How I’m Thinking About the New Year

The start of a new year is often a natural moment to pause and think deliberately about what comes next. For me, it is less about declaring resolutions and more about being deliberate in how I think about the year ahead. I do believe in setting goals, but only when they are grounded in a clear understanding of the technology decisions that actually shape outcomes over time. In modern organizations, those decisions span software architecture, data and analytics, AI, integration patterns, ownership boundaries, and tradeoffs between speed, reliability, and control. The constraints created by these choices rarely show up immediately. They surface later, often when systems scale, teams grow, and change becomes harder.

The past year reinforced a few convictions that will shape how I approach the one ahead with of course AI on my mind.

First, scale will amplify everything across technology systems. Decisions about architecture, data models, interfaces, ownership, and operational tradeoffs compound over time. When those decisions are sound, they quietly enable progress. When they are sloppy, deferred, or made in isolation, they resurface later as structural risk. What feels acceptable at small scale in pipelines, services, metrics, or controls becomes a liability as usage grows, dependencies multiply, and more decisions are automated or assisted by machines. AI does not fix weak foundations. It exposes them. Without clear ownership, reliable systems, and shared understanding of how things work, AI will not accelerate progress. It will accelerate noise, mistrust, and fragility.

Second, AI will raise the bar for alignment across technology and business, not lower it. There is a persistent misconception that smarter systems reduce the need for coordination. In reality, the opposite is true. Models and automation will move faster than organizations ever have. When product intent, system design, data, and execution are not aligned, AI simply makes that misalignment surface sooner and at greater scale. The teams that succeed will not be the ones experimenting the most or adopting tools the fastest. They will be the ones that invest in shared language, clear intent, and disciplined decision making before automation is embedded deeply into their workflows.

Third, AI’s impact will extend well beyond technical teams, and that shift is already underway. For many people, AI will change how work gets done long before it changes job titles. Writing, analysis, planning, research, and communication are becoming partially automated in ways that increase individual leverage. This will be broadly positive, but it will not be evenly distributed. People who understand how systems behave, how to frame problems, and how to validate outcomes will benefit far more than those who simply follow instructions or operate tools without context.

Related to this, coding will increasingly become a commodity. The ability to produce syntactically correct code will matter less than the ability to understand the system the code lives in, define the right problem, evaluate tradeoffs, and take responsibility for outcomes in production. This does not diminish engineering as a discipline. It raises the bar. Differentiation will come from architecture, reliability, integration, and the ability to connect software to real world business processes and constraints.

Fourth, leadership becomes more visible as technology driven ambiguity increases. AI expands the surface area of leadership rather than shrinking it. Decisions arrive earlier, with fewer precedents and more consequence. It becomes harder to hide behind process, hierarchy, or consensus building as a delay tactic. Leaders need to be explicit about guardrails, escalation paths, and where human accountability remains non negotiable. This will apply not just to executives, but to anyone responsible for systems, teams, or outcomes.

Fifth, technology, data, and AI only matter when they change behavior. This was a core theme in a recent keynote I delivered at Purdue’s Daniels School of Business, and it continues to hold. Insight without action is still waste. Systems can surface signals, predictions, and recommendations endlessly, but none of it matters if it does not change what people do next. The most valuable technology will not feel impressive in isolation. It will be embedded directly into workflows, shaping decisions where work actually happens, often quietly and without fanfare.

Looking ahead, my goals are not about doing more. They are about reinforcing the right things. They are grounded in building technology systems people can understand and trust, designing intelligence with downstream use in mind from the start, and creating space for teams to think and exercise judgment rather than simply move faster. They also require making decisions that favor long term credibility and clarity, even when short term velocity is tempting.

I am entering the new year bullish on AI, but not optimistic in a naïve way. AI will not be kind to ambiguity, weak systems, or leaders who confuse activity with progress. It will reward people and organizations that can think clearly, define problems precisely, and take responsibility for outcomes even when the tools feel powerful enough to hide behind.

For individuals, this will be confronting. For teams, it will be clarifying. Coding, analysis, and execution will continue to get cheaper. Judgment, systems thinking, and ownership will not. The gap between those who understand how technology actually works and those who simply operate it will widen. That is not a prediction. It is already happening.

As the year begins, I am not focused on chasing novelty or setting goals for their own sake. I am focused on reinforcing foundations that deserve to be accelerated and being honest about what does not. AI will move fast regardless. The more uncomfortable question is whether the systems, decisions, and incentives we have in place actually deserve that speed.

As technology continues to lower the cost of execution and raise the stakes of judgment, what are you intentionally building that should be allowed to move faster, and what are you still carrying that will break when it does?