This article is by: Sarah Murrow, President & CEO of Allianz Trade in North America
Companies are in a high-stakes race to lead in artificial intelligence. The pressure is intense, and headlines seemingly reward speed. But in my experience, both in risk management and now as CEO, the market does not reward those who move first; it rewards those who move well. In underwriting, timing is everything. Move too early without sufficient data, and you take unnecessary risk. Move too late, and you lose the opportunity. The same discipline applies to AI; timing matters more than speed.
We have seen this dynamic play out before. Early adopters of new technologies often bear the cost of inefficiencies, while fast followers scale what works. The same pattern is emerging with AI. There is real value in moving with intent, not urgency, ensuring that adoption is aligned to business outcomes rather than driven by fear of being left behind.
The perceived benefits of being first are clear: positive recognition, a reputation for innovation, and a sense of competitive edge. Yet many organizations are underestimating the risks of moving too quickly. Implementing AI without the right governance, data readiness, and clarity of purpose can create disruption, erode trust, and ultimately destroy value rather than create it. In industries like ours, where decisions carry real financial consequences and where trust is our license to operate, these risks are not theoretical; they are tangible.
One of the most important lessons I’ve learned as a leader is that transformation is fundamentally a people journey, not a technology one. AI is no different. It is not a technology strategy; it is a business and people strategy.
At Allianz Trade, our focus has been on using AI to elevate our people. When AI takes on repetitive, manual, or data-heavy tasks, it frees our teams to spend more time where they add the greatest value: advising clients, strengthening broker relationships, and applying judgment to complex decisions.
This shift is already changing how work gets done. Employees who embrace AI tools are working faster and working differently than before. They are able to move up the value chain, spending less time on administrative tasks and more time on analysis, insight, and interaction. In a relationship-driven business, that matters. More time with clients and partners leads to better understanding, stronger trust, and better outcomes.
There is also a meaningful impact on engagement. When people feel that their time is being used well, that they are contributing at a higher level, and that they are equipped with the right tools to succeed, it drives both performance and satisfaction. AI, when implemented thoughtfully, can be a powerful enabler of this. It allows organizations to unlock the full potential of their workforce by doing more with the talent they already have.
However, realizing this opportunity requires discipline. Leaders must start with a clear objective — what business outcomes are we trying to achieve, and how will AI help us get there? Without this clarity, it is easy to fall into the trap of deploying technology for its own sake.
The most effective approach is to start small and scale intelligently. Pilot AI in targeted areas where the value is clear and the risks are manageable, then learn quickly and expand with confidence. We have seen this firsthand. A recent AI pilot we launched to support a specific function is now being extended into other areas of the business, where we are uncovering value that was not originally anticipated. This is often where the real opportunity lies, not just in solving the problem you set out to address, but in discovering new ways to create impact. Importantly, what works in one market, product line, or function will not automatically translate to another. AI implementation must be tailored to the specific context, not applied as a one-size-fits-all solution.
Equally critical is investing in people. Building AI literacy across the organization is foundational. Employees need to understand not only how to use AI tools, but also their limitations. They need to feel confident in applying them, and equally confident in knowing when to rely on their own judgment. The goal is not to replace human expertise, but to enhance it.
This is particularly important when it comes to decision-making. AI can process vast amounts of data and surface insights at speed, but it does not replace context, experience, or emotional intelligence. In our business, where we are constantly balancing risk and opportunity, human judgment remains essential. The most effective organizations will be those that combine the analytical power of AI with the nuanced understanding that only people can bring.
Governance must evolve alongside adoption. As AI becomes more embedded in day-to-day operations, organizations need clear policies on data use, model oversight, and accountability. Cross-functional collaboration, across technology, operations, risk, and HR, is essential to ensure that AI is deployed responsibly and sustainably. Trust, both internally and externally, depends on this discipline.
Ultimately, leadership in AI will not be defined by who adopts it first, but by who integrates it best. The companies that succeed will be those that balance innovation with discipline, speed with judgment, and technology with humanity. They will be the ones that use AI to strengthen their people, deepen their customer relationships, and make better decisions.
Because in AI, as in business, timing is everything.
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