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Five companies show what it takes to adopt artificial intelligence without losing control of data, ethics, or accountability.
Artificial intelligence now touches nearly every industry, from travel bookings to customer service. Adoption has outpaced readiness in many boardrooms. Many companies rush to add AI tools without asking basic questions first. Who owns the outcome? What data gets exposed? When should a human step in? The tool a business picks matters less than most people think. What separates the companies doing this well is the guardrails leadership puts in place before they ever flip the switch.
Protect the Data, Update the System Often
In the travel industry, decision fatigue is one of the biggest hurdles to booking. AI Concierge, founded by Anish Kushalapa, spent nine months developing a supplier network. Its AI engine plans entire trips, including flights, hotels, and airport transfers, without exposing sensitive data. “We have very strict restrictions that segment the database so a user can only access their own data, not anyone else’s,” Kushalapa said. “On top of that, we’re working with technology from NVIDIA that will automatically redact sensitive information before it even reaches the AI layer.”
Kushalapa treats the system as a living structure rather than a finished product. “The advice I received from a lot of experts in the field is you need to periodically tear down your architecture and rebuild it based on what the latest models have available to them,” he said. “Don’t be overly attached to what you spent so much time building. If the newest model supports that capability, abandon it.”
He also keeps an eye on shifting regulations abroad. “Regulations in the EU are changing a lot,” Kushalapa said. “Now there’s data disclosure if you’re using an AI model, so staying ahead of that is very important, and making sure that you’re being very transparent with users about what the AI is capable of and not capable of.”
Audits Define AI’s Role and Limits
Alesha Brown is CEO of Fruition Publishing Concierge Services and editor-in-chief of Publish Magazine. For her, responsible AI integration is less about technology and more about process. Brown has seen too many businesses adopt AI tools without first auditing the workflows they hope to improve.
“If the business has poor processes and is inefficient, AI is just going to make it faster in its poor processes and inefficiency,” Brown said. “So you really need to audit your business first and maybe just use one tool. What is one main thing that your business could do better, and needs greater efficiency in?”
Her framework treats each AI tool like a new hire with defined limits. “Whenever I’m making a decision or consulting a client about using AI, you look at it like a new hire,” she said. “What are they supposed to do and what are they not supposed to do? What am I assigning them, and what do they not have access to? That’s the same way you need to look at each AI tool.”
Brown also places responsibility on the user, not just the software. “There’s also responsibility for the user of what you should not put in there, even when those safeguards are in place,” she said. “Regardless of what the promise is and the protections a tool has, you as the responsible user need to have an extra layer for ethics, legalities, and the protection of intellectual property.”
Clear Rules Keep AI Accountable
Cody Johnson, founder of echocody AI, warns against automating a process that already produces bad results. “If a qualification process is poorly designed and you automate it, you just qualify the wrong leads faster,” Johnson said. “AI can scale operations, but it can also scale operational mistakes.”
His approach starts by finding where information gets lost and where ownership becomes unclear. “The safest AI isn’t the AI that can do the most; it’s the AI that knows when not to act,” Johnson said. “Clear rules should exist before implementation. What is the system allowed to do, what is it not allowed to do, when should it escalate to a human, and who is accountable if something goes wrong?”
For Johnson, transparency should never be optional. “Transparency should generally just be a default with AI,” he said. “Responsible automation shouldn’t depend on convincing someone that a machine is a human. The biggest risk in automation is just moving faster than the accountability aspect of it.”
AI Adds Options, Not Job Cuts
Sherif Higazy came to AI through visual effects work in entertainment. A 2023 client project pulled him toward evaluating creative models directly, and it changed his career path. He later founded Megaton AI, a research firm that scores AI tools using human judges from film and media.
“AI should be presented as a menu of options,” Higazy said. “If you can shoot it in Sweden, go shoot it in Sweden; if you have the budget and the time, do that. But the reality is you may not. So here are your options. You present AI alongside all the other tools.”
He also questions the idea that AI forces layoffs. “If you’re a high-performing team and AI is only helping you, those people, if they’re smart and capable, will only be aided by AI,” Higazy said. “The companies using it as a crutch to say ‘we’re going to lay off 2,000 people’ just overhired. The best companies have not reduced headcount.”
Higazy encourages teams to use AI for hands-on practice rather than trend-chasing. “You have to actually engage in the practice, use it for coding, build software, run your contracts through it,” he said. “Nobody seems to have a well-calibrated sense of AI’s capabilities and drawbacks, and you need that.”
Daily Scrutiny Beats Written Policy
Priyank Jain, a data scientist at Boost Mobile, creates subscriber churn models and foot traffic forecasts. His work is detailed on Sessionize. After eight years watching machine learning evolve into large language models, he has noticed a pattern. “We should not consider responsible AI and ethics policy the same; that’s a trap,” Jain said. “A policy could tell you what’s prohibited, but responsible use of AI is a set of daily habits, checking whether the model is optimizing what you actually built it for, and looking where it fails rather than just this average score.”
His method depends on constant pushback rather than blind trust. “Just keep interrogating. Don’t just obey,” he said. “I always challenge what AI gives me—tell me your reasoning behind all these things.”
He also sees a shrinking edge in the models themselves. “The models are getting commoditized; everyone can buy the same capabilities,” Jain said. “What won’t commoditize is your own data and the operational knowledge of how your business actually runs. That’s why the winners will mostly be ordinary companies wiring AI into real workflows.”
A Disciplined Approach to AI Adoption
What these five individuals have in common is a disciplined approach to AI adoption. Every person here asks hard questions before automation begins, and every one of them treats data protection as non-negotiable.
AI is moving from a novelty into daily infrastructure. The companies that treat it as a responsibility, not a shortcut, are the ones keeping their customers’ trust.
