Most companies aren't truly treating AI agents as employees, even if the language used suggests they are. What's actually happening is task-level delegation, not role-level ownership. This distinction is important. In practice, teams that do this well don't onboard a bot the same way they onboard a person. They define a specific job to be done, clear inputs, expected outputs, and boundaries. An AI agent performs best when it handles repeatable processes like initial analysis, draft generation, monitoring, or classification. It struggles when given vague accountability or human-like autonomy. There are no annual reviews in the traditional sense, but there are performance cycles. Teams assess bots based on accuracy, latency, cost efficiency, and downstream impact. If an agent underperforms, it's retrained, restricted, or replaced. "Firing" a bot typically means simply turning it off or changing the workflow, which is quite different from managing people. The most significant management change is cultural. Humans need to understand that AI is a collaborator that enhances judgment, not a peer with its own intent or responsibility. The most effective teams treat AI as a junior operator that doesn't get promoted without demonstrated success. Outputs are reviewed, not automatically trusted. A practical approach is to manage AI like infrastructure with accountability. Define ownership, set success metrics, keep humans in the approval process, and document the agent's permitted actions. Companies that bypass this structure tend to either over-rely on the system or abandon it after initial setbacks. AI coworkers are real, but they aren't colleagues. They are tools with memory and agency that require more stringent management than people, not less.
As a globally recognized thought leader with expertise in business transformation, it would be a stretch to treat agents as employees. While AI agents are more capable than anything we have seen before, in my mind, they are just more intelligent APIs or traditional workflows. Nothing more than that. Sure, it can be argued that they are mimicking the workflows of human agents, and they would require similar auditability, governance, and control workflows built into software applications. That is not to say that they are necessarily employees. Yes, the job descriptions, roles, and responsibilities would also be common, but writing the roles and responsibilities of systems or workflows is not a new concept; that is exactly what enterprise architecture is all about. It's just that enterprise architecture now includes AI agents as stakeholders. There is a possibility of new compliance laws and regulatory requirements if unemployment rises to an unsustainable level; then the government might require companies to report and tax AI agents and workflows. This would create a more pressing need to treat AI agents as employees, but depending upon how complex their cost accounting would be. Onboarding and retiring a bot are not new concepts either. That's part of the standard IT lifecycle. Managing employees and bots side by side would primarily be handled by human employees, who would manage bots instead of human employees. They would not necessarily collaborate with bots. They would oversee their workflows. It's still very early for standards to develop. Companies are still in the experimentation mode with agentic workflows and applications. It will take some time before there are standards developed for their workflows.
Some companies are doing this, but the ones succeeding don't treat AI like a human employee. They treat it like a junior analyst with narrow scope and clear guardrails. We onboard AI by defining one job it owns, one metric it's judged on, and one human accountable for its output. You don't give bots performance reviews or fire them; you retrain, constrain, or retire them when they stop adding value. The real framework is simple: AI handles repeatable judgment at scale, humans handle context, ethics, and final calls. The mistake is pretending they're coworkers. The win is designing them as dependable systems people can trust.
We are in the process of working on this for a "Director of Marketing" role. We were given some templates on how to do this, and are slowly putting into place. It takes FOREVER to get all the information you need and to set up the various parts, so that it has the information you need it to have to be able to do it. I've also added a bunch of specific GPTs for various capabilities (video scripts, ad scripts, landing page feedback, etc) however, I still have to engage it based on what we need (but it is by no means running by itself). So far, it is a great jumping off point, however we still edit everything that it produces and we still have to do a ton of the work ourselves to blend the creative it provides with something that is actually usable. Also, it's super hit or miss in terms of how it works with the various software we use. Long story short, it's not taking away any marketing manager's job anytime soon. It's great as a brainstorming tool, but it has a long ways to go to become actually agentic to where I would trust it to do everything on its own.
