrics instead of making bold AI investments.
Waiting for Proven Brands: Companies often wait for major global software tools before acting. As a result, they miss early opportunities in deep-tech and specialized AI fields.
Linear Thinking: AI tech moves at an exponential speed. Planning with small, cautious steps guarantees falling behind.
3 Key Takeaways from the Discussion
1. Shift from Cost-Cutting to Creating Real Value
Do not use AI just to cut costs or reduce headcount. The real power of AI comes from building new products, boosting profit margins, and creating unique market value.
2. Build Open Data Communities
Small models cannot easily compete with massive global AI tools. To win, companies in sectors like chemicals or healthcare must share non-sensitive data. Working together allows local groups to train smart, domain-specific AI models.
3. Focus on "Synthesis Talent"
The industry used to focus on traditional data analysts who break data down into small pieces. Today, we need Synthesis Talent. These are people who can connect different fields, combine synthetic data, and see the big picture.
My Perspective: Focus on Experience Design, Not Just Numbers
During the session, I shared insights based on my work in digital experience and business strategy:
Build "Only One" Value Instead of Chasing Scale
This Only One is a concept from the Japanese SMAP song "Sekai ni hitotsu dake no hana (世界に一つだけの花)", which has an opening that says "Its not important to be number ONE, but be the exceptional and original ONE. Ironically it also is what usually has potenteial to make brands the Number One. Think Apple.
Japanese companies do not always need the biggest AI models to win.
"I do not focus on numbers alone. The main question is whether your product is an 'Only One' solution. Companies must build on their unique strengths. Align your tech with your brand purpose, and focus on Customer Experience (CX) rather than copying competitors."
The Importance of Experience Design
Data without clear context is just noise. To succeed with generative AI, leaders must focus on Experience Design:
From Analysis to Synthesis: The last decade was about analyzing data. The next decade is about synthesis—combining insights, synthetic data, and real-world context into smooth customer journeys.
Understanding the 'Why': Finding target customers is standard practice. However, synthesis talent looks deeper. They ask why customers make decisions and design experiences around those needs.
Empowering Your Team: AI adoption cannot be forced from the top down. Leaders must share a clear vision. When employees understand why the company uses AI, they lose their fear and start trying new ideas.
Looking Ahead
Boosting AI competitiveness is not about waiting for a perfect tool from abroad. Japanese enterprises must adopt bold thinking, train cross-disciplinary talent, and share data in open ecosystems.
When we move away from risk avoidance and focus on creative experience design, AI stops being a trend and becomes a tool for real business leadership.