日本語 
Mr. Suzuki, can you tell us about your professional background and your current role at Discoveries Inc.?
I am currently a Customer Success Manager at Discoveries Inc., where I help organizations adopt Microsoft 365 Copilot in a practical way that leads to real usage and measurable progress. My work includes enablement programs, KPI design, community building, and data analysis to help companies move from initial interest in AI to meaningful adoption.
Before joining Discoveries, I spent more than 20 years in enterprise sales and customer success roles at NTT and Microsoft Japan. That experience gave me a front-row view of how large organizations adopt new technology, where they struggle, and why transformation often falls short when tools are introduced without enough attention to people, habits, and day-to-day work.
What motivates me today is the idea of making AI accessible to everyone, not just specialists. I believe the real value of AI comes from helping ordinary business users work more effectively and make better decisions in their daily roles.
Mr. Suzuki, after more than two decades in the Microsoft ecosystem, what is the most fundamental shift you’ve seen in how Japanese enterprises adopt technology, especially in how they think about ‘tools versus transformation?
Over the past two decades, one of the biggest shifts I have seen is that Japanese enterprises have become more outcome-oriented in how they think about technology adoption. In the past, success was often defined by whether a tool was introduced smoothly and deployed across the organization. Today, more leaders want to know what happened after adoption: whether it improved the quality and speed of work, supported better decisions, or produced clear business outcomes.
Many organizations are still in transition. They expect results, but often continue to approach adoption as a rollout exercise rather than as a change in how work is actually done.
For me, the most fundamental shift is this: companies are becoming less satisfied with simply introducing tools. They increasingly expect technology to create measurable value in real business operations.
From your experience at Discoveries working closely on Copilot adoption, what is the biggest misconception enterprise leaders have about what Copilot can realistically deliver in the first few weeks of deployment?
The biggest misconception is that Copilot will create immediate, organization-wide impact simply because it has been deployed. Many leaders expect that once licenses are assigned, employees will start using it effectively right away and clear business results will appear within a few weeks.
In reality, the first few weeks should be seen as a learning and discovery phase. Employees need time to understand where Copilot is useful, which tasks it supports well, and how to incorporate it into their actual workflows. At that stage, the most meaningful signals are not large ROI numbers, but early usage patterns, practical use cases, and examples that can be shared and repeated across teams.
The key point is that early deployment should be evaluated less as a final results stage and more as the stage where the organization learns where value can realistically emerge.
When organizations move from pilot programs to full-scale rollout, what tends to become the first real bottleneck: data readiness, employee usage habits, or leadership alignment?
If I had to identify the first real bottleneck, I would point to employee usage habits.
Pilot programs often look promising because the participants are usually early adopters or highly engaged users. Full-scale rollout is different. It exposes whether average employees can actually incorporate the tool into their routines, apply it to real tasks, and continue using it without constant support. That is where many organizations discover that deployment is much easier than habit formation.
Leadership alignment matters a great deal, and poor alignment can slow everything down. Data readiness also becomes more important as usage expands. But the first bottleneck that organizations usually feel most directly is whether employees can turn initial exposure into consistent, practical behavior.
Across global deployments, there is often a pattern where AI tool usage peaks during pilots and declines afterward. In Japan, does this drop-off behave differently, and what factors typically influence whether usage sustains or fades?
Yes, the same basic pattern exists in Japan. Usage often peaks during pilots and then declines afterward, so I would not say Japan is fundamentally different in that respect.
What can be different is that in Japan, the decline is often quiet. If employees do not find a clear place for the tool in their daily work, or if they do not see it being used visibly around them, usage can fade without much discussion. That is why the transition from pilot to rollout is so important.
If expansion is treated simply as a larger deployment, usage often falls. But if organizations prepare for that drop-off in advance, they can maintain strong usage much more effectively. In my experience, that means reinforcing adoption through internal promotion, practical training, and clear top-down messages that the tool is not just an experiment but part of how work should evolve. Sustained usage is rarely automatic. It usually comes from anticipating the decline and responding with deliberate action.
Across industries like manufacturing, finance, and the public sector, have you observed meaningful differences in how Copilot is adopted, or does the core challenge of driving workstyle change remain surprisingly consistent?