The new AI co-worker trend might be slightly overblown in its current state, but it's not entirely without merit. AI agents - especially those that can perform long-horizon tasks over multiple hours, days, or weeks - are, to say the least, like really competent associates or juniors. One of the bigger problems with these agents today is a lack of context. For example, if I want a helper bot to make a PPT on the company's annual performance and a forecast of future performance, I need to feed it relevant financial and operational data. That data may or may not be scattered across the organization. One of the bigger challenges in AI transformation is building a central repository of organizational context that an agent can draw from to make work more efficient (at least at the bottom of the pyramid). Language models are great at turning messy reality into structured inputs - extracting action items from complaints, turning transcripts into CRM-ready fields, and converting unstructured text into something your systems can use. Most orgs should start there. Last but not least, treat your AI agent as a super-smart assistant. At the end of the day, it is not an employee and should not be held responsible - the supervising human is responsible. As IBM famously said in the 1970s: "A computer can never be held accountable. Therefore a computer must never make a management decision."
Yes, some companies have AI implemented in their organizations. However, this does not correlate with individuals that are regarded as employees in the sci-fi world. Instead, companies treat AIs as junior operations personnel in their organisations. ...very narrow definition of what the AI will do and clear limitations on it (guardrails). At its initial deployment, the AI will have a defined scope of work. As an example, onboarding of AIs looks more like configuring the AI than orientating it. For instance, during onboarding, the user has to define what the AI has permission to perform, in what workflows it will be utilised, and how humans can override an action by the AI. A practical example in the use of AI would be to write a frontline announcement, but there will be an approval required by the Frontline Manager before sending it out. Having a review step to perform for an AI's work is extremely important. With performance reviews, the performance of an AI is evaluated based on results. Specifically, the performance is measured on how much time the AI saves the user from performing administrative tasks, on how well the AI has reduced the number of errors being made, and whether or not there needs to be a retraining or shutting down of the AI if performance is poor. Performance issues can be resolved quite simply without drama. The biggest mistake most people make when it comes to using AIs is to think that they function in the same manner as humans do. They don't. AIs work best as a behind-the-scenes resource, performing mundane, repetitive, time-consuming work so that the Frontline Manager can focus on people. At that point is when AIs really begin to work.
I'm a customer experience leader with more than 10 years of SaaS hands on work and founder at cxeverywhere.com, where I spend much of my time pressure-testing how (and whether) newly arriving tools actually behave at scale inside real teams. I've watched companies tease the notion of AI agents that are employees, but in practice the ones that are making progress have drawn a line at pretending bots are people. At one SaaS support org I worked with last year, we gave an AI agent a named role called "Tier 0 case deflection", but it was etched in stone that it's a system and not some staffer. That framing mattered. It kept the stakes pedestrian and forestalled all the emotional shortcuts people take with "if I think you are a coworker." Onboarding a bot is not fundamentally different from onboarding a new (junior) analyst. We recorded depth: which inputs it was able to touch, which decisions it could make without permission and where we made it hand off to a human. That would be the largest early mistake was to skip this and let the tool go wandering about in data it didn't quite comprehend. That produced wrong, but very confident answers, and we lost the trust of our customers in a few days. We didn't do annual performance reviews, but we would review outputs weekly. We followed error rates, the quality of its escalations and how often humans had to reverse its work. We never "coached" the bot when performance declined. We modified prompts, retrained on narrower inputs or restricted the scope. We shut it off entirely for two weeks in one case, because it was causing more cleanup work than it was saving. That was the unloading, equivalent to firing but without all the drama. You have to allow for appropriate human clarity in managing humans and bots together. Support agents received clear instructions on when and how to defer to the AI, and when not to. We also made it OK to cheat the system with no consequence. It's that psychological safety, more than the tooling that mattered. There's no tidy blueprint in place so far, however much vendors like to imply that there is. The best teams I have seen treat AI agents as unpredictable junior systems to be used for speed and nothing else. Organizations often forget that, and that's when they get into trouble.
