GenAI Adoption Lessons from Resolve.ai: Data Quality, Use Cases, and Build vs. Buy

Quentin Packard, VP of Sales at Conduktor, spoke with Spiros Xanthos, Founder and CEO of Resolve.ai, about the challenges and opportunities of AI-driven innovation. They covered data quality, strategic AI adoption, and the entrepreneurial mindset required to thrive.

Early Adopters Prioritize Data Quality Over Data Volume

"The future of AI doesn't belong to the companies with the most data. It belongs to those who can use their data most effectively."

Spiros highlighted that if even a fraction of current GenAI expectations materialize, the impact will be massive. Early success depends on pragmatic steps:

Data quality matters more than quantity. Organizations must ensure data is clean, accurate, and tailored to specific use cases. They must also balance securing data with keeping it usable for AI training.

Start with internal problems. Instead of chasing external opportunities, address internal problems where AI can make a measurable impact. Some use cases need a simple search algorithm. Others require robust AI systems.

Validate before buying. Early in the journey, experimentation is critical. Validating models internally before making large investments often pays off more than buying ready-made solutions.

Entrepreneurial Lessons from the AI Space

For those considering the entrepreneurial path, Spiros shared hard-earned wisdom:

Commit fully. "Burning the boats" applies here. Success requires full commitment and persistence. Half-hearted efforts fail in competitive spaces like AI.

Optimize for upside. Taking calculated risks rather than avoiding downside separates stagnation from transformation.

Understand the weight of leadership. Entrepreneurs must recognize the responsibility of rallying investors, employees, and customers behind their vision.

"The learning curve in a startup is vertical. You're not just learning to swim. You're diving into the deep end every day."

Infrastructure Builders Will Win, Not Model Makers

The conversation drew parallels between GenAI and the early internet. Like previous paradigm shifts, infrastructure builders may not capture the lion's share of value. Quentin and Spiros predicted the emergence of vertical applications tailored to specific industries.

Quentin invoked the gold rush metaphor: "Once again, the picks and shovels, those who enable the ecosystem, will emerge as winners."

The Bottom Line

GenAI success requires thoughtful strategy, bold execution, and willingness to learn from missteps. Whether refining data quality practices or starting a company, the key is balancing ambition with pragmatism.

Startups and GenAI adoption are fast tracks to growth and learning. The pace is relentless, but for those ready to embrace change, the rewards are significant.

"The riskiest thing you can do is to avoid risk altogether. Upside doesn't happen without taking chances."