I’ll admit it: I’m addicted to speed.
My mind races from the moment I wake up, and honestly, I like it that way. I can juggle a lot at once—and sometimes, without meaning to, I expect the people around me to keep up too.
Now, with AI? I can move even faster. And it’s tempting to think everyone—and everything—should move just as fast.
AI is transforming what’s possible—unlocking new speed, new scale, new capabilities we could barely imagine a few years ago. I believe in it.
But lately, I’ve been wondering: at what cost?
A few weeks ago, I was in a meeting with a client who manages the legal documentation at their organization. We were brainstorming ways to speed up their QC process with automation.
Right now, it’s slow. Painfully so.
They are spending countless hours on manual review—and even more money hiring external lawyers at $300–$500 an hour.
Midway through the conversation, someone threw out: “Can’t we just use ChatGPT?”
I paused.
My risk radar went off.
Not because the idea was wrong—AI absolutely can help. But because I’ve seen what happens when we move too fast without asking better questions.
General-purpose large language models often aren’t trained deeply enough for nuanced, high-stakes tasks. Tasks like spotting whether an investor is U.S.-based or foreign, or whether a contract is structured to comply with new jurisdictional laws.
Without tailored fine-tuning and expert validation, these systems can miss critical details. And when they miss, the cost isn’t just technical—it’s financial, legal, reputational.
Real-world failures make the stakes clear. In 2018, Amazon scrapped an AI hiring tool after it was found to penalize resumes with female-associated terms like “women’s chess club”—because it was trained on biased historical data. In 2019, Google faced a €50M fine under GDPR after its ad-targeting AI failed to meet standards for transparent data consent.
Not because AI wasn’t powerful. But because it wasn’t governed.
If we lean entirely on AI—bypassing the people who know the domain best—we’re betting that the system’s creators thought of every possible edge case. And who are those creators? Often, staff who just learned how to prompt a model on their own.
Large language models can struggle with subtle distinctions unless specifically tuned. Without human oversight—validating outputs against industry benchmarks, stress-testing compliance, checking for hidden bias—we’re flying blind.
It’s not about avoiding AI. It’s about not forgetting why humans are essential—for validation, for judgment, for protecting what actually matters.
And it’s not just compliance at stake.
We have to think about broader risks too. Biases that could quietly shape decisions. Environmental impacts, when training a single model can consume the energy equivalent of thousands of homes.
AI brings incredible power. But power without thoughtful integration doesn’t serve people—it misses the mark.
I get it—speed is critical. In competitive markets, being first can mean capturing market share or meeting investor expectations. Falling behind feels like a death sentence.
But rushing AI adoption without responsibility isn’t just risky—it’s a missed opportunity. Because human-centered AI isn’t about slowing down. It’s about accelerating innovation that people can trust, adopt, and build on.
In our AI initiatives atAI Project Solutions, we build in multiple layers of protection:
Clear data governance standards.
Human-in-the-loop checkpoints.
Regular audits against evolving frameworks like GDPR, CCPA, and NIST’s AI Risk Management Framework.
It’s not enough to trust the initial build. Systems need continuous validation—stress tests for edge cases, bias detection, drift monitoring—as regulations and realities change.
Moving fast is important. But moving responsibly is what ensures we’re still standing five years from now.
(If you’re looking for more around responsible AI frameworks and operational strategies, I share resources here: www.ai-projectsolutions.com)
Responsible AI Checklist:
▢ Establish Clear Data Governance: Define how data is accessed, stored, and used—aligned to frameworks like GDPR, CCPA, and evolving standards.
▢ Implement Human Oversight: Design processes that intentionally keep humans in the loop for critical outputs—especially legal, compliance, and customer-facing decisions.
▢ Audit Regularly: Schedule quarterly audits to check for bias, model drift, regulatory gaps, and security vulnerabilities.
▢ Train Your Teams: Educate everyone—not just developers—on AI risks, ethical use, and data privacy responsibilities.
▢ Monitor Compliance Continuously: Use automated tools or third-party services to track compliance with evolving legal, ethical, and industry standards.
▢ Hire or Consult Experts: Bring in specialists who understand AI ethics, regulatory compliance, and technical integrity—especially if you don’t have deep in-house expertise.
▢ Test for Edge Cases: Validate outputs against complex, less common scenarios—before they turn into real-world failures.