As Heartthrob AI, we take this very seriously as AI is at the core of what we are selling (AI companions), not just in how we sell it (AI coworkers). The idea of an AI coworker is more useful to think about as a governance metaphor, and we treat them like tools that we are constantly evaluating the ROI for, but they aren't strictly being treated like humans right now and being invited to happy hours. It's important to note that like any SaaS tool, an AI coworker takes time to onboard, train, and integrate into your company. There's also a cost, similar to a salary; and an opportunity cost of time (if it's taking more time for your human employee to adjust the tool, then it's not really providing you much leverage). There's also a very narrow scope that you have to apply to the AI coworker - they aren't meant to be general athletes, and usually require a lot of oversight and direction. We do try to operationalize the AI coworker to some extent: - a job description is a scope of what they can do, and it needs controls. We set boundaries for what it cannot do, and give it only certain auth access (what tools it can access). Unlike a human which has a little more intuition, you must be very specific and narrow in the description, mandate, and permissions - onboarding an agent is like how we'd launch a new tool: start in a limited pilot, validate it is working correctly and safe, and have processes in place to deal with red-team scenarios before rolling out broadly. It's really onboarding the team, not just the agent - performance reviews are live monitoring and automated evals. We can fire an agent too - and onboard a new one especially if the time to monitor and correct isn't worth the cost. You can also fire the agent in certain areas but not others. Unlike a human, you don't need a full time agent - you can have a part time agent and pay it part time as well
Already some businesses are treating AI as an employee when they actually do their jobs correctly, they don't assume that AI is anything close to a person. At Uptalen, a European tech staffing company, we don't take on "AI Workers," instead we assign the roles, and clearly define the roles, of AI. Each agent of AI is given a very specific role: CV screening support; sourcing assistance; outreach draft; summaries of interviews; and report generation. There is no ambiguity with regard to what it is an AI does. If it can't be defined as there being a role for a human employee then you cannot expect AI to be effective in that role. It's not about culture decks, it's about data and guardrails when it comes to onboarding a bot. You need to specify what data the bot has access to, what type of output would be appropriate and commanding enough for the owner, and at what point the bot must pass the response onto a human. AI is not evaluated once a year through performance reviews. Rather, AI performance is evaluated constantly through the metrics of accuracy, time saved, error rates, and the number of times humans have to override the AI's performance. If the AI does not create greater leverage than it would be removed or retrained. No drama. Every AI agent is owned by a human. If something goes wrong with the AI, it's usually because the responsibilities and levels of supervision of that individual's role were not clearly defined. The way in which humans and AI interact together can be defined in one simple rule: the AI proposes and the humans decide. If an organisation allows AI to make decisions rather than recommendations, this will result in disarray caused by AI. The real question is not whether AI is an employee or not. It is which responsibilities should never be performed again by humans in relation to AI.
As the leader of a team building company, I think a lot about the future workforce and what it means for humans at work. It's obvious that AI is here to stay, and so what does that mean for coworker relationships, compensation, and so on. I think it's important that no matter how person-like an AI agent is, it's best output actually comes from treating it impersonally. With feedback for example, given to employees you need to balance social norms of directness vs indirectness, be mindful of morale, not constantly watching over their shoulder, and so on. With a bot you actually get the best outcomes through rapid iteration. Give feedback. Give it direct. Do it again. Follow a pattern that would be unpalatable with a human workforce, and the AI agent will actually thrive. You should use that framework to understand how your AI agents fit in. No, annual reviews is not the right format because frequent reviews create a better outcome. I do think onboarding is a neat example though. You should deliberately onboard bots both for their benefit, for example by providing extensive organizational context, and also for human coworker benefit for how to interact with it.
