意見：迅速で柔軟なAIテストは戦略的リーダーシップの基盤である

Cathy Wolfe,Executive Vice President and General Manager ,Wolters KluwerApril 6, 20264 Min ReadBlackJack3D via Getty ImagesGenerative AI has quickly moved from background capability to a highly visible boardroom mandate, and the pressure is on leaders to set their organizations up for success.Instinctually, most organizations execute the traditional approach to tech innovation, including commissioning a task force, building a multiyear roadmap, establishing a center of excellence and publishing AI strategy documents along with external resources. While these are reasonable steps, they assume the current environment will stay the same long enough for their plan to play out. But it's more likely that conditions will change based on the rapidness of current technology cycles. AI is developing faster than regular planning cycles can accommodate, and the organizations depending on a polished strategy before they act are falling behind the ones that started testing months ago.Three leadership pivots can be made to help close the gap. All of them will require leaders to rethink how strategic technology decisions are made.Related:The Real AI Shift Isn’t New Models. It’s Control.Separate AI Experimentation from Daily OperationsProduct and operations teams are already working at full capacity. They are maintaining existing systems, responding to customer needs and managing the ongoing demands of compliance and regulatory requirements. Asking these teams to also evaluate and test AI capabilities can cause technological advancement to lose priority.A more effective approach is to create a group dedicated to taking pain points from across the business, then building corresponding proofs of concept. The team does not need to be large. Five people with the right mix of functional expertise and a specific mandate can be enough. Testing if AI applications solve organizational problems within a defined timeframe is a manageable starting point. If the concept proves its benefits, the AI testing group should present it to the product or operations team for full development. If it does not meet organizational needs, the learning gets documented, and the team moves on to other use cases.This structure helps prevent AI initiatives from ending abruptly when product teams are too busy with current work to give them attention. It also keeps unproven or ineffective AI tools out of situations where a bad output could cause serious damage which matters even more in compliance-intensive industries. A confident wrong answer from an AI tool can be worse than no answer at all.Establish Accountability Before Fixing TechnologyOne of the biggest barriers to effective AI implementation is organizational bottlenecks. At senior levels, responsibilities overlap, ownership of decisions becomes unclear and teams spend more time discussing who should act rather than deciding what to execute.Related:OpenAI GPT-5.4-Cyber is More Open Than Claude MythosBefore investing in AI tools or hiring AI-specific talent, leaders should clarify where one group's responsibility ends, and another's begins. When accountability is clear, decisions happen faster and internal fragmentation decreases. But when responsibility is ambiguous, even well-funded AI initiatives stall because nobody is sure who owns the outcome.This extends to how leadership teams are composed. Organizations that bring cross-functional partners into strategic conversations early by giving technology, HR, legal and operations representatives an equal voice can move more effectively. Different perspectives point out problems earlier in the implementation process and can encourage broader team commitment to an agreed upon direction. People support decisions they helped shape.Choose Opportunity Over Risk as the Starting PointEnterprise conversations about AI tend to start with risk. What if the model hallucinates? What if we invest in the wrong approach? What about regulatory exposure? What if it fails publicly?Related:Anthropic Releases Good but not Great Claude Opus 4.7These are legitimate concerns, but when risk dominates the conversation, it creates decision paralysis. Teams spend months debating what could go wrong instead of running small tests to find out what would happen. This risk-first approach can become a hindrance because the organization might gain less practical insight than it would through direct testing, while its competitors are testing, adjusting and learning.The answer is to reframe questions around risk. Instead of fixating on what could go wrong, explore what the smallest test could teach your organization about whether these tools work. Design experiments that are contained enough to reverse if they fail. A 90-day test with a small team and a defined problem is not a bet-the-company decision. It is a way to gather real data about what AI can and cannot do in specific business applications, and that data is far more valuable than any theoretical risk assessment produced in a conference room.The first few experiments take courage. After that, the organization builds confidence from accumulated experience rather than speculation. Teams start moving from slow, endless discussion to agile action, and the quality of strategic decisions improves because they are informed by what the business experienced rather than what it feared might happen.Why This Matters NowAI capabilities are not going to slow down to let planning cycles catch up. Organizations that treat 2026 as a year to finalize their AI strategy will find themselves in a similar stalling position, still planning while the industry has moved on again.The alternative is to build an organization that can learn and adjust faster than conditions change. That does not require a perfect plan, but it requires clear accountability, a dedicated space for experimentation and leaders willing to focus on opportunity rather than getting stuck in risk analysis. The leadership challenge is not just about figuring out the right AI strategy. It's about building an organization capable of finding the right strategy through action.About the AuthorExecutive Vice President and General Manager , Wolters KluwerCathy Wolfe is executive vice president and general manager at Wolters Kluwer Corporate and Legal Compliance, a global provider of compliance services and solutions.