There are definitely meaningful differences across industries. In manufacturing, organizations tend to be especially sensitive to accuracy and hallucination risk. In finance, data governance is often already relatively strong, which can make it easier to build a workable foundation for generative AI adoption. In the public sector, many organizations are still dealing with more basic digitization challenges, including paper-based processes and a heavier reliance on face-to-face meetings, so in some cases the issue is not only AI adoption but digitalization itself.
At the same time, what stands out is how much the underlying motivation is shared. Across all of these sectors, organizations clearly feel that AI is changing the competitive and operational environment around them, and many are looking for practical ways to respond.
So yes, industry context changes the form of adoption. But the deeper challenge remains surprisingly consistent: how to connect AI to real work, build trust in everyday use, and turn initial interest into lasting behavioral change.
Beyond productivity gains, where have you seen the most unexpected impact of Copilot in organizations, especially in how teams communicate or how internal roles begin to evolve?
One unexpected impact I have seen is that Copilot can make digital inequality inside an organization more visible. Employees who already use digital tools regularly often adopt AI quickly and start getting value from it early. But employees who are less used to working digitally in their daily tasks often struggle to find a clear entry point.
That means the impact of AI is not always evenly distributed. In some organizations, the people who were already comfortable with digital work become even more productive, while others remain largely outside that change. This can affect not only productivity, but also who speaks up, who experiments, and how confident different employees feel about contributing in an AI-enabled environment.
For me, one of the most important lessons is that AI adoption is not just about the AI tool itself. It also reveals whether the organization has built enough of a digital foundation for broad-based adoption in the first place.
You’ve worked deeply in enterprise environments where internal communication and knowledge systems play a critical role. In your experience, how much does the structure of tools like SharePoint and Teams influence whether Copilot succeeds or struggles inside an organization?
It matters a great deal. SharePoint and Teams are part of the data foundation that determines how much value Copilot can actually deliver inside an organization.
But I would go one step further: this is not only about system structure. It is also about the company’s culture of information sharing. For Copilot to work well, important files should not remain on individual desktops or in personal storage. They need to be saved in shared environments such as SharePoint as part of the organization’s shared knowledge base. In the same way, communication should not happen only through offline conversations. Teams also needs to function as a space where digital communication happens actively and where ongoing knowledge can accumulate over time.
That is why I see SharePoint and Teams as more than technical platforms. They reflect whether the organization is building a usable digital knowledge environment. Copilot can only work from what has been shared, structured, and accumulated. Companies do not need a perfect environment before they start, but they do need to recognize that the quality of their digital knowledge foundation has a major impact on whether Copilot succeeds or struggles.
Given your background spanning security and enterprise systems, how should companies think about balancing AI governance with speed of innovation, without slowing down actual team adoption?
I do not think governance should be understood mainly as a mechanism for control. Especially for employees who are new to AI, concerns about security and information leakage are often the biggest barrier to adoption. In that sense, governance is not just about managing risk. It is about creating enough clarity and reassurance for people to use AI with confidence.
I sometimes think of it like the rules in a public park. Those rules are not there to prevent people from using the space, but to help everyone use it safely. AI governance should serve a similar purpose. It should reduce uncertainty and support responsible use, rather than create an atmosphere of fear.
The right balance comes from practical guardrails. Companies need clear guidance on acceptable use, data handling, and escalation points, while still allowing teams to start in lower-risk areas and learn through real usage. Good governance should support adoption by making responsible use feel possible and safe.
As we move toward agent-based systems inside Microsoft’s ecosystem, how do you see the role of a Customer Success leader evolving? Will it become more about technology expertise, or more about guiding organizational behavior and decision-making?
As agent-based systems become more common, Customer Success leaders will definitely need stronger technical understanding. They need to understand how agents work, where they are effective, what risks they introduce, and how they should be governed.
At the same time, the role will require a more business-oriented, BPR-like perspective. Agents are not simply new tools. They often force organizations to rethink the work itself: what should be automated, what should remain human-led, and how workflows should be redesigned to deliver better business outcomes.
For that reason, Customer Success leaders will need both technical skill and business skill. They will need to understand processes, identify which tasks are truly suitable for AI, choose the right solution rather than assuming one tool fits everything, and advise on what business goals an agent should serve. I believe this is exactly the kind of role where human judgment will remain essential.
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