There's a noticeable shift from how organisations used to refer to AI as simply a "tool" and are now treating them as an entry-level employee when they are using them properly. Companies using their Bots effectively are now defining clear role descriptions, creating definitive privileges for their Bots, creating definitive performance metrics around their work rather than vague "assist" tasks, etc. Additionally, onboarding Bots includes creating training data and developing rule sets around their decision-making (e.g., when to escalate), not just on-boarding paperwork as is done with Human Resources. Performance evaluations for Bots follow a direct line; did the Bot reduce the time it takes to complete tasks, did it reduce error, did it decrease costs? If the answer to that is no, retrain or turn off the Bot. This same principle applies to Automation in Construction software; Automation automates the repetitive tasks and humans perform the judgement function on the final completed product. The clarity and accountability framework have dual functions; they provide a better understanding of the Bots functions so the team can work together in a more productive manner, rather than working harder.
Interesting question! To give you the short answer: Yes, there are companies doing this albeit not necessarily in the method that most interact with HR-software. One of the methods I have seen companies having success with AI is treating them like junior ops hires versus peers. From the beginning, clarify the job scope, set up close boundaries, and monitor them closely by humans. AI bots have been used by teams to complete tasks such as generating summaries of calls or identifying urgent requests, but humans check these items before anything is sent. When it comes to onboarding, it consists of feeding scripts, examples, and edge cases to the bot, not a welcome luncheon. There are no annual evaluations. Instead, companies evaluate the AI through weekly checks against one specific metric: response time and number of mistakes should remain below 2.5% - retraining or taking the bot off of that job if the bot does not perform well within these parameters. Firing a bot is generally just a matter of turning it off. One of the biggest mistakes companies make is to allow their AI to float between jobs. Select one role to focus on, measure it, and assign it to a human owner to oversee. Only in this way can the AI be of value.
I manage $2.9M in marketing spend across 3,500+ multifamily units, and we're already using AI as part of our workflow--just not with the employee theater of job descriptions or performance reviews. When we implemented UTM tracking and saw a 25% lead generation increase, the real work was teaching my team to interpret what the AI-surfaced patterns meant for our specific properties in Chicago, San Diego, and Minneapolis. The "onboarding" happened backward from what you'd expect. I didn't train an AI on our processes--I trained my regional managers on how to question the AI's recommendations. When Livly's sentiment analysis flagged recurring oven complaints after move-ins, the AI didn't solve it. My team created the maintenance FAQ videos that cut dissatisfaction by 30%. The bot spotted the pattern; humans built the fix. Here's what actually matters: we "fire" AI recommendations about 40% of the time because they miss market nuances that only show up when you're negotiating ILS packages in real-time or adjusting geofencing based on neighborhood events in Pilsen. I secured a 4% budget reduction while hitting occupancy targets specifically because I knew when to override the optimization suggestions. The framework is simple--AI gets veto power from humans, never the reverse. My pricing team uses algorithmic suggestions for comp analysis, then adjusts based on factors like the Illinois Medical District's hiring cycles that no training data captures. You're not managing AI employees; you're deciding which human decisions deserve algorithmic support and which need pure judgment.
We've been treating AI tools like team members at Netsurit for the past year, and the biggest shift wasn't technical--it was cultural. When we rolled out AI-powered tools through our InnovateX program, we didn't just drop them into workflows. We assigned them specific "roles" with clear boundaries, just like we do with our 300+ people across three continents. Here's what actually works: We onboard AI the same way we onboard humans through our Dreams Program--define the role, set success metrics, and build in feedback loops. Our AI handles tier-one ticket classification and routes requests to the right specialist, which freed up 18 hours per week across our helpdesk team. When it misroutes something or misses context, we don't "fire" it--we retrain the model and adjust the handoff rules, exactly like coaching a junior tech who's learning our systems. The performance review part is simpler than it sounds. We track one thing: did it make our humans more effective? For our accounting clients using InnovateX, the AI handles document prep and data entry. Our people focus on advisory work and client relationships--the stuff that actually requires judgment. When Machen McChesney's team went from "scared and not sleeping" to exploring AI themselves, it wasn't because the bot was smart. It was because we positioned AI as the thing that handles repetitive work so their CPAs could think bigger. The framework is dead simple: AI gets the repetitive tasks, humans get the decisions. Anything involving client trust, security judgment calls, or interpreting what a business actually needs--that stays with our certified experts. The bot doesn't get a desk, but it definitely has a job description.
