Technology leaders have never had more choices. Every week seems to bring another platform promising to revolutionize customer engagement, streamline operations, or unlock the full potential of artificial intelligence. Salesforce. Adobe. HubSpot. Databricks. Snowflake. Agentic AI. Customer data platforms. The list grows longer by the day.
The problem is that organizations often begin the conversation in exactly the wrong place.
During a recent episode of the On the Right Stack podcast, veteran technology executive Kevin Reynolds challenged one of the most common mistakes companies make: selecting technology before clearly defining the business problem they’re trying to solve. It’s an observation born from more than two decades of leading enterprise technology initiatives across media, marketing, and customer data ecosystems, and it’s a lesson that deserves far more attention than the latest product announcement.
Instead of asking, “Which platform should we buy?” Reynolds argues that leaders should first ask a much simpler question:
What Business Outcome Are We Trying to Improve?
Whether the goal is increasing revenue, reducing customer churn, improving campaign performance, or launching a new product, the objective should come first. Only after defining success—and the measurable KPIs that demonstrate it—does technology selection become meaningful. Otherwise, organizations risk falling victim to what Reynolds describes as “shiny object syndrome,” where impressive product demonstrations distract from actual business needs.
That philosophy aligns closely with what many industry analysts are seeing today. Gartner recently reported that organizations achieving meaningful AI results invest significantly more in foundational capabilities such as data quality, governance, and change management than organizations focused primarily on the technology itself. In other words, the winners aren’t simply buying better tools; they’re building stronger foundations.
Change Management Isn’t the Final Step
One of Reynolds’ most compelling observations centers on change management. In many technology implementations, organizations spend months gathering requirements, negotiating contracts, and configuring systems, only to think about user adoption near the end of the project. By then, resistance is almost inevitable.
His recommendation is refreshingly practical: involve the people who will actually use the technology from the very beginning. Let them influence requirements. Give them ownership in the process. Make change management part of project planning—not a final checklist item before launch.
It’s advice that extends well beyond CRM implementations. As generative AI continues to reshape enterprise software, organizations are discovering that successful adoption depends as much on people and processas it does on algorithms. Companies that treat AI as an organizational transformation instead of a software installation are generally seeing stronger, more sustainable results.
AI Is Only as Smart as Your Data
Reynolds is equally pragmatic when discussing AI itself. Rather than framing it as either a miracle technology or an existential threat, he compares today’s skepticism to the early days of Google and Wikipedia. Many leaders dismissed those tools because they weren’t perfect. Today, they’re indispensable.
His point isn’t that AI is flawless. It’s that leaders who refuse to learn how it fits into their business risk falling behind those who do. The competitive advantage won’t belong to organizations that simply “use AI.” It will belong to those that combine trusted data, thoughtful governance, and clearly defined business objectives with the capabilities AI brings to the table.
That becomes especially important when discussing customer data. AI can analyze enormous volumes of first-party information, identify meaningful patterns, and help organizations deliver more relevant customer experiences. But none of that matters if the underlying data is incomplete, poorly governed, or disconnected. McKinsey recently noted that as organizations attempt to scale AI, data readiness has become one of the biggest constraints separating successful initiatives from stalled pilots.
Technology Should Empower People, Not Replace Them
Near the end of the conversation, Reynolds offers another observation that deserves attention, particularly from private equity firms and executive leadership teams evaluating technology investments. He encourages organizations to look beyond simply cutting costs or reducing headcount. Instead, examine whether existing platforms are fully utilized, eliminate expensive “shelfware,” and invest in technologies that empower employees to create more value rather than simply replacing them. That’s a much healthier—and ultimately more profitable—way to think about digital transformation.
The Best Technology Decisions Start Before Technology
Perhaps that’s the biggest takeaway from the entire discussion.
Technology has never been more powerful. AI is evolving at breathtaking speed. New platforms will continue to emerge.
But organizations that consistently succeed won’t necessarily be the ones buying the newest tools. They’ll be the ones that know exactly what they’re trying to accomplish before they ever sign the contract.