Mike Gianoni is President, CEO and Vice Chairman of the Board of Directors at Blackbaud.
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Every wave of digital transformation shifts where competitive advantage lives. The one we’re living in now will be no different.
The AI era isn’t just changing how software is built; it’s redefining which companies will lead. For years, leadership in enterprise technology was earned through visibility. Systems of record helped organizations capture, organize and understand their most critical data, replacing manual processes with clarity and confidence. That visibility became the foundation for scale.
Over time, those systems evolved. Analytics, benchmarks and machine learning layered insight on top of record‑keeping, enabling more informed decisions and more personalized engagement. Software moved from helping organizations see what was happening to helping them understand why it was happening.
Now, we are entering the next chapter—one where systems don’t just provide insight, but act on it.
As AI capabilities mature, the role of technology must evolve again. Insight alone is no longer the endpoint. Increasingly, value comes from systems that can interpret what’s happening, recommend what to do next and, when invited, execute.
This shift marks the transition from software that informs work to software that propels it forward.
In the past, horizontal platforms won by delivering coherence across organizations, and vertical solutions won by embedding deep domain expertise. The next era belongs to companies that can do both: connecting systems end‑to‑end while applying distinctive, purpose‑built context that allows technology to drive work with greater effectiveness and velocity than ever before.
That combination—data, context and motion—is what transforms software from a passive tool into an AI engine for impact.
In the AI era, one powerful path to leadership is building a true data moat: a proprietary, accumulated body of information that cannot be easily replicated or acquired. Scale alone does not create this advantage. What turns data into a moat is exclusivity, depth, breadth, progressive accumulation over time and continued usefulness beyond its original system.
Yet even the strongest data foundation has limits. Data can tell you what happened. It cannot, on its own, explain what it means or what should happen next.
That’s where context becomes essential.
Context is a structured, domain‑specific understanding of how data should be interpreted, weighted and applied within real workflows, decisions and success criteria. It enables systems to reason about trade-offs, understand intent and generate recommendations that align with real‑world constraints.
When context is embedded deeply, it becomes a moat of its own.
The solutions that will lead in this era are those that combine proprietary data, deep contextual intelligence and the ability to translate both into action.
When data and context are properly combined, user interaction looks different. Action doesn’t require a person to be inside an app interface. People may engage intelligence through the language model of their choice, or they may delegate execution to agents operating under human direction and guardrails.
But if the balance is off—if data is unmoored from context, or intelligence is unbounded by purpose—the result isn’t progress. It’s noise, wasted energy and motion you wouldn’t trust with the mission.
This is where the ultimate differentiator emerges.
Trust is what moves systems from potential direction to real motion.
Better data and richer context lead to better outcomes. Better outcomes build trust. And as trust grows, people become more comfortable shifting from acting themselves to allowing systems to act with them, or on their behalf, under clear direction and safeguards.
Each trusted action generates new outcome data and feedback, strengthening the system and restarting the cycle. Over time, this compounding effect creates an advantage that goes beyond data or context alone. It creates a trust moat.
In trust-sensitive environments—where decisions carry moral weight, resources are limited and the cost of getting it wrong is high—this dynamic becomes even more pronounced. Leadership in these settings won’t come from optimizing isolated tools. It will come from reducing friction, strengthening coordination and enabling people and organizations to act with greater confidence and clarity.
The true value of technology has always been about more than efficiency. Ultimately, it’s measured by how well systems support human relationships and human progress.
As AI moves from insight to action, the bar for leadership rises. The companies that lead the agentic era will not be those that generate the most intelligence, but those that build systems people trust enough to put that intelligence into motion.
That is the standard this next era will demand and the opportunity ahead for technology leaders willing to build for it.
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