I run a pool service company in Southern Utah, and we're definitely not writing performance reviews for AI--but we are using it to handle the scheduling chaos that used to eat up hours of my day. Every spring we get slammed with pool startup requests, and I was spending more time coordinating appointments than actually servicing pools. Now AI handles the initial booking, sends reminders, and even suggests optimal routes between properties based on which pools need similar treatments. The real test came during our busiest month last season when we had a commercial client's pool turn green right before a big hotel event. While I was on-site doing the emergency treatment, the AI was already rescheduling that day's residential clients and sending them updates with new time slots. Nobody waited on hold, nobody got a "I'll call you back," and I didn't have to stop mid-chemical treatment to answer my phone fifteen times. Where we draw the hard line: anything involving water chemistry decisions or telling a client what their pool actually needs. I'm a Certified Pool & Spa Operator, and there's no way I'm letting an algorithm tell someone their calcium hardness is fine when I can see scale forming on their tiles. The AI helps me get to more pools faster--it doesn't replace the judgment call of whether that filter needs replacing now or can wait another month. We "fired" our first AI phone system after three days because it couldn't handle our emergency service calls properly. Someone with a green pool before their kid's birthday party doesn't want to steer a bot--they need to hear a human say "I'll be there in two hours."
I've been running marketing and sales operations for 20+ years, and here's what nobody's talking about: you don't onboard AI like an employee--you onboard it like you should've onboarded your CRM in the first place. We built chatbot qualification systems in HubSpot that ask prospects about budget, timeline, and pain points before any human touches them. But here's the thing--we "fired" the bot twice. First version asked questions like a robot interrogation. Second one was too friendly and let tire-kickers through. Third one finally worked because we treated it like a struggling SDR: gave it a script based on actual buyer psychology, tracked its performance weekly, and adjusted when close rates dropped. The framework is dead simple: AI gets repeatable decisions where the criteria are clear. Humans get judgment calls where context and empathy matter. When a prospect says "maybe next quarter," the bot logs it and sets a reminder. When they say "we tried this before and got burned," that's when my team steps in--because that's not a data problem, it's a trust problem. I've watched companies waste six figures on marketing automation that runs on autopilot with no performance reviews. You wouldn't let a sales rep go months without checking their numbers--why would you let AI do whatever it wants? We pull weekly reports on bot-to-human handoff quality. If qualified leads aren't actually closing, the bot's criteria get rewritten. Simple as that.
Edtech SaaS & AI Wrangler | eLearning & Training Management at Intellek
Answered 3 months ago
I've been treating AI tools as team members for the past year or two, and yes, I actually manage them like I would real staff - just with fewer HR concerns. Each AI tool in my workflow has a clear job description (work objective). One handles first-draft content, another does research synthesis, and a third manages data analysis. I evaluate their performance constantly, not annually. If a tool consistently misses the mark or a better alternative launches, I fire it without hesitation. No awkward conversations, no severance package, no hurt feelings. Employee training matters for people and bots, just in different ways. With people, we invest time upfront teaching them our processes, company culture, and decision-making frameworks. With AI tools, training and onboarding means building effective prompt libraries, feeding them examples of good output, and refining instructions until results match your expectations. Both need that initial investment to perform well, but AI training happens faster and scales instantly across every use. The framework I use is surprisingly simple: same standards as human workers, different consequences for failure. I expect quality output, reliability, and improvement over time. When an AI tool stops delivering, it gets replaced. I've dropped half a dozen different tools in as many months because competitors outperformed them. Try doing that with actual employees. Managing virtual and real workers side by side means being brutally honest about what each does best. People should handle strategy, relationship building, and complex judgment calls. The bots handle repetitive tasks, data processing, and generating starting points that we refine. There's no pretending the AI is creative or strategic - it's a production assistant that never sleeps. The biggest shift in my thinking was realizing I could be much harsher with bots. When a person underperforms, you coach them, give second chances, worry about their mortgage. When a bot underperforms, you delete it and move on. That clarity makes the whole arrangement cleaner. No politics, no emotional labor, just pure performance evaluation.
