Is the right question "What Tech Do We Need?" or "What Outcome Do We Want?"

Left-to-Right Thinking Slows Us Down In Achieving Faster Business Outcomes

Most technology initiatives start by selecting a tool, implementing it, driving adoption, and then measuring usage. This is left-to-right thinking, and it’s clear why this became the default. It shows progress right away, but seldom delivers the right outcomes.

Adopting a new tool creates immediate, visible activity and often eases short-term pain. That feels like momentum, but it doesn't guarantee the outcomes the business expected. The impact is also easier to track through pre-defined metrics such as usage, integrations, tokens, and credits.

The market reinforces this habit. New data and AI tools come out every day, each with targeted capabilities and a compelling pitch. What's concerning is the limitations/overheads and long-term effort each new tool brings, and with so much market noise, these often get overlooked. Integrating with existing systems, getting access to data, governance, maintenance, and ongoing management should all be weighed in.

So, most data investments begin with picking a tool or platform, implementing it, then hoping that sustained value will follow. Once the technology is adopted, teams then try to figure out what value means.

By then, the buyer is paying for activity instead of results. Integrations, tokens, credits, and compute hours all show up on bills whether or not the original business problem has moved. Adoption can look strong while revenue, customer experience, and operational efficiency stay where they were.

Right-to-Left Thinking: Start With the Outcome

Right-to-left thinking begins by identifying the problem with the greatest impact, defining the desired outcome, and working backward to determine the data and technology needed to achieve it. It also means resisting the temptation to solve everything at once. Start with a focused, manageable use case. An early, measurable win builds credibility and gives teams a strong foundation for tackling related challenges.

Doing this requires business, data, and technology teams to plan together. They need to agree on what success looks like, whether the data can support it, and what needs to change. That coordination makes the approach harder than simply selecting a tool.

What Outcome-Aligned Looks Like in Practice

For a company looking to reduce churn, the best place to start may be a specific customer segment. Rather than trying to fix everything at once, it can focus on the customers most at risk and build from there.

The first use case is very targeted: test whether earlier outreach improves retention in that segment. Set a target and agree on how to compare results with the current approach. Then work backward. Analytical workloads would flag at-risk customers, and alerts would prompt account teams to reach out. Those requirements guide which technology to use, adapt, or build.

Measurements are crucial too. An early, measurable win builds credibility and gives the team a foundation for expanding into other customer segments or adjacent use cases, such as improving onboarding.

Other domains and business units can apply the same approach in parallel, each starting with an important problem and a manageable first use case. Reusing relevant data, workloads, and increase throughput without making every initiative a bigger project.

The strongest questions to ask are “What outcome matters most, and what is the first meaningful part we can deliver?”