AI’s Newest Employee: Who Bears the Burden of Your Digital Coworkers?

Digital coworkers are no longer hypothetical. AI-driven agents—agentics—are creeping into every function, every decision process, and every interaction within organizations. In some ways, they are the executive dream—they don’t need coffee breaks, demand raises, or call in sick. And yet, they’re reshaping work in ways few leaders are prepared to handle.

But here’s the real question: Who actually bears the burden of these AI-driven coworkers? Who trains them, maintains them, and—when they inevitably fail—who is held responsible?

 

IMG SRC: Adobe Firefly

 

[Something tells me this group of coworkers won’t get the “fail fast-learning culture-EQ” training if they made a $10,000 online trade when they were supposed to make a $1000 trade.]

Despite the rush to leapfrog into AI-first solutions, most organizations are sleepwalking into a governance nightmare where digital agents operate in a murky no-man’s-land between automation and human accountability. Leaders want the efficiency of AI but haven’t reckoned with its organizational consequences. They are eager to scale digital agents. AI-driven digital coworkers (‘Agentics’) exist in a no-man’s land between automation and human accountability. Who will manage them, where do they fit in the organization, and what happens when they make bad decisions?

So before Agentics become the silent majority of your workforce, let’s unpack what this means for small, mid-sized, and large organizations—and most importantly, who is ultimately responsible when things go wrong.

From AI Assistants to Digital Coworkers: The Rise of Agentics

AI-driven automation started as a support tool—helping humans make decisions faster, providing insights, and streamlining processes. But the shift we’re witnessing now isn’t just about automation. Agentics are being positioned as decision-makers themselves.

These AI-driven coworkers are not just supporting humans but replacing cognitive work. They write reports, make hiring recommendations, analyze risk factors, and even generate creative content. The impact of this transition will fundamentally alter who does what in an organization and who is responsible for AI-driven actions.

Consider a few key shifts already happening:

In each case, Agentics aren't just supporting—they are working. They are employees in all but name.

And that raises a dangerous question: If AI performs employee-like tasks, should it also be treated like an employee?

The Messy Middle: Leaders’ Desire to Leapfrog to AI-First Without a Plan

Most leaders are trapped in what I call "The Messy Middle"—the uncomfortable space between current AI capabilities and their vision of an AI-first organization.

They want end-to-end automation, real-time decision-making, and efficiency at scale, but they also want none of the baggage associated with hiring, training, and governing employees. They want Agentics to behave like employees—but without any HR, legal, or accountability structures that apply to humans.

This is not just wishful thinking—it’s a governance catastrophe in the making.

Consider these unresolved questions:

  • Who trains Agentics? If we consider them "employees," do they receive onboarding or ongoing training? If so, who is responsible for updating their knowledge base when regulations change?

  • Who maintains them? Who intervenes if an AI agent makes inaccurate, biased, or unethical decisions? IT? HR? A new AI governance team?

  • Who owns their mistakes? Who is accountable if an AI-driven agent makes a bad call—denying a loan, rejecting a qualified job candidate, or approving a fraudulent transaction?

Today, Agentics are being deployed with no clear answers to these questions.

This is why many organizations will stumble into a two-tier AI workforce:

  • The Majority: Most service teams (creatives, data teams—even legal and probably the development, non-compliance side of human resources) will become glorified “prompt stations,” feeding AI requests but never truly owning the programs, initiatives, or decision-making with their business stakeholders.

  • The Top 10%: Teams that actively shape and govern their services using AI to enhance their skills, create quantifiable business value, and push strategic decision-making forward. They will successfully be able to link their operational data with business outcomes and tell an end-to-end story.

The difference between the two will determine who thrives in an AI-first world and who gets replaced by it.

Agentics as Employees: Who Bears Their Burden?

If we acknowledge that digital coworkers behave like employees, who is responsible for them?

Right now, organizations are dodging this question entirely.

For Small Businesses:

For small businesses, Agentics offer an unprecedented opportunity—they allow lean teams to scale operations without hiring additional human employees. AI can handle customer service, marketing automation, financial forecasting, and even administrative tasks.

But with that convenience comes a major risk: who ensures these AI-driven coworkers align with the company’s goals and ethical standards?

Most small businesses lack dedicated AI governance and, as a result, risk deploying digital agents they don’t fully understand.

  • If a small business uses AI to screen resumes, who checks for bias?

  • If AI handles customer service, who ensures responses don’t alienate customers?

  • If AI automates financial projections, who verifies its accuracy?

Without oversight, small businesses could unknowingly create AI-driven failures that damage their reputation, customer trust, or even their legal standing.

🔹 What small businesses need: A lightweight but structured AI governance approach—assigning clear responsibility to someone (or an external partner) who monitors and fine-tunes AI-driven processes before issues escalate.

