Editorial
Research Report
The ROI of Employee Experience
It’s 2026 and the AI honeymoon is over. Companies that spent the last year bolting generative models onto their systems are now auditing the results. What they’re finding is a massive gap between those polished ethics statements and what's actually happening on the front lines.
Consider a common scenario: A mortgage officer reviews a loan application. The bank’s new AI risk model flags the applicant for rejection. The officer sees a steady, high-earning freelancer with a perfect rent history, but the algorithm cannot reconcile his unconventional income. She suspects the machine is wrong but lacks the technical vocabulary to challenge the "risk score." Meanwhile, the data scientists who built the tool are unaware their model over-penalizes "gig economy" markers. The officer hits "deny," losing a lifelong customer because nobody felt confident enough to question the AI.
This is the real ethics problem. It’s not about the hypothetical scenarios in boardroom presentations; it’s about the daily friction between policy and practice. The question isn't whether your company has an AI ethics committee. It's whether your people can actually use the tools you've given them.
When Ethics Moves to the C-Suite
In the early days of generative AI, safety lived with engineering teams focusing on technical bugs. As AI scaled, "safety" was rebranded as "AI Responsibility" and moved to Legal or Communications.
The move protected the company's reputation, but didn’t do much for the workers.
When ethics exists inside a bubble, an organization gains polished reports and moral approval for disruptive tools, but doesn't receive the technical authority to stop a high-risk product launch. Ethics functions as a communications exercise: a headline to be announced, not a protocol to be followed.
In practice, AI-driven efficiency often turns into work intensification. If a tool saves an employee two hours, managers fill that time with more tasks. As a writer on LinkedIn observed, "More efficient tools can create an expectation for more output.” Somewhere, an ethics committee signed off on this because the tool met technical safety requirements, ignoring the impact on the human workflow.
The Deloitte "State of AI in the Enterprise 2026" report found that 42% of organizations felt prepared for AI, but only 30% felt capable of managing the risk. That 12-point gap isn't about technology; it’s about the distance between corporate talk and employee reality.
Breaking the Silos
The mortgage story? That’s a silo problem. When information gets trapped in departments, nobody has the context to make responsible decisions.
Legal manages privacy in one building. Engineers build models in another. The people who actually use the tools are somewhere else entirely, dealing with the consequences of systems they don't understand and can't challenge. Of course, this isn’t a new problem. History is littered with ideas decided in the boardroom without any input from those affected. AI is simply the latest, and most powerful, technology to suffer from this top-down blindness.
Some companies are starting to fix this with cross-functional rotations. Engineers spend a week in customer-facing roles. Business leaders sit in on technical architecture reviews. It's uncomfortable at first. A data scientist watching a loan officer struggle with an opaque interface isn't "efficient," but when everyone speaks the same language of risk, decisions get faster and better.
The alternative is a "not my job" culture where the legal team protects privacy, engineering builds models, and frontline workers silently work around broken systems.
What the Experts Say
Beena Ammanath of the Deloitte AI Institute, said hiring a single "AI Ethicist" is often a search for a unicorn: someone with engineering skills, philosophical training and sociological insight. Instead, she argued that AI ethics must be a "team sport" where insights from across the organization come together.
Steven Mills, Global Chief AI Ethics Officer at Boston Consulting Group, put it bluntly: "You need to own your work product at the end of the day." His point is that AI should be a peer reviewer, not a replacement for human judgment. When workers take pride in their output while staying critical of automated suggestions, you get better results.
Sanjay Srivastava, Chief Digital Strategist at Genpact, urged leaders to assume they'll be wrong. "Build telemetry to know when you are, and systems to learn and pivot," Srivastava said. That mindset shifts focus from perfection to resilience: systems that improve through feedback rather than failing silently.
Five Operational Standards
Here's what operational accountability actually looks like on the ground:
- Build ethics into sprint planning. Treat responsibility as a technical requirement that gates code releases, not a rubber stamp after launch.
- Make data sourcing transparent. Healthcare clinicians or financial advisors need to know the limitations of training sets. A model trained on historic data might bake in past biases that don't reflect current market realities.
- Protect human override authority. Give workers the power to overrule the algorithm when it's clearly missing context. A loan officer should be able to approve a mortgage when the AI rejects it based on incomplete or misunderstood data.
- Shift to real-time monitoring. Replace one-time approval with continuous tracking. Watch for bias and model drift as the system runs in production.
- Enforce shared ownership. Every AI system needs both a technical owner and an operational owner. When something breaks, there should be a clear path from the business unit back to engineering.
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Building Systems That Work
Right now, most companies optimize for speed, hoping to worry about consequences later. That works until the mortgage model costs you a generation of customers, or until Legal gets a discrimination lawsuit because nobody checked the training data for bias.
Authentic accountability means treating AI as a tool that sharpens human judgment. In your next review, don't ask "did we consult ethics?" Ask "who is accountable when this fails?" Move ethics out of the committee room and into the workflow. Polished statements that don't match reality are just theater, and your employees can tell the difference.
Editor's Note: What other considerations should inform AI rollouts?
- 5 Questions Every Leader Should Ask Before Building AI Solutions — AI isn’t the enemy — or the magic fix. Most failures come from leaders skipping the hard questions. Here are 5 that separate hype from real impact.
- Fundamentals of a Responsible AI Framework — Building trustworthy AI requires more than good intentions — it demands systematic frameworks that prioritize responsible AI at every decision point.
- Who's Responsible for Responsible AI? — Responsible AI frameworks will inevitably evolve, but you can start building the foundation today that sets your business — and employees — up for success.
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