Why the Future of AI Isn't Faster — It's Fairer

Entrepreneur

3 days ago

AI should be used to reduce friction and give overworked teams the tools to focus on what matters most.
Opinions expressed by Entrepreneur contributors are their own.
AI is showing up in every corner of the business world — but in high-stakes fields like finance and tax, its real value isn't speed for speed's sake. It's about reducing friction, increasing accuracy and giving overworked teams the tools to focus on what matters most.
At our company, we apply AI to interpret complex, unstructured tax documents — dozens or even hundreds of pages long — and turn them into clean, structured data that flows directly into tax software in a matter of seconds.
This kind of automation isn't about creativity. It's about getting the details right the first time — accurately, consistently and in full compliance. When AI is designed as infrastructure, it doesn't replace people; it supports them, earning trust while taking pressure off the teams who do the hardest work.
But speed alone isn't enough. In regulated industries, how you build is just as important as what you build. Our team treats AI governance the same way the IRS treats human preparers: with rigorous standards and complete traceability. By embedding guardrails from the start, we can move quickly and safely without sacrificing quality, compliance or user trust.
Related: These Are the 50 Best AI Tools Right Now
Design with guardrails, deliver with confidence
AI has been the core infrastructure at our company since day one. We engineer our patented tax-prep software to the same strict standards the IRS applies to human preparers because, in a regulated field, compliance and design are inseparable. We treat AI governance as a non-negotiable engineering requirement.
Here are some examples. Once our software update is in production, we maintain vigilance by logging all inputs, outputs, and model versions to monitor for drift. We also define thresholds for model performance conditions that instantly notify an on-call owner, and clearly designate who holds the "rollback switch" and the protocol for using it. Underpinning everything, a well-managed, structured model development life cycle must track data sources and training sets, model versions, evaluations, and incidents for a more transparent record of what changed and why.
This is how you maintain speed without spraying risk into production.
Related: 75% of Workers Say AI Is Hurting Productivity
Use AI to cut burnout, not corners
Our tax season data reveals that customers' usage of our product nearly disappears after 5 p.m. and on weekends. This doesn't mean a lack of work; it's a sign that customers are getting their evenings back because the work is now powered by AI that's designed to cut burnout, not corners. And when our customers have questions, AI helps resolve 73% of support inquiries on the first try, alleviating long queues and reducing the mental strain on everyone involved.
Your company can achieve similar results by being more deliberate. Start by mapping the biggest energy drains. Ask each team to name at least three tasks that consistently sap focus and morale. Commit to automating one pain point each quarter; even small wins like AI‑assisted inbox triage stack up quickly.
After implementation, track the hours you reclaim and make them visible in reviews and all‑hands. When people can see time coming back onto their calendars, skeptics become champions of your vision, and the momentum takes care of itself.
Make AI innovation everyone's responsibility
At my fintech company, we run 'Innovation Days' that bring fresh ideas to life in every corner of the organization. It's our team's chance to step away from the familiar and use their curiosity, creativity and collaboration to dream up not just product innovations, but also process optimizations, market expansion ideas, and, of course, superior customer experiences.
But we don't stop there. Our customers rely on our products to automate away the most tedious parts of tax preparation. So they naturally share their own ideas about where our products should go next. And we listen. Some months, nearly all of our product release updates are originally sourced as customer ideas via our website or our regular community check-ins.
This is exactly the kind of bottom-up creativity that helps us land in the top growth tier for SaaS companies our size, and achieve twice the revenue per employee compared to peers.
To make innovation part of your company's operating system, start by granting no-code tool access to every team, not just those with technical chops. Remove permission gates that stifle experimentation. Offer small micro-grants — $1,000 is often enough to get an idea off the ground.
Then, turn those ideas into real momentum with quarterly demo days that celebrate solutions that improve revenue, cut costs, or deliver better service. The best ideas don't always come from the top, but from the people closest to the pain points.
Related: We Will Inevitably Lose Skills to AI, But Do The Benefits Outweigh The Risks?
Stress-test your AI regularly
We approach model safety like a contact sport, where we scrimmage the trained models to ensure they can handle the plays we make with efficiency and accuracy. Make these scrimmages part of your AI lifecycle.
Fresh eyes, especially from other departments, surface hidden drift that routine tests might miss, surfacing feedback loops and other signals where your AI performs can be extremely beneficial. Recognize contributors who find bugs or edge cases with public shoutouts or small bonuses.
After each session, publish a brief post-mortem that details what broke, how it was fixed, and what was learned. These rituals don't slow you down; they sharpen your edge.
Scale smarter, not just faster
Speed gets the headlines. But if you want staying power, you have to build with intention. That means baking in ethical standards from the start, before anything ships, breaks, or backfires. Guardrails shouldn't be reactionary. They should be part of the design.
I believe we need to redefine what automation means. While it is a tool for efficiency, it must also serve to create space for in-depth thinking and empower teams to excel. The goal is not replacement but elevation. This leads to a healthier definition of scale.
The best companies out there build adaptive systems founded on feedback and ownership. They understand that real momentum isn't found in the frantic race to the next launch but in the deliberate act of building something that will endure.