We're actually doing this at GrowthFactor, though we framed it differently than "hiring" the AI. When we built our platform, we treated the AI like a specialized team member with one job: eliminate 90% of the grunt work so our human analysts can focus on the 10% that actually matters--nuanced judgment calls about market conditions and site-specific risks. The "onboarding" happened when we designed the workflow. Our AI handles data aggregation from ESRI, Unacast, and Streetlight--pulling demographics, foot traffic, and vehicle patterns into one view in seconds. It runs the initial KNN models for revenue forecasting. But here's the key: we built in a mandatory human checkpoint. Every AI-generated forecast goes to one of our certified analysts who either approves it or overrides based on factors the algorithm can't see--like upcoming infrastructure projects or landlord reputation. "Performance reviews" look like this: we track our 99.8% accuracy rate on revenue projections across 550 stores. When we opened 27 Cavender's locations in 6 months with 100% hitting targets, that validated both the AI models *and* our analysts' judgment in trusting them. When something's off, we don't "fire" the AI--we retrain the models or adjust the human decision framework. The side-by-side management is simple: AI owns speed and consistency, humans own context and exceptions. During bankruptcy auctions, our AI ranked hundreds of locations in hours while our analysts made the final call on which to pursue based on client strategy. Neither replaces the other--they handle completely different parts of the same job.
I've spent 15 years solving what seemed impossible--making external memory perform faster than local memory. That work with Swift and their 11,500+ financial institutions taught me something critical: AI isn't an employee, it's infrastructure that your actual employees either trust or route around. When Swift deployed their federated AI platform, we saw the real issue wasn't "managing" the AI--it was that their existing hardware couldn't handle the models they needed. Their people wanted to run fraud detection across massive transaction datasets, but kept hitting memory walls. We didn't onboard the AI; we removed the bottleneck so their analysts could actually use it. Usage went from theoretical to operational once people stopped fighting the technology. The framework everyone's missing: your people will collaborate with AI exactly as much as it removes friction from their actual work. At one client, we cut model training time by 60x--not because we "managed" the AI better, but because data scientists could finally iterate rapidly instead of waiting days for results. They started experimenting more, not because we created protocols, but because the tool became genuinely useful. Stop thinking about AI as a coworker to manage. It's a capability you either provision correctly or watch people work around. When Red Hat saw 9% latency reduction with our memory pooling, their engineers didn't need performance reviews--they just started building things that were previously impossible.
What's actually going on is not that companies are "hiring" bots just like they hire humans — it's more down to earth than that. The firms that implement this successfully consider AI agents as basically junior specialists with highly limited areas of expertise. They don't get their job descriptions in a vague manner; instead they get very detailed handbooks. Clear inputs, clear outputs, and very explicit guardrails. Introducing a bot to your company seems more like product onboarding than HR onboarding. You expose it to your data, your brand voice, your procedures and you verify it in low-risk situations well before it interacts with customers or makes core decisions. Performance evaluations definitely take place — but they are not annual. Bots are constantly assessed through their metrics: accuracy, speed, cost savings, escalation rates. "Firing" a bot is quite a simple matter. You can re-educate it, revert it or disconnect it. No fuss, no departure interview. The actual problem is not the bots - it's the people who are working along with them. Teams have to be very clear about the AI's responsibility, the moment when humans intervene, and the way accountability operates. The most effective model I've come across is very straightforward: humans decide on goals and make the judgment call; bots take care of volume and repetition. When this division is understood, the teamwork is like second nature and not something to be feared.