For Mid-Sized Companies

Mid-sized companies are at a crossroads—large enough to have multiple AI-driven systems but too small to have a dedicated AI ethics or governance team.

Many are layering Agentics into existing workflows, using AI for HR hiring processes, automated analytics, and customer engagement. But who is supervising AI when it replaces decision-making roles?

The burden of managing AI often falls to HR, IT, or data teams—who were never trained to “supervise” digital coworkers.

  • HR departments might be tasked with monitoring AI-driven hiring processes but lack the technical expertise to detect algorithmic bias.

  • IT teams may be expected to "maintain" AI systems, even though AI training and ethical risk assessment fall outside traditional IT roles.

  • Data teams might be forced into governance roles simply because they work with AI models, despite having no mandate to oversee AI-driven decision-making.

This governance gap means AI failures may go unnoticed until something goes wrong—and by then, the damage is already done.

🔹 What mid-sized companies need: A cross-functional AI governance task force that includes business leaders, legal advisors, and technical experts, ensuring AI systems remain aligned with business objectives and ethical guidelines.

For Large Enterprises

Large enterprises face the greatest liability risks because Agentics are making high-stakes decisions at scale. AI is embedded in financial transactions, legal compliance, hiring, supply chain management, and customer experience.

This means companies must decide who “owns” AI agents. Is it:
✅ IT? Because they maintain AI systems.
✅ HR? Because AI affects hiring, promotions, and performance assessments.
✅ Legal? Because AI decisions carry regulatory and liability risks.
✅ A newly formed AI governance unit? Because existing teams are not designed to manage this responsibility.

If an AI model discriminates in hiring, is the company liable?

  • If an AI-driven financial model leads to unethical lending practices, who takes responsibility?

  • If AI-generated content spreads misinformation, who owns the legal risk?

Despite these high stakes, many large enterprises still lack formal AI governance structures.

🔹 What large enterprises need: A dedicated AI oversight team, clear accountability structures, and ongoing audits to ensure AI remains ethical, unbiased, and legally compliant.

So, Who Is Ultimately Responsible?

Let’s cut to the core truth: Agentics don’t work in isolation. They reflect their organizations' choices, biases, and governance structures (or lack thereof).

AI is not an autonomous employee—it is an extension of leadership.

This means the real responsibility for digital coworkers falls on executive leadership.

Like human employees, Agentics need as much structure oversight, training, and ethical guardrails. If a company wouldn't hire a human employee without straightforward onboarding, performance management, and accountability, why are they deploying AI without the same guardrails?

The Final Provocation: AI is Running Your Business. Are You Running It?

It’s time to admit it: what you’re doing isn’t working.

We’ve been here before. The last two decades of data investments promised business transformation, but what did we get? A messy middle of data silos, disconnected dashboards, and operationalization bottlenecks. Now, history is repeating itself—only this time, it’s on an exponential scale with AI.

Right now, your teams are poised to take one of two paths:

  1. They become deeply embedded, strategic partners—leveraging AI to work through the messy middle, operationalizing AI not as a shortcut but as a tool to refine thinking, improve decision-making, and master complexity.

  2. Or they become glorified prompt stations—watching AI replace the very problem-solving and contextual understanding that data teams spent decades struggling to develop.

AI does not enable you to “leapfrog” maturity. You cannot skip over the messy middle. If anything, it will magnify the gaps that already exist. In the same way, companies spent years trapped between data collection and actual insight, AI will expose the limits of an organization’s ability to think critically, operationalize knowledge, and connect AI-driven decisions to real-world business outcomes.

This moment isn’t about automation. It’s about learning how to think with AI. It’s no longer about coding; it’s about reasoning. AI should be a force multiplier for human intelligence, not a replacement for the hard work of understanding context, alignment, and execution.

Organizations that get this right will not just automate faster—they will think, adapt, and evolve better. Those who don’t will watch AI become another expensive, fragmented tool that reinforces old failures rather than unlocking new possibilities.

  • If your organization is stuck in the AI hype cycle, it’s time to break out. AI-first doesn’t mean AI does the work for you—it means learning to use AI to do the work better.

Are you willing to change how your organization thinks, or will you repeat the same mistakes—only this time, at AI speed?

👉 The teams that figure this out will redefine competitive advantage in the AI-first economy. If you’re ready to move beyond the hype, let’s talk.

CHRISTINE HASKELL, PhD, is a collaborative advisor, educator, and author with 30 years in technology, driving data-driven innovation and teaching graduate courses in executive MBA programs at Washington State University’s Carson School of Business and is a visiting lecturer at the University of Washington’s iSchool. She lives in Seattle.

ALSO BY CHRISTINE

Driving Your Self-Discovery (2024), Driving Data Projects: A comprehensive guide (2024), and Driving Results Through Others (2021)