What The Shift From Business Applications To AI Means For Leadership
Rob Buller, Founding Partner, Cyberhill Partners.
Business leaders face a growing challenge: keeping pace with the ever-accelerating volume of data while still making timely, strategic decisions. Traditional business applications—once central to daily operations—are rapidly becoming outdated in an era increasingly defined by artificial intelligence (AI).
Earlier in his career, a mentor shared a candid reflection about the nature of executive leadership:
“Rob, I don’t work the way I used to. These days, I’m paid to be in the room, to listen and to make the right decision when it matters. That’s my job—three or four critical decisions a day. That’s what they pay me for.”
The point wasn’t about doing less—it was about the value of decisive leadership. Across industries, senior executives are compensated not for busy work but for making the right call at the right moment. Those decisions depend on a combination of experience, intuition and, increasingly, access to real-time intelligence.
This is where AI comes in. Artificial intelligence doesn’t replace leadership—it can amplify its impact by pulling insights that support faster, more confident decisions in high-stakes environments.
Execs have long relied on traditional business applications—CRMs, ERPs and dashboards—to support those decisions. But in today’s AI-powered landscape, the role of those tools is rapidly evolving.
The Shift Away From Business Applications
Microsoft CEO Satya Nadella discussed this shift in an episode of the BG2 podcast, reflecting on how traditional business applications are losing relevance in an AI-driven workplace. He noted that most enterprise users rarely interact directly with SaaS tools anymore. Instead, they rely on others to input data while executives increasingly expect AI agents to surface relevant insights automatically—pulling from CRMs, the web and internal systems to provide real-time intelligence on demand.
As business applications are becoming less relevant in daily decision making, this transformation marks a critical turning point. Business applications are no longer the primary interface for decision making. Instead, AI-powered intelligence is emerging as the new front end for executive insight.
AI As An Executive Advisor
Today’s executives don’t need more tools—they need smarter, faster access to intelligence. AI-powered systems are increasingly capable of:
• Extracting key business insights from multiple data sources in real time
• Automating the analysis of financial, operational and market trends
• Delivering personalized, role-specific insights through natural language queries
Instead of manually pulling reports, leaders can now interact with AI in plain language, asking questions like:
• “How did our sales pipeline change in the last 30 days?”
• “Compare our Q1 performance to last year, factoring in supply chain issues.”
• “What are the top operational risks we should anticipate next quarter?”
This ability to receive real-time responses helps to reduce dependence on outdated interfaces and repositions technology as a strategic enabler. The architecture of the underlying AI is everything. If architected incorrectly, incorrect answers could appear—which brings us to a company’s data strategy.
Why AI Adoption Often Requires A New Data Strategy
Artificial intelligence can only provide meaningful insights if it has access to accurate, well-structured and properly governed data. Mark Twain’s famous observation remains true in the AI era: “Data is like garbage. You better know what you are going to do with it before you collect it.”
Many organizations struggle with data sprawl, duplication and siloed information, making it difficult for AI to deliver consistent, reliable insights. To fully capitalize on AI-driven decision making, businesses must implement a structured approach to data management:
• Keep data at its source. Avoid unnecessary duplication. AI should query data where it resides rather than rely on redundant copies.
• Use a data catalog. AI systems require metadata to understand context. A structured data catalog enhances accessibility and accuracy.
• Implement a context engine. AI must do more than retrieve data—it needs to interpret and contextualize it for decision makers.
• Ensure strong data governance. Sensitive business information must be protected through well-defined access controls and compliance measures.
• Leverage knowledge graphs. Structured relationships between people, projects, transactions and historical trends allow AI to surface deeper insights.
Building an accurate and thorough ontology is at the heart of this endeavor. Without a strong AI-ready data foundation, organizations risk building solutions that deliver fragmented, unreliable or misleading intelligence.
The Leadership Imperative: How Executives Can Prepare
AI is not just reshaping how businesses operate—it’s redefining how leadership happens.
To remain competitive in this evolving landscape, leaders must evaluate their organization’s AI readiness by assessing whether their data infrastructure can support real-time intelligence. They should also reassess their software investments to determine if current applications are delivering actionable insights or merely collecting data. Adopting an AI-first mindset is crucial, encouraging teams to prioritize AI-driven solutions over outdated reporting methods.
AI isn’t replacing leadership—it’s elevating it. As the shift away from legacy applications accelerates, the real question is whether today’s leaders are equipped to embrace the speed, agility and intelligence that the future demands.
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